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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

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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

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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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Introduction

In today’s rapidly evolving business landscape, data-driven decision-making is paramount. Enterprise organizations require advanced tools and technologies to harness the full potential of their data. One such solution that stands out is AnswerRocket, a platform that integrates generative AI technology throughout the data analysis process. But what sets AnswerRocket apart, and why should analytics and insights leaders take note? In this blog post, we’ll highlight perspectives from AnswerRocket’s Co-Founder and CTO, Mike Finley to explore the unique approach that makes AnswerRocket a game-changer in the world of data analytics.

Understanding Generative AI at AnswerRocket

AnswerRocket’s approach to generative AI extends far beyond merely answering questions. It’s about understanding data long before the questions are asked and comprehending both the queries and the database results. Generative AI isn’t just a tool; it’s an integral part of the entire analytical process. This differentiates AnswerRocket from augmented analytics solutions that rely solely on question-answer mechanisms, making it a true AI assistant in data analysis for everyone in an organization.

What Makes AnswerRocket Different?

A crucial aspect that distinguishes AnswerRocket is its evolutionary journey. Over a decade of development, the platform was continually refined even before incorporating large language models. The unique combination of traditional capabilities with modern language models enables AnswerRocket to be enterprise-ready, secure, governed, reliable, and powerful. It’s not just about providing answers; it’s about narrating the story hidden within your data, making it relatable and actionable.

The Power of Skills in AnswerRocket

Skills in AnswerRocket are fundamental units of analysis. Your business might have its unique way of forecasting sales, and no large language model can replace that. However, AnswerRocket empowers you to create Skills that encapsulate these analyses, seamlessly integrating them into the language model conversation. The platform offers a range of pre-built Skills and the flexibility to create your own using the Skill Studio. This approach ensures that insights specific to your business become a part of your enterprise analytics arsenal.

Connectivity and Data Access with AnswerRocket

Accessing and connecting to diverse data sources is a common challenge for enterprise organizations. AnswerRocket, from its inception, aimed to tackle this challenge. It can connect to traditional data sources like SQL databases, DAX, and relational models, but it goes further. It seamlessly integrates with unstructured data sources, including emails, PowerPoint presentations, and PDF documents, transforming “dark data” into valuable insights. This capability enables AnswerRocket to access all the same documentation and training that a human analyst would, serving as a true AI assistant in your analytical journey.

The Magic of Skill Studio

The Skill Studio within AnswerRocket is where the magic happens. It empowers you to combine data from disparate sources in innovative ways, giving you a comprehensive view of your business landscape. With the ability to access structured and unstructured data, as well as real-time data through APIs, AnswerRocket can provide unique and thoughtful insights. It’s not just about reporting the weather and sales; it’s about understanding the causal relationship between the two. This capacity to merge different data sources makes the language model’s analysis invaluable to your business. With Skill Studio, developers and analysts can “teach” their AI assistant how it should analyze your data, what kinds of insights it should be looking for, and even how the findings should be presented. It’s the way that enterprises can capture their specific data analysis processes and methodologies and enable an AI assistant to do that work for them.

Conclusion

AnswerRocket represents a new frontier in the world of generative AI-powered analytics. Its unique approach, blending traditional capabilities with modern language models, allows it to offer secure, reliable, and enterprise-ready insights. The power of Skills and the flexibility of the Skill Studio make it adaptable to your business’s unique needs. Moreover, its unparalleled connectivity and data access capabilities ensure that no data source is out of reach. 

For analytics and insights leaders at enterprise organizations, AnswerRocket is more than just a tool; it’s a strategic assistant in your quest for data-driven success. Embrace the power of generative AI, and see how AnswerRocket can transform your data into actionable insights. The future of analytics is here, and it’s waiting for you to unlock its full potential with AnswerRocket.

Blog image by pch.vector on Freepik. 

The post Unlocking the Power of Generative AI with AnswerRocket first appeared on AnswerRocket.

Enterprise organizations that hope to better understand their business have more access to information than ever before. Data can come from a multitude of sources, with very little lag time between when it is collected and when it is delivered to the analysts who will leverage it. 

The problem that analytics and business users have faced for years isn’t the availability of data, but how they analyze and extract insights from that data. The current state of data analysis is manual, non-standardized, time-constrained, and biased. 

And the problem of data analysis isn’t limited to analytics and insights teams, it creates a snowball effect that affects the entire organization. 

In this blog we’ll talk about the current state of data analysis and how AnswerRocket aims to simplify data analysis and democratize insights for all. 

The Vicious Cycle of Data Analysis

Companies usually have no shortage of tools that they’ve invested in, dashboards to access, recurring reports to review, and other tools that they can review to evaluate their performance, but this typically only gets them so far. These views of data typically prompt follow-up questions and require further exploration to understand:

What is going on?

Why is this metric up or down?

These are questions that business users or key decision makers who are trying to take an action can’t easily answer on their own. This means they then have to involve an analyst or insight team member who has to dig into the data, compile supplemental data, evaluate it, interpret it, and then translate it into an answer that the business user can understand. 

Analysts, Business Users, Executives and more can all relate to the “vicious cycle” of gleaning insights from their organization’s data.

This then prompts more follow up questions, and the vicious cycle continues. This process is currently very time-consuming, and inefficient. 

Why Does This Matter?

Organizations that are stuck in this vicious cycle are likely experiencing one or more of these side effects as a result:

• Wasted time and resources
• Wrong strategies and tactics in the market
• Missed company targets
• Vulnerability to competitors

The challenge to analysts and insights leaders is, “How do we get people the answers they need, FASTER?” 

Faster insights means better, informed business decisions can be made more quickly, which can mean the difference between outpacing your competitors or getting left behind.

Who Does the Vicious Cycle Affect?

The vicious cycle of data analysis is a broad enterprise problem that doesn’t just stop at the analysts’ desks. At the core of this challenge is the fact that despite the vast amounts of data available, gleaning actionable insights remains a significant stumbling block. 

• For executives, this cycle means strategic decisions are often delayed or are based on incomplete insights, potentially resulting in poor market tactics or even missing crucial business targets.
• Business users, typically in need of quick answers for operational decisions, find themselves stuck in a loop of follow-up questions for which they are often reliant on analysts to help answer.
• Analysts, entrusted with the task of data analysis and interpretation, often find themselves overwhelmed by the demands of multiple stakeholders, which they juggle alongside business-critical initiatives.
• For data scientists, this process can be particularly frustrating as the tools and dashboards they’ve crafted, while advanced, still don’t yield the streamlined, quick insights the business desperately seeks.

What do these personas need to break out of the vicious cycle?

What Does the Vicious Cycle Look Like in Real Life?

Global Pharma Manufacturer

This pharmaceutical manufacturer has been trying to understand their performance in the market, as it relates to the sales team’s performance. 

They have a huge sales force that is in the field talking to doctors and hospitals on a regular basis, promoting the company’s prescriptions. The ability to analyze the efforts of this sales force to see what’s working, what isn’t working and change tactics on the fly is crucial to the success of the company.

Up until now, they have never been able to combine their market performance with what their sales time is doing. Questions like:

• How is our sales team impacting a change in the market?
• Does more prescriptions written equate to more share in the market?

AnswerRocket developed the omnichannel sales driver copilot, combining data that has never been combined in the past: market performance + sales teams efforts. Sales leaders can now see what’s working, what’s not, what’s driving growth, what to avoid, all with the goal to drive additional share gains.

By implementing this copilot and follow up questions, this allows them to pinpoint regions or districts that are struggling (to help) or pinpoint areas that are succeeding and implement what they’re doing in other areas. 

Global Beverage Leader                       

This multinational enterprise has prioritized working with brand equity data to drive their global marketing efforts and pricing strategies. This data captures how consumers perceive  their brands in the marketplace. 

In the past, someone like the CMO might ask “why is brand equity down in Europe?” and then everyone would drop what they’re doing and try their best to tell a story from very complex data.

Their data provider partner does not currently have great tools for storytelling. They have dashboards, but that’s it. 

With the Max brand guidance copilot, they can  glean insights and answer questions faster than ever before,  taking a 21-day process each quarter down to just 5 days (a 75% reduction). 

Previously, it was nearly impossible to keep a “real time pulse” on what was going on in the marketplace. But now that they’re able to cut out about 4 business weeks of wait time, they’re able to be as agile as they want to be. 

How are We Breaking the Vicious Cycle?

With our first-of-its-kind AI assistant for data analysis, Max, we put the power of data analysis in the hands of everyone in your organization, not just a select few. No matter your role, your technical experience, or what your insights goals are, Max is here to help. 

By integrating with the power of GPT with AnswerRocket, Max allows users a chat-based analytics experience that allows them to ask questions in basic, conversational English, and get answers back in plain English. By getting “beyond the dashboard” and avoiding complex reports, there is no longer a steep learning curve. 

This element of self-service allows business users and executives to  investigate changes on their own, and ask follow up questions on the fly for deeper insights. 

Check out this 90-second Max demo below to see more of what Max can do. 

Bringing Max to the Enterprise

At AnswerRocket, we have been working with each individual customer to thoroughly understand the problems they’re experiencing. Each customer situation is nuanced, and has a workflow that we need to fit into.

We like to understand:

• What is the current state of data analysis in the organization?
• What should the future state of data analysis look like?
• What is their vision for what an AI assistant could do?
• What should the output look like?
• What insights are they missing today that they would like to have?

The process is very consultative and individualized. 

We are trying to understand the skills that are required to solve each customer problem. Information such as what are the data sources and formats, how do we insert our tool to make it a seamless part of their existing workflow?

In this way, we can co-create a purpose-built analytics copilot that addresses our customer’s needs head on. 

Max is helping to achieve the AnswerRocket mission more than ever before, automating meaningful insights to help decision-makers take action, faster. 

The post Breaking the Vicious Cycle first appeared on AnswerRocket.

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.

Watch the video below or read the transcript to learn more.

Ryan: My name is Ryan Goodpaster. I’m one of the sales guys here at AnswerRocket. Been with the company for six years. And what we do is we help our customers get insights out of their enterprise data in seconds, using techniques like natural language processing, natural language generation, AI, and machine learning, model integration, and now GPT. 

How does AnswerRocket help CPG’s accelerate data analysis?

Ryan: We’re fairly industry agnostic, but we have a pretty heavy focus in consumer goods. We help them with a lot of their internal data, their third party data, like Nielsen and IRI and Kantar. This data is really important to a lot of departments, so they see a lot of value across lots of different business areas in their companies. 

How does AnswerRocket uncover insights from syndicated market data?

Ryan: Every month, the Nielsen data updates. And for the most part, it takes a company maybe a week or two to get through a comprehensive analysis of what’s going on with their business. With AnswerRocket, you can do a full deep dive in seconds, and you don’t have to wait a week or two. So you get those insights much faster and you can really figure out what to do, what actions to take with those insights, and get that to their customers even quicker. 

Why are customers excited about Max, our AI assistant for analytics?

Ryan: Most everybody that I’ve talked to is really excited about Max. They want Max yesterday. So we are working very hard to deliver that to them, especially our current customers. I don’t see very many people being scared of it, because it’s one of those things where you have to get on board with it or you’ll get left behind, right? Everybody and every company that I’m talking to is trying to figure out how do we leverage GPT, not only for analytics, but across the entire organization? How do we make ourselves more efficient in these current times? 

Conclusion: AnswerRocket’s industry-agnostic approach is particularly beneficial for consumer goods companies, where they provide valuable insights by leveraging both internal and third-party data sources like  Nielsen and IRI. Our solution enables CPGs to accelerate data analysis, allowing them to dive deep into syndicated market data within seconds, facilitating quicker decision-making and actions based on the obtained insights. Customers are excited about Max, which is powered by GPT, as it promises enhanced efficiency and competitiveness across organizations. 

The post Transform CPG Analytics with AnswerRocket: Max, the AI Assistant for Accelerated Insights first appeared on AnswerRocket.

Will One Large Language Model Rule Them All?

While single-model language systems have been groundbreaking, they have limitations such as biases, inflexibility in handling different tasks, and the risk of overfitting. The multi-model approach offers a solution by combining the strengths of multiple models. By using different models for specific tasks, businesses can enhance their data analytics capabilities and overcome the limitations of relying solely on a single dominant model.

The Evolving Landscape of Large Language Models

In the near term, it is unlikely that a single large language model will emerge as the dominant player. Instead, we can expect a range of models tailored for specific tasks and excelling in their respective domains. Looking further into the future is challenging, but ongoing development will continue to improve both domain-specific and general models. Companies will invest in building robust large language models that cover a wide array of knowledge and information. Instead of narrowing their focus, these models will incorporate multiple domains and subjects, creating a diverse ecosystem of models.

A Nuanced Approach: Leveraging Multiple Models

A multi-model approach recognizes the limitations of a single dominant model and embraces the diverse capabilities offered by different models. By combining domain-specific and general models, businesses can achieve more accurate and contextually relevant language understanding and generation. This approach allows for the utilization of smaller, specialized models that excel in specific areas, providing more relevant insights.

The Benefits of Multiple LLMs in a Multi-Model Language System

Multi-model language systems integrate multiple Large Language Models (LLMs), each specializing in different areas. This approach offers several advantages:

– Businesses can leverage the unique strengths and expertise of each model. For example, one model may excel in generating creative content for a digital marketing agency, while another might be proficient in assessing market trends for a consumer goods manufacturer. This blending of models enables businesses to achieve comprehensive language processing capabilities.

– The potential applications of multi-model language systems are vast. In the pharma sector, integrating LLMs trained on medical literature, patient treatment therapies, and clinical trial data could facilitate drug research, development, and improved patient outcomes. Likewise, in insurance, combining models trained on claims data, policies, and regulatory documents could enable accurate predictions, effective risk management, and regulatory compliance.

The Future of Multi-Model Systems

The future of multi-model systems is incredibly promising. Ongoing research and development will likely lead to even more advanced capabilities and increased efficiency. Businesses are progressively adopting multi-model approaches to leverage their data analytics endeavors.

However, it’s crucial to acknowledge the challenges and limitations. Developing and refining multiple Language Learning Models (LLMs) require significant resources and expertise. Ensuring seamless integration and addressing biases inherent in individual models are complex tasks.

Multi-model language systems are reshaping the world of large language models. By combining the strengths of multiple LLMs, businesses can unlock new levels of language understanding and generation. The advantages of a multi-model approach over a single-model system are clear: enhanced capabilities, broader applicability, and improved performance. Executives in the data analytics space should recognize the potential of multi-model language systems and incorporate them into their AI strategies. Continued research and development will be vital in harnessing the power of a multi-model world and shaping the future of data analytics. Embrace the potential, explore the possibilities, and embark on the journey towards a multi-model future.

The post The Future of LLMs: Embracing a Multi-Model World first appeared on AnswerRocket.

The existing augmented analytics solution has done a considerable amount to democratize data within organizations, but the newest AnswerRocket offering, Max, furthers that mission leaps and bounds. Max is a ChatGPT-like AI assistant for data analysis that creates a truly conversational analytics experience. 

Read the transcript of the interview below or watch the video on our YouTube channel:

Question: What are the current pain points around data in organizations that AnswerRocket works with?

Joey Gaspierik: First, there’s a lot of it. Everybody has a ton of data. It’s everywhere. It’s in spreadsheets, pivot tables, dashboards, usually hundreds and thousands of these different things across the bigger organizations. Also there’s not many ways for them to access this data. You have dashboards that have been delivered to the business, but the business can’t answer the questions that they really have from these dashboards, like, hey, why is this KPI up or down? Why am I losing volume? Why are our margins in trouble over the past couple of months? That’s really where they run into these walls over time because of the manual ways that they’re reviewing this analysis and Excel spreadsheets and pivot tables, and also just the limitations from a dashboard perspective where they have filters and different dashboards, and where’s that dashboard that I used to answer that question last week. 

I’d say the biggest problem is around how much data, the overall volume of data that exists, and just the inability for business users to get to the answers that they need. 

Question: How does Max alleviate customer pain points?

Joey Gaspierik: AnswerRocket alleviates those pain points by giving users a very approachable, a very easy way to answer business questions. Instead of having to be a data scientist or analyst or “I’ve got an MBA in some advanced analytics capability,” I can just be a business user. I can be a salesperson, marketer and have a question; 

     Why is my brand up? 

     What’s going on with my brand? 

     Why is my market share up? 

     Why are my sales up? 

     What should my pricing be, right? 

I have these questions, and I can just ask them AnswerRocket and get answers immediately from the data. I have really the ability to be curious about anything that data allows me to be curious about. I’m not restricted to the questions that I can ask. I like to say this: It reduces the cost of curiosity to zero, as a business user.

Question: Who are the AnswerRocket power users?

Joey Gaspierik: I would call them the data literate folks in the organization, the ones who know the different metrics, the different KPIs, who understand the types of business questions and how to answer them. You’re going to find analysts, insights teams, maybe different types of sales analysts that are plugged into different parts of the business. I would say the primary power users right now are the folks who are closest to that data that have to then do that analysis manually, and who traditionally have relied on Excel spreadsheets and hours upon hours of digging through raw data to try to find answers. Those are the folks who, when they get AnswerRocket and can turn these answers around in seconds, it really just adds a ton of value to their day to day and then ultimately back to their organization because they’re able to get to these answers so quickly. 

Question: How does Max change the profile of power users?

Joey Gaspierik: Max really changes everything from many different perspectives. First off, those users who traditionally have been close to the data or like I said, data literate, it really opens the door up to those less data literate folks. I don’t want to make them sound like they don’t know anything about data. These are the folks that have the questions; 

     Why is this happening? 

     What’s going on? 

     What’s this problem? 

     What are the key drivers behind that? 

Historically, these are the users that have never been able to answer their own questions, have relied on other folks in the business, the analysts, the different insights teams. Now, Max presents this really approachable way to do business analysis, where I can ask questions in plain English, open ended questions, and have a conversation with an AI agent that can help me find answers to my business questions. Without ever having to tag an analyst or that insight person, which then allows those insights and analyst teams to focus on way more value added tasks that can continue to add value to the company and allows me, as a business user, to get that answer, make a decision and ultimately find growth. 

Question: What are customers saying about Max?

Joey Gaspierik: What are our customers saying about Max? Well, they love Max. They love the ability to have this AI agent that they can interact with. First off, it’s such a great opportunity for them to solve this challenge of turnover or of bias in decision making as it relates, because every human that’s digging through data has to put their bias aside and say, maybe it’s not that thing that happened last month. Maybe it’s a new thing. Max being AI machine learning driven, max has no bias. Max looks at the data and he gives you insights on why things are happening and where that growth is coming from. Customers see this as a huge competitive advantage for them to make decisions, to get to answers quickly, and to really move faster than their competition in the marketplace. 

Question: What is Max?

Joey Gaspierik: Max is a new feature from AnswerRocket that essentially is an AI assistant for your data analysis. Max is a chat experience where it doesn’t matter what time of day where you are in the world. You can ask Max a question about your business data and get answer and not just get any simple answer. Max has different advanced capabilities built in to where not just telling you what happened, but what’s going to happen next, why something might happen. What happens if I raise my price 6%? How is that going to impact my sales? Max is one of the smartest analysts in your business and available all the time. And, oh, by the way, he’s powered by AI machine learning. Max is able to answer any question quicker than anyone in your company can because of that compute power. 

In Conclusion

There is an overwhelming volume of data in organizations and it is difficult for business users to access the answers they need. AnswerRocket addresses these challenges by offering an easy-to-use solution that allows users to ask questions and receive immediate answers from their data. Their AI assistant, Max, empowers both data-literate users and less data-savvy individuals to explore insights and make data-driven decisions. Customers have praised Max for its unbiased insights and competitive advantage, while its chat-based interface and AI capabilities provide fast and accurate responses, surpassing traditional manual analysis methods.

The post Max: Relieving Data Analysis Pain Points first appeared on AnswerRocket.

ChatGPT Enters the Scene

ChatGPT launched in November 2022 and within 5 days it had reached one million users, shattering every previously held record by an online service. Users piled on to create free accounts and dive into the new technology headfirst. Some were just curious, others wanted to understand the rapidly changing artificial intelligence landscape. Still others, like AnswerRocket, wanted to understand how to utilize this new technology to the benefit of their customers.

Making Waves in AI and Beyond

There’s a concept from marketing guru Seth Godin that talks about the elements that make up the much sought-after “brand crush.” A brand crush is a love affair that consumers have with a brand that they can’t live without. They consume said brand frequently, talk about it fervently, and no substitute will do.

The elements that create a brand crush? Magic and Generosity. 

The magic of ChatGPT is easy to see. OpenAI has taken the power of artificial intelligence, plus billions of data points, and put them together to provide users answers within seconds. It was unlike any tool any of us had ever seen and we couldn’t wait to show our friends. 

The generosity piece of the equation is that ChatGPT is completely FREE. That’s right. Some of the most powerful technology in the world, boxed up with a ribbon on top and handed over to the world. 

THAT is how they reached over one million users in just 5 days. They created a brand crush between ChatGPT and the world, and it went viral. 

What Can Analytics Leaders Learn from ChatGPT?

In a world where a 25% adoption rate is accepted as the norm, analytics leaders can borrow a few tricks from ChatGPT’s playbook to spark adoption of analytics tools within their own organizations. 

1. K.I.S.S. (Keep It Simple Stupid)

Your non-technical users are more likely to be intimidated by complex processes and dashboards that stand in the way of getting the answers they need. So make it unbelievably easy to use, right away. 

Upon signing in, ChatGPT users are greeted with a simple dashboard that outlines some basic examples, capabilities and even limitations of the platform, followed immediately by a search bar where users can enter their prompt for the tool. 

Recent activity is saved in the right hand column, making it easy for users to resume previous activities. Also on the right side near the bottom are the standard account/settings/help options. 

That’s it. No fancy bells and whistles, no setup, no training, just create an account and you’re in, ready to start using the tool. 

Analytics leaders can lean on this example when preparing their teams to use a new analytics platform.

→ Identify what content is the highest priority to review, and limit yourself to only showing that content. This way, you avoid overwhelming users with too much information to start.  

→ Focus on meeting people where they are now, instead of what they could be doing with the tool in the future. A gradual build up of question complexity grows confidence. While ChatGPT 4 has the ability to write code for a website, you can bet most users are not starting there.

→ Start with simple functions and walk users through the process, step-by-step at a slow pace. This allows time for and creates an environment that welcomes open dialogue and questions.

2. Build Off Existing Skills

Part of why ChatGPT is so successful is because the general public has already learned how to interact with a chat interface. If you handed someone who’s never had a computer an open laptop with ChatGPT up – they’d really struggle. But ChatGPT shines because it knows that its most common user base is made up of folks who are familiar with this kind of setup. It’s natural to find the text box, use a “send” button (paper airplane) and continue to interact.

→ For analytics leaders, find what’s familiar to your users and their skill sets. This could be building off of presentations they’re used to reading, or referring to another tool (like Excel or PowerPoint) to ground explanations in your analytics tool.

3. Deliver Immediate Value

In the same way that users could create a free ChatGPT account and immediately get a recipe for that evening’s dinner, users want to see immediate value in the tool.

Because of the simplicity of the interface, ChatGPT users can choose to dive right in if they decide to. 

When preparing their own teams to use a new tool, analytics leaders can lean into the ChatGPT experience by offering a thoughtful first experience. Loading data, testing, and training prep all go a long way to make sure the solution is polished and functional when your team sees it for the first time.

→ Keep training sessions streamlined and focus on delivering a quick win. This helps create excitement and drive adoption.

→ Address a couple of key use cases in your training sessions and demonstrate how the tools improve upon the current processes.

→ Offer resources for users to dive-deeper on their own AND be available for one-on-one problem solving sessions. This allows users to get answers in the best environment for them.

4. Make it fun!

While it may seem counterintuitive for data analysis to be fun, delivering a useful tool that makes the 9 to 5 a little bit easier should make anyone happy, be ready to share your excitement!

→ Offer prizes for participants who ask or answer questions during your training.

→ Sprinkle humor into your presentation and deck.

→ Take frequent breaks, encourage open dialogue and ask about current pain points. You’ll learn a lot about the team you’re working with, and create trust. 

→ Remember people want to be talked to, not at. 

5. Find your raving fans

As users piled into the ChatGPT platform and discovered how easy it was to use and how much fun it was, excitement grew and word spread quickly. In a sense, ChatGPT made mini-influencers out of all of us.  

We’ve found some of the best ways to drive adoption of our augmented analytics solution in various organizations is by spotlighting your most excited, most passionate users. This goes beyond your own role as an analytics leader, as you’ll need to find advocates on each team that you’re working with. 

→ These individuals talk the talk, understand the hurdles and headaches, and can help relay unvarnished feedback back to you.

→ Showcase the success stories of your users and the different ways they’re getting value from your analytics tools. When users can see their own peers succeeding, it makes it seem less intimidating and within their reach.

In Conclusion
In just 5 days, ChatGPT launched and broke records by acquiring a million users. Its overnight success can be attributed to a viral brand crush born of the generous sharing of “AI magic” for free. 

The good news is: brand crushes aren’t just reserved for leaps in AI technology. No matter the industry or the audience, leaders can fuel crushes for their own brands by embracing magic and generosity.

Magic can be as simple as a tool that works, and makes life easier. 

Generosity may not be in the price of the platform itself (free won’t work for most business models) but can come in the teamwork, relationships, and resources of a mutually beneficial partnership. A thoughtful, intentional roll-out will help teams focus on the immediate value your platform brings. The continued collaboration of an engaged team makes all the difference for success.

The success of ChatGPT serves as an inspiration for analyst leaders who are trying to create a similar following in their own organizations.

The post What Analytics Leaders Can Learn from ChatGPT first appeared on AnswerRocket.

The Data Democratization Dream

For those who recognize the power of data-driven decisions in business, the “data dream” typically looks something like this: Plentiful, rich data with insights to unlock future success. A business intelligence or analytics platform to analyze the data. Employees of varied skill sets using data to glean insights and make decisions across multiple functions. An organization that is data-literate, data-efficient, and truly benefitting from data democratization. 

So what is it that stands in the way of that info-utopia? If the data is there and the technology exists, what is preventing that widespread wildfire of adoption that data leaders dream of?

The Obstacles to Widespread Adoption

“The percentage of employees actively using BI/analytics tools is currently 25% on average.”
– Strategies for Driving Adoption and Usage with BI and Analytics Survey, BARC and Eckerson Group, March, 2022

A 25% adoption rate may be the start of data democratization, but it’s still a long way off from getting the majority of your organization to use a tool.

While business intelligence and analytics tools have taken big strides to cater to business users, they are still viewed as “something else to have to learn.”  In an already busy workday where resources–people, time, budgets–are spread thin. Some of the most common challenges to adoption include:

• Technical Barriers
  Employees who aren’t as skilled in data analysis may be uncomfortable with the perceived Excel mastery or SQL knowledge needed to use these tools.
• Resource Limitations
  Teams whose functions are not directly tied to analytics may hesitate to allocate valuable resources to learn and utilize BI and analytics tools. Instead, they lean on insights teams, data leaders, and analysts to provide insights when they can.
  
• Staying Stuck
  If a team is using a tool that isn’t 100% of what they need, the idea of migrating their data and learning a new tool is just too daunting to undertake. Usually, if the current solution is working “well enough” for now, the team hobbles on for as long as possible.

Utilizing the Power of ChatGPT to Analyze Data 

With the launch of ChatGPT, a chatbot from OpenAI, users now have the ability to ask questions and have artificial intelligence parse through billions of data points in real-time to provide an answer within seconds. The underlying large language models used by ChatGPT–GPT-3, GPT-3.5-turbo, and GPT-4–have demonstrated huge advancements in using AI to understand and respond to natural language prompts.

At AnswerRocket, we saw the power of these large language models and saw the opportunity to pair them with our augmented analytics solution to create a first-of-its-kind, chat-based analytics experience. 

Meet Max

Max is the latest offering of AnswerRocket that brings the power AI + Machine Learning and the ease of conversing with a chatbot together to offer users an AI assistant for data analysis. 

• No Technical Barriers
  Using Max is as easy as sending a text or instant message. Because Max requires no Excel skills, no SQL knowledge, and no analyst on-hand to get answers, the traditional obstacles to adoption are removed. Our hope is that instead of the traditional adoption rate of 30% for BI & analytics tools, Max will see closer to a 60% to 75% adoption rate in the organizations that utilize it.

Max leverages OpenAI’s large language models to understand user prompts and compose the answers that teams need quickly through conversational analytics. And just like a new coworker that you would onboard, Max can be trained to understand your preferences and objectives as you interact with it. 

• No Additional Resources to Learn
  Because using Max is as easy as speaking to a coworker, there’s no need to set aside days or weeks of training to users up to speed on how to use the platform. We’ve designed the experience to enable users to get started quickly, with sample questions and in-tool guidance.
• Use the Data You’re Using Now
  You can analyze the same data you’re already working with in the AnswerRocket platform. Leverage our data connectors to connect Max to your data warehouse or upload CSV files for analysis.

A tool like this would be much less intimidating, much less time-consuming, and much more widely used among teams.

A simplified, chat-based analytics experience can be the linchpin to a data analysis tool that organizations will actually willingly adopt. 

The Future of Analytics is Here

By incorporating accessible technology that enables a ChatGPT-like user experience for analytics, data leaders will be that much closer to achieving true data democratization within their organizations. 

Imagine the efficiency of multiple business teams, all over the world, operating on insights they’ve gleaned themselves from data that’s been underutilized for years? Mind blowing. 

Being an early adopter of this technology could give your business a competitive edge in the marketplace. 

We’d love to share Max with you and your team. Join the waitlist now.

The post Driving BI & Analytics Adoption with ChatGPT first appeared on AnswerRocket.

AnswerRocket was excited to be a platinum sponsor of the 2023 Category Management and Shopper Insights Conference at Caesars Palace. It’s an event we look forward to each year and this was our fifth year participating. We were pleased to meet and network with many of the 900+ attendees and host our own Emerging Technology Session.

We partnered with Abdul Raheem, Director Global Analytics Lab of Mondelēz International, to speak to a packed audience of about 200 attendees.

The topic? How ChatGPT and AI will help consumer packaged goods companies get faster category insights. 

The Challenge

Mondelēz was experiencing the same pain points that most large companies have when it comes to their data. That frustration is that while dashboards and reporting are great, they can cause you to fall into a vicious cycle of waiting hours, if not days, to answer a question. If you have a follow up question or want to dig deeper into the data points, that will take even longer.

As one might assume, mountains of cumbersome data and time delays are less-than-conducive to success in the competitive snacking industry.

Why Augmented Analytics?

Augmented Analytics uses AI and machine learning to drastically cut-down on the amount of manual analysis that business users and analysts have to do, to reach their end goal of actionable insights. 

What They Used Augmented Analytics For

For Mondelēz, the switch from traditional to augmented analytics allowed them to “increase their computation efficiencies.” They no longer had to depend on a data scientist to create a model for a specific scenario, they could now enable business users to do that on the fly using AI and machine learning to interact with their own data sets. This means faster insights and outcomes for all involved.

Mondelēz leveraged AnswerRocket’s augmented analytics platform to solve a variety of business problems: 

• Predicting the impact of COVID stimulus on cookie demand
• Forecasting Easter Egg sales in Brazil to avoid overstocks and buy-backs at the end of the season
• Numerous other Nielsen data use cases globally and locally

Increased access to these insights and forecasts gave Mondelēz the competitive advantage that they value as a pillar in their business. 

However, despite the power of augmented analytics to help overcome their data hurdles, driving broad adoption across the organization was still a challenge.

The Next Leap in Analyzing Data with AI

Abdul is excited about the future of data analysis which includes pairing ChatGPT technology with an augmented analytics platform.

Joining the two technologies enabled AnswerRocket to create  an AI assistant for data analysis that’s available anytime, anywhere, to chat about your data and provide insights. We’re calling it Max.

The implications of offering an on-demand analyst are huge.

Max In Use

For companies who are building Max into their future data analysis plans, what does that look like?

And just like a coworker who you might have a conversation with, Max can be trained to understand your preferences and objectives through reinforcement learning. 

Because of how easy it is for anyone, in any role, with any skillset to use, Max removes traditional “adoption obstacles”. The team at AnswerRocket anticipates a much higher adoption rate across organizations than what we typically see with business intelligence tools. Where most BI tools can expect to see about a 20 to 30% adoption rate, we hope to see closer to 60 to 75% adoption rate by organizations utilizing the Max platform. 

Max gives us the tremendous ability to put AI + Machine Learning + Answers in the hands of entire teams.

So why not just use ChatGPT on its own?

ChatGPT is a large language model that’s been trained on the internet but it doesn’t understand your business, your playbook, or your customers. You can ask ChatGPT general questions about your industry, but it won’t be able to provide insights into your brand’s specific data.

How Will ChatGPT + AI Accelerate Category Insights?

Category Managers and Buyers want data-driven insights, but they typically don’t want to spend most of their time in the weeds analyzing data and learning new BI tools. A conversational analytics experience powered by ChatGPT removes that burden by giving end users an approachable, familiar chat interface to interact with. 

An AI-powered data analyst empowers category managers to tell a better story, faster, to their buyers. It gets users out of data wrangling and into decision making and market strategy. 

Democratizing Data. Equipping Teams to Make Better Decisions.

ChatGPT has done more to bring AI to the masses in the last 4 months than we’ve seen in the last decade. By offering access at no cost to everyday users, ChatGPT is encouraging widespread adoption and use by any-and-everyone for any-and-everything. This effectively removes the “shroud of mystery” around AI as being this big, intimidating, expensive thing for use only by the most intelligent minds for the most complex tasks. 

ChatGPT is making AI usable for everyone.

Because of this, more and more teams are leaning into AI and trying to understand how it applies to them, and how it can be used in their jobs to make their lives easier, regardless of industry or specialty.

In the augmented analytics space, the need for “doing more with less” – less budget, less time, less people, is more prevalent than ever. Analysts are swimming in a sea of data with more questions to answer and insights to provide. 

Combining the capabilities of ChatGPT with an augmented analytics platform means more insights for more teams, faster than ever before. It is the democratization of data in its truest form.

Taking the Next Leap

From traditional analytics, to augmented analytics, to an AI assistant for analyzing your data, organizations are only getting more agile and competitive with each step in the evolution. Abdul shares the AnswerRocket team’s excitement for the future of chat-based analytics. Though the technology is in its early stages, the implications of offering an on-demand analyst are huge. 

Attendees of our session got a sneak peek at AnswerRocket’s ChatGPT-based analytics solution. We encourage you to reach out to us if you’d like to see what we’re working on. 

We loved the opportunity to visit with everyone at the conference and chat with our friend Abdul about the changing world of augmented analytics. We look forward to seeing you again next year at CMA|SIMA!

The post ChatGPT & AI: Accelerating Category Insights 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.

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.

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

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

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

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

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

What are Data Science Unicorns and the Last Mile Problem?

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

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

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

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

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

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

In other words: frustration on both sides.

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

Solving the Last Mile Problem (Without Data Science Unicorns)

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

To perform the analysis, data scientists must:

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

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

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

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

This is where the Last Mile Problem takes hold.

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

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

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

Simply put, augmented analytics:

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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.

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.

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

Data Visualization as a Tool for Storytelling

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

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

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

Finding a Data Narrative Using BI Software

Your data story is not complete without a proper narrative.

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

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

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

Context for your Data Story

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

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

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

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

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

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

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

The Importance of Storytelling with Data

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

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

AnswerRocket and Data Storytelling

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

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

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

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

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

The post Data Storytelling – Explained first appeared on AnswerRocket.

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.

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

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