ATLANTA and NEW YORK – August 13, 2024 – AnswerRocket, a pioneer in GenAI-powered analytics, and Kantar, the world’s leading marketing data and analytics company, are excited to announce a go-to-market partnership on joint clients that will use GenAI to help reduce the time needed to understand data, produce actionable insights, and make this information accessible to marketing decision-makers at all levels.  

Meeting the Urgent Need for Faster Insights

Brands today face immense pressure to quickly identify trends, discover opportunities, and make informed decisions. By combining AnswerRocket’s GenAI analytics platform with Kantar’s unparalleled market knowledge and data expertise, this collaboration will deliver tailored GenAI solutions that significantly decrease the time required for data analysis from days or weeks to mere hours or minutes. 

Combining Kantar’s Market Knowledge with AnswerRocket’s AI Expertise

Kantar brings a deep understanding of market dynamics and consumer behavior, proven methodologies for collecting, managing, and interpreting vast amounts of data, and proprietary frameworks and models to generate actionable insights. AnswerRocket contributes an advanced GenAI platform powered by LLMs, designed to streamline and enhance data analysis processes, along with custom AI applications tailored to specific customer needs, ensuring seamless integration and optimal performance. Additionally, AnswerRocket provides comprehensive technical support to ensure smooth implementation and ongoing system efficiency.

Through this partnership, Kantar will leverage AnswerRocket’s platform on joint clients to create custom GenAI assistants ingrained with Kantar’s proprietary data, models, and analytical frameworks. This collaboration empowers brands to quickly access and act on valuable insights, supporting data-driven decision-making.

AnswerRocket has supported brands like Anheuser-Busch InBev and Cereal Partners Worldwide (a partnership between Nestlé and General Mills) to identify, develop, and productionalize custom GenAI analytics solutions in a matter of weeks. Results show a 40% productivity gain for Insights teams, significantly reducing the time spent on manual data analysis and enabling business users to self-service insights. 

Advantages for Brands: Speed, Efficiency, and Expertise

• Accelerated Time to Insights: Brands can reduce the time required to analyze data and generate insights from days or weeks to hours or minutes.
• Enhanced Decision-Making: Brands will have access to advanced analytics to help make more informed and strategic decisions.
• Increased Operational Efficiency: By automating data analysis and insights, brands can reallocate resources to focus on core business activities and innovation.
• Expert Guidance: Kantar’s extensive market knowledge combined with AnswerRocket’s technical support ensures effective navigation and implementation of GenAI solutions.

“AI and GenAI are not only helping us be more effective: they are giving us, and therefore our clients, a competitive edge,” said Ted Prince, Chief Product Officer of Kantar. “Working with AnswerRocket on joint clients means brands and marketers at all levels can talk to our data and get access to valuable insights faster than ever before using AI and new technologies – vital in the fast-moving landscape they’re operating in.”

“We are excited to team up with Kantar to bring the power of GenAI to more brands around the world,” said Alon Goren, CEO of AnswerRocket. “Our partnership will help businesses unlock the full potential of their data and accelerate their journey to actionable insights.”

END

About AnswerRocket

Founded in 2013, AnswerRocket is a generative AI analytics platform for data exploration, analysis, and insights discovery. It allows enterprises to monitor key metrics, identify performance drivers, and detect critical issues within seconds. Users can chat with Max—an AI assistant for data analysis—to get narrative answers, insights, and visualizations on their proprietary data. Additionally, AnswerRocket empowers data science teams to operationalize their models throughout the enterprise. Companies like Anheuser-Busch InBev, Cereal Partners Worldwide, Suntory Global Spirits, and National Beverage Corporation depend on AnswerRocket to increase their speed to insights. For more information, visit www.answerrocket.com.

About Kantar

Kantar is the world’s leading marketing data and analytics business and an indispensable brand partner to the world’s top companies. We combine the most meaningful attitudinal and behavioural data with deep expertise and advanced analytics to uncover how people think and act. We help clients understand what has happened and why and how to shape the marketing strategies that shape their future. For more information, visit www.kantar.com/.

This Press Release Syndicated In:

• News Break
• Yahoo Finance
• Chronicle Tribune
• MarTech Cube
• CityBiz

The post AnswerRocket and Kantar Join Forces to Accelerate Time to Brand Insights with GenAI first appeared on AnswerRocket.

The Challenge of Hallucinations in Data Analysis 

Recognizing the potential risks posed by hallucinations, AnswerRocket has developed robust mechanisms within Max to minimize this occurrence and ensure that every piece of information generated by the AI is accurate, verifiable, and grounded in reality.

To combat LLM hallucinations, AnswerRocket employs several key strategies:

1. Providing Correct and Full Context: We provide Max with the data observations generated through AnswerRocket’s analysis of the data to compose the narrative. Max is instructed to only leverage the supplied data observations and no other sources to form its response. By ensuring that the model is presented with the full picture, including the nuances and specifics of the dataset, we significantly reduce the chances of hallucination. This context-setting enables Max to “tell the story” accurately and generate answers that are directly tied to the data.
   
2. Acknowledging When Unable to Answer: Max is instructed to provide answers only when there is sufficient data to support a response. If the model does not find a concrete answer within the supplied data, it is designed to acknowledge the gap, rather than fabricate a response. This disciplined approach prevents the model from venturing into speculative territory and maintains the reliability of the insights it generates.
   
3. Providing Transparency and Traceability with References: Max supports its responses with references, such as the SQL queries run, Skills executed,  or links to source documents. This transparency allows users to trace the origin of the information provided by the AI, enabling users to easily see how answers were derived and to verify the results as needed. Establishing this ground truth is crucial in minimizing hallucinations, as it ensures that the model’s outputs are plausible and factual.
4. Iterative Loop for Testing & Refining: Through AnswerRocket’s Skill development process, Max undergoes continuous cycles of human-in-the-loop testing within our Skill Studio. This process includes validating the language model’s behavior across a wide range of questions and scenarios to ensure appropriate guardrails are in place. By rigorously testing and refining Max’s responses under the review of human experts, we can confidently deploy the AI in diverse analytical tasks with minimized risk of hallucination.
5. Conducting a Fact Quality Check: During Skill development, narratives generated by Max using LLMs are reviewed against the supplied data observations to confirm that they are high-quality, useful, accurate and reflective of the analysis findings. This check protects against any ambiguity in the data observations that may have been misinterpreted by the LLM in composing the story. This process can also be performed against prior answers to highlight areas for improvement.
   

The Path Forward: Trust and Transparency in AI

By implementing these strategies, AnswerRocket ensures that interactions with Max are accurate and reliable. Preventing LLM hallucinations is crucial for building and maintaining trust in AI systems, particularly as they become more integrated into our decision-making processes. At AnswerRocket, we’re not just developing technology; we’re nurturing trust and transparency in AI, ensuring that Max remains a reliable partner in analytics and beyond. 

Learn more about how AnswerRocket is delivering AI-powered analytics that businesses can rely on for accurate, actionable insights. Request a demo today.

The post Preventing LLM Hallucinations in Max: Ensuring Accurate and Trustworthy AI Interactions first appeared on AnswerRocket.

The post Demo: Meet Max, Your Generative AI Assistant for Data Analysis first appeared on AnswerRocket.

Throughout last year, the COVID-19 pandemic severely affected businesses. It impacted their ability to make accurate predictions, fulfill consumer expectations, and assess performance.

Restaurants were closed or shut down permanently. The financial sector adapted to a decrease in business profits and consumer disposable income.

Some CPGs and retailers coped with out-of-stocks and delayed supply chains, while others capitalized on “free” product trials as consumers flocked to whatever items were available during periods of pantry stocking.

Thus, many companies are poised for portfolio evaluations, whether to tighten their operational costs or to gain more market share.

Portfolio health can be enhanced by reducing products, changing factors like price, and investing in winners. To make these decisions, businesses require an examination of individual SKU performance, which is far easier said than done.

SKUs can be affected by many factors, such as segment, geography, price, promotions, channel, retailer, distribution, or competitors–and all to varying degrees.

Analysts must grapple with these factors to gain an accurate view of SKU performance, but most businesses still struggle to determine their big-picture portfolio performance.

SKU rationalization, and how you approach it, can make a difference.

What is SKU Rationalization and Why Does it Matter?

SKU rationalization is the process of determining which products should be kept, retired, or improved based on the myriad of factors that contribute to performance. Sometimes referred to as SKU optimization, this process enables organizations to refine their product portfolios to improve their financial outlook.

By prioritizing SKUs that drive growth and cutting the tail on laggards, leaders can build healthier portfolios of revenue-generating, valuable products.

In addition to choosing which SKUs to keep and cut, analysts might also focus on solving product cannibalization to ensure SKUs aren’t competing with each other.

By understanding performance at the SKU level, companies can invest in winning products, eliminate low performers, identify opportunities for new innovations, and optimize for efficiency.

How is SKU Rationalization Typically Performed…and Why is it Insufficient?

For large companies, performing granular analysis at the SKU level would be a seismic task, especially if the analysis is manual. Thousands of SKUs would need to be audited along with the many factors affecting their performance.

In actuality, companies tend to group and analyze SKUs based on a limited number of factors.

One way of measuring SKU performance involves comparing SKUs in the same category. For example, a company might compare the performance of all its sparkling waters against each other, rather than comparing the category to its soda products.

This approach is the most accessible way to analyze SKUs, but it’s lacking because category is a single performance factor among many.

A flavor of sparkling water that’s only sold in one region may perform poorly compared to a national flavor but perform well compared to other regional products.

Grouping SKUs by category doesn’t tell the whole story or show meaningful product performance, which can lead companies to draw the wrong conclusions.

The lack of visibility is a cumbersome hindrance in the way of organizations’ success.

Data science, however, provides a different approach. Data science enables accurate decision-making by analyzing SKUs with the business case in mind.

Machine learning (ML) algorithms can consider every important factor, which isn’t possible for a human analyst contending with deadlines. Additionally, ML speeds up the analysis process significantly, delivering compelling insights directly to business teams within minutes, on-demand.

It can also optimize SKU rationalization on the granular level. Instead of only comparing SKUs within the same category, SKUs can be compared based on likeness. While two different flavors of sparkling water can appear to be similar enough to draw conclusions about performance, this assumption is based on limited characteristics.

ML analyzes many elements beyond just category, allowing the company to gain the greatest amount of insight into SKU performance.

In the next section, let’s further discuss how to use data science and ML to automate SKU rationalization.

A Different Approach: Applying Machine Learning to SKU Rationalization

ML can automate SKU rationalization by analyzing the many factors that affect SKUs. It can then group SKUs together based on overall performance, showing a big picture view of product portfolio health.

This approach groups SKUs as high, medium, and low performers. This performance is determined by a number of factors, including comparisons with SKUs in and outside the category, as well as competitors, and the overall trend of the segment, retailer, and region.

ML also enables teams to generate granular, SKU-level recommendations. While manual SKU rationalization would require significant translation, ML can automatically serve these recommendations in natural language.

Natural language generation enables a business person to receive recommendations like “cut this item” or “intervene in 2023.” Essentially, business teams can take action without waiting for back-and-forth with an analyst (and the analysts are freed from translating the minutia of their findings).

Recommendations to intervene should be bolstered with forecasts that show future SKU performance by year.

The automated process compresses the workflow and produces compelling, on-demand insights. Instead of yearly, SKU rationalization can occur daily, weekly, quarterly, or monthly to suit the needs of the organization.

The continual analysis is a huge competitive advantage among changing consumer behavior. Companies have instant access to actionable insights from relevant data, rather than data that is weeks or months old.

How, then, do we correctly automate the SKU rationalization process to ensure our companies have access to actionable insights?

We’ll explore the essential elements of automating SKU rationalization and how to successfully incorporate ML software into the process in the next paragraph.

The Essential Elements of Automating SKU Rationalization

The “learning” part of ML starts with successful examples that can be used to teach a machine how to do something.

For a machine to learn SKU rationalization, the first step is to use human insight that identifies high-performing SKUs from which the machine can learn. This is an example of how ML cooperates with human experts to generate successful, scalable results.

ML software creates an idealized model of great SKUs based on examples from human experts. This model is then applied to all SKUs to assess current and probable future performance.

Using historical results, machine learning can create a view of how SKUs will sell over time. This forecasting capability of ML allows organizations to predict SKU performance.

To test whether the forecast is accurate, machine learning models can be used to “predict” what has already happened. This is called backtesting, and it should be used to establish whether ML has accurately learned how SKUs perform.

In SKU rationalization, the goal is to decide how to use limited resources to improve the performance of a large number of SKUs. Price, promotion, distribution, advertising, and innovation all are levers that a marketer can pull to change the trajectory of a SKU.

Deciding which of those levers to use one SKU at a time is ideal, but tedious and expensive, doing so for arbitrary groups is not likely to create optimal results. A better answer is to use ML for this task.

ML is used to make groups out of SKUs according to their characteristics such that marketers can act on these groups instead of on each SKU, while still achieving near-optimal results. The ultimate question is then: “How do we know what actions to take for each group of SKUs?”

The groups of SKUs that ML assembles should make sense to the human experts that provided the training information. If the groupings don’t make sense, the machine learning algorithm likely needs additional iterations with more specifics on which factors are important when determining how to group SKUs.

Once the groups do make sense, the actions needed to be taken will be clear to the marketers receiving the analysis.

Of course, some products will be too new to be meaningfully evaluated. A good ML model should highlight those innovations and exclude them from analysis. Products that are approaching the end of their shelf life should also be excluded.

Apart from the technical essentials, successful SKU rationalization automation requires intelligent change management. Company leaders must endorse AI and ML automation to ensure it’s adopted from a top-down approach. Since this analysis will enable cross-functional collaboration, teams should be aligned from the start of adoption.

The benefits of this alignment are clear: marketing, finance, and data teams often don’t have enough visibility into each other’s realms. A standardized approach to SKU rationalization creates a clear through-line for decision-making on which products to keep, promote, or cut.

Conclusion

Analytics from automated SKU Rationalization provides companies with the SKU-level insights they need to make better investment decisions and increase the health of their portfolios.

It reduces costs as companies can proactively cut the tail on low-performing SKUs and invest in winners based on predictive insights. They can also track the performance of all SKUs over time.

The real-time analysis provides organizations with a competitive advantage compared to other companies that have not adopted this type of technology. The automation streamlines the entire SKU rationalization process by providing team members with ease of access and more granular insights.

To illustrate, National Beverage, a company with a distinctive portfolio of sparkling waters, juices, and carbonated soft drinks, needed to obtain actionable insights to solve complex problems like SKU rationalization.

The organization utilized AnswerRocket’s AI-powered analytics software to understand what exactly was driving category and product performance with efficient and timely insights, eliminating “analysis paralysis.” Learn more about this use case.

Originally published August 3, 2021.

The post The SKU Rationalization Guide: Optimize Your Product Portfolio first appeared on AnswerRocket.

The post How Max Helps AB InBev’s Insights Team Do More first appeared on AnswerRocket.

Atlanta, GA – March 22, 2023 – AnswerRocket, an innovator in delivering augmented analytics to the enterprise, is excited to announce the launch of Max, a revolutionary conversational AI assistant designed to help businesses explore, analyze, and uncover insights from their data. 

Max combines AnswerRocket’s augmented analytics platform with OpenAI’s GPT-4 large language model to deliver a simple conversational AI experience for insights discovery. With Max, users can ask natural language questions and get back accurate insights and visualizations in seconds. GPT-4’s advanced language processing capabilities allow Max to understand and respond to a wide range of queries, making it easier than ever before to get the information they need. 

“The record-breaking adoption of ChatGPT is driving a paradigm shift in how business users interact with software. This shift aligns with AnswerRocket’s mission to empower business teams to quickly get answers from their data,” said Alon Goren, CEO at AnswerRocket. “Powered by GPT-4, Max makes it easy for everyone to understand and act on data, no matter their level of technical expertise.”

BI adoption may be as low as 25%, according to research by Business Application Research Center (BARC) and Eckerson Group. Interacting with BI and analytics platforms represents a key barrier to broad enterprise adoption. AnswerRocket’s patent-pending use of large language models to enable chat-based analytics addresses the user adoption challenges that have plagued business intelligence and analytics teams for decades. Max enables businesses of all sizes to easily access and analyze their data in real-time with zero training. Simply type your questions, and Max will provide insightful answers and visualizations based on your data.

Some of the key features of Max include:

• Easy data exploration: With Max, you can ask natural language questions and receive instant insights on your data. Whether you’re looking for specific answers, drivers, trends, or outliers, Max can help you uncover hidden insights so you can take action.
• Advanced analysis capabilities: In addition to answering basic questions, Max can perform advanced analysis, including statistical, diagnostic, and predictive analytics. This allows businesses to expand access to deeper insights across their teams. 
• Accelerated data setup: Users can connect, prepare, and begin analyzing their data in minutes thanks to a streamlined data configuration experience powered by GPT-4 to support automated data classification, definitions, synonyms, and suggested questions.
• Trainable model: Users can train Max to understand their business and analysis preferences. Max continuously learns from user input to improve its insights over time. 

“Anheuser-Busch InBev has long recognized the power of analytics to spur growth and innovation in a highly competitive market. It’s why we partnered with AnswerRocket to deliver faster, deeper insights to our business,” said Sabine Van den Bergh, Director Brand Strategy & Insights Europe at Anheuser-Busch InBev. “A chat-based tool like Max can help more users feel comfortable interacting with data. Having an on-demand assistant that can quickly answer the questions that pop up throughout the day would enable our team to make data-driven decisions at scale.”

Beam Suntory is currently leveraging AnswerRocket to deliver automated, interactive consumer insights across its portfolio of 50+ premium spirits. Abraham Neme, Global Head BI & Analytics at Beam Suntory said, “With Max, Beam Suntory can automate routine tasks and gain valuable insights from data, allowing us to make more informed decisions. I see the potential for Max to become a powerful tool for analyzing a combination of external, macro, and internal data.”

Cereal Partners Worldwide (CPW)—a joint venture between Nestlé and General Mills with over 50 brands—launched AnswerRocket’s core augmented analytics platform to their enterprise in early 2023. Regarding the new chatbot experience, Chris Potter, Global Applied Analytics at CPW said, “Max will take AnswerRocket to the next level! We need our teams to make informed, fact-based decisions. Max will enable users across all levels of CPW to quickly access data and insights through intuitive questions and responses.” 

AnswerRocket is excited to bring this innovative technology to businesses in Q2 2023. For more information about Max or to view a demo, please visit www.answerrocket.com/max. 

To learn more about AnswerRocket’s augmented analytics platform which enables businesses to automate analysis, generate interactive presentations, and operationalize data science models, visit www.answerrocket.com/product.

About AnswerRocket

Founded in 2013, AnswerRocket is an augmented analytics platform for data exploration, analysis, and insights discovery. It allows users to monitor key metrics, identify performance drivers, and detect critical issues within seconds. AnswerRocket’s latest release harnesses OpenAI’s ChatGPT technology to enable conversational analytics on proprietary data. Users can chat with Max–an AI assistant for data analysis–to get narrative answers, insights, and visualizations. Additionally, AnswerRocket empowers data science teams to operationalize their models throughout the enterprise. Companies like Anheuser-Busch InBev, Cereal Partners Worldwide, Beam Suntory, Coty, EMC Insurance, Pabst, Hi-Rez Studios, American Licorice Company, and National Beverage Corporation depend on AnswerRocket to increase their speed to insights. To learn more, visit www.answerrocket.com.

The post AnswerRocket Introduces Max, an AI Assistant for Data Analysis first appeared on AnswerRocket.

Max Integrates GPT-4 With AnswerRocket

Since Day 1, AnswerRocket’s mission has been to empower users to easily interact with and understand their data. To this end, our platform has always leveraged natural language search and natural language generation to make analytics accessible to business users, enabling them to ask questions and get answers without having to know SQL.

In November 2022, ChatGPT was launched. It quickly grew to reach millions of users with record-breaking speed. We were in awe of its language comprehension and saw ChatGPT’s underlying large language model (then GPT-3) as a powerful enabling technology to support our mission.  

With Max, we have integrated OpenAI’s GPT-4 large language model into AnswerRocket’s augmented analytics platform. We’re using GPT-4 to augment our own natural language querying and generation capabilities–as well as our robust ontology of over 6,000 business concepts–all of which have been developed over the course of nearly 10 years. By bringing this all together, we’ve created a familiar, chat-based experience for exploring, analyzing, and uncovering valuable insights from data. With the scalability and security of the AnswerRocket enterprise solution.

How Max Makes Data Analysis Easier

We believe an assistant like Max can empower even more business users to engage with their data better than ever before. It’s a tool for answering those ad-hoc questions that pop up throughout the day. But one that can also introduce you to more complex analysis in a friendly way. 

Here’s how Max helps to improve the data analysis experience:

More flexibility to ask questions in your own words

Similar to ChatGPT, you interact with Max just by chatting. What’s great is the increased flexibility you have to ask questions in a variety of ways, and in your own words. Max leverages GPT-4 here to understand the intent of your question, serving as the interpreter between users and the AnswerRocket platform. This allows even more robust question-asking, allowing for synonyms, abbreviations, misspellings, and more — with zero configuration. 

Answers are clear and easy to understand

Max responds to your question with a narrative answer, calling out key insights found. Here, we leverage GPT-4 again as an interpreter–taking the data facts produced by AnswerRocket’s analysis and composing it into a concise summary. An accompanying data visualization and table round out the answer, allowing you to see the data in different ways. 

Max helps guide you toward forming good questions

When Max is unclear about what you want, it prompts you to provide the details needed. In this way, you don’t have to be a data expert or have deep knowledge about a dataset to be able to ask questions. 

Here, the user didn’t specify which metric they wanted to analyze, but Max was able to get the information required to return a meaningful answer.

Transparency on analysis scope and assumptions

Max provides visibility and transparency into how questions were interpreted into analysis parameters. This helps give you confidence and trust in the answers. It can also help you troubleshoot and make adjustments when an answer doesn’t look quite how you expected.

Easy access to advanced analytics capabilities

Max is capable of leveraging AnswerRocket’s library of AI- and ML-powered analytics skills, RocketSkills. Max can help gently introduce users to advanced diagnostic, predictive, and prescriptive capabilities organically within a conversation.

Here’s an example of our Driver Analysis RocketSkill, which uses machine learning to determine the drivers of a metric increase or decrease. 

Accelerated data setup 

With Max, you can connect, prepare, and begin analyzing your data in minutes thanks to a streamlined data configuration experience powered by GPT-4 to support automated data classification, definitions, synonyms, and suggested questions. 

A key challenge of analysis is not knowing what data is available. A data dictionary view helps users understand what metrics and dimensions are contained in a dataset.

Ability to adapt to your preferences and feedback

The more you use it, the better Max gets at understanding how you like to look at your business, what visualizations you prefer, and what types of insights are most valuable to you. You train Max each time you provide clarifying guidance (like in the top 5 products example above) or give a thumbs up/down on an answer.

Ready to Give Max A Try?

We’re excited for you to try Max for yourself and experience analytics powered by GPT-4 in action. Currently, Max is in private preview, with plans for a general release in Q2 2023.

To request access to your own Max account, join our waitlist here.

To gain access sooner, schedule a consultation with our team to see a demo and discuss your analysis needs.

The post Meet Max, Your AI Assistant for Analytics first appeared on AnswerRocket.

Average Order Value shows you the amount your customers spend on average for each order that they place with your store. AOV also helps your company evaluate the effectiveness of marketing efforts and allows you to implement changes where you see fit.

Below, we’re exploring why AOV is so important, and how you can leverage its power to increase your revenue and profitability as an eCommerce business.

What is Average Order Value?

AOV tracks the average amount each customer spends per order on each purchase from your store. To calculate your brand’s AOV, you need to divide the total revenue by the number of orders you’ve received over a specified period. The formula looks like this:

Average Order Value (AOV) = Revenue / Number of Orders

A simple example of an AOV calculation is if your store did $10,000 in sales last month. If customers placed 200 orders during the month, your AOV would equal $50.

Many retailers track AOV over weeks, months, and years. Analyzing the AOV over these periods lets you see if AOV is getting better or worse over time.

Why Is AOV Important?

Average Order Value is crucial to the success of ecommerce brands because it shows marketers how they should spend their advertising dollars and which channels they should focus on the most. AOV also helps determine strategies involving pricing, presentation, user experience, and merchandise selection.

Tracking how AOV is trending over time gives digital marketing teams the ability to run A/B campaigns to test out the most effective strategies and which ones are not working well. Using AOV in conjunction with other ecommerce metrics like lifetime value (LTV) and customer acquisition cost (CAC) gives you further insight into the best ways you can optimize your marketing efforts.

How To Optimize AOV

Increasing your Average Order Value will improve profitability and your Customer Acquisition Cost (CAC). Some of the best ways you can optimize your AOV include:

• Customer loyalty programs
• Upselling
• Offering free shipping
• Bundling your products

CUSTOMER LOYALTY PROGRAMS

Loyalty programs are a proven strategy that reward customers for spending money at your store, and many well-known brands worldwide are choosing to implement these programs. 

These reward programs encourage customers to spend more to reach different tiers and qualify for other free rewards.

Customer loyalty programs can be especially beneficial for ecommerce stores that sell products that customers need to repurchase. Products like shaving cream, candles, and other consumable products can work very well with a loyalty program since customers will continue to purchase the products, increasing their lifetime value (LTV).

UPSELLING

Upselling your ancillary products to customers before they checkout is one of the oldest and most effective ecommerce and sales strategies. However, overdoing your upselling offers can put off your customers and cause them to abandon their order cart.

It’s also important to price your upsells effectively. If your customer is about to purchase $50 worth of merchandise, it doesn’t make sense to upsell them on a different product that costs $100. Use upsells by offering smaller products that complement the customer’s order that they can add when they’re about to finalize their purchase.

OFFERING FREE SHIPPING

Giving your customers free shipping drastically increases the likelihood of adding items to their cart and following through with their purchase. 

Ensuring you offer free shipping at the right price is essential so your profits aren’t adversely affected by your shipping costs. Depending on your location and the size and weight of the package, shipping can be costly when sending packages across the country or internationally. 

To find the right threshold to offer free shipping, you should determine your AOV and then set the order value to 30% higher than this figure. For example, if your AOV is $35, customers must spend at least $45 to get free shipping with their order.

BUNDLING YOUR PRODUCTS

If your goal is for customers to buy more products and increase your AOV, bundling your products will be one of the best strategies you can implement. Bundling your products is done by offering discounts to customers who purchase multiple items together or offering a package of products for one flat price.

For example, if you are a retailer of cooking equipment, you may choose to sell a frying pan along with items like hot pads, cooking utensils, or baking sheets in a complete package for one fixed price.

The Bottom Line

Running a successful ecommerce store takes a lot of time and effort and extensive knowledge of digital marketing strategies, sales copywriting, and graphic design. By increasing the AOV of your customers, you will increase your company’s profitability, decrease your customer acquisition cost, and drive up the lifetime value of your customers.

Implementing the strategies we’ve covered have been proven to optimize AOV and will position your ecommerce store for near-term and long-term success.

With AnswerRocket, brands can optimize their AOV via automated analysis and insights that allow them to easily track performance changes, identify issues, and understand which segments are helping or hurting AOV.

The post Hit Your Ecommerce Sales Goals By Optimizing AOV first appeared on AnswerRocket.

To reach effective brand analysis, pressing questions must be answered promptly:

• What’s the overall category trend?
• How are my brands performing compared to the category and to my competitors?
• What’s causing my market share growth/decline?
• Where should we invest?
• How do we gain more basis points?

Yet, answering these questions is often easier said than done. Data stays underutilized in “data lakes” where vast amounts of data pool together. Data analysts then sort through the data to run reports and generate insights. It can take days to several weeks, depending on the scope of the question.

Even data scientists, with their advanced skill sets, encounter tedious and time-consuming obstacles as they try to translate complex analysis into actionable insights.

In both cases, it’s difficult for data analysts and data scientists to quickly and effectively communicate their findings to the business team. The insights probably won’t be current and may not even be actionable to answer the pressing questions.

This past year revealed how quickly environments can change, especially in the CPG and retail space. Businesses have seen ten years of change in one year. Customers’ attitudes shifted, supply chains were disrupted, and uncertainty spread.

Static dashboards and traditional data analysis couldn’t track the rapidly changing conditions as managers felt the pain of out-of-stocks and missed opportunities.

Accurate and timely brand performance analysis is vital to a company’s success, now more than ever. In this article, we’ll break down how to achieve brand performance analysis that overcomes uncertainty and generates proactive, actionable insights.

What is Brand Performance Analysis?

Brand performance analysis ideally occurs when brand, category, manufacturer, and competitor data are analyzed to evaluate performance and generate insights that help businesses make decisions.

The insights from this analysis should help managers and teams understand how their organization performed compared to other brands in the category over short-, medium-, and long-term time periods. Brand analysis should also reveal drivers behind brand performance, or the “why,” and opportunities to gain market share.

Drivers contributing to brand performance could be anything from pricing to distribution. Likewise, drivers detracting from brand performance could range from the channel mix to product performance or package type.

With so many factors in play, managers need to understand which ones are impacting performance the most.

There are innumerable metric combinations that could explain the root cause behind change. At most companies, brand performance analysis is not yet sophisticated enough to handle this complex analysis.

In the next section, we’ll discuss some of the shortcomings of the current approach and how to improve your process.

 Brand Performance Analysis Today

These innumerable metric combinations provide many options to consider, such as pricing, promotions, out-of-stocks, and distribution. Unfortunately, there’s not enough time to analyze them all.

The manual analysis process itself is often repetitive and inefficient. Analysts, working in Excel, must pull and prepare data on repeat, test their assumptions, and build a narrative with visualizations and insights.

When business teams have follow-up questions, the process repeats, leaving little time for strategizing and planning.

Even dashboards, which may seem user-friendly to managers, aren’t built for more complex questions. Dashboards simply display what happened (“Brand A increased sales by 9% in Q2”), but not why.

Analysts can help pinpoint some causes, but the manual process prevents them from testing everything. This simple answer doesn’t help brand and category managers discover what actually caused the increase in sales.

Data scientists may have more advanced tools to dive deeper into brand performance. However, it’s hard for them to share the insights due to the complexity of the ML (machine learning) algorithms and the lack of common business language within the programs.

These difficulties result in incomplete business intelligence that’s manual, non-standardized, biased, and time-constrained. As a result, brand and category managers struggle to gain actionable, timely insights that help them make proactive decisions.

Analysis is less of a competitive advantage and more of a struggle to get a basic understanding of performance.

There’s a better way to perform brand performance analysis.

Better Brand Performance Analysis

How can companies modernize their brand performance analysis?

Instead of shoehorning brand performance analysis into an outdated and ineffective process, organizations should think about how brand performance can actually lead analysis.

ML algorithms can be tailored to the specificity of the business, compared to dashboards that remain static. Imagine if brand and category managers can ask questions like “How is Brand A doing?” An answer is available in seconds–one that fully explores the important dimensions of the brand.

AI and machine learning can automate this analysis, testing every factor to ensure no stone goes unturned. Natural language processing lets business teams ask questions directly.

Managers won’t have to depend on data analysts and data scientists to interpret results or answer their pressing questions. Likewise, analysts and data scientists are freed from addressing the same question over and over again.

To round out a strong solution, natural language generation produces insights that business people can understand. The insights describe how many basis points a brand gained or lost, and why. It also includes context for how that performance has changed over time.

These are basic components of any good analytics solution, but moreover, they actually solve the challenges with brand performance analysis:

• Prioritizing thorough, contextual analysis that actually “understands” the components of brand performance.
• Putting insights directly into the hands of the decision-makers who most need them.

To incorporate this way of thinking into existing business processes, managers should first build a consistent understanding of the brand performance use case, start to examine the existing process, and solidify the type of information the company requires.

Managers should ask themselves:

• What problem are we trying to solve?
  • As mentioned, brand performance analysis should help brand and category managers make better decisions and drive growth.
• What type of insights do we need?
  • Identify insights that will move the needle on decisions: key drivers, opportunities, etc. These insights are the outcomes that should lead the analysis process.
• What’s the existing process?
  • Identify top analysts and see what they’re doing well. These ideal workflows can serve as the basis for automation, to be replicated and distributed to the business via natural language queries.
• What data do we need?
  • Only now is it important to consider the data. Many companies make the mistake of adding a dashboard on top of data, but this leads to the cumbersome process described previously.
  • Rather, quality brand performance analysis requires that the data suit the use case–meaning data need not be perfect before organizations can reap the benefits of AI and ML.

Once these questions are answered, brand and category managers can look into automating the analysis. This process will further ease the strain on business teams and data teams.

Automating analysis provides companies with a competitive advantage. In the following section, let’s discuss the specifics of automated brand performance analysis.

The Competitive Advantage of AI and ML on Brand Analysis

Incorporating automation into companies’ existing data analysis processes saves organizations time and money by providing actionable answers and unbiased insights. Additionally, it’s becoming more critical for CPGs to invest now so as not to get left behind.

According to Forbes, “Marketers use AI-based demand sensing to better predict unique buying patterns across geographic regions and alleviate stock-outs and back-orders…Having this insight alone can save the retail industry up to $50B a year in obsoleted inventory.”

Since retail marketers are using AI technology, CPGs must incorporate it into their business practices as well. This ensures their knowledge of shelf presence, market share, and customer shopping behavior is current.

Instant insights enable better retailer partnerships and the kind of proactive decision-making that helps CPGs move faster than their competitors.

The other advantage of AI is that data insights are no longer at risk of biased processes or time constraints.

Brand and category managers and business teams now have access to insights presented in plain language and clear visualizations. They no longer need to ask data analysts and data scientists follow-up questions to understand what’s truly going on.

Take National Beverage, one of the largest soft drink companies in the United States. The data and analytics team knew its manual process hindered its ability to gain actionable insights.

The company turned to automated analysis through AnswerRocket’s AI-powered analytics software. Using AnswerRocket, National Beverage was empowered to create a culture of data-driven-decision making, which transformed its existing data processes. Learn more with the National Beverage Case Study.

Ultimately, brand and category managers should consider automation for brand performance analysis to streamline existing business practices.

Ready to transform your brand performance analysis? Talk to our team.

The post Getting Actionable Insights with Brand Performance Analysis first appeared on AnswerRocket.

On May 5th, AnswerRocket took to the virtual stage for the 2021 Gartner Bake-Off: Modern Analytics and BI Platforms.

The Bake-Off is a mainstay of the annual Data & Analytics Summit, and we were honored to be selected as a featured vendor, along with Tableau, Power BI, and Qlik.

You can watch AnswerRocket’s full demo at the Bake-Off below, or scroll down to read our synopsis.

The Bake-Off tasked industry-leading vendors with analyzing COVID-19 data to demonstrate their product capabilities and differentiators. Gartner provided vendors with data and the directive to review the state and efficacy of vaccination efforts, as well as other containment measures. Rita Sallalm, a VP Analyst at Gartner, hosted the event and gave expert commentary on key differentiators.

We approached the task with a number of questions:

• How are vaccines affecting mortality rates?
• Which countries and regions are performing well in their vaccination efforts?
• How will vaccines impact unemployment, and when will we start to see the effect?

Then, we leveraged AnswerRocket’s augmented analytics to analyze data, diagnose drivers, and predict future outcomes. Our Chief Data Scientist, Mike Finley, presented our findings to a live audience, including pandemic expert Donna Medeiros.

Here’s what we learned.

Insights Highlight Reel

To create a comprehensive picture of vaccinations, the AnswerRocket team combined various data sources, including data from:

• Our World in Data
• Humanitarian Data Exchange
• The CDC
• The Department of Labor

AnswerRocket analyzed all of this data to generate insights like this:

The Insight: New deaths are down 4.8% worldwide

How We Got Here: AnswerRocket trended vaccinations and deaths from COVID using a RocketBot, a specialized analytical app that automates analysis and surfaces insights in the form of data stories. The most compelling insights were bubbled up to a browsable NewsFeed based on the end user’s interests. Whenever new data was added or existing data refreshed, the NewsFeed automatically generated insights, ensuring the most up-to-date vaccine information without prompting. The stories and insights you see in the NewsFeed below were all composed by AnswerRocket, using natural language generation.

The Insight: The majority of new vaccination increases month-over-month were concentrated in 3 countries: India, China, and the United States. Further, nearly 90% of the gains came from just 15 countries.

How We Got Here: AnswerRocket was able to answer a natural language query to produce these insights: “What were the new vaccinations given month over month by country in Mar 2021?” AnswerRocket understood we were looking to compare vaccinations across countries between two time periods. It produced a bridge chart showing month-over-month variance. It also generated natural language insights to explain the result and uncover additional interesting facts about the data.

The Insight: By June 5th, weekly vaccines will drive the unemployment rate below 4.5%

How We Got Here: With a natural language question (“When will weekly vaccines drive the unemployment rate below 4.5%?”), AnswerRocket generated an answer in seconds. AnswerRocket understood the intent of the question, selected the right machine learning Skill to answer it, and provided visualizations and insights that gave context to the answer. We used our Impact Skill, which leverages machine learning to predict an outcome based on modeling a scenario.

The Insight: When it came to containing the spread of COVID cases, countries with a higher prevalence of domestic travel restrictions and mass population testing measures faired better than those that relied predominantly on awareness campaigns.

How We Got Here: We used a custom Cluster Comparison Skill to automatically cluster different countries together based on their COVID testing rate and case rate, allowing the end user to easily compare containment measures between the leaders and laggards. While there is a Python code base powering this Skill, end users do not need to know how to work with code, they can simply ask a question or leverage a shortcut to invoke this interactive application.

This is how AnswerRocket enables operationalization of advanced analysis to business users.

Shifting the Dashboard Paradigm

With COVID data constantly evolving, meaningful insights must be 1) timely and 2) accessible to decision-makers.

Automation of analysis and insights generation achieves this. In our Bake-Off prep, it became increasingly clear that augmented analytics provides essential capabilities that traditional dashboards simply don’t possess.

While dashboards are important visualization tools that won’t be replaced anytime soon, they must be paired with accessible AI and machine learning techniques, as well as natural language technology, to enable end users to take action.

Our augmented analytics capabilities accelerate decision-making in the following ways:

• Conversational search with natural language processing enables frontline vaccination experts to get fast, up-to-date information on their own.
• Curated news and daily digests highlight insights based on interests, meaning end users get vaccination information without having to ask questions or trigger analysis in the first place. This helps to fill the gaps between what end users know to ask versus what they need to know.
• Skills leverage AI and machine learning to understand user intent, select the best possible model to answer questions, and automatically generate the appropriate insights and visualizations. End users have access to the best analysis techniques without having to learn SQL. Likewise, data scientists can fine tune models based on their in-depth knowledge with openly extensible AI.

AnswerRocket doesn’t approach data analysis from the same angle as dashboards. It’s not simply sitting on top of and visualizing data. It’s automating analysis, responding dynamically to the data, and enhancing discoverability.

We were thrilled to showcase AnswerRocket at the Bake-Off and demonstrate our unique perspective.

Do you have questions about AnswerRocket? Talk to our team!

Answer Your Questions and Solve Business Problems. Try AnswerRocket With Your Data!

The post AnswerRocket Joins Gartner Bake-Off: Analyzing The Impact of COVID Vaccines first appeared on AnswerRocket.

This announcement follows a surging trend in data analytics— to make data and insights more accessible to business people. Many companies now recognize the value of data science in this endeavor and are striving to put advanced analysis skills into the hands of business people.

Since data science and its successful deployment is something we’re very familiar with at AnswerRocket, we want to jump into the conversation.

Tableau’s Business Science Sparked a Data Science Conversation— What Should Businesses Consider?

With uncertainty from COVID-19, businesses must navigate unprecedented scenarios with an overwhelming amount of noise in their data.

Many businesses fire on all cylinders to simply analyze historical data, let alone capitalize on current or future growth opportunities.

Data science provides the diagnostic and predictive capabilities that enable businesses to make proactive decisions with sufficient precision, speed, and context.

However, data science teams are stretched thin, tasked with shepherding their models through the business and selling their findings to decision-makers. Meanwhile, business teams lack the technical context to make use of the models’ output and take action.

There’s a gap between what data science can do and how businesses can gain value. One approach to solve this problem is to put no-code AI in front of business people, guiding them to adjust models based on their understanding of the data.

In theory, this allows data scientists to focus on higher-level analysis, while enabling business people to answer everyday questions with data science skills.

However, there are two potential gaps in this approach:

• Data scientists still aren’t empowered to “sell” high-level analysis to business people
• Business people aren’t necessarily clear on the use cases that call for data science, meaning data scientists must undertake significant change management work

In both cases, data science teams must perform work that’s best left to the realm of the data science unicorn— an expert storyteller and modeler who’s too rare to count on.

The work of data science is not the same work of designing outputs that make sense to business users and helping decision-makers take action. Nor is it the same work of getting business users to adopt a new modeling tool, no matter how user friendly.

With that in mind, what other approaches can businesses take to incorporating the strengths of data science into their organizations?

Approaching Data Science for Best Results

Let’s see how business users and data scientists can solve problems without requiring either to level up their skills in areas outside their scope.

1. Data science as a service — This approach brings a team of data scientists into your business to do the hard work of building and deploying models to end users. Data science as a service pulls rare talent into your organization without a long hiring process.
2. Operationalize data science models — High-level data science can be incorporated into self-service analytics and automated for business people. This approach enables domain experts to assess output and refine analysis, while preserving a single source of truth for every end user. Data scientists can leverage their expertise to fine tune the model, while business people can get visualizations and natural language insights that speak in their language.
3. Access pre-built machine learning skills — Models that have already been refined and packaged for critical use cases can accelerate data science skills without overhauling their process.

In each of these approaches, data scientists can lean into their data science skills, and business people can lean into their business skills.

To learn more about these strategies, check out RocketScience, AnswerRocket’s data science services.

The post Tableau Announces Business Science: A Data Science Tool first appeared on AnswerRocket.

What does it mean to be a Top Workplace?

The awards are based solely on employee feedback gathered through a third-party survey. The anonymous survey measures 15 culture drivers, such as alignment, execution, and connection.*

“This year’s recognition as a Top Workplace is especially meaningful given the dramatic changes to all of our lives over the course of the year,” said Alon Goren, AnswerRocket CEO. “I’m incredibly proud of our team for fostering a culture that has thrived in both remote and in-office settings with humility, empathy, and poise. That this honor was granted to us based on the feedback of our employees is humbling and furthers our commitment to creating a modern, best-in-class workplace where we can all thrive.”

With a quick shift to fully remote work, AnswerRocket’s Rocketeers experienced significant change but have been able to thrive regardless.

Let’s take a look at some cultural highlights over the last year:

AnswerRocket Goes Remote

Within a week, AnswerRocket employees were working remotely without losses to productivity. We met our colleagues’ pets, toured their homes, and embraced sweatpants.

Swag Bags Arrive for Every Season

Connection is a foundation of AnswerRocket’s culture. T-shirts, water bottles, hoodies, blankets, and more made their way to our Rocketeers’ doorsteps.

Virtual Events Kick Off

From virtual trivia to scavenger hunts, Rocketeers have found fun ways to stay engaged and meet new colleagues—all while embracing the competitive spirit.

As a company, AnswerRocket quickly pivoted to virtual industry events as well, presenting at conferences like the Digital Transformation Conference 2021 and Analytics Unite. We enjoyed meeting industry leaders over virtual conference room tables and ending a long day of keynotes with a quick walk to the living room couch.

AnswerRocket Launches RocketScience

Despite the challenges of the past year, Rocketeers have continued to innovate and drive AnswerRocket’s vision forward.

In 2021, the AnswerRocket team launched RocketScience, a new data science as a service offering. RocketScience leverages AnswerRocket’s data science talent to solve critical business problems.

With so much uncertainty, RocketScience can help businesses make sense of their data, assess different possibilities, and decide on the best course of action.

We’re looking forward to helping businesses grow in the post-COVID world.

AnswerRocket Looks to the Future

The team is only continuing to grow, now with Rocketeers across the country. We’re honored to offer them a Top Workplace experience.

To learn more about our mission, vision, and leadership, check out our About Us page.

To join the AnswerRocket team, check out our open positions.

*This survey was administered via Energage, a purpose-driven company that helps organizations turn employee feedback into useful business intelligence and credible employer recognition through Top Workplaces.

The post AnswerRocket Named Top Workplace 2021 first appeared on AnswerRocket.

It’s no secret that data scientists are in demand.

These professionals leverage their understanding of the business’s needs to build models that solve specific problems. This advanced form of analysis requires extensive knowledge of machine learning, statistics, and the business data— skills outside the scope of a typical data analyst.

While these skill sets are essential for answering complex questions and making predictions of future outcomes, simply analyzing the data doesn’t help business people actually enact solutions.

Decision-makers need to understand how this analysis should impact their strategy, and ultimately, their actions. Thus, data scientists are also tasked with effectively communicating their findings to the business.

Herein lies one of the largest challenges facing data science teams today.

What are Data Science Unicorns and the Last Mile Problem?

The Last Mile Problem refers to the difficulty of making data science output actionable. While data scientists are experts at the analytical process, they often aren’t exceptional storytellers.

It’s incredibly difficult to translate complex insights into something business people (without any analytical background) can understand. Moreover, the ability to present and visualize data analysis is an entirely different skill set than building machine learning models. The rare employees who are able to accomplish both are known as data science unicorns.

A data science unicorn is fully capable of wrangling data, performing analysis, visualizing data, and presenting the findings to decision makers.

They’re so scarce yet in such high demand that hiring one, let alone a team of them, is unrealistic.

As a result, many companies struggle to maximize data science. Instead, they have The Last Mile Problem, with twofold results:

1. Data scientists know they’re sitting on valuable insights, but they struggle to sell them to stakeholders. Decision-makers misunderstand or oversimplify the analysis, expecting the right answers to all their questions even when the answers are nuanced and complex.
2. Executives don’t receive the guidance they need. They invest a lot of money in data science operations, but they don’t see tangible results— because the results aren’t communicated in their language.

In other words: frustration on both sides.

How can companies successfully leverage data science, knowing that data science unicorns are just as rare as their namesake? Simply hiring more data scientists won’t fill the gap.

Solving the Last Mile Problem (Without Data Science Unicorns)

First, let’s illustrate the data science process in more detail.

To perform the analysis, data scientists must:

• Gather and scrub data
• Plan and build models
• Test and validate models
• Evaluate models
• Deploy models
• Run models

As previously discussed, this is where most data scientists excel.

However, once the models are created, the data scientists find themselves stuck as the steward of that model. Every time the business wants to run the model on different parameters, the data scientist is pulled in to facilitate the rest of the cycle on repeat:

• Visualize data
• Form conclusions
• Present findings to decision-makers

This is where the Last Mile Problem takes hold.

The process itself is complicated, time-consuming, and repetitive. Data science teams can solve the Last Mile Problem and automate repetitive steps of the analytics process with augmented analytics.

Augmented analytics can simplify the entire workflow, helping both data scientists and decision-makers in the process.

Augmented analytics is the combination of machine learning and natural language technology to automate insights. Augmented analytics represents a collision of traditional business intelligence solutions and data science, allowing both producers of analytics and consumers of analysis to achieve their goals in a single workflow.

Simply put, augmented analytics:

1. Enables end users to ask questions by typing them into a search bar.
2. Selects the appropriate machine learning algorithm to perform analysis across the business’s data.
3. Produces an answer in the form of visualizations and natural language insights in seconds or minutes.
4. Automates insight production to create a continuous, proactive feed.

How does augmented analytics work? It employs similar “thinking” as the manual analysis process, but speeds up and scales up the work.

For example, decision-makers may want to know expected sales through the end of the year by week.

Just as a data scientist would perform a time series forecast, augmented analytics would do the same, choosing the model from a myriad of options (such as clustering, gradient boosting, or selecting a deep learning network). Augmented analytics understands that a time series forecast is the best choice to answer the question, and it automatically selects the model topology, parameters, and confidence.

Minutes later, augmented analytics delivers the forecast with visualizations and insights tailored to business people. As such, decision-makers can directly receive answers to business questions without needing to query a data scientist. This self-service analytics solves the Last Mile Problem for common business use cases.

Now, how does augmented analytics handle even more complex analysis or unique cases?

Data scientists can leverage openly-extensible platforms to deploy their own custom algorithms. Data science models are invaluable, created from extensive knowledge of business data.

Augmented analytics allows data scientists to input their models into the platform, developing custom workflows that can be performed with natural language queries.

In this way, business users and data analysts are given an approachable way to leverage these models to produce user-friendly visualizations and insights, without any data science technical know-how. This enables business teams to tap into advanced analytics capabilities—which they previously relied on technical resources for—all on their own.

Thus, data scientists can operationalize their machine learning models, automate steps of the analytics process, and allow business users to ask questions and get answers in their own language.

To sum up, augmented analytics makes advanced analysis accessible, approachable, and actionable by:

• Allowing data scientists, analysts, and business leaders to play to their strengths.
• Streamlining the most time-consuming, tedious portions of the data science process.
• Automating model running to enable proactive, continuous insights.

As a result, businesses gain the competitive advantage of speed. Faster insights enable decision-makers to act quickly, instead of reacting to change once it’s already happened.

Lastly, in addition to augmented analytics, it’s worth noting the value of Data Science as a Service (or DSaaS).

This solution allows companies to outsource their data science needs to a third party. A careful selection of a vendor can enable companies to tap into data science unicorns without having to hire them. It’s worth considering this option, especially for urgent business problems.

Do you have a business problem to solve with data science? Are you unsure where to start? RocketScience can help! Request a free consultation.

The post Solving for Data Science Unicorns and the Last Mile Problem first appeared on AnswerRocket.

Answering these questions can be the difference between a growth-oriented, proactive strategy and floundering, reactive decisions.

With advances in AI and machine learning (ML) technology, the answers themselves are more achievable than ever before. Yet, how to answer these questions (and the myriad others), is outside the scope of most businesses internal resources. They simply don’t have enough data scientists, enough technical capabilities, or enough time, to develop and deploy the right ML models to decision-makers.

Data science as a service can fill the gap.

What is Data Science as a Service?

Data Science as a service, or DSaaS, refers to outsourcing data science skills and capabilities to suit the needs of a business.

Businesses usually opt for DSaaS to boost their internal data science resources— whether to build ML models or to fill a hiring gap for these in-demand professionals.

DSaaS, however, is more than a supplement. For many companies, it’s a means of scaling their analytics capabilities to meet the critical needs of the business.

Data science itself is the act of modeling specific problems and synthesizing an understanding of the data adapted to the business. Through this process, ML models investigate options, predict outcomes, and find solutions.

As such, the DSaaS provider must closely align with the business on the desired outcomes, as these outcomes drive the development of the model.

The actual implementation of the model will also vary based on need.

In practice, DSaaS consultancies can provide data scientists to:

• Perform advanced analysis for specific projects.
• Build ML models to deploy to the business.
• Enable self-service analytics so business users can access the models and output (i.e. insights and visualizations).

The value of DSaaS is growing in tandem with rising analytics needs— now, more than ever.

Why is DSaaS Critical Now?

Even before COVID-19, data analytics has evolved to meet the growing needs of end users.

DSaaS is the latest phase of analytics maturity, broadly following this order:

1. Database Tools — provide access to data
2. Business Intelligence Tools — allow users to create canned reports and dashboards
3. Self-Service Analytics Platforms — enable data exploration, performs complex metric calculations, and produces visualizations and conditional insights
4. Data Science as a Service — model data to solve business problems and achieve critical outcomes, leveraging tools like automation where necessary

DSaaS fills the gap between the needs of the business and the data that can inform decisions.

It’s no longer a competitive advantage to simply see the results of the previous quarter, to see bar graphs and pie charts demonstrating basic computational and visualization skills.

To make informed decisions that impact performance, businesses need more advanced skill sets that typically fall to data scientists.

In the midst of COVID-19, data science capabilities are necessary to make realistic predictions about the impact of external factors, such as stimulus checks. Less mature analytics solutions will struggle to make sense of pandemic behavior, especially for industries that saw significant growth or loss. In fact, most solutions will be completely incapable without data science at the core.*

These forecasting needs fall beyond the scope of reports that simple analytics platforms churn out on schedule.

Inadequate technology coupled with urgent need and scarce data science resources has led to the rise of DSaaS.

For many businesses, the question is no longer about the value of these services, but: how can we leverage DSaaS for a competitive advantage post-COVID?

*Even advanced analytics solutions can hamper data science efforts with proprietary software that needlessly restricts access to the source code. Only open source solutions with open extensibility have the right foundation to operationalize data science models within their platforms.

How to Successfully Implement DSaaS

While DSaaS will not necessarily require implementation à la software, it will require focused effort on part of the business and the service provider.

A good provider will follow these essential steps to maximize the value of data science across your organization.

1. Identify key analytics questions

The provider should connect with the business to gain a thorough understanding of the problem the business is trying to solve. Common analytics questions include:

• How might COVID impact demand?
• How are new products contributing to growth?
• How would a pricing change impact sales?

2. Develop the machine learning model

Next, the provider will develop, train, and test the model to achieve a high degree of accuracy. Once ready, the DSaaS team will deploy the model and continue to fine tune it. (For more information on ML models in analytics, check out this blog post).

3. Design output for end users

In many cases, end users are business people who need insights and visualizations to make decisions. DSaaS can put ML models in their hands, but the output must be consciously and carefully designed. Insights should be served in natural language that’s immediately understandable to a non-technical person. Likewise, visualizations must render with intelligence and options for customization.

4. Integrate analytics tools for self-service

Operationalizing data science across the business enables end users to tap into advanced analytical capabilities regardless of their role. A good DSaaS provider will understand the value of pairing ML models with self-service analytics and will work with the business to meet cascading needs at every level.

The end result of these efforts should be faster insights delivered directly to the business people who most need them. To see an example of DSaaS in action, check out the next section.

Data Science as a Service Case Study — Modeling the Impact of COVID-19 Stimulus Checks

One of the top snack companies in the world, with $26 billion in annual revenue, needed to better understand the potential impact of COVID-19 stimulus checks on demand.

This company’s sophisticated forecasting system was unable to predict extraordinary demand created by fiscal stimulus at the local level. They needed a new model that could learn from the previous stimulus and make educated predictions — enter RocketScience, AnswerRocket’s DSaaS solution.

Working with AnswerRocket, the customer built a scenario modeling tool that would learn from 2020 and adapt to 2021 as the year evolves. As a result, the customer identified hundreds of millions of dollars of opportunity. They would be able to meet this opportunity by fulfilling customer demand, instead of disappointing them with empty shelves.

This case study is just one example of the significance of DSaaS in the post-COVID world. Every business will have its own unique problems to solve amid the impact of the pandemic. DSaaS can meet this need and help companies grow.

Do you have a business problem to solve with data science? Are you unsure where to start? RocketScience can help! Request a free consultation.

The post Data Science as a Service: Navigating Post-COVID Uncertainty first appeared on AnswerRocket.

In fact, many CPGs and retailers don’t even have a centralized view of their data. As a result, cross-functional collaboration is difficult, and organizations struggle to act with precision and agility. Growth opportunities get left on the table, especially during times of accelerated change (like COVID-19).

Ada Gil is here to help. Ada is a former Unilever Marketing Director, currently specializing in automating data analysis for CPGs and retailers. Check out her advice for making the most out of consumer data in these two Fireside Chats:

• For CPGs — Making the Most of Consumer Data with Consumer Goods Technology
• For Retailers — Making the Most of Consumer Data with Retail Information Systems

Watch to learn more, and check out the Additional Resources that compliment each video.

For CPGs — Making the Most of Consumer Data with Consumer Goods Technology

In this video, Ada discusses the proper strategic approach to consumer data analysis as well as the technological prowess that will allow consumer goods companies to turn data into insight, and insight into action.

Ready to learn more about how CPGs can make the most of consumer data? Check out these additional resources:

• CPG Analytics Guide — Learn what’s possible with with CPG analytics, what to look for in a solution, and how various roles can leverage this technology for success.
• AnswerRocket for CPGs — Check out AnswerRocket’s approach to automating CPG analysis, tailored to critical use cases.

For Retailers — Making the Most of Consumer Data with Retail Information Systems

In this video, Ada discusses the current state of retailers’ data approach for planning, how retailers can partner with CPG to maximize their data, common data pain points and opportunities, and more.

Ready to learn more about how retailers can make the most of consumer data? Check out these additional resources:

• Retail Analytics Guide — Learn the benefits of advanced retail analytics, common roadblocks to automation, and how to solve business problems with analyses.
• Category Analysis: How to Determine Performance Drivers — Understand how retail category managers can leverage analyses techniques to pinpoint the root causes behind performance.

The post Watch: Making the Most of Consumer Data first appeared on AnswerRocket.

Even prior to the current state, CPG leaders have been leveraging the power of AI and machine learning to automate analyses, saving time and resources to improve their bottom lines. These existing use cases can act as models for CPGs that now have an urgent need to upgrade their technology.

This blog post will cover how CPGs use machine learning for:

1. Brand health analysis
2. Market share analysis
3. Category analysis

Machine learning and AI are more than temporary trends; they’re fundamentally changing how CPGs understand their consumers.

Practically, machine learning algorithms can identify and tailor critical insights across different data sets, scenarios, and functions— meaning, the algorithm “understands” what information is most important and relevant for a category analysis whether the data represents cosmetics or food and bev.

To learn more about machine learning, check out this resource: Machine Learning in Business Intelligence Solves the Puzzle. Otherwise, let’s dive into the myriad of ways machine learning is used in data analysis.

1. CPGs use machine learning for brand health analysis.

Understanding brand health is critical for CPGs.

Just as data has become more complex in recent years, brand health too has evolved beyond the consumer experience to encompass a brand’s overall performance:

As revenue models and customer expectations continue to evolve rapidly, every aspect of a business can affect the brand—from logistics and inventory management to the in-store experience. As a result, organizations increasingly are considering the connection between their brands and their underlying business operations with a focus on how performance can affect brand health.

Source: https://deloitte.wsj.com/cmo/2018/01/11/assessing-brand-health-risk/

This evolution means there are an incredible amount of factors to consider when it comes to brand health. Yet, at the core of brand health analysis is the simple question: “how did my brand do?”

In order to answer this question, brand managers need to understand which factors are driving a brand’s performance.

Which metrics, if changed, would create the largest ripple effect? Is location critical to brand health, or does advertising channel selection matter more? Where should employees focus their time and energy for the best results?

These answers aren’t always intuitive. It’s not uncommon for CPGs to leave growth opportunities on the table because they’re acting on partial information or assumptions.

For example, a brand’s strong sales performance can mask losses like declining market share. Furthermore, it could be reasonable to attribute this strong performance to a sales value increase in the Southeast and subsequently double down on promotions in that region. Meanwhile, data indicates that an increase in brand penetration in the Northeast would offer the greatest growth opportunity, but this insight is obscured by numbers on the surface.

Some of the most intuitive and obvious assumptions can be the most dangerous, simply because it’s so challenging to look past them. Machine learning approaches brand health without preconceived notions or this kind of bias.

CPGs are using machine learning algorithms to parse data and identify brand health drivers.

Specifically, machine learning can:

• Analyze data intelligently and exhaustively, using brute force models to evaluate all possible data permutations and focusing attention on factors that matter the most.
• Perform contribution analysis, weighing and understanding how much each factor contributes to overall brand performance.
• Determine which data is relevant to brand health, without a biased approach.

To put these theoreticals into real life, read this case study. The Consumer Insights department at one of the top 10 CPGs with more than $50 billion in revenue needed to better understand their brand health. Here’s how they used machine learning to do so.

2. CPGs use machine learning for market share research.

One of the most important metrics that CPGs must consider is market share, whether across a specific brand, segment, category, or industry.

For CPGs, market share insights indicate how brands perform against competitors. This context is critical to building a long-term growth strategy. The goal with comprehensive market share research is to leave no stone unturned and to fully understand where a brand fits into the larger landscape.

Machine learning is capable of performing this in-depth and complex research in seconds.

To do so, machine learning algorithms thoroughly cull through syndicated and first-party data sources, test every data combination to determine relationships, and generate insights.

A complete machine learning-generated market share report would include the following information:

• Market share performance — Year-over-year, has market share increased or decreased and by how many basis points?
• Market share momentum — How does recent movement in market share compare to the long-term trend?
• KPI drivers of market share growth — which key performance indicators (KPIs) are driving market share growth (or decline) the most, and to what degree?
• SWOT analysis— What are the brand’s Strengths, Weaknesses, Opportunities, and Threats based on its performance and that of its competitors?

With this information, CPG professionals can zero in on the factors that drive market share and make decisions based on the big picture.

What were the top gainers and decliners? Which contributors most determined market share performance? The answers to these questions can help teams take action with the greatest possible ROI.

What’s particularly compelling about machine learning in this instance is that its speed and efficiency enable immediate follow-up research.

When marketers learn that distribution contributed 250 basis points to the positive trend, they can quickly drill down into distribution and find out why. Essentially, machine learning enables more targeted, informed decision-making.

To learn more about market share research and brand health analysis for CPGs, check out this quick-read blog post. Or, watch this quick video demonstration to see this analysis in action.

3. CPGs are automating category analysis.

Syndicated data sources are an important investment for CPGs wanting to understand their performance, especially within the context of their competitors. To take advantage of these massive data sources, CPGs are using machine learning algorithms to analyze category data quickly and effectively.

Machine learning has advanced to the point that it can automate the bulk of what is largely a manual process. That means CPG professionals can start with a question (like “how did my category do?”) and get an answer in seconds without having to wrangle disparate data sources, formulate and test assumptions, pull data, and repeat ad nauseum.

Within the manual process, analysts spend so much time pulling data together that they tend to follow the same pathways when it comes to analysis. As a result, analysis can easily become biased; analysts don’t have time to look below the surface, so they stick with what’s worked in the past. Unfortunately, COVID-19 data is beyond historical precedent, and the old methods of analysis are quickly becoming obsolete (if not now, then in the next 5 years).

In contrast, machine learning is unbiased, approaching the data without a set pathway or preconceived notions.

Once the machine performs analysis, it generates insights and data visualizations that explain the drivers in a category. This analysis should, for example, identify how different brands contribute to the overall category and draw the user’s attention to the most relevant and important insights.

While data visualizations are familiar mainstays at many CPGs, insights, on the other hand, vary widely, and machine learning has significantly elevated the baseline expectation.

The value of insights is no longer limited to simply pointing out what’s happening in the data, i.e. “market share increased 50 basis points.” Now, insights can be structured in complete data narratives that explain analysis in natural language.

“You have that ‘a-ha’ moment; that’s really what an insight is, where you see things differently than what you saw before. And you can follow that train of thought without these interruptions,” states Laura Braunecker, Founder and Principal Consultant of Zeerio, in this Food Dive Playbook. “[CPGs] want their best and smartest talent to be more productive, to get the answers they need and to be proactive, not reactive.”

That’s the key with automated analysis — generating insights that lead to action. And generating insights quickly enough that action can be taken proactively, not reactively.

This automation means CPG professionals like brand managers, CMOs, finance teams, and sales teams can perform analysis themselves, quickly and without intervention from an analyst.

Of course, the potential applications of machine learning are nearly endless, and they can fulfill analytical requirements for a myriad of departments and functions.

A few additional examples of machine learning in action include:

• Scenario analysis — ask “what if” questions to see what happens when metrics are altered.
• Granular forecasting — predict revenue and profit performance.
• Opportunity analysis — identify and prioritize growth opportunities.

Each of the above examples are active use cases for machine learning.

Hopefully, these examples inspire ideas for how you can use machine learning to solve problems and drive growth at your organization.

Additional Resources

• CPG Analytics Guide: Learn everything you need to know about CPG analytics with this ultimate guide.
• Data Analytics and Machine Learning: Learn more about machine learning in this more technical overview of AI in analytics.

The post How CPG Companies Are Using Machine Learning Right Now first appeared on AnswerRocket.

Performance drivers dictate where category teams can take actionable steps that will result in the greatest potential impact.

With the uncertainty of COVID-19, this degree of precision is essential.

Category managers at CPGs are feeling the pressure of siloed data and retail partners who are increasingly investing in their own analytics resources. Likewise, retailers are juggling rapidly changing consumer behavior and massive amounts of data. Plus, retailers still have a lot of ground to cover when it comes to building up their in-house analytics teams.

Let’s dive into how CPGs and retailers can determine performance drivers with category analysis.

1. Why performance drivers are essential for category analysis
2. Centralizing data for category analysis
3. Understanding category performance with KPI trees
4. Automating category analysis

Why performance drivers are essential for category analysis

When changes occur in category and brand performance, tons of different metric combinations could be the root cause. For example, let’s take any of these possibilities into consideration:

• Changes in shopper baskets
• Growing channels like curbside pickup and e-commerce
• Supply chain disruption
• New consumer trends
• Fluctuating consumer confidence
• Shifting customer loyalty due to early pandemic out-of-stocks

…and so on. This list is nowhere near exhaustive, and each of these factors could be simultaneously impacting performance. What matters the most is understanding the degree to which each factor contributes to and detracts from brand and category performance.

Only with this precise information can category teams create efficient and effective strategy.

Otherwise, category teams risk losing both growth opportunities and market share.

To put this problem into context, many shelf-stable categories like rice and pasta have excelled during the pandemic. With healthy top-line metrics, it’s easy to take that performance at face value. However, a performance driver analysis could reveal a significant decline in total distribution points, masked by other high-performers like volume price.

Without this critical information, supply chain issues could go unaddressed, and CPG category teams could lose market share in the long run as consumers steadily commit to alternative brands on the shelf.

That’s why understanding performance drivers is so essential. It creates the full picture of what’s happening to a category or brand so that every growth opportunity is on the table.

Centralizing data for category analysis

CPGs and retailers have different challenges with data, but both struggle to centralize data for a comprehensive view into business performance. Retail category managers need access to store and shopper data, such as panel, scanner, and loyalty data. Likewise, CPGs need access to internal and syndicated sources, such as retailer,  finance, and marketing data.

In both cases, data is often siloed— meaning, category teams can’t get a 360° view to truly understand performance drivers.

At least, not without manually merging these sources together. This process is time-consuming and prone to human error. In addition, siloed data creates an obstacle to cross-functional collaboration between category teams and other business functions. When every team has their own view of data, the complete picture is skewed.

The ideal outcome of centralized data is a holistic view of top-line and margin growth drivers. From there, teams can seamlessly collaborate to determine results from a single source of truth.

As such, category teams should strongly consider an analytics solution that enables them to easily connect their various data sources to a single hub. This kind of enabling technology should extend beyond data to the actual category analysis, which brings us to our next discussion point.

Understanding category analysis with KPI trees

Category analysis usually centers around a key performance indicator, or KPI. Sales in a category or brand is a common KPI to demonstrate overall performance across a specific period of time.

Understanding the drivers behind sales means understanding the complex relationships between metrics in large data sets. When it comes to analysis, there are innumerable metric combinations that could explain the root cause behind change. Out of all the possibilities that could impact category sales, which combination is right?

It’s impossible for humans to perform an exhaustive analysis that accounts for all of these factors. CPGs and retailers need insights that they can act on, so even the best category teams can’t assess every possible data combination, especially with tight deadlines and pressure from COVID-19.

This is where enabling technology like AI and augmented analytics can become a competitive advantage.

AI creates a common metric hierarchy and analyzes category performance across datasets. Machine learning algorithms can parse through data to analyze all potential drivers of a KPI in minutes or even seconds. From there, the ML process can determine which drivers are contributing positively and negatively to the overall performance.

Comparatively, an analyst would take days or weeks to identify drivers manually (and even then, their analyses would be limited). Plus, analysts approach data with their own biases and assumptions, whereas AI does not.

With COVID-19, safe assumptions are actually quite risky. Consumer behavior has changed rapidly, and it’s unclear which trends will stick; as a result, category teams need real-time insights into performance drivers to stay on top of opportunities and avoid pitfalls.

Granular, precise consumer insights can move the needle towards success.

That’s where a KPI tree is so valuable. A KPI tree provides a decomposition of the relationships between metrics so that category teams can easily understand the degree to which each metric affects the main KPI. This view enables category teams to weigh metrics based on their relative level of impact and make decisions accordingly.

This KPI tree illustrates that declining total distribution points is the key detractor of sales with a -$21.5M impact.

If category teams can achieve this view into their data, they can direct their time and energy into the strategic activities that will drive growth.

While a KPI tree serves as an incredibly valuable visualization tool, it’s not complete without insights that explain the “why” behind category and brand performance.

Automating category analysis

The category analysis workflow we’ve outlined thus far includes:

1. Centralizing various data sets into a single source of truth
2. Analyzing every data combination to determine key performance drivers
3. Generating category and brand insights (which we’ll speak to in this section)

This process can be automated with augmented analytics and AI. As described in the previous section, AI can actually perform analysis to exhaustively test every data combination and generate a KPI tree. This is only one component of what AI can do for category teams.

Along with a KPI tree, AI can generate natural language insights that highlight important information. These insights can draw attention to critical metrics, frame KPIs within the context of larger trends, and explain not just what’s happening in a category, but why.

This “why” is critical because it puts performance drivers into a larger narrative. Total distribution points may be the most significant decliner impacting sales, but what does that mean in dollars? To what degree are other metrics, like out-of-stocks, playing a role in the TDP losses?

Insights can answer these questions, providing a comprehensive narrative of brand and category performance.

In order to manually generate these insights, category teams would need to interpret data and visualizations to draw conclusions, selecting which aspects of the story to include. The challenge with this method is that it’s time-consuming and prone to bias, just like merging and analyzing data.

Ideally, category teams should spend their time acting on the insights they receive and making data-driven decisions. Understanding performance drivers enables them to operate with agility, precision, and certainty— essential advantages for the COVID-19 landscape.

Ultimately, category teams should consider automation through augmented analytics to get the best view into performance drivers.

Additional Resources

• CPG Analytics Guide: CPG analytics allows you to leverage syndicated and first-party data to understand category and brand drivers. Learn how to enable cross-functional collaboration and get faster insights amid COVID-19.
• Retail Analytics Guide: Retail analytics can connect all of your data sources, producing real-time insights that help you stay on top of changing consumer behaviors. Learn how to plan effectively even amid the pandemic.

The post Product Category Analysis: How to Determine Performance Drivers first appeared on AnswerRocket.

Before COVID-19, businesses with a loose handle on their business performance might have missed 1-2% of top-line growth opportunities. Now, drastic shifts, stressed processes, and the speed of change have compounded the situation, putting closer to 5-7% of top-line opportunities at risk.

Technology like AI and augmented analytics can help businesses understand performance and identify growth opportunities, mitigating potential losses.

For detailed information on how retailers and CPGs can navigate COVID-19, check out this webinar and whitepaper. Or, keep reading for a preview of the content.

1. Drive cross-functional collaboration and data sharing

With changes affecting every department, CPG and retail leaders must enable collaboration and data sharing.

Yet, centralizing data is easier said than done. Marketing, finance, and supply chain departments often work in silos, making data sharing and quick collaboration difficult. These silos must be removed, however, as informed decisions depend on data sharing. For example, to determine when to start a campaign, teams must know if consumers are overstocked and if production rates can balance the expected demand and cost of goods.

When data is shared, CPGs can create a holistic view of top-line and margin growth drivers and identify demand drivers in light of recent trends.

2. Recover and improve shelf presence

Shelf presence will be more challenging now than ever before.

To recover and improve shelf presence, CPGs and retailers must stay in constant communication.

Timing and speed are key— the faster teams can receive data and insights, the faster they can initiate these discussions. Additionally, real-time insights can help manufacturers prove the importance of their portfolios, an essential advantage when shelf space is on the line.

The question then becomes: which insights are most valuable, and what should teams look for?

3. Closely manage your market share

Unfortunately, market share lost during a crisis or recession is very difficult to recover. However, CPGs and retailers can actually gain market share if they can identify opportunities and avoid losses.

Consumers are participating in “free” product trials that would be difficult to generate in normal circumstances. Even loyal customers are trying new brands. For CPGs, these product trials can be wins; by that same token, CPGs risk losing customers if they can’t manage market share effectively.

Companies who can quickly understand their market share can take faster actions, better positioning themselves for quick recovery and even gains. AI and analytics can aid in these efforts.

4. Adapt to new shopping behaviors

Apart from managing the day to day, CPGs must consider future trends in retail and adjust their plans accordingly.

According to Ulf Mark Schneider, CEO of Nestlė:

“The coronavirus crisis is likely to be a ‘breakthrough event’ for online sales of food and beverages, as people who previously would not have bought groceries online discover how convenient it is.”

Part of the challenge with trends is understanding temporary versus long-lasting change. Some trends may flatten while others may stick. Surveys and quantitative research can help CPGs and retailers understand consumer motivation, providing insight into which trends are here for the long haul.

To build a strong omnichannel strategy and capitalize on opportunities, CPGs can leverage AI and ML to identify behaviour across channels.

5. Plan for the new norm

CPGs and retailers typically have several months of lead time for planning purposes. Planning during COVID-19 requires a different mindset.

Rather, CPGs and retailers must prepare to manage continuous and dynamic change. Teams should aim to work 18-24 months ahead while planning for short term periods to make decisions. How can teams plan successfully with so much uncertainty?

First, taking a “what if” strategy can help CPGs and retailers plan for various plausible scenarios. Second, CPGs can learn from the past. In the short-term, teams can look to countries that are ahead in terms of crisis management as well as past data on economic recessions.

Even in uncertainty, CPGs and retailers can draw realistic conclusions from existing data and adapt accordingly.

Learn More With the Webinar and Whitepaper

Ready to take your knowledge to the next level? Access the following resources:

Webinar: Promise & Peril: How Augmented Analytics Helps CPGs & Retailers Navigate COVID-19

• Featuring: Ada Gil, Former Unilever Marketing Director
• You’ll Learn: Detailed, actionable advice in the span of 20 minutes. The points in this blog post are a simplistic preview of the webinar content.

Whitepaper: The State of AI in Analytics and Business Intelligence

• You’ll Learn: Which AI capabilities are available today and how they can help your business analyze data. This COVID-neutral resource is not speculative. Instead, it foregrounds technology and the capabilities that are essential to ensuring adoption.

The post 5 Ways Retailers & CPGs Can Adapt and Grow During COVID-19 first appeared on AnswerRocket.

After all, companies must come away from these costly investments with actionable insights. Where’s the value if you can’t generate insights to help drive growth, cut costs, and ultimately move the needle on performance?

Many companies are facing the fact that their traditional business intelligence (BI) investments in reporting and dashboards don’t necessarily get them closer to actionable insights, at least not at the scale needed to go on investing in those solutions.

Instead, the hope is that a new generation of big data and machine learning will mean that employees can ask a myriad of complex questions specific to their department, function, or role and receive answers in seconds or minutes, without intervention or support from a data analyst.

Insights should make teams smarter, more agile, and more efficient. They must be embedded in the decision-making process, accessible to business people without technical expertise.

Insights produced by traditional BI platforms tend to lack the depth, speed, and ease of understanding to be truly actionable at scale. Employees are accustomed to reporting that is either irrelevant or fails to point out the key problems.

In this post, I discuss why traditional business intelligence is insufficient to scale insights generation.

Traditional business intelligence is insufficient

Traditional analytics platforms rely on dashboards to illustrate stats and trends in the data. Dashboards are typically composed of distinctive visualizations that answer simple questions, such as “what were sales in Q3?” or “Report on Sales In Q3.”

While dashboards can show data, they can’t explain the results with drivers and analysis. An analyst must still interpret dashboards to find insights and piece the story together.

In reality, analysts download data from dashboards into Excel to determine root causes and uncover meaningful, actionable insights. While dashboards can provide helpful visualizations, analysts are still performing the bulk of the work, often outside the BI tools themselves.

That means analysis is not uniform across departments because best practices are difficult to convey and enforce. The occasional big success punctuates an expensive and mundane flow of largely irrelevant charts and grids.

These limitations can lead to low adoption, since business people need support from analysts to get actionable insights. In turn, data and analytics leaders who’ve carried out traditional BI are left with lackluster ROI.

As such, there’s an impetus for companies to upgrade traditional BI and analytics tools to drive the results they want to achieve.

These considerations illustrate the depth of the need for better insights at scale:

• Surface-level insights aren’t sufficient. A BI tool may show a trend or perform statistical analysis, but this information doesn’t help a business person get closer to understanding why and what steps they can take to impact a business outcome.
• The cost of analytics is increasing, along with demand for insights. Analysts are challenged to better serve the business, while at the same time, they’re asked to reduce costs. The insights generation process is too manual and time-consuming— the same way it’s been for 10–20 years.
• Existing BI and analytics tools are not broadly accessible. While dashboards can provide helpful visualizations, they often require technical expertise to interpret or build.
• The turnaround time for quality insights is too long. Insights can quickly become outdated when it takes days or weeks to produce reports or build dashboards. Even fast analytics tools don’t solve this problem, as quick insights can be disparate and lacking context. A competitive environment requires speed.

To solve for these challenges, companies need fast insights that replicate the knowledge of a data scientist— insights that can be infused with the business expertise of a department or category manager, a supply chain guru, or a strategic executive. In other words, companies can’t sacrifice deep analysis to get quick insights, and vice versa.

The solution cannot be traditional BI and dashboards. These tools have been around for 10–15 years with user adoption rates stuck around 30%. To help companies achieve the outcomes that measurably improve performance, they must look to future-facing technology: augmented analytics and GPUs.

Operationalize data science with augmented analytics and GPUs

Augmented analytics leverages the power of AI to put data science capabilities into the hands of business people.

This data democratization — essentially, allowing business people to access and understand data without intervention from a data scientist or analyst — is enabled by two key components of augmented analytics:

1. Natural language generation (NLG) — NLG produces insights in plain language. Advanced NLG can produce entire data narratives that tell the story of business performance, trends, and opportunities.
2. Machine learning (ML) — ML algorithms perform exhaustive data analysis, surfacing hidden insights by testing every data combination. To accelerate ML, companies can pair augmented analytics with GPUs (more on GPUs in a moment).

With NLG and ML, augmented analytics automates the analytics workflow. Augmented analytics not only performs the analysis far more quickly and exhaustively than a person could, but it also mitigates the need for interpretation.

As discussed, a report generated by traditional BI would likely include visualizations with simple answers that state surface-level trends. These answers might state a percentage increase or decrease for important metrics like sales or market share, but would require technical expertise to understand the “why” behind the numbers and generate actual insights.

Augmented analytics, in contrast, can answer “why” questions directly; ML algorithms determine core contributors and detractors, developing a full data narrative that helps business people understand where to focus their attention.

There’s no need to bring an analyst into the conversation. Augmented analytics enables business people to drill-down, to ask follow-up questions, and to quickly gain an unbiased, 360° view of performance.

Augmented analytics is a powerful tool. To generate insights above the gold standard, augmented analytics requires significant processing power, especially for the large data sets common in enterprise organizations.

Most analytics platforms currently run on central processing units, or CPUs. That’s the kind of tech that powers the computer you’re using to read this. While CPUs work well for lots of different types of analyses, such as trending and contribution, their use is limited for AI and ML. CPUs simply don’t have the speed that can provide a competitive advantage. As companies move to reap the benefits of AI and ML, they’ve also seen the need to move from CPU-based systems to GPU-based systems.

GPUs, or graphical processing units, can cost-effectively accelerate augmented analytics, scaling insights generation through massive processing power. Originally developed to create the kind of realistic 3D environments now seen in video games, GPUs can now be applied to advanced AI-driven technologies well beyond that humble start.

Where a typical CPU server may have 10, 20, or 25 cores, a GPU server can have over 5,000.

As such, GPUs enable complex analyses and insights generation that simply can’t be accomplished with CPUs. This GPU revolution runs ML algorithms faster, meaning insights can be delivered in seconds, even for complicated questions.

GPUs scale insights generation in four primary ways:

1. GPUs help augmented analytics scale up from a handful of experts to enterprise rollout. For companies to achieve true intelligence with insights, they’ll need enough processing power to support every department and role that needs fast, on-demand answers.
2. GPUs enable proactive analysis, finding insights before business users even ask the question. AI can leverage processing power to understand a user’s interests and behaviors, generating insights that are relevant to them in a newsfeed. As data changes, the platform can highlight notable changes or anomalies without being prompted.
3. GPUs can help to understand the intent behind a user’s question. One question can contain a multitude of questions within it. Nested in a question like “why are sales increasing this year” is the question “explain the drivers of sales success this year, and identify any areas where we were not successful.” GPUs can answer the entire question, quickly, combining automated research with the work of many collaborators to build a coherent, comprehensive and actionable response.
4. GPUs help advance the sophistication of questions that AI can answer. GPUs can support more complex ML algorithms without sacrificing speed or depth, unleashing enormous potential for the future of automated analytics workflows.

Together, augmented analytics and GPUs allow users to get answers to complex questions quickly. Pragmatically, this unique combination is currently implemented with two leading technologies: AnswerRocket’s RocketBots and RAPIDS— an open source initiative started by NVIDIA.

RocketBots

RocketBots are powerful ML algorithms for proactive insight generation.

Here’s how they work:

1. RocketBots are invoked with a natural language question or through scheduled or event-triggered analysis.
2. The RocketBot gathers the data and uses ML to analyze all possibilities, producing meaningful analysis and insights.
3. The RocketBot composes a clear, concise story showcasing the most relevant visualizations and a high-quality insights narrative.

RocketBots allow companies to operationalize data science on demand. After a user asks a question, RocketBots get to work, automating the analytics process so that business people can focus on taking action (instead of waiting for answers or trying to divulge meaning from static dashboards).

Data scientists can also take advantage of this technology by launching their own ML algorithms within AnswerRocket’s platform and adjusting RocketBots based on their deep understanding of the business and its data.

RAPIDS

RAPIDS is a software suite for GPU-accelerated data analytics and machine learning. Pioneered by NVIDIA, RAPIDS enables faster, deeper data insights powered by ML and AI. Because it’s open source, software developers can leverage this technology for their custom needs.

The NVIDIA accelerated computing technology has enabled breakthroughs in AI across industries— from driving intelligent retail to optimizing content creation workflows. It’s no surprise that NVIDIA would power the next wave of disruptive AI-driven analytics.

RAPIDS allows RocketBots to analyze an entire data warehouse and return the best answer at lightning speed. For companies with enormous amounts of data and complex analysis needs, RAPIDS provides unprecedented insights that would take a team of analysts days or weeks to uncover.

Together, RocketBots and RAPIDS can automate game-changing analysis.

For example, the Category Overview RocketBot performs a deep-dive analysis into CPG categories, providing insights tailored to category managers. With this one RocketBot, category managers can:

• Uncover the key contributors and detractors impacting their category.
• Compare category performance to competitors with comprehensive market share analysis.
• Gain insight into product attribute performance.
• Forecast future performance.

Essentially, RAPIDS makes sure that RocketBots leave no stone unturned because they have the processing power to evaluate every aspect of a question, every assumption in analysis, and every intention of the user.

What RocketBots and RAPIDS mean for the future

RocketBots and RAPIDS ultimately enable teams to achieve better business outcomes.

For companies that want to identify and drive growth, leaders need to know more than what they’ve gained or lost. They need to know which gains and losses most affected the end results and where to focus their attention to net the most growth.

When these insights are fast, exhaustive, and intelligent, business leaders can achieve an enormous competitive advantage. Understanding “why” in seconds means companies can move the needle in several respects.

First, companies can reduce analytics costs and better distribute resources to high-priority projects, instead of allocating data analysts to churn through routine reports (reports that often contain outdated insights by the time that they’re produced).

Second, companies can act proactively instead of reactively. While business leaders are waiting days to simply understand what happened last quarter, the market is moving. The ability to quickly gain a 360° picture of performance enables companies to move faster than their competition.

GPUs and augmented analytics can scale insights generation for companies, where traditional BI and analytics tools lack the power to do so.

By operationalizing data science, companies can gain the deep and fast insights that they need to achieve critical business outcomes.

The post Scaling Insight Generation with Augmented Analytics and GPUs first appeared on AnswerRocket.

A sweeping economic downturn further complicates matters, as hard-hit industries face unprecedented shifts in operations.

To manage these changes, business leaders have accelerated adoption of digital tools to maintain operations. Communication and collaboration solutions like Slack, Zoom, and Microsoft Teams have experienced an uptick in usage, as companies strive to keep their business moving in uncertain times.

Though it’s unclear how long the COVID-19 crisis will last, the global pandemic has sparked widespread digital transformation efforts that will make a permanent impact on the way we work.

What is Digital Transformation?

Digital transformation involves reimagining how technology, people, and processes can come together to impact business performance.

At its core, digital transformation enables organizations to meet specific business outcomes, for example: driving greater efficiencies, increasing the quality of customer experiences, or creating new revenue streams.

In the case of sudden remote working requirements spurred by COVID-19, many digital transformation initiatives have included implementing virtual conference software or creating secure VPNs. While fundamental, these solutions don’t address the larger problem companies are facing.

Big picture, the critical challenge for businesses is making decisions in light of an uncertain and changing landscape.

How can digital transformation enable the essential decision-making that will help companies weather the storm?

How Digital Transformation Enables Faster Decisions

Access to timely data is critical for companies to understand what’s happening in their business at any time, but it’s especially important during the COVID-19 crisis.

With daily — even hourly — updates about the virus, consumer behavior is changing rapidly. To react effectively, companies need to quickly assess the situation, understand changing customer needs, and make data-driven decisions.

From Manual Analysis to Augmented Analytics

In this landscape, spreadsheets and static dashboards just won’t cut it. These legacy tools rely on manual, time-consuming analysis to understand and interpret business data.

Without digital transformation, many organizations depend on analysts to manually:

1. Gather, cleanse, and prep data
2. Develop research hypotheses and test each assumption individually
3. Build data visualizations and dashboards
4. Interpret these visualizations to find insights
5. Develop a story or narrative
6. Present their findings to key decision-makers

This process can take days or weeks. In the COVID-19 world, these insights could be rendered completely irrelevant by the rapid pace of change. Businesses simply don’t have time to wait; critical decisions must be made today.

To get to faster decisions, businesses can look to technology like augmented analytics. Cloud-based augmented analytics solutions complement the need for remote accessibility, allowing business people access from both mobile and desktop devices.

Pushing it further, augmented analytics platforms enable business people to ask questions and receive answers on demand, leveraging AI and machine learning technologies to perform the heavy lifting of data analysis.

This capability is incredibly valuable in today’s remote work environment, making it possible for business people to pull analysis and generate insights without relying on an analyst. It’s even more relevant in times of uncertainty, when data analysts are inundated with high-priority tasks, such as ensuring data pipelines operate effectively.

Self-service analytics can alleviate analysis bottlenecks since both business people and analysts can use this technology to get insights in seconds. Compare seconds to the days or weeks of manual analysis. Faster insights enable faster decisions.

How Digital Transformation Enables Better Decisions

Faster insights are critical, but in-depth, actionable insights are just as important.

To deliver more value to business users, digital transformation initiatives can go beyond implementing simple natural language interfaces that allow business people to ask questions.

Today’s AI-enabled augmented analytics solutions are capable of automating entire analysis workflows to deliver exhaustive analysis and richer insights.

Insights Grounded in Domain Knowledge

AI-driven analytics leverages machine learning algorithms to perform analysis based on a deep understanding of the user’s domain. Essentially, business people can ask questions specific to their team, function, and industry.

For example, category managers in the CPG industry are particularly invested in analyses that divulge actionable category insights, such as market share, competitor analysis, and key performance drivers. Analytics that delivers those insights as a complete narrative brings immense value because it applies the right frame of reference to the data.

This level of insight is a competitive advantage as companies scramble to stay ahead of market changes. AI allows humans to understand data in context, without sacrificing the speed of insights.

Unbiased Analysis

In unpredictable circumstances, it’s especially important to remove bias from analysis to understand the root causes behind changes.

Consumer needs can be overlooked or misunderstood when analysts, facing unprecedented urgency, make assumptions to get the business closer to actionable answers.

Assumptions can surface in unexpected ways when it comes to data. Take, for example, this data set regarding technology and loneliness from the Kaiser Family Foundation, the Economist’s survey on loneliness, and Gartner’s Consumer Values Survey.

While survey participants perceived that technology use increased loneliness, the data indicated that the strongest contributing factor to loneliness was location. Technology use, in fact, increased feelings of both connectedness and loneliness, based on self-reported data from the same survey.

Machine learning algorithms identified these insights, addressing questions that many people wouldn’t think to ask.

The changes brought by COVID-19 evade safe assumptions; we’re in uncharted territory. The previous example shows how easy it is for biases to take over, especially when correlations seem obvious on the surface.

Companies can take advantage of unbiased, exhaustive data analysis that tests every data combination in a matter of minutes to uncover hidden data relationships, like key drivers in performance.

Indeed, many digital transformation initiatives have recently been borne out of urgent necessity in response to COVID-19. Organizations are doing their best to adapt to a new way of working.

Regardless of the impetus for these changes, investing in digital transformation will certainly leave many companies in a better place to drive toward business outcomes and mitigate future disruptions going forward.

Digital transformation is one way to gain more control and understanding in an unprecedented situation; however, the challenges of COVID-19 are not easy to overcome, and there are no simple solutions.

Hopefully, understanding what digital transformation offers can help companies factor these opportunities into their decisions. Knowing where companies can gain ground in the future can help inform next steps as employees at every level work through this uncertainty together.

The post Digital Transformation in the Midst of COVID-19 first appeared on AnswerRocket.

NEW YORK, NY / ATLANTA, GA (Feb. 25, 2020) — AnswerRocket and Nielsen’s Global Connect business announced today that Nielsen will leverage the AnswerRocket platform to automate and scale business intelligence and insights generation for the consumer packaged goods (CPG) manufacturers and retailers it serves around the world.

AnswerRocket’s analytics solution provides Nielsen with the means to efficiently scale insights generation using artificial intelligence and machine learning. AnswerRocket’s platform will advance Nielsen’s ability to merge multiple data sources, query data using natural language, apply machine learning algorithms and produce insightful stories to guide decision-making. Further, AnswerRocket’s pioneering RocketBots—advanced machine learning analysis bots—allow Nielsen to automate entire complex workflows, reducing the time it takes to conduct category overview analyses.

“CPG manufacturers and retailers depend on Nielsen Global Connect to provide the trusted data, solutions and insights that empower them to make bold business decisions,” said Waqas Cheema, Nielsen’s Senior Vice President and Global Head of Client Service Delivery. “AnswerRocket will help us increasingly deliver on this commitment, unlocking valuable AI-driven capabilities that help us deliver faster, superior insights to our clients.”

Alon Goren, AnswerRocket’s CEO, said, “Our work with Nielsen aligns with AnswerRocket’s vision of enabling on-demand access to actionable analytics and insights through intelligent automation. With AnswerRocket’s augmented analytics capabilities, Nielsen Global Connect is well-positioned to help clients scale their insights-driven decision-making—a competency necessary to succeed in today’s competitive markets.”

Nielsen’s Marc Taylor, Vice President of Global Client Delivery, will be presenting a Nielsen and AnswerRocket case study at the Category Management Association & Shopper Insights Management Association’s Annual Conference on February 26, 2020 in Dallas, Texas.

About Nielsen

Nielsen Holdings plc (NYSE: NLSN) is a global measurement and data analytics company that provides the most complete and trusted view available of consumers and markets worldwide. Nielsen is divided into two business units. Nielsen Global Media, the arbiter of truth for media markets, provides media and advertising industries with unbiased and reliable metrics that create a shared understanding of the industry required for markets to function. Nielsen Global Connect provides consumer packaged goods manufacturers and retailers with accurate, actionable information and insights and a complete picture of the complex and changing marketplace that companies need to innovate and grow.

Our approach marries proprietary Nielsen data with other data sources to help clients around the world understand what’s happening now, what’s happening next, and how to best act on this knowledge.

An S&P 500 company, Nielsen has operations in over 100 countries, covering more than 90% of the world’s population. For more information, visit www.nielsen.com.

About AnswerRocket

AnswerRocket is the AI-powered analyst for impact makers. Named a Cool Vendor by Gartner, AnswerRocket helps business people make faster insights-driven decisions, empowering them to access all of their data, ask questions in plain English, and get quick answers and insights in seconds. AnswerRocket’s pioneering RocketBotsTM automate time-intensive analytics workflows and proactively generate stories to keep teams informed about business performance, trends, and opportunities. A fully extensible platform allows data science teams to easily operationalize machine learning models, supported by enterprise-grade scalability, administration, security, and governance.

For more information, please visit www.answerrocket.com.

The post Nielsen Selects AnswerRocket’s AI-Powered Analytics Platform to Automate Insights Generation first appeared on AnswerRocket.

To stay competitive, CPGs need to understand the trends that are shaping the industry. What are the major themes underscoring 2020? The rising influence of AI and machine learning on analytics, the need for better insights, and increasing consumer expectations.

Ensure your technology is up to date and your strategy is set to capitalize on these trends.

1. CPGs will take advantage of AI and machine learning to inform decision-making.

It’s estimated that AI and analytics could add as much as $13 trillion to total output by 2030, increasing the annual rate of global GDP growth by more than one percentage point.* These projections for 2030 are rooted in AI advancements that have already started. CPGs will continue to invest in AI in 2020, specifically in regards to decision-making.

AI and machine learning are revolutionizing the way CPGs can use data to make business decisions. CPGs are combining first-party data with syndicated sources, and to make the most of these investments, they’ll look to advanced technology like AI to uncover actionable insights.

In other words, CPGs have tons of data at their fingertips; now, they need help understanding what the data can tell them about how to find growth and what actions they should take to drive the business.

AI provides CPGs with fast data analysis that generates actionable insights, all without the help of a data analyst. Machine learning algorithms can identify performance drivers from hundreds of thousands to millions of data points, so CPG professionals know which factors most impacted their results.

In 2020, CPGs will continue to invest in AI-driven analytics because of the advantages it provides. AI pinpoints the “why” in the data, so decision-makers know where to focus their efforts for the best ROI. Even now, CPGs can leverage AI to understand brand health, market share, trends, performance, and “what if” scenarios.

The use cases for AI will continue to grow, and CPGs will become more comfortable with the automation enabled by this technology.

*McKinsey Global Institute: The Coming of AI Spring
https://www.mckinsey.com/mgi/overview/in-the-news/the-coming-of-ai-spring

2. CPGs will invest in AI-powered innovation analysis.

Innovations are expensive endeavors for CPGs. To succeed, CPGs need to move quickly and accurately; once a new product hits the shelves, there’s a very short window to adjust strategy to either course-correct or capitalize on opportunities.

In 2020, CPGs who invest in AI-powered analysis can reap significant rewards. First, AI can assist with innovation planning by helping CPGs identify growing segments. Second, centralized analytics can assist with cross-functional collaboration by assembling relevant data from every department into a single source of truth.

Once innovations are launched, AI provides CPGs with a fast and deep understanding of performance. It takes time to build distribution, and AI can help fill in the gaps without sacrificing the quality of the analysis.

CPGs who leverage AI for innovations can be more agile, acting during the launch instead of waiting until year-end to get a report on performance, essentially leaving money on the table.

3. The role of CPG category managers will shift.

In 2020, retailers will be well-equipped with syndicated data sources like Nielsen. Plus, they’ve invested in their own data scientists and analysts who build reports and generate insights. As a result, retailers are looking for new insights that they can’t produce themselves.

CPG category managers can fill in that gap and bring new value to the table. Specifically, category managers need to tell a different data story.

Category managers can, for example, leverage data sources across marketing channels. By engaging in social listening, category managers can better understand how consumers talk about products and adjust their strategies accordingly. This kind of analysis will become more critical to the category manager role as retailers continue to invest in their own analytics resources.

Want more? Download the whitepaper to see all 11 CPG Trends.

The post 11 CPG Industry Trends to Anticipate in 2020 first appeared on AnswerRocket.

This TDWI report, Driving Digital Transformation Using AI and Machine Learning, reveals the latest research on the business intelligence and analytics market, as well as best practices for a successful transformation.

TDWI specializes in educating business and IT professionals about the strategies and tools necessary to create, maintain, and enhance data and analytics across an organization.

Designed for business leaders, this report explores AI and machine learning as they’re currently implemented and where these technologies will go in the future.×

Click here to download “Driving Digital Transformation Using AI and Machine Learning.”

This TDWI report covers topics like:

How AI is Used in Digital Transformation Right Now

Why do 90% of respondents surveyed in this report think AI is a competitive advantage?

From automating work to augmenting intelligence, the use cases for AI are growing, as are the investments companies are putting toward deployments.

AI has already arrived. What do businesses need to know to capitalize on this technology before they get left behind?

The State of AI and Augmented Intelligence

Half of respondents believe automating the generation of insights with augmented intelligence tools is a dominant use case for AI.

This report dives further into the ways humans can augment their own intelligence and workflows with AI to make smarter decisions.

Plus, it discusses the nuances of machine learning and why it dominates AI technology. As stated in the report:

“BI and analytics solutions can also employ machine learning to explore data automatically and spot trends and patterns that users working with standard querying and reporting capabilities may not have seen.”

The Case for Open Source AI

Concern over the “black box” and biased insights aren’t unfounded, which is why transparent, open source AI technology is critical.

Survey respondents are “big believers” in tools such as R and Python. Open source AI vendors recognize the value in allowing IT departments to deploy their own models within a proprietary solution.

Success Factors for Digital Transformation with AI and Machine Learning

Do you know what data infrastructure you need to support AI? Quality data and streamlined pipelines are critical. Having the right team is paramount, too.

The best practices outlined in this report explain which users can leverage augmented intelligence analytics and how to determine the skill sets needed for successful AI deployment.

Get the report recommendations for a successful digital transformation with AI and machine learning.

The post How to Drive Digital Transformation With AI and Machine Learning first appeared on AnswerRocket.

Identifying the Problem

At many companies, the approach to business intelligence and analytics is inefficient and unscalable. Worse, most people don’t recognize that this is a problem. Even fewer realize that there’s a simple solution.

Check Your Symptoms

Mark which of these statements apply to you and your organization:

Processes

• You have more analytics requests than you can possibly keep up with.
• Other ad hoc reporting requests aren’t even submitted, because they’ll never get prioritized in time.
• The business units know the way to expedite their requests (and it usually includes name-dropping one of their executives).
• This “fast pass” option is getting used more frequently.
• After completing the initial request, you often get a follow-up request for more analytics. This effort is like starting from scratch and may keep you working late to complete.
• Your team is routinely asked to provide the same types of reports, just with updated data.

Tools

• You already have more than one BI tool, obtained when you or your predecessors tried to fix this problem previously.
• These data analytics tools are not getting used. Or at least not to the degree that was envisioned when purchased.
• Your data is expected to grow significantly in the next few years, and you don’t have a practical solution for how to manage this.

People

• Even though your analytics team has grown in the past few years, the time it takes to fulfill data requests hasn’t improved.
• Your team has a tense relationship with the business teams whose reports you provide.
• You hired data scientists and advanced analytics experts who are stuck answering basic questions like “what were sales by month for product X?”.
• Members of your team tell you they’re feeling burned out or bored by their work.
• Some team members talk about leaving or have already left.

Begin Treatment Now

AnswerRocket provides self-service analytics that is easy to use and powerful. Business users can compile their own reports and dashboards by simply asking natural language questions. What takes days using traditional BI solutions can display within seconds using AnswerRocket.

Request a custom demo to see how AnswerRocket can improve the health of your analytics.

The post The Cure for Data Dysfunction first appeared on AnswerRocket.

One of the latest developments in BI is advanced analytics, or AI-driven platforms that autonomously produce data insights at a greater speed and scale than traditional data analytics. Why are advanced analytics so important right now?

Many business leaders are operating off of data insights that are “good enough” based on the time and labor constraints that limit their depth. In tandem with this data are hunches, gut feelings, and other anecdotal experiences that help inform decisions.

These senses can’t compete against truly data-driven decision-making, especially in competitive markets backed by large sources of syndicated data.

The data analysis brought by AI and machine learning is more than a competitive edge. It’s a solution for common business challenges and problems that are actively hindering growth.

With strategically implemented advanced analytics, businesses can turn data analysis into a revenue-generating investment.

Let’s talk about these solutions in more detail, using AnswerRocket’s advanced analytics platform as an example.

1. Advanced analytics converts insights bottlenecks into insights factories.

Traditional data analytics are often bogged down in cycle of routine reports. Additionally, data analytics software is often technical in nature, revolving around dashboard displays that are created by data analysts and consumed by business people.

This means that business people who want to make data-driven decisions must go through an analyst to find the answers to their questions. This process slows down momentum and actively discourages business people from asking every question and exploring every avenue.

When data analysts produce insights for business people, business people must weigh the pros and cons of follow-ups, where the “cons” can be hours or even days of time spent waiting instead of acting. Similarly, analysts, operating against the priorities of business people, may not have the time to produce insights that are as thoroughly researched as they would be in ideal circumstances.

Plus, the process of manually pulling data from different sources, compiling this data, and filtering it on repeat is time-consuming. When each hypothesis or potential answer is buried in the labor of wrangling data, data analysts simply don’t have the time to test their theories to their full extent.

When each question adds considerable time to the production of deliverables, getting deep in the weeds of data can be a disadvantage. Waiting on reports is the kind of red tape that not only slows projects but discourages an iterative, agile approach.

Advanced analytics changes this dynamic. Advanced analytics solutions, like AnswerRocket, automate the production of meaningful insights, eliminating the human element of compiling and analyzing data.

With the power of AI and machine learning, advanced analytics platforms can research an entire data warehouse exhaustively and intelligently; AI knows what data to look at, where to find it, and how to explain it.

“AI knows what data to look at, where to find it, and how to explain it.”

With the computational power of machines, AI can complete this entire process in seconds.

This fast, deliberate, and comprehensive analysis enables the concept of an insights factory.

The insights factory is a centralized approach to data in which different data sources are integrated for analysis through standardized processes and tools. The machine (in this case, the AI) pulls the data together and automates the production of insights.

When targeted to specific, high-priority use cases, insights factories can deliver quick wins by pointing users toward the data that’s most relevant to their roles and functions. Just as raw materials are turned into unique products, the factory packages data based on market-specific customer and shopper insights.

In other words, insights factories make the data work for the business in a streamlined, efficient way that’s driven by the power of machines.

As such, business people can get faster, more comprehensive insights for every question and follow-up they have. Advanced analytics dismantles the traditional division between business people and data analytics.

Let’s discuss this more in the next section.

2. Advanced analytics enables effective utilization of different roles and teams.

Because advanced analytics automates data analysis, these tools are (or should be) self-service. Self-service analytics enable business people to use the tools directly, without having to go through data analysts (a positive for both roles).

Self-service analytics leverages natural language technology so that business people can ask questions as they would normally in everyday language.

For business people, this means they can get the information they need on their own with instant answers to their questions.

Time that would be spent waiting for a report or acting on hunches can instead be spent on the work with the most value. After all, when your analytics can tell you exactly which metrics impact your KPIs, it’s much easier to prioritize work accordingly.

In a broad sense, advanced analytics allows people to put their time and energy toward the kind of creative, interpretive work that’s currently outside the scope of machines.

Business people can focus on:

• Making decisions. With data at their fingertips, business people can quickly determine what to focus on, with metrics that clearly outline potential growth and/or opportunity in hard numbers.
• Taking action. Once business people know what they need to do, they can start creating campaigns, adjusting sales pitches, and strategizing based on their data. The ability to spend more time doing ultimately supports innovation. Often, it’s not until we put something concretely on paper that we realize the scope of a task, the potential roadblocks, and important follow-up questions. With advanced analytics, business people can quickly ask questions based on these situations as they occur. Meanwhile, the process moves forward.
• Measuring impact. Fast data analytics also enable business people to keep up-to-date tabs on their efforts. Instead of waiting until Q2 to find out how a brand performed in Q1, advanced analytics enables users to get answers derived from the most recent refresh of the data source, meaning that they can measure results and act accordingly.

In this sense, business people can spend their time actually creating, instead of waiting on reports or circularly talking through strategies without data to illuminate a clear path of action.

Similarly, data analysts are freed from the slog of routine reports and can instead focus on working with advanced analytics tools that enable them to use their skills directly. Data analysts and scientists can build custom machine learning algorithms and statistical models to run analyses with open source platforms, for example.

In both cases, advanced analytics facilitate a more proactive and action-oriented workflow, which we’ll discuss in the next section.

3. Advanced analytics facilitates proactive decision-making.

Proactive decision-making enables companies to act more innovatively and secure wins without the time and labor costs of a reactive approach.

After all, knowing how a campaign is performing as it is performing enables business people to turn the tides as necessary. And with targeted insights, users will immediately know what they need to focus on to accomplish their goals.

Further, the risks of testing new products and trying new markets increase exponentially when a team can’t react to the results of the campaign in critical time frames. In this sense, teams may only know the extent to which a campaign succeeded after the campaign has been completed.

The self-service component of analytics is important for generating speedy insights. As more analytics platforms implement AI, technology like natural language processing and generation will become more mainstream.

Advanced analytics that’s truly on the edge of innovation involves even more revolutionary tech that reframes the way business people access their data.

With AI, advanced analytics can actively track data fluctuations and determine which changes a user should know. These changes can be targeted to specific users, roles, and functions.

In other words, AI can proactively generate insights that are relevant to a person, without that person having to ask for the insight.

In this sense, advanced analytics tells you what data deserves your attention. This value cannot be understated; the process of determining what to look at is the beginning of all the data discovery that follows.

Asking the wrong questions can obscure valuable insights that should take precedence. AI can take guessing out of the equation and highlight the data most relevant to performance and trends, proactively.

By staying on top of these changes, businesses can act immediately to capitalize on the biggest opportunities for growth and swerve to avoid pitfalls.

Want to see advanced analytics in action? Check out AnswerRocket’s advanced analytics solution.

The post 3 Ways Advanced Analytics Tech Resolves Business Challenges first appeared on AnswerRocket.

To capitalize on this data, businesses must frame their approach strategically. After all, having the data is not enough to:

1. Interpret and understand the story it’s telling.
2. Determine which data is most relevant to which audience.
3. Instill a culture of data discovery in employees, especially when acting on hunches can be habitual.

Business leaders understand the value of data that’s tailored to each function and the role analytics tools play in the overall employee experience of accessing that data.

In this sense, analytics software that organically promotes data-driven decision-making provides a competitive advantage.

The advent of AI analytics has changed the premise of the conversation. With the automation and augmentation capabilities of AI, analytics tools are no longer facilitators of data analysis but are capable of performing the actual labor that was once unique to humans.

These advancements mean that businesses have an incredible opportunity to capitalize on data (as we’ve mentioned), but they must do so with an eye toward scale, change management, and curiosity culture.

In this article, we’ll specifically discuss the advantages of machine learning analytics and how it fits into the larger picture of AI in business intelligence.×

Learn more about the state of AI in business intelligence with this in-depth eBook for business leaders.

The difference between traditional data analytics and machine learning analytics

Data analytics is not a new development. From the beginning of business intelligence (BI), analytics has been a key aspect of the tools employees use to better understand and interact with their data.

However, the scale and scope of analytics has drastically evolved. Let’s discuss these differences in more detail.

Data Analytics

Traditional data analytics platforms typically revolve around dashboards.

Dashboards are constructed of visualizations and pivot tables that illustrate trends, outliers, and pareto, for example. Technical team members like data analysts and data scientists play a role in constructing these dashboards; generally, the humans are still performing the bulk of the analysis, and the software helps facilitate the results.

Current state analysis with traditional data analytics software looks something like this:

1. The data analyst starts with a core question, likely sourced from a business team. In this case, the question is “how did market share do last quarter?”
2. The data analyst accesses different spreadsheets from different locations.
3. The data analyst merges multiple spreadsheets manually.
4. The data analyst conducts analysis by filtering data based on their hypotheses around market share’s performance. This process is constrained by time restrictions, so the analyst can’t fully test every scenario. As the analyst iterates on their hypotheses, they may need to access data again.
5. The analyst presents the story, or the findings from their analyses. While these stories can be well-researched and accurate, they’re not a complete picture of what’s happening in the data and rely on the analyst’s initial assumptions.

This process is labor-intensive, time-consuming, and often frustrating. Data analysts have advanced skill sets that they can’t use effectively when they’re spending their time stuck in a cycle of routine reports.

The limitations of this process have paved the way for machine learning to take hold in analytics.

Machine Learning Analytics

Machine learning analytics is an entirely different process.

Machine learning automates the entire data analysis workflow to provide deeper, faster, and more comprehensive insights.

How does this work?

Machine learning is a subset of AI that leverages algorithms to analyze vast amounts of data.

These algorithms operate without human bias or time constraints, computing every data combination to understand the data holistically. Further, machine learning analytics understands boundaries of important information.

If asked to identify changes in sales figures, the machine can learn the difference between a $200 fluctuation and a $200,000 increase, only reporting the latter because that’s the info that actually impacts the company.

In other words, machine learning also tests out hypotheses to answer key business questions — but it can test all of them in a much shorter timespan. Think seconds instead of weeks. Then, it tells a data story that’s accurate, exhaustive, and relevant to the person asking questions.

Practically, machine learning is invoked in techniques like:

• Clustering — The machine determines commonalities between different data to understand how certain things, like customers, are alike. These customers can be grouped together in ways that may not be immediately apparent or intuitive to a person performing the same exercise.
• Elasticity — The machine determines causes behind results. If many factors are changing simultaneously, how do you determine which factor is credited with which outcome? This technique tells employees that an increase in household income resulted in boosted sales, rather than product promotions, for example.
• Natural language — The machine maps phrases like “sales” to their coding language counterparts. In this way, business people don’t have to understand R or Python to perform deep analysis. They can simply ask everyday questions like “what were sales in Q2?” and the machine translates those words.

With these techniques, machine learning analytics determines the drivers beneath the data and the opportunities to grow the most.

Significantly, machine learning that invokes natural language is also targeted toward business users who can perform the analysis themselves (a development known as augmented analytics).

Machine learning analytics are taking off…but why now?

The amount of data that companies have access to is much greater now than it has ever been before.

This data is a goldmine for businesses as it can inform the decision-making process, assist with targeting customers and prospects, and deepen the level of analysis that can be performed.

However, as the amount of data grows, so too do the challenges with harnessing its power:

• The roles and functions that make data-driven decisions are often removed from the data itself. CMOs, brand managers, sales teams, and other business-driven roles need data to act, but don’t have the time or training to divulge insights from the data without user-friendly tools or support from technical team members like data scientists and analysts.
• The data itself is more complex. Businesses need to invest resources into data cleaning, structuring, and maintenance to ensure that data pipelines are supported properly.
• The value of data is becoming more apparent. As more businesses invest in syndicated data sources, how do businesses gain a competitive advantage, especially when competitors are accessing the same data?

In tandem with this growth in data is a growth in computational processing power.

Cloud computing, the technology that ultimately supports this data, is becoming more advanced, and machines have more processing power than they have previously.

Companies are investing in both big data and cloud infrastructure.

According to SVP Pete Reilly in this CGT webinar, they’re investing toward an AI-driven end:

“They’ve got all this data available, and now they’re saying, what are the big business problems we could apply this to that would have a huge impact?”

After all, at the intersection between the expansion of data and computational power is machine learning.

Machine learning is essentially what you do with these resources to leverage them as business assets. Without machine learning, companies simply have a sea of disparate information. With machine learning, companies have a hierarchical structure of the information that’s most specific, relevant, and important to each role and function.

As indicated in Reilly’s quote, specific business problems can focus the implementation of machine learning.

Key considerations for data analytics and machine learning

The potential gains from machine learning have enormous appeal, and companies are looking to invest in advanced analytics solutions.

Yet — as with the larger conversation around AI in business — the pathway to successful implementation of machine learning is not as easy as it may appear. Change management strategies are critical for ensuring that employees use machine learning analytics effectively.

Machine learning is new in most industries, and its benefits aren’t necessarily obvious to employees who haven’t been exposed to the larger conversation. This is especially true when employees are concerned about being replaced by automation.

A good implementation strategy is key.

This strategy should be driven by:

• Specific business outcomes that clarify what machine learning analytics will accomplish and automate.
• Alignment between tech and business teams, so that both parties understand the benefits of workforce augmentation.
• Accurate data, supported by system maintenance and AI expertise.
• Change management fundamentals, which are often lost in the excitement of new technology.

These considerations will help ensure that machine learning analytics take root in the business and help employees become more effective in their jobs.

Request a Demo

See how AnswerRocket leverages machine learning to transform data analytics.

The post Data Analytics and Machine Learning: Let’s Talk Basics first appeared on AnswerRocket.

In the past, data storytelling was a data analysis method restricted to data scientists or analysts. This has now changed with the introduction of self-service business intelligence (BI) tools such as AnswerRocket.

Data Visualization as a Tool for Storytelling

Most effective data stories begin with a relevant visualization. Relevance refers to both the visualization’s depiction of the data as well as its usefulness to the audience in question.

For example, a relevant visualization of regional sales figures could employ a map that illustrates these numbers intuitively. For a salesperson who wants to use the visualization in a presentation, the map may be perfect.

For the team member who wants to dive deep into the nitty-gritty numbers, a pivot table may be more appropriate. A visualization tool that’s relevant, and therefore effective, should provide customization options so that said team member can quickly pick the chart type that makes the most sense.

Finding a Data Narrative Using BI Software

Your data story is not complete without a proper narrative.

Historically, data narratives were culled together and put into a report by data scientists and other analytics experts. Then, the business user would be left to draw conclusions and build out data stories from that predefined narrative.

Now, with the help of AI and machine learning, a non-technical user can go straight to their business intelligence tool and ask their first question. For example, a sales leader might be interested in, “What were sales by territory for Q4?” The ability to ask an every day business question in conversational, organic speech is the beauty of natural language processing, one of many technical advancements brought by AI.

Once that answer is generated in a matter of seconds, the user can leverage results to go further down the process of building out a data story. AI speeds up this storytelling process immensely.

Context for your Data Story

When storytelling with data, the audience should be considered when choosing the way data is framed or positioned.

“It’s the context around the data that provides value and that’s what will make people listen and engage” – Gartner.

The context of the data story will help guide the audience to an elevated understanding of the data being presented.

For example, an increase of 10% of sales in Q4 will look promising until provided the context that the goal of Q4 was a growth of 20%. Further, positive trends can overshadow stagnating metrics and opportunities for immense growth if taken at face value.

It is also important to consider who you are building the story for, as a salesperson is likely to have more interest in data that’s actionable for their role than in data that’s targeted to the finance department.

As we discussed before, the ability to ask questions in natural language dramatically increases access for non-technical people, allowing them to tailor data storytelling to their own needs and interests.

Data without context is just that, just data. It can contain certain insights, but these insights can only be unlocked through the use of context. Random data points will mean nothing to the audience or user until you show them what to look at and what to compare them to.

The Importance of Storytelling with Data

Data storytelling can assist non-technical members of an organization by simplifying complex sets of data into digestible, relevant content.

It can empower entire organizations down to the employee level to make informed business decisions to optimize business operations.

AnswerRocket and Data Storytelling

Through the use of an AI analytics tool such as AnswerRocket, data storytelling can be largely automated. AnswerRocket is a query-based data analytics software that can automatically detect trends and other insights based on the questions you ask.

The software is also simple enough for anyone in an organization to use by implementing natural language processing (NLP) and natural language generation (NLG) in the platform itself. This means that a user can ask a question of their data in natural human language, and the system will output an answer in the same easy-to-understand language. U

pon asking questions of your data, AnswerRocket will use machine learning algorithms to analyze a large scope of data to uncover many different insights that may not be visible on the surface level.

These insights can provide a narrative to your data story and answer the “why” questions the user may pose. The platform will also perform analyses on your data to discover the perceived best visualization based on the question asked.

AnswerRocket also provides the ability to continue to ask questions that stem off the first initial query in order for the user to come to a productive conclusion, which is an essential component of data storytelling. In short, using AnswerRocket can provide full data stories and data storytelling tools when asked just one question.

The post Data Storytelling – Explained first appeared on AnswerRocket.

Yet, not all solutions are designed to meet the unique needs of large-scale enterprises. Here, we cover 6 key enterprise-grade capabilities to look for in an augmented analytics platform.

But first, let’s define advanced self-service analytics.

What is Advanced Self-Service Analytics?

First, self-service analytics refers to business intelligence (BI) platforms that allow business users to access and interact with their data directly, instead of relying on a technical team member like a data analyst to compile data for them. 

Self-service analytics enable business people to get the insights they need to act, all while streamlining data access and combating bottlenecks caused by routine reports.

While self-service analytics have grown in popularity, advanced self-service analytics bear distinctive traits that elevate them above their more routine counterparts.

According to Advanced Analytics: The In-Depth Guide:

“Advanced analytics leverages AI-based technologies in business intelligence tools to produce deep insights that help business people uncover and understand the stories hidden in their data. Advanced analytics combines technologies like machine learning, semantic analysis, and visualization to automate analysis.”

This automation of data analysis is key. Next-level analytics should provide deep, contextual, and user-friendly insights for business users without technical expertise– and theses solutions should provide these insights in seconds.

Now, let’s discuss the features needed to scale advanced self-service analytics solutions to the enterprise level.

Enterprise Capabilities for Advanced Self-Service Analytics

1. Open Data Platform

An analytics platform is only as valuable as the data that it’s connected to. As an open solution, AnswerRocket can flexibly connect to your existing data platform, whether it’s on-prem, in the cloud, or a hybrid solution.

We currently support over 25 different data platform providers and are continually adding new ones.

We can also host your data for you.

2. Security & User Administration

Security is a priority for AnswerRocket. Admins can customize functional permissions and set row-, column-, and table-level security to ensure that access to sensitive data— such as employee performance— is carefully controlled and governed.

Admins can also define entitlements guiding access rights at the user-, role-, and group-level. Authentication is handled with Active Directory and single sign-on integration.

3. Metadata Management

What good is self-service analytics if you can’t trust the answers?

AnswerRocket enables users to leverage a centralized semantic model and metadata.

This management feature helps establish a single source of truth capable of generating consistent, accurate answers you can have confidence in.×

See AnswerRocket’s enterprise capabilities with a custom demo of our advanced self-service analytics solution.

4. Data Storage and Loading

One of the biggest benefits of a solution like AnswerRocket is being able to access all of your important data in one place.

ETL tools allow you to extract, transform, and load data into a self-contained storage layer. Easily index data, manage data loads, and refresh scheduling.

5. Support for AI & Machine Learning Libraries

If you have existing AI and machine learning algorithms in use, then having an extensible analytics platform that can leverage those assets is key.

In addition to the AI automation and ML algorithms included out-of-the-box, AnswerRocket also makes it easy for you to build and operationalize your own machine learning algorithms.

Use any open source machine learning library, such as TensorFlow or scikit-learn.

6. Branding and Personalization

Need to provide a seamless experience for your team members?

The AnswerRocket platform can be white-labeled to reflect your company’s logo and branding. Customize colors to complement your style guide. Define your preferred format and default colors for visualizations to ensure they work harmoniously within your company’s templates.

Each of these capabilities is important for an enterprise-grade analytics solution.

Ready to see an example in action?

Get a custom demo of AnswerRocket.

The post Advanced Self-Service Analytics: 6 Must-Haves for Enterprise first appeared on AnswerRocket.

First, Google announced that it would be acquiring Looker on June 5, reportedly to leverage the platform’s data visualization capabilities for Google Cloud services.

Days later, Salesforce announced its upcoming acquisition of Tableau, a deal scheduled to close on October 1st of this year.

It’s not yet clear how these analytics platforms will be made accessible to business users or how they’ll be entrenched in Google and Salesforce’s mainstay software.

Yet, in light of this news, the analytics space is buzzing with questions about the state of the market and the role of analytics in the larger scheme of business intelligence.

What the Looker and Tableau Acquisitions Signal About the Future of Analytics

These back-to-back purchases demonstrate that tech companies are investing in analytics as a critical aspect of their enterprise suites.

Users, perhaps now more than ever, need tools that can interpret the astronomical amounts of data that most companies wield— a demand that Salesforce and Google, in their data-centricity, would be well aware of.

For the average business user, analytics are a means of transforming abstract data into something more actionable. The data visualization capabilities of platforms like Looker and Tableau are one method of providing more insight into metric relationships by indicating general trends, outliers, and so on with customizable graphs and charts.

But, the hunger for analytics solutions seems to point to a larger need for the kind of tangible insights that lead to data-driven decisions.

Beyond visualizations, advanced technology like natural language insights can weave complete data narratives that address the causes beneath the numbers on the surface. Data exists as a complex web of relationships, and trends that can be captured in visualizations are part of a larger story.

The more companies invest in tech that drives insights, the more innovative, intuitive, and routine insights will become in the decision-making process.

As the need for user-driven, insight-fueled analytics in business intelligence software grows, so too has interest from outside software companies, like Google and Salesforce. Likewise, this interest will help propel the analytics market forward, as independent platforms continue to refine their offerings to differentiate themselves with better, faster, and more advanced insights capabilities.

The market is ripe for innovation— which brings us to the next frontier in the space.

AI: The Next Frontier of the Analytics Market

Analytics solutions help users make smarter decisions based on data. In a sense, they enhance our insight and intelligence.

It’s a logical, natural step that the future of analytics will be led by AI. 

AI continues to bridge the gap between a user and the data they seek to interpret. AI is adept at the tasks that are time-consuming, laborious, and often frustrating for employees.

For example, AnswerRocket’s AI-driven analytics software leverages machine learning and natural language technology to generate the data narratives discussed prior. In practice, this means AnswerRocket parses through an entire data warehouse to identify the most important and relevant data relationships, triggered by user queries like “how did Brand A perform last quarter?” or “why are sales down?”

Machine learning algorithms perform this analysis in minutes, a feat that would take a person hours or days, depending on the complexity of the query and the amount of wrangling they’d have to do to gather, prep, and analyze all of their relevant data sources.

AnswerRocket makes quick work of evaluating all potential combinations of data, testing every possibility without bias.

Once the analysis is complete, AnswerRocket generates data visualizations and natural language narratives that explain and reveal hidden insights from the data, such as key drivers, trends, correlations, and anomalies. Where possible, the opportunities with the most ROI are also presented, guiding the decision-making process for business users.

This level of AI is innovative and unmatched in the analytics market.

Learn more about AI analytics with this ultimate guide.

How the Google and Salesforce Acquisitions Can Expand the Breadth of Analytics Use Cases

These acquisitions provide an interesting opportunity to see how analytics can be adapted and refined for the wide variety of use cases that customers of Google Cloud and Salesforce no doubt have.

At AnswerRocket, AI has been the means of tailoring analytics to different roles, departments, and industries.

Specifically, AnswerRocket employs specialized machine learning algorithms purposefully designed to address specific business use cases. CPGs can, for example, easily automate time-intensive market share and brand health analysis with algorithms that break down these metrics in-depth. Algorithms designed to run financial analysis quickly reveal core contributors to top-line growth and bottom-line profits.

We’re continually developing new algorithms for automated analysis to meet the growing and diversified needs of companies who’ve invested in our AI-driven analytics.

The analytics buy-in from Google and Salesforce can similarly lead to more nuanced applications of analytics as a whole. In a future state, companies could come to expect tailored analytics that speak to their unique needs.

Ready to get ahead of the analytics curve? Try AnswerRocket today!

The post Looker & Tableau Acquired: The State of the Analytics Market first appeared on AnswerRocket.

Even more so, it’s a challenge to understand what AI can pragmatically accomplish for the business user.

In this eBook, The State of AI in Business Intelligence, we discuss how AI is revolutionizing BI, and in turn, how employees can be more effective in their roles.

Let’s break down the key elements that we discuss in the eBook and why they matter.

Let’s Talk AI in BI

With this resource, you’ll learn about:

Natural language technology 

Have you heard of natural language processing? You probably use it every day in tools like Google search. BI solutions are catching up with their own implementations.

What about natural language generation? Are you familiar with data insights that can be easily understood by non-technical users?

We cover both topics in depth.

Machine learning

All machine learning is AI, but not all AI is machine learning.

Understand how ML is actually used in BI tools and learn the difference between advanced applications of these algorithms and misleading marketing jargon.

Augmented analytics

Analytics technology designed for a business user is key to turning data into real action.

AI can “augment” our approach to data by handling the heavy processing, so that people can spend more time doing what they do best– creating innovative strategies to reach consumers, instead of wrangling numbers into reports.

And more…

With this resource, our goal is to educate readers on the technical terms they’ve either been hearing or will be hearing soon. AI has already impacted BI, and it’s unlikely to stop.

That’s why we want to both define these terms and explain their implementation in current BI tools, like AnswerRocket. In other words, you’ll get info on the basics of machine learning and how it parses through data to identify causes and correlations that wouldn’t be apparent on the surface of a standard data visualization.

Ready to get started?

Get your copy of “The State of AI in Business Intelligence: The Features You Should Look for Today.”

The post The State of AI in Business Intelligence: The Features You Should Look for Today first appeared on AnswerRocket.