The post Ai4 2024 Session Demo: Accelerating Brand Insights with GenAI first appeared on AnswerRocket.

AnswerRocket is your trusted partner for rapid GenAI results, now offering full-spectrum GenAI services. Take advantage of our team of AI and analytics experts, who bring unparalleled full-stack GenAI capabilities to the table. We focus on delivering results-oriented solutions that drive impactful business outcomes. Our services are tailored to meet your unique needs, ensuring that you receive the most effective and customized GenAI solutions for your enterprise.

Download the Solution Brief

Services Include:

1) Discover

GenAI Technical Assessment

Use Case Identification & Prioritization

1) Design

Enterprise Architecture Design

GenAI Integration Plan

LLM Enablement & Prototype

Data Preparation, ETL & Augmentation

1) Develop

GenAI Solution Development

Vector, Chat, Function & Prompt Engineering

User Interface

Metadata Grounding

4) Launch

Deployment & Rapid Response

Training & Onboarding

Change Management

Roadmap Execution: Use Case Expansion

ROI Measurement

1) Run

Day-to-Day Operation

User Support

Continuous Improvement

Scaling

The post GenAI Consulting Services first appeared on AnswerRocket.

Our latest resource, “Brand Growth Beyond Crisis: Leveraging Pandemic Insights to Future-Proof Your CPG & Retail Strategies,” explores critical strategies for harnessing the power of Artificial Intelligence (AI) and Machine Learning (ML) to steer through turbulent times. Here’s why this guide is a must-read:

Navigate Through Data with Precision

The pandemic taught us that the necessity for precise, timely, and frequent analysis of complex business performance has never been more critical. Augmented analytics—melding AI with natural language generation—empowers businesses to make intelligent decisions swiftly, ensuring that you’re not just keeping up but staying ahead.

Transform Challenges into Opportunities

From enhancing cross-functional collaboration to improving shelf presence and managing market share, AI can transform your operational challenges into competitive advantages. Learn how AI helps you gain real-time insights into market demands, enabling you to make informed decisions rapidly.

Insights from Industry Leaders

Gain inspiration from leading companies like Coca-Cola and Procter & Gamble, who have successfully navigated the pandemic’s challenges by innovating and adapting their strategies. Understand the shifts in consumer preferences and how these giants are leveraging technology to stay relevant and resilient.

Ready for the Future

As the lines between digital and physical shopping experiences blur, understanding and implementing a robust omnichannel strategy is key. Discover how AI and ML are crucial tools in understanding these dynamics and preparing your business for the future consumer landscape.

Embrace the New Normal with AI

The insights offered in “Brand Growth Beyond Crisis” are more than just theoretical—they’re a blueprint for action. By embracing AI and sophisticated analytics, you can ensure that your business is not only reacting to changes but is proactively prepared for future shifts.

Curious to uncover the full strategies and detailed insights? Download the full eBook now and transform your approach to meet the demands of a rapidly evolving market. Let AI be your guide in future-proofing your CPG and retail strategies.

The post Post-Pandemic Lessons Learned: Harness AI for CPG & Retail Growth Amid Crisis first appeared on AnswerRocket.

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

CONTACT INFO

answerrocket.com/cpg

Anywhere, Anytime

POWERED BY

AnswerRocket

Open AI GPT-4

USE CASES

• Investigate business issues & opportunities
• Generate proactive insights & analysis
• Support business planning & strategy development
• Support research projects

ANALYZE DATA FROM

• NielsenIQ
• Kantar
• IRi
• Spins
• NPD
• Toluna

EXPERIENCE

Category & Brand Insights Assistant | Fortune 500 Global Beverage Leader

March 2023-Present

Automating over a dozen analytics workflows for a Fortune 500 global beverage leader, reducing time to insights by 80% and empowering decision-makers to respond quickly to changes in market share and brand equity with data-driven action plans.

Sales Effectiveness Analysis Assistant | Fortune 500 Pharmaceutical Company

March 2023-Present

Helping a Fortune 500 pharmaceutical company generate groundbreaking insights revealing the direct impact of sales activities on market share.

Macro Market Trends Research Assistant | Global Consumer Packaged Goods Leader

March 2023-Present

Empowering a global consumer packaged goods leader to generate insights from unstructured market research alongside structured company performance analysis to better understand and respond to macro market trends.

AI Assistant for CPG Analytics & Insights

Powered by AnswerRocket

An AI Assistant for Category Managers & Insights Teams

ABOUT ME

I’m a generative AI Assistant that helps you explore, analyze, and get insights from your CPG data. Powered by AnswerRocket’s augmented analytics platform and GPT-4, I’m focused on making it easier than ever to get answers to questions about your categories and brands–just by chatting.

CAPABILITIES

Explore Data

Analyze & Visualize Data

Run advanced analysis to understand, diagnose, and predict business performance

Generate Insightful Narratives

Compose easy-to-understand data stories highlighting key insights from analysis

Follow-ups

Answer follow-up questions and pick back up on past conversations in an instant

Automate Analysis

Generate recurring analysis reports
and presentations on a set schedule
or as new data is available

ANALYTICAL SKILLS

• Current Performance: Evaluate your latest performance and track key metric changes
• Competitive Performance: Assess performance against major competitors and spot improvement opportunities.
• Metric Drivers: Identify and drill into the drivers behind metric increases and decreases.
• Metric Trends: Trend business performance over time, spotting outliers, and forecasting future performance.

REFERENCES

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.”
– Sabine Van den Bergh, Director Brand Strategy & Insights Europe

Cereal Partners Worldwide, a joint venture between Nestlé and General Mills

“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.”
– Chris Potter, Global Applied Analytics

Suntory Global Spirits

“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.”
– Abraham Neme, Global Head BI & Analytics

Share Max’s Resume with Your Team

The post Max’s CPG Resume first appeared on AnswerRocket.

Chat with Max for 10x faster insights on your CPG data with generative AI

Conversational UX

Understands your business and data

Skilled in advanced analytics

Turns raw data into actionable insights

Safe and secure

Download the Solution Brief

Get actionable insights for better decisions
across your organization

Insights Teams

Category Managers

Marketing

Field Sales Team

Drive brand and category growth
with the power of AI Analytics

Analyze CPG data just by chatting

Chat with Max to gain valuable insights on your brand, category, market, and customer data – anywhere, anytime. Our integration with OpenAI’s GPT-4 LLM lets you chat naturally in your own words.

Automate recurring brand and category analysis

Say goodbye to manual analysis, while giving your team the same reports and presentations they know and love. Max automates your daily, weekly, and monthly analysis on schedule or upon data refresh.

Tailored to your CPG business

Max is fully customizable to reflect the way your business analyzes, visualizes, and talks about data. With Skill Studio, you can create specialized Skills and AI Assistants to help tackle your unique data analysis needs.

Make Max a GenAI Assistant on Your CPG Team

The post Meet Max, a GenAI Assistant for CPG Teams first appeared on AnswerRocket.

You can get answers to your critical questions just by chatting with Max

Communicating with Max is as easy as speaking to a human analyst.

Ask questions in your own words and get answers you can understand. Interact with visualizations and even ask follow up questions in the same conversation.

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We really enjoyed being a Platinum Sponsor of this event, and connecting with so many great people while we were there!

What is Big Data LDN?

The UK’s leading data, analytics and AI event. Big Data LDN (London) is the UK’s leading free to attend data, analytics & AI conference and exhibition, hosting leading data, analytics & AI experts, ready to arm you with the tools to deliver your most effective data-driven strategy. Discuss your business requirements with over 180 leading technology vendors and consultants. Hear from 300 expert speakers in 15 technical and business-led conference theaters, with real-world use-cases and panel debates. Network with your peers and view the latest product launches & demos. Big Data LDN attendees have access to free on-site data consultancy and interactive evening community meetups. Learn more at BigDataLDN.com.  

While we were there, we met with lots of people at our booth (pictured below) and hosted 3 different sessions. We were lucky enough to have some our great customers and even a partner join us on stage for those sessions.

Check out some of the highlights from the show in the gallery below.

Gangs all here!

A bird’s eye view of the conference floor.

Connecting with the audience at the booth.

Our own Alon Goren alongside Ted Prince of Kantar.

Chris Potter, CPW, Alon Goren, AnswerRocket, Liz Davies, AB InBev

Our presentation with Anheuser-Busch

Creating some content alongside Chris Potter, CPW.

Enjoying the local pub scene after a great day at the show.

Cheers to great partnerships!

If you weren’t able to attend the show, or if you’d like to rewatch one of our sessions, you can click on the session titles below to watch a recording. 

WHAT: How Cereal Partners Worldwide Scaled Data-Driven Decisions with Augmented Analytics & Gen AI

WHEN: Wednesday, Sept 20, 4:40 p.m.

WHERE: Gen AI & Data Science Theatre Session

WHO: Chris Potter, Global Applied Analytics, Cereal Partners Worldwide

Joey Gaspierik, Enterprise Accounts, AnswerRocket

Step inside the transformative journey of Cereal Partners Worldwide (CPW), a joint venture between industry giants General Mills & Nestlé, as they redefine decision-making in the era of AI & Big Data. Hear how CPW modernized its analytics processes, turning to augmented analytics and generative AI to realize their vision for a data-driven culture. We’ll share challenges faced, strategies implemented, and the tangible results achieved in this ongoing journey towards democratized analytics.

WHAT: How Anheuser-Busch InBev Unlocked Insights on Tap with a Gen AI Assistant

WHEN: Thursday, Sept 21, 2:40 p.m.

WHERE: Gen AI & Data Science Theatre Session

WHO: Elizabeth Davies, Senior Insights Manager, Budweiser – Europe, Anheuser-Busch InBev

Joey Gaspierik, Enterprise Accounts, AnswerRocket

Gaining a competitive edge in today’s business landscape requires instant, actionable insights. Hear how global beverage titan Anheuser-Busch InBev is transforming its workflows with AI assistants for automated and ad hoc analysis and insights. We’ll discuss real-world use cases and highlight how Insights teams are empowering their business counterparts to make faster, better decisions at scale.

WHAT: Maximizing Data Investments with Automated GenAI Insights

WHEN: Thursday, Sept 21, 4:00pm

WHERE: X-Axis Keynote Theatre 

WHO: Ted Prince, Group Chief Product Officer, Kantar

Alon Goren, CEO, AnswerRocket

We have more data at our disposal than ever, but extracting true value from it remains a challenge. Generative AI and machine learning have opened up new possibilities for transforming raw data into actionable business insights with unprecedented efficiency and precision. Hear how Kantar, a data and insights leader, and AnswerRocket, a Gen AI analytics platform, are applying these powerful technologies to help companies analyze business performance, forecast market trends, and spot anomalies in seconds. Attendees will gain a comprehensive understanding of how to leverage their data assets more effectively, ensuring every data investment drives business growth and innovation.

If you weren’t able to attend but would still like to connect with us, click the link below. 

Request a demo with a member of our team here.

The post Past Event: AnswerRocket at Big Data LDN first appeared on AnswerRocket.

What is Panel Data?

Panel Data refers to data gathered from a group or “panel” of consumers about their buying habits, brand loyalty, brand perceptions, pre and post purchase behaviors, and more. Information can be gathered from tracking consumer loyalty programs at grocery stores, interviewing panels of consumers about their preferences and perceptions of brands, and also from self-selected respondents who provide post-purchase details. Panel Data is vital to not only understanding your brand’s current position in the market and market share, but also provides insights to help grow your brand for the future.

Companies like Nielsen, IRI and Kantar lead the multi-billion dollar industry, providing valuable information to brands with years of trusted data gathering practices.

“A successful brand strategy starts with an accurate knowledge of consumers and their habits. We track more than 450,000 consumers worldwide who provide us with invaluable information on their household’s shopping decisions.” – Kantar Consumer Panels

The Panel Data Investment

When it comes to the best CPG brands in the business, the question is not if your brand is using panel data, but rather, are you getting the most out of your panel data?

Investing in panel data can cost brands in the hundreds of thousands, if not millions of dollars on an annual basis. The cost is relative to what the perceived payoff will be: increased sales and market share.

Panel data can provide valuable insights to help you gain a competitive advantage

• Brand awareness: How is brand awareness trending? Are there certain segments where awareness is higher or lower?
• Brand equity: How valuable is the brand name in the marketplace? What brand names are more valuable than ours?
• Brand affinity: What customer values does the brand align with? Where do we need to improve on this?
• Pricing/marketing effectiveness: How does our brand’s pricing compare to other brands in the marketplace? Was our recent marketing campaign effective?
• Shopper behavior: How often do customers purchase our brand? What other brands do they purchase at the same time? 
• Competitor tactics: How are our competitors priced? What marketing campaigns are they running?

The Hurdle of Traditional Panel Data Analysis

Once the panel data is acquired, the greatest hurdle is extracting actionable insights from it. The analysis process tends to be very manual and cumbersome which leaves room for human error. For brand managers, this can mean waiting days or even weeks to get the valuable insights needed to inform business decisions, and then not knowing if it’s completely accurate.

The other challenge is the limited ability to drill down further when additional analysis is required. Asking “simple” follow-up questions typically means additional time delays and more manual work on behalf of the analyst or agency tasked with analyzing the data.

This inhibits brand managers from being as nimble and responsive as they’d like to be, and slows down the decision making process.

If you opt to use the services offered by a panel data provider, or have an agency partner that analyzes panel data for you, this can mean facing a different set of challenges.

Whether you’re analyzing panel data internally or using a 3rd party, the results are the same: delays, frustrations, and missed opportunities.

The AnswerRocket Advantage

Use AnswerRocket’s augmented analytics platform powered by natural language search to get answers easily and quickly. Analysis can also be automated to run as soon as new panel data is available. Either way, this means getting valuable insights in hours and not weeks.

Users can easily change metrics like geography, time period, brands, etc. all in real time while simultaneously building out a presentation.

Need to dig deeper? With AnswerRocket you can pick any data point, and select “Drill Down” from the pop up. Then dig deeper into any of the dimensions in the data set. No more waiting on analysts or agencies to spend days to rework the data.

Presentations are then easily shared across teams with the ability to download, email and print right from the AnswerRocket dashboard.

Before AnswerRocket: 

• Brand analysts create quarterly brand equity deck 
• Business teams ask follow-up questions, causing delays and missed opportunities
• Response time for follow-up questions can range from hours to days or not being answered at all

After AnswerRocket:

• Quarterly decks are automatically generated as soon as data is refreshed
• NLQ functionality allows anyone in the business to quickly answer follow-up questions
• Brand analysts have time to do deeper analysis and teams are able to act quickly, resulting in increased productivity and share gains

Panel data is a powerful tool that can help brands better understand their consumers as well as their place in the market. While there are some challenges associated with using panel data, the benefits far outweigh those hurdles. Brands that use panel data can expect to see improved marketing ROI, increased sales, and greater customer loyalty. Analyze your panel data with AnswerRocket and get critical market insights you need NOW.

The post Panel Data Insights: Key to Unlocking Growth 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.

CPGs and FMCGs need to understand their data and insights. Augmented analytics is disrupting how businesses access this vital information. Learn how augmented analytics automates time-consuming analysis and leverages natural language to power insights that CPGs can use to get an edge over their competition.

Speakers:

• Mike Finley, Chief Scientist at AnswerRocket
• Pete Reilly, SVP of Sales, Marketing, and Implementation at AnswerRocket

The post How AI is Powering the Next Wave of CPG Analytics first appeared on AnswerRocket.

Augmented analytics is the combination of natural language generation and machine learning to automate insights. In practical terms, this means businesses can leverage the power of machines to analyze their data in seconds, so employees can spend more time setting business strategy instead of playing catch-up.

Speakers:

• Mike Finley, Chief Scientist at AnswerRocket
• Pete Reilly, SVP of Sales, Marketing, and Implementation at AnswerRocket

The post Using Augmented Analytics to Gain a Competitive Business Advantage first appeared on AnswerRocket.

With 10+ years leadership experience at companies like Unilever and Coca-Cola, Ada Gil has expertise in all things CPG. She discusses how AI can help CPGs better reach their customers with methods like personalization. Learn more about how CPGs can implement AI to stay competitive for the future.

Speakers:

• Ada Gil, Former Marketing Director at Unilever
• Pete Reilly, SVP of Sales, Marketing, and Implementation at AnswerRocket

The post How AI Can Launch CPGs Into the Next 5 Years 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.

Join Ada Gil, former Unilever marketing director, to learn how CPGs and retailers can capitalize on the new opportunities and avoid the dangerous pitfalls of COVID-19. With the power of augmented analytics, CPGs and retailers can make sense of unprecedented data and quickly adapt to new consumer behavior patterns. CPGs and retailers can’t operate with an old mindset; they need to learn quickly and move fast to emerge stronger at the end.

Speakers:

• Ada Gil, Former Unilever Marketing Director
• Pete Reilly, SVP of Sales, Marketing, and Implementation at AnswerRocket

The post Promise and Peril: How Augmented Analytics Can Help CPGs & Retailers Navigate the Post-COVID World 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.

COVID-19 has forced businesses to enact digital transformation years earlier than they’d planned. With so many changes to consumer behavior, teams need tools that can help them sort through vast amounts of data, identify growth opportunities with precision, and quickly understand trends– and to be competitive, this analysis must happen fast. Learn how automation can accelerate digital transformation where it matters most.

Speakers:

• Pete Reilly, SVP of Sales, Marketing, and Implementation at AnswerRocket

The post The Future of Decision-Making: Empowering The Post-COVID Workforce with Automated Analytics 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.

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.

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.

Historically, an enterprise-level CPG would work with a traditional business intelligence (BI) platform that was designed for data scientists and very technical to deal with. Now, similar consumer goods companies can rely on what Gartner has coined to be “augmented analytics” solutions.

An augmented analytics platform uses machine learning and natural language programming to automate insights for non-technical users. In other words, business teams can easily run reports and drill down into data to answer core questions and make informed, data-driven decisions.

If you’ve clicked on this article, you likely recognize the value in transitioning to an AI-powered, augmented analytics solution and have started the search for a platform. To aid in your search, this article breaks down a three-step process for picking the right platform for your CPG.

Click below to jump ahead to any step in the process:

1. Evaluate your current state and determine if new data analytics software is needed.
2. Define what a successful implementation will look like.
3. Review your options and make a decision.

Or, continue scrolling to read the full article.

1. Evaluate your current state and determine if new data analytics software is needed.

Before you start a comprehensive search, you need to look inward and identify the strengths and weakness of your current analytics process.

You can divide your internal assessment into three categories:

1. Review of the actual process
2. Appraisal of the tool(s) you’re already using
3. Consideration of how performance in the first two categories impacts your various departments

First, review the process. The goal here is to understand how a report goes from concept to reality and if there is a possibility for further investigation after the initial report is generated. Think critically about process efficiency and effectiveness.

Key questions to ask include but should not be limited to:

• Does the analytics department field more requests than they can possibly keep up with?
• Do business users frequently decide against ad-hoc reporting because they need an answer sooner than your process allows for?
• Are your data scientists and analysts consistently generating nearly identical reports that just have updated data?
• Are follow-up requests generally as time consuming and labor intensive as initial requests?

As you can tell, if you answered yes to a number of those questions, there are definitely inefficiencies in your analytics process.

You can circumvent those inefficiencies with a self-service solution that uses natural language to let non-technical users drive their own reporting process.

For example, imagine that one of your brand managers wants to see a report for sales by state over the past year. After seeing that report, she decides she wants to also analyze product sales in the southeast over the past year.

With a self-service, augmented solution, that brand manager can handle all of this on her own. As she continues down this path of curiosity, she can also pivot from these “what’s happening” questions to “why is this happening” questions. With a traditional business intelligence solution, that same brand manager is looking at a drawn-out process to even get her first question answered.

After you have reviewed your process, consider the functionality of the tool or tools you’re already using.

• Do you rely on more than one BI tool, acquired over time to solve for issues that a single tool could now solve?
• Do you feel that your analytics solution is not being used to its fullest potential?
• Are you struggling to use your current analytics solution to handle the massive amounts of data you have (both in-house data and syndicated data)?
• Does your tool require you to house your data in a specific format (i.e., cloud-based vs. on-premises)?

Like with the exercise above, answering yes to a number of those questions indicates that you’re in a place to consider finding a tool that works better for a leading CPG’s needs.

Finally, you also want to evaluate how your analytics status is impacting your team members. This evaluation needs to look at both business users and experts like analysts and data scientists, as both camps are heavily affected by the data analytics software that you use.

Consider:

• Are your data scientists and advanced analytics experts stuck running basic reports rather performing high-level work?
• Are you business users so frustrated by delays in the process that they refrain from asking questions and truly leveraging data?
• Has the time it takes to complete data requests remained stagnant, even though you have expanded the analytics department?
• Do your non-technical users have little-to-no ability to use your current tool without assistance?

Answering yes to these questions implies that your analysts and business users are likely equally frustrated by the process. It also implies that your current solution is not efficient or properly utilizing the strengths of your employees.

This breakdown should give ample guidance for your internal evaluation, but if you’d like an even more thorough checklist, head over to our interactive resource, “The Cure for Data Dysfunction.”

2. Define what a successful implementation will look like.

Now that you’ve determined that you do in fact need a new data analytics platform, you should define what a successful implementation will be for your CPG. Then, you’ll be able to use your definition of success as a guiding principle in assessing potential analytics vendors.

Success is obviously subjective and will vary from industry to industry and even company to company. To help define what it means for your consumer goods company, consider three options:

1. Tactical: This kind of objective will be highly quantifiable. For example, you might want to reduce the time it takes users to get business questions answered. You could aim for 25 business questions answered in the time it previously took to answer a single question.
2. Cultural: This category is a bit more of an intangible, although it is certainly valid. You might, for instance, want to encourage more curiosity and data-driven decision making across your business. You can learn more about how curiosity can benefit your company by checking out our case study with SnapAV.
3. Bottom-line oriented: This is probably the first success metric you thought of because the bottom line matters. It is massively important. Maybe you want to insights from analytics to increase revenue by 10%. Maybe you want to reduce churn by a certain amount. Regardless, knowing your hardline goals before shopping can help you narrow your list of potential analytics vendors.

CPGs that are looking to edge out the competition should define success across all three of the above categories prior to shopping. Your goals don’t have to be fixed, but getting into that hyper-specific mindset is how you’ll end up with a solution that meets your company’s full breadth of needs.

Additionally, if you have data scientists or analysts on your team, you should check in with them, too.

A successful implementation would include syncing up your data with your analytics software and ensuring that more extensive functionality is available for the deeper data questions that would be beyond the scope of the average business person’s role.

So, ask your team questions like “what kinds of AI libraries or frameworks should the new solution integrate with?.”

Or, keep it simple and invite your data team to any product demos so that they can ask these kinds of questions themselves.

3. Review your options and make a decision.

For this stage in the process, be as thorough as possible. Any vendor you consider should wow you in with their demo and offer a comprehensive proof of concept.

As you narrow down the list, there are going to be countless points and questions to consider. To help you make sense of all the features that are thrown at you, here’s a list of six questions to ask, accompanied by more detailed follow-ups.

1. Is there truly a user-friendly interface for business users (think: CMOs, brand managers, category managers, etc.)?
   • Interface is user friendly and requires minimal training for end users.
   • Product meets all accessibility guidelines.
   • Product uses a search interface.
   • Product is fully accessible on mobile.
   • Queries can be made by typing or speaking.
   • Product understands queries made in natural language, broad business terminology, terminology specific to a company, and languages besides English.
   • Product resolves terms that have been mistyped.
   • Reports can be scheduled, shared by users and non-users of the product, and printed, exported, and saved as CSV or XLS files or PDFs.
   • Users can add and share comments.
2. Are the data visualizations intuitive and readily digestible?
   • Answers are automatically provided as visualizations, without users having to select a specific type.
   • Product supports common visualization forms needed by the business as well as advanced forms.
   • User can easily change the type of visualization presented, drill into a specific data element in a visualization and ask a follow-up question, drill into a visualization to get the underlying data in a table format, and add notes on the visualization to highlight key activity.
   • Large amounts of data can be represented in a single data visualization.
3. Is there a feature that provides deeper insight or next-level thinking?
   • Product calculates advanced business queries based on questions asked.
   • Product offers advanced analytics — forecasting and predictive analytics.
   • Reports can be compiled into an interactive storyboard.
   • Product produces a natural language narrative based on queries.
   • Related questions are suggested and enable the next query.
4. Is the solution open and flexible to any data source?
   • Data can be stored on-premises.
   • Data can be stored in the cloud.
   • Product can handle incremental data loads.
   • Data import and updates can be handled without vendor involvement.
   • Data can be formatted in numerous ways.
   • Product can handle structured and unstructured data.
   • Product can support large quantities without performance disruption.
   • Data is backed up routinely to allow for quick restoration if needed.
5. Will the vendor be able to get up and running smoothly and efficiently?
   • Deployment can be done in a quick time frame.
   • Product deployment includes user training and a library of help materials.
   • Vendor offers live support during and after business hours.
   • Vendor offers support in languages beyond English.
   • Product includes a means of sending feedback to the administrator and/or vendor point of contact.
6. Will your data be safe?
   • Vendor has taken steps to defend platform against vulnerabilities and hacks.
   • Users can mask data to hide protected / sensitive information.
   • Vendor does nightly backups and maintains backups for agreed-upon length of time.
   • Access to the product is handled via username/password functionality.
   • User privileges are set to determine which data each user can access.
   • Administrators can define and set up roles and groups.
   • User activity is recorded for administrator review, and audit trails exist for successful and failed logins, queries that provide zero results, data imports/exports, and more.

As you can see, this is lengthy, but it is not even fully comprehensive. Use this as a springboard to dive into your vendor research and selection.

For more on the selection and purchase, you’ll want our free eBook on buying analytics software. Download it here.

Related Reading

• Machine Learning in Business Intelligence Solves the Puzzle — If this article has piqued your interest in the intersection of AI and business intelligence software, check out our article that explains just how AI, machine learning, and business intelligence fit together to make an immensely valuable product for the end user.
• Natural Language & Analytics: A Cheat Sheet for Business People — Want to understand more about how natural language options are deployed in analytics solutions? Want to see what natural language accessibility can mean for your company’s bottom line. Check out our in-depth resource on all things natural language.

The post 3-Step Process for Selecting the Right Data Analytics Software for Your CPG first appeared on AnswerRocket.

CPGs can leverage the power of AI to understand the story in their data, eliminate the grunt work of routine reporting, and focus their efforts on strategic growth planning. With AI-powered automation of analysis, CPG professionals can receive a full breakdown of the actions that will impact your bottom line the most. Learn how analytics can make your team more agile.

Speakers:

• Pete Reilly, SVP of Sales, Marketing, and Implementation at AnswerRocket

The post The AI Opportunity: How CPGs Can Turn Speed Into Growth first appeared on AnswerRocket.