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

Navigate Through Data with Precision

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

Transform Challenges into Opportunities

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

Insights from Industry Leaders

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

Ready for the Future

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

Embrace the New Normal with AI

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

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

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

Addressing CPG-Specific Challenges with AI

Integrating AI into data analysis isn’t without its hurdles. Here are some challenges specific to the CPG sector:

• Data Collection Issues: CPG companies pull data from many sources, like sales and supply chain info, which makes consolidating data tough.
• Quality and Privacy Concerns: Keeping data accurate and navigating privacy regulations is crucial.
• Integration and Analysis Obstacles: Siloed data and the complexity of integration pose significant challenges, along with the usual struggles with BI tool adoption and finding data science talent.

The key to overcoming these challenges is a strategic approach to data management, focusing on data quality, privacy compliance, and breaking down data silos.

The Transformative Impact of AI on CPG Analytics

AI’s potential in CPG analytics is immense. Beyond automating processes and improving safety protocols, AI shines in data-driven decision-making. Tools like AnswerRocket’s Max make complex analytics accessible through natural language processing, democratizing data analysis across all organizational levels.

Forward-Looking Solutions for Data-Driven CPG Companies

CPG companies aiming to leverage AI for a competitive edge should focus on:

• Actionable Data Insights: Emphasize conversational data exploration and custom AI assistants tailored to specific needs, like brand analysis and SKU rationalization.
• Data Preparation and AI Integration: Ensure comprehensive data collection and preprocessing to make CPG data AI-ready.
• AI-Powered Predictive Analytics: Use AI for demand forecasting, sales and marketing optimization, and customer segmentation.
• Supply Chain Optimization and Sustainability: Utilize AI for predictive maintenance, route optimization, and achieving sustainability in operations.

The Future of AI in CPG

As the CPG industry evolves, integrating AI into analytics and strategic decision-making will enhance operational efficiencies and pave the way for innovative solutions to market challenges. The journey towards AI adoption might be complex, but the rewards—faster insights, better decisions, and a competitive edge—make it essential for today’s businesses.

To download the full eBook, “Generative AI Accelerates its Impact on CPG Analytics,” click here.

The post The Rise of Generative AI in CPG Data Analytics first appeared on AnswerRocket.

The Challenge of Hallucinations in Data Analysis 

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

To combat LLM hallucinations, AnswerRocket employs several key strategies:

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

The Path Forward: Trust and Transparency in AI

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

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

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

CONTACT INFO

info@max.ai

www.max.ai

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

COMPATIBLE DATA WAREHOUSES

• Amazon RedShift
• Azure Synapse Analytics
• Google Big Query
• Snowflake

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 Data Analysis

ABOUT ME

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

Beam Suntory

“With Max, Beam Suntory can automate routine tasks and gain valuable insights from data, allowing us to make more informed decisions. I see the potential for Max to become a powerful tool for analyzing a combination of external, macro, and internal data.”
– Abraham Neme, Global Head BI & Analytics

Share Max’s Resume with Your Team

The post Max’s Resume first appeared on AnswerRocket.

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

CONTACT INFO

answerrocket.com/cpg

Anywhere, Anytime

POWERED BY

AnswerRocket

Open AI GPT-4

USE CASES

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

ANALYZE DATA FROM

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

EXPERIENCE

Category & Brand Insights Assistant | Fortune 500 Global Beverage Leader

March 2023-Present

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

Sales Effectiveness Analysis Assistant | Fortune 500 Pharmaceutical Company

March 2023-Present

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

Macro Market Trends Research Assistant | Global Consumer Packaged Goods Leader

March 2023-Present

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

AI Assistant for CPG Analytics & Insights

Powered by AnswerRocket

An AI Assistant for Category Managers & Insights Teams

ABOUT ME

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

CAPABILITIES

Explore Data

Analyze & Visualize Data

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

Generate Insightful Narratives

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

Follow-ups

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

Automate Analysis

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

ANALYTICAL SKILLS

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

REFERENCES

Anheuser-Busch InBev

“A chat-based tool like Max can help more users feel comfortable interacting with data. Having an on-demand assistant that can quickly answer the questions that pop up throughout the day would enable our team to make data-driven decisions at scale.”
– Sabine Van den Bergh, Director Brand Strategy & Insights Europe

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

“Max will take AnswerRocket to the next level! We need our teams to make informed, fact based decisions. Max will enable users across all levels of CPW to quickly access data and insights through intuitive questions and responses.”
– Chris Potter, Global Applied Analytics

Suntory Global Spirits

“With Max, Beam Suntory can automate routine tasks and gain valuable insights from data, allowing us to make more informed decisions. I see the potential for Max to become a powerful tool for analyzing a combination of external, macro, and internal data.”
– Abraham Neme, Global Head BI & Analytics

Share Max’s Resume with Your Team

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

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

Conversational UX

Understands your business and data

Skilled in advanced analytics

Turns raw data into actionable insights

Safe and secure

Download the Solution Brief

Get actionable insights for better decisions
across your organization

Insights Teams

Category Managers

Marketing

Field Sales Team

Drive brand and category growth
with the power of AI Analytics

Analyze CPG data just by chatting

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

Automate recurring brand and category analysis

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

Tailored to your CPG business

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

Make Max a GenAI Assistant on Your CPG Team

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

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

The post AI Vision: The Future of Data Analysis first appeared on AnswerRocket.

ATLANTA—Dec. 12, 2023—AnswerRocket, an innovator in GenAI-powered analytics, today announced the launch of Skill Studio, which empowers enterprises to develop custom AI analysts that apply the business’ unique approach to data analysis.

“Skill Studio has immense potential to transform our approach to analytics,” stated Stewart Chisam, CEO of RallyHere Interactive, a platform for game developers to run multi-platform live service games. “With Skill Studio, we can create a customized AI analyst that deeply understands the nuances of the gaming industry and how we analyze our data. Its ability to automate complex analyses like game performance, player interactions, and usage patterns is groundbreaking. The insights generated by Max can help drive strategic decisions to enhance both our platform and user experience.”  

Say hello to specialized AI copilots

AI copilots have emerged as a powerful tool for enterprises to access their data and streamline operations, but existing solutions fail to meet the unique data analysis needs of each organization or job role. Skill Studio addresses this gap by providing organizations with the ability to personalize their AI assistants to their specific business, department, and role, which enables users to more easily access relevant, highly specialized insights.

Skill Studio elevates Max’s existing AI assistant capabilities by conducting domain-specific analyses, such as running cohort and brand analyses. Key enhancements include:

• Full Development Environment: End-to-end experience supporting the software development lifecycle for developers to gather requirements, develop, test, and deploy Skills to the AnswerRocket platform. Skill Studio allows developers to leverage the Git provider and integrated development environment (IDE) solution of their choice.
• Low-Code UX: User-friendly interface for developers and analysts to create customized Skills for the end users they support.
• Reusable Code Blocks: Accelerate custom Skill development by leveraging pre-built code blocks for analysis, insights, charts, tables, insights, and more.
• Bring Your Own Models: Skill Studio extends the analytical capabilities of Max, enabling enterprises to deploy their existing machine learning algorithms within the Max experience.
• Multi-source and Multi-modal Data Support: Analysts can perform complex analyses using multiple data sources, including structured or unstructured through a single tool. This allows businesses to glean insights from siloed data sources that were previously inaccessible.
• Create Purpose-Built Copilots: Construct copilots designed for specific roles by giving them access to the Skills needed to perform a set of analytical tasks.
• Quality Assurance & Answer Validation: Testing framework for validating accuracy of answers generated by Skills. 

“AI copilots have revolutionized the way organizations access their data, but current solutions on the market are general-use and not personalized to specific use cases,” said Alon Goren, CEO of AnswerRocket. “Skill Studio puts the power of AI analysts back in the hands of our customers by powering Max to analyze their data in a way that helps them achieve their specific business outcomes.”

A collaborative experience for creating fit-for-purpose AI assistants

Skill Studio enables cross-functional design, development, and deployment of AI copilots:

• Data Scientists and Developers: Technical team members can democratize specialized data science algorithms and models as reusable Skills that can be leveraged by Max, enabling users to successfully retrieve the advanced answers they need quickly and securely.
• Analysts: Analysts can customize Skills and Copilots to capture their company’s best practices for analyzing and retrieving insights from data. This allows repetitive, manual data analysis processes to be executed by Max for automated analyses. 
• Business Users: Users can enjoy an easy-to-use experience for interacting with their data by chatting with an AI analyst who understands their business, analytical processes, and insights needs.

For more information on Skill Studio, please visit: https://answerrocket.com/skill-studio/

About AnswerRocket

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

Contacts

Elena Philippou
10Fold Communications
answerrocket@10fold.com
(925) 639 – 0409

The post AnswerRocket Unveils Skill Studio to Empower Enterprises with Custom AI Analysts for Enhanced Business Outcomes first appeared on AnswerRocket.

What is machine learning? What are the business implications of AI? How can these advancements in technology impact analytics and add dollars to your bottom line? We discussed these topics at TAG Summit 2018; now you can watch this educational, academic-style talk in this webinar.

Speakers:

• James Hunter, Product Manger at AnswerRocket

The post How Human is the Machine first appeared on AnswerRocket.

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.

We loved being a Diamond Sponsor for this event in Atlanta, right in our own backyard!

What is COLLIDE?

The collision of data and industry is at Data Science Connect’s COLLIDE Data Science Conference. This event showcased the latest trends and advancements in data-driven decision making and how it is revolutionizing industries such as healthcare, finance, and marketing.

Learn more at https://datasciconnect.com/events/collide-2023/.

If you were unable to attend the event, you can watch our session recordings, linked below:

Tuesday, October 3rd, 2:50-3:10pm
Copilot Cooking Show: How To Build a GenAI Assistant for Analytics
With Mike Finley, AnswerRocket Co-founder, CTO and Chief Scientist

In this quickfire session, we’ll demonstrate how AnswerRocket enables enterprises to create customized GenAI-powered assistants for data analysis. Key ingredients include OpenAI’s GPT LLM, AnswerRocket’s augmented analytics platform, and tough business questions. Come see how you can apply these game-changing technologies to produce a custom AI assistant that boosts your team’s analytical productivity.

Wednesday, October 4th, 2:50-3:10pm
Bridging the Gap: From AI Hype to Real-world Impact
With Pete Reilly, AnswerRocket Co-founder and COO

Artificial Intelligence, with its seemingly endless potential and promise, is now front and center as a hot topic in boardroom meetings and strategy sessions. But how does a business move from awe and curiosity to actively realizing benefits in real-world scenarios? This session is tailored to steer organizations from mere contemplation of AI’s power to the tangible and transformative results it can deliver.

Are you interested in learning more about adding a Generative AI Analytics Assistant to your team?

Request a demo with a member of our team here.

The post Past Event: AnswerRocket at COLLIDE 2023 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.

ATLANTA—Sept. 19, 2023—AnswerRocket, an innovator in GenAI-powered analytics, today announced new features of its Max solution to help enterprises tackle a variety of data analysis use cases with purpose-built GenAI analysts.

Max offers a user-friendly chat experience for data analysis that integrates AnswerRocket’s augmented analytics with OpenAI’s GPT large language model, making sophisticated data analysis more accessible than ever. With Max, users can ask questions about their key business metrics, identify performance drivers, and investigate critical issues within seconds. The solution is compatible with all major cloud platforms, leveraging OpenAI and Azure OpenAI APIs to provide enterprise-grade security, scalability, and compliance. 

“AI has been reshaping what we thought was possible in the market research industry for the past two decades. Combined with high-quality and responsibly sourced data, we can now break barriers to yield transformative insights for innovation and growth for our clients,” said Ted Prince, Group Chief Product Officer, Kantar. “Technologies like AnswerRocket’s Max combined with Kantar data testify to the power of the latest technology and unrivaled data to shape your brand future.”

Following its March launch, AnswerRocket has been working with some of the largest enterprises in the world to solve critical data analysis challenges using Max. Highlighted applications of the GenAI analytics assistant include:

• 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 market share and brand equity changes with data-driven action plans.
• Helping a Fortune 500 pharmaceutical company generate groundbreaking insights revealing the direct impact of sales activities on market share.
• Empowering a global consumer packaged goods leader to quickly respond to macro market trends by generating insights from unstructured market research alongside structured company performance analysis.

“Today’s enterprises demand instant insights, and the traditional methods are no longer sufficient on their own,” said Alon Goren, CEO, AnswerRocket. “Max is enabling several of the world’s most recognizable brands to understand better what’s driving shifts in their business performance, effectively turning their vast data lakes and knowledge bases into a treasure trove of business insights.”

Max’s advanced capabilities solidify its position as the first GenAI assistant for data analysis built for the enterprise. Enhancements to the solution include:

• Customizable Analyses: Out-of-the-box Skills used by Max for business performance analysis, including search, metric drivers, metric trend, competitor performance, and more. AnswerRocket also offers support for custom Skills using enterprises’ own models. Skills can be configured to reflect unique business rules, processes, language, outputs, etc. 
• Structured and Unstructured Data Support: Max supports both tabular and text-based data analysis, allowing companies to glean insights from vast enterprise data, documents, and multiple data sources seamlessly in a single conversation.
• Automation of Routine Analysis Workflows:  Max can execute multi-step analytics processes to free up analyst time for more strategic projects while giving business stakeholders timely analysis and self-service answers to ad hoc follow-up questions.
• Integration with Third-party Tools: Embed the Max chat experience into tools like Power BI, Slack, Teams, and CRMs, enabling users to analyze their data in tools they’re already using.

“Max brings forward a seismic shift in how companies can transform their data into actionable intelligence with unprecedented speed,” continued Goren. “With Max, everyone within the enterprise can have immediate access to an AI analyst, providing them with prescriptive recommended actions and helping to guide them towards data-driven decisions.” 

AnswerRocket is a Platinum Sponsor of Big Data LDN, taking place on September 20-21, 2023 at Olympia in London. They will be showcasing their revolutionary GenAI analytics assistant, Max, alongside early adopters of the technology in three sessions:

• Wednesday, September 20 from 4:40 – 5:10 p.m. – How CPW Scaled Data-Driven Decisions with Augmented Analytics & Gen AI (Chris Potter, Global Applied Analytics, Cereal Partners Worldwide; Joey Gaspierik, Enterprise Accounts, AnswerRocket)
• Thursday, September 21 from 2:40 – 3:10 p.m. – How Anheuser-Busch InBev Unlocked Insights on Tap with a Gen AI Assistant (Elizabeth Davies, Senior Insights Manager, Budweiser – Europe, Anheuser-Busch InBev; Joey Gaspierik, Enterprise Accounts, AnswerRocket)
• Thursday, September 21 from 4:00  – 4:30 p.m. –  Maximizing Data Investments with Automated GenAI Insights (Ted Prince, Group Chief Product Officer, Kantar; Alon Goren, CEO, AnswerRocket)

For more information on AnswerRocket’s industry-leading solutions, please visit: https://answerrocket.com/max.

About AnswerRocket

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

Contacts

Vivian Kim
Director of Marketing
vivian.kim@answerrocket.com
(404) 913-0212

The post AnswerRocket’s GenAI Assistant Revolutionizes Enterprise Data Analysis 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 of the interview to learn more.

How is AnswerRocket making these emerging technologies enterprise-ready?

Mike: I would start simply by saying that the idea of keeping data secure and providing answers that are of high integrity is table stakes for an enterprise provider. Making sure that users who should not have access to data don’t have access to it and that the data is never leaked out, right? That’s table six for any software at the enterprise. And so it doesn’t change with the advent of the AI technology. So AnswerRocket is very focused on ensuring that data flowing from the database to the models, whether it’s the OpenAI models or other models of our own, does not result in anything being trained or saved. So that it could be used by some third party, so that it could be leaked out, so that it could be taken advantage of in any way other than its intended purpose. So that’s a core part of what we offer. 

The flip side of that is, as you mentioned, many of these models are sort of famous at this point for producing hallucinations, where when you under specify what you ask the model, you don’t give it enough information, it fills in the blanks. It’s what it does, it’s generative, right? The G in generative is what makes it want to fill in these blanks. AnswerRocket takes two steps to ensure that doesn’t happen. First of all, when we pose a question to the language model, we ensure that the facts supporting that question are all present. It doesn’t need to hallucinate any facts because we’re only giving it questions that we have the factual level answers for so that it can make a conversational reply. The second thing we do is when we get that conversational reply, like a good teacher, we’re grading it. We’re going through checking every number, what is the source of that number, is that one of the numbers that was provided? 

Is it used in the correct way? And if so, we allow that to flow through and if not, we never show that to the user, so they never see it. A demonstration is not value creation, right? A lot of companies that just kind of learned about this tech are out and are out there demonstrating some cool stuff. Well, it’s really easy to make amazing demonstrations out of these language models. What’s really hard is to make enterprise solutions that are of high integrity that meet all of the regulatory compliance requirements that provide value by building on what your knowledge workers are doing and making them do a better job still. And so that’s very much in the DNA of AnswerRocket. And it’s 100% throughout all the work that we do with language models. 

How can enterprises avoid data leakage and hallucinations when leveraging GPT?

Pete: A lot of the fear that you hear people saying, oh, I’m going to have leaking data and so on, a lot of that’s just coming from ChatGPT. And if you go and read the terms and conditions of ChatGPT, it says, hey, we’re going to use your information, we’re going to use it to train the model. And it’s out there. That’s where you’re seeing a lot of companies really lock down ChatGPT based on the terms and conditions that makes sense. But when you look at the terms and conditions of, say, the OpenAI API, it is not using your data to train the model. It is not widely available to even anybody inside of the company, it’s removed in 30 days and so on. So those are much more restrictive and much more along the lines of what I think a large enterprise is going to expect.

You can go to another level. And I think a lot of our, what we’re seeing is a lot of our customers, they do a lot of business with, say, Microsoft. Microsoft also can host that model inside the same environment that you’re hosting all your other corporate data and so on. So it really has that same level of security that if you trust, say, Microsoft, for example, to host your corporate enterprise data, well then really trusting them to sort of host the OpenAI model is really on that same level. And what we’re seeing is large enterprises are getting comfortable with that. And in terms of hallucinations, as Mike said, it’s really just important how we use it. We analyze the data, we produce facts, and there are settings in these large language models to tell it how creative to get or not. And you say, don’t get creative, I just want the facts, but give me a good business story about what is happening. 

And then we also provide information to the user that tells them exactly where that information came from, traceable all the way down to the database based, all the way down to the SQL query so that it’s completely audible in terms of where the data came from and being able to trust. 

In Conclusion

Analytics and Insights leaders who wish to utilize ChatGPT technology in their organizations have to balance the possible rewards with the risks. We’re committed to providing a truly enterprise-ready solution that leverages the power of ChatGPT with our augmented analytics platform to securely get accurate insights from your data. By providing AI models with complete and correct supporting facts, we can eliminate the possibility for hallucinations and maintains full control over the generated responses in the platform. Furthermore, we use astringent grading process to validate the AI-generated insights before presenting them to the users. 

The post Preventing Data Leaks and Hallucinations: How to Make GPT Enterprise-Ready first appeared on AnswerRocket.

The post AnswerRocket and Your BI Tech Stack: The Perfect Match 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.

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 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. 

What problem does AnswerRocket help solve?

Ryan: Most of our customers, when they first come to AnswerRocket, they’re leveraging something like a dashboard to track their business performance. And when they see something has changed, a number has gone up or down, their next question is typically, why? And it takes a long time to answer that. It’s a lot of manual analysis. And so we leverage AI and machine learning and now GPT to take a natural language question like, why are my sales down? And help them get an answer to that in seconds so that they can spend more time doing what they were hired to do. And that’s being human and coming up with solutions and strategy, not digging through data. 

How does AnswerRocket improve enterprise data analysis?

Ryan: The solution to the problem of “it takes too much time to get through data” is I need to hire more analysts. But what I’m sure everybody has seen in the marketplace and in the talent pool is there’s just not enough analysts to hire. Right? And it’s hard to retain them. So by giving them things that make them more efficient and get them better answers to serve up to their customers, you have happier analysts. They’re more efficient, and you don’t have to hire as many. You get more production out of all of the talent that you’ve already got with all of the business knowledge that they’ve learned over the years. 

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

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 analytics AI assistant, Max, powered by GPT, excites customers as it promises to enhance efficiency and effectiveness across organizations. Organizations recognize that embracing AI and GPT is crucial for staying competitive in today’s dynamic landscape. By leveraging GPT and AI technologies, companies can stay competitive and optimize their operations while making data-driven decisions swiftly and effortlessly. 

The post Discover AnswerRocket: Unlocking the Power of AI for Your Enterprise Data Analysis first appeared on AnswerRocket.

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

Pete: Hi, I’m Pete Reilly. I’m one of the co-founders of AnswerRocket and the COO of the company. 

Mike: My name is Mike Finley. I’m also a co-founder of AnswerRocket and the CTO of the company. 

What is AnswerRocket and Max?

Pete: AnswerRocket is ChatGPT meets enterprise analytics. Generally, the problem we solve is when we go into really large organizations, they have mountains of data that’s growing every day, and they struggle to make good business decisions quickly from it. And so we dramatically accelerate that process. We’ll take a process that might take a week to figure out how a brand says it’s performing in a particular market and bring it to 60 seconds. What that does is then it enables the business folks to then go ahead and act on that information instead of spending days or weeks analyzing it. 

How does AnswerRocket leverage AI and GPT?

Mike: AnswerRocket has been a leader in this process of engaging naturally with business users for ten years. We’ve introduced many of the popular concepts that are core to this technology now. The advent of GPT, or really large language models in general, has meant an enormous leap forward in AnswerRocket’s ability to both understand the user, not just the facts that they’re asking, but the intent of their question. Intents are things like, is there a comparison? Are we looking for outliers? Are we trying to determine a specific course of action to take? Right. So understanding that as well as the answer that they get back from the solution being something that is far more natural and usable as human language, powered by the language models. 

What makes AnswerRocket different?

Pete: There are other companies in the market that I would say we differentiate additionally in a couple of ways. One is this ability not just to ask a natural language question and get answer from a database, but to actually run a model on that data, to do a forecast on that data, to do a driver analysis on that data, to do a deep market share analysis on that data. Those are capabilities that come with this addition of data science, machine learning, but also combined with deep domain experience to solve really thorny, challenging problems in these vertical spaces. 

How does AnswerRocket unlock answers from structured and unstructured data?

Mike: So it’s important to realize that enterprises have their data siloed in two major sections. Right? There’s databases, traditionally that have been built up over time with imported and loaded data. And then there are also the more informal, unstructured sources that are all the enterprise flows of information, whether that’s email, meeting transcripts, PDF files, reports, PowerPoints. And so one of the key things about the AnswerRocket vision and the vision for Max, the AI agent, is to be able to have enterprise users access data from either one of those repositories at the same time and have them work together to produce answers and insights that really can power opportunities that weren’t available before this technology.

How does AnswerRocket fit in alongside other enterprise BI tools?

Mike: Are we trying to displace the dashboards and the reports and all the things that businesses do today? It’s not about displacing those technologies, it’s about removing the friction, right? So we’ve seen so many examples of companies that have 600 different reports.  They have so many reports, they don’t even know which one to use, right? With something like AnswerRocket in place. You can just ask your question and it will find the right source, whether that’s stored in an existing dashboard, in an existing business intelligence tool. Wherever that information is stored in documents, we’re going to be able to go retrieve it, pull it into the language model, and let the language model answer the question based on those facts. Right? So it’s not about eliminating the value that’s been created by the calculations and the historical trends and things that have been observed, because all those business practices are very valuable and they’re key to how enterprises run. It is about removing the friction of leveraging those things, right? So it’s all about being able to take advantage of them in an easier way, to be able to get to that information faster, to be able to compete better in a way that’s more satisfying to the human users. Because they’re no longer in the tedium of working through the processes of finding data and joining it up and putting it into a spreadsheet.

Pete: Dashboards aren’t going away anytime soon. Look, most companies, the major key performance indicators that people’s bonuses are based on are on these dashboards, right? And it’s a unified way for a company to look at their overall performance, see how we’re tracking, are we hitting our goals or not? That’s here to stay. Changing that out would be herculean effort. But what you end up seeing is that a lot of times these dashboards, they’re really good for just reporting the news, like what happened. They’re really not great then at helping you understand why that thing happened, or what would happen if I did something differently, or what’s going to happen next, or what should I do. It’s terrible at all those things. And that’s where really, I think we come in, is automating those questions and getting users to these business decisions sooner, because what they do today, they go to that dashboard and somebody downloads a bunch of data so they can do all this analysis. And I think I agree with Mike. To me, what ends up happening is those dashboards are in place. They become a data source or something like AnswerRocket. It’s just another place to get governed information that’s been cleansed and approved and so on that people can then use to automate their analysis and make good decisions easily. 

In Conclusion

By leveraging cutting-edge technologies such as GPT and combining data science, machine learning, and deep domain expertise, AnswerRocket brings natural language querying, advanced modeling, and deep analysis capabilities to the fingertips of business users. These capabilities give AnswerRocket the ability to revolutionize how analytics leaders access and leverage both structured and unstructured data, streamline workflows, and extract meaningful insights that drive tangible business outcomes. 

Get AnswerRocket and get meaningful insights from your data now.

The post How AnswerRocket’s AI-Driven Insights Revolutionize Enterprise Analytics first appeared on AnswerRocket.

You can listen to Mike’s episode of Inside Analysis on Apple Podcasts or Spotify. You can also watch the video below on YouTube.

On this episode of the podcast, Eric sits down to talk with our very own Mike Finley about all things chatbots, large language models, and generative AI. 

AnswerRocket has long been using natural language to help businesses have conversations with their data. With the emergence of OpenAI and ChatGPT, this capability has taken a huge leap forward. This is because LLMs are really good at:

1. Understanding what humans mean.
2. Understanding what they are trying to achieve.

How is this different from how we’ve traditionally gleaned insights from our data?

For years, we’ve made humans learn how to talk like computers, and speak in computer code. We can now finally ask a question in our language and get an answer back in our language. That is the true benefit of large language models and generative AI.

Mike makes a couple of great points about working with LLMs: 

You have to to “treat it like a human coworker…you have to train them on your business, make sure they are an expert in that area that you’re talking about, and you would fact check their results…”

LLMs like GPT are great because it knows a lot about general things, but it doesn’t know anything specific about your business and your data. 

This is where Max comes in. Max is a co-pilot for your business that helps with AnswerRocket’s BI initiatives.

AnswerRocket’s Max can dive in and understand your business and your data while GPT can communicate those insights in plain English with end users. 

While organizations may have concerns about sharing their data with the most used chatbot in the world, Mike assures us that the Max solution and its connection with OpenAI is built for enterprises.

→ When we connect to OpenAI on behalf of customers, we use a private instance purchased just for the customer, it’s just as private and secure as anything else you might be using in the cloud.

→ Max comes with all the features of an enterprise level BI tool such as role management, security, role-level data isolation, etc. 

The implications of this type of technology are huge for businesses looking to get valuable insights quickly. As Mike points out, large language models or “intelligence on tap” is the ultimate power tool for business. 

Visit answerrocket.com/max/ to learn more exploring and analyzing your data 10x faster with AI. 

The post Mike Talks Max on Inside Analysis Podcast first appeared on AnswerRocket.

Fast forward to the launch of ChatGPT in November 2022. The AI landscape has evolved leaps and bounds in just a few short months and presented a unique opportunity to organizations of all industries to consider how they would take advantage of the technology. 

We sat down with Alon to get his insights on ChatGPT, large language models, and the evolution of data analysis. He shares how AnswerRocket has layered in ChatGPT with AnswerRocket’s augmented analytics software to create a conversational analytics AI assistant for our customers.

Read the transcript of that interview below.

Question: Why was AnswerRocket started?

Alon Goren: We started AnswerRocket with the idea that anybody should be able to get easy answers from their data and it should be as easy as interacting with a personal assistant. That whole idea came from the frustration of sitting in many meetings where a discussion was had around some critical thing being presented, whether it was a board meeting or a management meeting. A PowerPoint was presented with “here’s the reason why we should do X.” 

Inevitably there were follow up questions that couldn’t be answered by the PowerPoint. There were requirements to say can we go out and do analysis? And those would take days or weeks. And that felt very wasteful. It felt like the data is there, why can’t we just go out, ask the question of the data and get back the response? We wanted that experience to be something that was available to everybody in the enterprise. 

Question: What does the current AnswerRocket offering include?

Alon Goren: The current AnswerRocket offering is kind of a full pipeline that starts with connecting to data sources and then the end product is some kind of an automatic visualization narrative in response to a user question. 

So along the way, the technology we have to build is certainly connecting to a wide range of data sources, including all the major data cloud providers. We built a pipeline that starts with a natural language question that the user is posing, breaks that down to an understanding of how to query the underlying source. Sometimes the analysis requires us to do further things than just querying the database. It requires us to do a forecast or some kind of a machine learning based algorithm to answer the user’s ultimate question. Then the presentation of that answer is in the form of a chart, a narrative, a combination of both those things. The technology to achieve all those are part of the kind of the AnswerRocket modules. Now, when we get into enterprise deployments, which is our core market, there’s lots of surrounding stuff that you have to do so around security and authentication and robustness for enterprise deployment. There’s lots of infrastructure that comes along for the ride. The differentiated modules are kind of at the heart around deep analysis of underlying data and presenting sophisticated answers, but in an easy to read way. 

Question: How has the data and analytics space evolved in the last decade?

Alon Goren: AnswerRocket was founded almost ten years ago and since then a lot has happened in the space, a lot has happened with technology in general. I kind of pointed out, I guess, several things. One is the number of data sources that are accessible to enterprise users have grown tremendously. It used to be the case that maybe there was a corporate data warehouse with some critical ERP kind of information in it, maybe basic sales information, but over the years it’s grown to the point where any interaction that happens in the enterprise, most of those interactions are captured digitally, and those interactions can be made into data. 

So whether you think about website interactions or HR interaction, customer experience interactions, any of those things usually leave a trail of data behind them. There are more and more digital products or applications that are used by the enterprise, the number of applications for enterprise has probably doubled in those ten years. What we see is just a diversity of kinds of data sources that are accessible, and the need therefore, to accommodate all of those. 

The second thing that’s really interesting is the pressure to get answers out of your data in a self service mode has probably increased over time. As the data sets grow, as the kind of questions that could be asked have grown, it puts more and more pressure on the data science team or the data analytics team to field those requests by business users. Because of that pressure, it’s impossible to keep up with that demand. 

And so, self-service in theory is the way to solve that problem, where users can ask their own questions and get their own answer. That started with a movement to visualize data with dashboards. Over the years, what’s happened is the proliferation of dashboards has really made it hard for users to find what they’re looking for because they have to understand, “Well, which of the hundred dashboards that I have accessible is the answer actually in?” That evolution of everyone essentially is their own analyst to some degree is a change in the space that technologies have to keep up with. Most significantly, the recent inflection point in large language models has created an opportunity to start dealing with users’ questions in the most natural possible way in terms of language and the response to those questions. I would say the natural language technology stack has really hit that part of the growth curve where everything now appears. 

There’s going to be massive disruption and massive changes in the ability to answer users underlying questions. 

Question: Why is ChatGPT a revolutionary technology for knowledge workers?

Alon Goren: Technology like ChatGPT is going to have a huge impact on technology, broadly on knowledge workers, probably broadly in many ways for us at AnswerRocket, because we started this journey ten years ago looking for a way to essentially make a solution that feels more like an assistant than a software tool. We’ve been in this mode of trying to understand how we can harness language models and other aspects of natural language processing to achieve that mission. What we see now is that, as is evidenced by the growth of ChatGPT users, that there is a huge appetite for interaction, kind of this natural language level. Right? Before, I would say before the launch of ChatGPT, it was more of an interesting, maybe in academia circles, like the idea of how well is natural language evolving? What problems can it tackle? 

Once ChatGPT hit the public web and a million users had access to it within the first week, and something on the order of magnitude of 100 million users have accessed it over time, it has changed the way, I think, the perception of what natural language can achieve. Not just in the sense of “can a machine tease apart what the sentence means, but can a machine carry on a conversation to some productive end?”

Which I think is the biggest kind of revelation with a chat-style interface is that it’s not just about the initial question, it’s about the context of that question phrase and the follow up opportunities to explain what’s in the answer and refine it. So that technology is tremendous. I think it’s going to have a broad impact, not just in analytics, but in any knowledge worker type of tasks where if your interactions to accomplish a job is with a computer, you have to ask the question, well what could that computer be doing for me in a way that doesn’t require me to understand where the buttons and the menu options are in order to achieve whatever I’m trying to do? 

Question: How does AnswerRocket use ChatGPT’s large language model?

Alon Goren: We span so many different data sources that a user can connect to and so many different systems that the kinds of questions they can ask are very broad. Our ability to then tackle those questions through the usage of a large language model where we’re not just confined to, “oh, the underlying data that to your question says that the right answer is the number x”, but rather it’s a story that explains what’s going on in the data. 

So, for instance, asking a question like, let’s say you work in a consumer goods company and you’re a multinational and you want to know about what’s going on in Southern Europe, how well are we doing versus the competition, that kind of a broad statement implies that there’s an understanding of this competition. 

• What’s “my” brands? 
• What’s “their” brands? 
• How do I measure performance? 
• Is it in currency, is it in share, is it in share of volume? 

Those are all variables or interesting kinds of KPIs that you can’t answer that question. We use a language model, it lets us back off the idea of saying all the information that’s in the data has to be queried very specifically and narrowly. The final number is X to more of an assessment that says, “oh, we understand in this data set that we have here’s how your business is presented and here’s how the competition presented.”

We’ve gone through that data set and in fact, looked at all things that are of interest to you based on a process where you tell us what you care about. Now we can pull from that information and weave together a story that combines information from any of those kinds of analytics. Not only that, but we actually combine that information with any other information that you have potentially connected us to. For example, if you have PowerPoints or PDFs or other documents or websites that incorporate interesting information that relate to the ultimate problem that you’re trying to solve, those are now accessible. Not just accessible, but summarizable in the same process of looking at your underlying data sources. 

You get a much richer story about how things happen and you can have that experience of asking and receiving that story and refining what you’re looking for in a natural language kind of way. 

Question: What are the challenges with GPT and other large language models?

Alon Goren: There are many challenges with large language models. They are moving targets though. The kinds of things that we see as challenges today, the techniques by which we solve them will evolve over time. 

Kind of a snapshot today would be core issues are:

Hallucinations, which is the idea that the model essentially gives you information that is what you would consider fictional, right? The language models; what they understand is whatever they’ve read and they’ve read a lot of fiction and nonfiction in the course of essentially reading the entire web. The model doesn’t distinguish between those two things per se as far as it’s concerned, you’re asking it to tell a story and it’s going to tell a story and sometimes it’s a fiction writer and sometimes it’s a nonfiction writer just depending on the best resources that it found to answer the question. 

In that, our challenge is to make sure because people are asking for factual information from us, right, they want to know what’s going on in the real world, not in some fiction, so we make sure we put the right kind of pre- and post-processing to the natural language model. That means when we ask the question, we provide context to say here is relevant information that you should use in answering your question. In post-processing, meaning we look at the answers that it provides and examine it for truthfulness in terms of does it connect back to the facts. So that is a core challenge. Now, outside of how effective the language model is doing that work, there are things like price and performance that will continue to improve. 

There are, let’s say, other technological aspects to it that are a moving target in terms of the kind of information that the model has access to and how to connect to it. For instance, in this recent week actually OpenAI introduced the concept of plugins, which is the idea that you can take a chat experience and extend it, almost like if you think of an app store that lets you download things to your phone or browsers to let you connect plugins. The language model itself serves as a basis for having a conversation across a lot of information that it has. For instance, it doesn’t know real time stock prices, it doesn’t know how to place an order online. Those are things that can be achieved through usage of plugins, meaning that the model has to be taught that if the user is asking to book an appointment somewhere, what tool do I need to achieve that result? 

The extension of these models is a very critical area that’s I would say fairly nascent at the moment. We expect that area to grow by a lot in terms of the sophistication and the kinds of things that the models can achieve. AnswerRocket sits in this interesting position where we want to use the model as the basis for the conversation, but we want to augment it both in the sense of providing the tools to answer questions and those tools can appear in the form of a real time interaction with AnswerRocket APIs. Another mechanism is to actually retrieve information and use that as the context. These techniques are called tool augmentation and retrieval augmentation. There are ways of extending what a model can do given that the model is trained on some generic but very broad set of data. The kind of challenges that we face today are engineering challenges by and large of wrestling the existing language models into doing our bidding. 

It takes energy to make it suitable for enterprises and our enterprise customers in terms of the end results they get. It doesn’t feel like they’re having a conversation that’s partially fiction, partially nonfiction.

Question: Where will AI and data analysis be in 5 years?

Alon Goren: The pace of the technology and the change in technology for language models and chat experiences is such that there’ll be huge pressure to create very narrow answers or narrow solutions that are really deep for certain fields, right? It’s easy to imagine a world where instead of having one large language model or several large language models that are very broad, those then get operationalized or customized for various use cases. Having an assistant that helps you deal with data analytics could be one of ten assistants that you talk to. They all maybe share some common interface where it’s a team that’s helping as opposed to an individual, but it’s all accessible, let’s say, with the same kind of chat paradigm. 

Those deep models, you can imagine each model becoming better and better at serving its users. In our space, we would imagine that if you’re an ecommerce company and you’re trying to do analysis on promotions right. That is probably powered by a bot that’s learned a lot about the ecommerce space and learned a lot about promotional activities and customer behavior, which could be very different from, let’s say, the kind of bot that you’re talking to if you’re trying to do planning for a wedding. Both those scenarios are equally valid in terms of can you have an assistant that helps you do tasks, basically? Anywhere where there is a computer centric task. You have to ask the question, what would a really smart assistant who had access to all the information that it needed to make recommendations me? What could it do for me? And then the possibilities are somewhat endless. 

Now, how fast can we realize that vision? It appears that right now, based on the improvements, so if you look at the technology side of it, the capabilities, both in terms of the kinds of information that’s available through a chat bot and the speed at which it operates, those are growing kind of a Moore’s Law or better kinds of numbers. We’re talking about doubling every year or so. That’s because both the hardware and the software are improving in this case, right? Both the algorithms are getting more efficient and the hardware that they’re running on, GPUs, is becoming faster. You get this kind of effect that multiplies those two improvements and that unleashes at the moment. When we look at large language models increasing the size of the parameters that they use, increasing the amount of data that they see, increasing the amount of time they get to train on that data, all those have not been tapped out. 

All those seem to continue to add capabilities. Those emergent capabilities create a future where you say, okay, what questions shouldn’t it be able to answer, right? If it’s given access to all the information it needs, what are the emergent things that we will find? Because it was a total surprise that suddenly language models can write poems in any number of styles, the creative side of doing tasks was not the thing that AI was supposed to automate. It was supposed to automate routine things, not things that we consider creative tasks. It’s been very surprising to see that actually, as it learns more and more about language and content, that language or the world through text, but the capabilities have increased tremendously. If you put it back down to the concept of five years from now, I feel like this kind of conversation will probably be one or more assistants on this call, and they’ll participate in some ways that help you sharpen your answer, help you better understand the content. 

It feels like there’ll be a world where whatever you write to communicate, an assistant will help you. Whoever’s reading might say, well, just give me a summary across all of the information I got. It feels like we’ll have a system of both the receiving and the sending side of the conversation in various ways.

Question: What unique value does Max deliver?

Alon Goren: The way we approach building Max and what we think we could achieve is unique and very valuable. 

Probably first and foremost revolves around the idea of getting deep within the customer base that we choose to serve. We’re not trying to necessarily go across all industries and all use cases. We’re trying to be much more targeted because we believe that by being targeted you can get much deeper. You could better build a deep understanding of what users want and how to deliver it. Especially in the case of having conversational type interactions. 

It’s challenging or currently impossible to teach a bot to know everything about all kinds of questions. If we focus on something really interesting, like we spend a lot of time with consumer goods companies, we spend time with the finance companies and healthcare more broadly, in each of those areas, there are needs to understand their domain, understand their particular, not just the vocabulary, but what drives the business. 

• How do they measure performance of a business?
• How do they set up the objectives for those businesses?
• How do they go about their work, of planning out what to do? 
• Where are the opportunities, where are the threats? 

The unique thing that we can bring to the table is working closely with those customers to ensure that the domain that we build, the knowledge that we build into the system to work alongside them, is second to none. 

That’s in contrast to probably the broadest solutions that are out there, that are designed more to be platforms to serve all use cases, right? Where they have to be agnostic about the kind of data and the kind of questions that they answer, which I think will work great for a lot of broad use cases, but won’t be the best choice for those companies who have a need for deeper analysis and the desire to automate more of the work that they’re doing.

In Conclusion

Looking ahead, the advancements in language models and chat experiences hold tremendous potential for the field of AI and data analysis. The future may see the emergence of specialized AI assistants customized for specific domains, capable of providing deep and tailored insights. These assistants, powered by advanced large language models, could transform various industries by offering efficient and personalized assistance. As technology continues to improve, with hardware and software enhancements complementing each other, the possibilities for AI and data analysis are expanding rapidly. With ongoing developments, the vision of having smart assistants with access to vast amounts of information and the ability to provide valuable recommendations is within reach. The trajectory of progress suggests that the limitations of what these assistants can achieve will continue to be pushed, unlocking new and unforeseen possibilities in the near future.

To learn more about the Data Superpowers within your reach, watch our YouTube playlist here. 

The post Conversational Analytics with Max 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.

Max Integrates GPT-4 With AnswerRocket

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

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

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

How Max Makes Data Analysis Easier

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

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

More flexibility to ask questions in your own words

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

Answers are clear and easy to understand

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

Max helps guide you toward forming good questions

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

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

Transparency on analysis scope and assumptions

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

Easy access to advanced analytics capabilities

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

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

Accelerated data setup 

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

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

Ability to adapt to your preferences and feedback

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

Ready to Give Max A Try?

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

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

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

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

Driver analysis is a powerful type of analysis that can help you identify what factors are impacting your KPIs, either positively or negatively. In this blog, we’ll cover what driver analysis is, the benefits and challenges of it, and how it can be automated with AnswerRocket.

What is Driver Analysis?

Driver analysis is a statistical technique used to identify the key factors that influence a particular outcome. In the context of business, it is often used to identify the factors that drive KPIs such as customer satisfaction, revenue, or profitability.

Driver analysis typically involves a three-step process:

1. Data collection: The first step in driver analysis is to collect data on a range of variables that could potentially impact the outcome of interest. This might involve surveying customers, analyzing sales data, or conducting market research.
2. Statistical analysis: Once the data is collected, statistical analysis is used to determine which variables have the greatest impact on the outcome of interest. This might involve running a regression analysis to identify the strength of the relationship between each variable and the outcome.
3. Actionable insights: The final step in driver analysis is to use the insights gained from the statistical analysis to inform business decisions. This might involve optimizing marketing campaigns, improving product features, or making changes to the customer experience.

Benefits of Driver Analysis

Driver analysis can help businesses optimize their performance in a number of ways:

• Targeted decision-making: By identifying the key drivers of success, businesses can make more informed decisions about where to focus their efforts. For example, if customer satisfaction is identified as a key driver, businesses can invest in initiatives that improve the customer experience.
• Optimization of marketing efforts: Driver analysis can help businesses identify the marketing campaigns and strategies that are most effective at driving customer engagement and revenue. This allows businesses to allocate resources more efficiently and optimize their marketing spend.
• Product optimization: By identifying the product features that have the greatest impact on customer satisfaction or revenue, businesses can optimize their products to better meet the needs of their customers. This can help drive growth and profitability over the long-term.
• Improved customer experience: Driver analysis can help businesses understand the factors that have the greatest impact on customer satisfaction, allowing them to optimize the customer experience and improve retention rates.

Challenges of Driver Analysis:

• Access to the right data: Within an organization, teams are often working on a multitude of platforms, all with their own reporting tools and interfaces. The onus is on individuals to share data and make the connection of how it affects KPIs. 
• Avoiding your own biases: It’s natural to lean on personal experiences and organizational habits when looking to data to glean insights. This is why an objective third party or tool can be useful in gaining a true understanding of what’s going on.
• Finding time to analyze data: Many business teams are seeing decreased headcounts and increased expectations for delivering results. The need to “do more with less” lends itself to busier employees and longer days, leaving little time to dig into data. 
• Identifying actionable insights: While it may be easy to see what’s changed, the challenge comes in identifying why something is up or down. Once you can identify the “why,” it’s much easier to take action to change or capitalize on a situation. 

Automate Driver Analysis with AnswerRocket

With AnswerRocket’s Driver Analysis, teams can easily track and understand why KPIs are changing—in seconds. The solution is designed for the end user who wants to monitor and understand business performance changes quickly. It’s perfect for answering the “what, where and why” questions that you may come across when analyzing your data. 

The KPI Dashboard

The Driver Analysis KPI Dashboard gives you a view of your key metrics at a glance, enabling you to quickly see what’s up, what’s down, and the WHY behind any changes.

Some of the types of metrics you can pull into a driver analysis dashboard:

• eCommerce 
• Google Analytics
• Email
• Social Media
• Sales Data

Criteria that you can refine your data by includes:

• Timeframe
• Comparison period
• Geography
• Brand

Users are able to click on a KPI in the dashboard and quickly understand why something is happening.

AnswerRocket does the heavy lifting with standard business intelligence capabilities like:

• What’s happening? 
• What was it year over year?

It will also be running a trend analysis in the background, to see if changes are above or below where we expected. 

Users can also easily see pacing as it relates your targets and growth rate over time. 

If the selected KPI is dipping or spiking, then we get into the next piece of “why did that happen?”. Scroll down further and you’ll see what the Top Drivers of that change are.

AnswerRocket Highlights Top Drivers

The Top Drivers section of the KPI analysis shows what’s having the greatest impact at a glance. 

In the example below, we can quickly see, spelled out in simple terms, that mobile was the main driver of the increase in Gross Sales.

AnswerRocket also features:

• Issues to Investigate
  
  Highlights things like device categories, source channels or campaigns that may not be performing up to par. Instead of guessing or searching around for a possible issue, users can focus their time and get to the root of a problem quickly.
  
• On the Bright Side
  
  Highlights things like device categories, products or product categories that are performing better than expected. This information can help users understand if a recent investment like a mobile update, or marketing campaign focused on a certain item is paying off.

Drill down even further by switching from “Summary” to “Top Drivers” view and seeing additional details within each segment and subsegment. Details such as:

• News 
• Current Period
• Comparison Period
• Amount Change
• Percent Change
• Driver Impact 
• Driver Rank

Normally, this analysis could take days or even WEEKS to complete.

In conclusion, driver analysis is an important aspect of data analysis that helps determine what segments are impacting your KPIs. While it can be time consuming, driver analysis offers benefits such as uncovering hidden information and providing value-focused insights. With the right tools and technology, it can be automated to minimize manual effort and generate useful results quickly. Whether you are trying to increase sales, optimize performance, or just make informed decisions about budgets, understanding the drivers behind your organization’s success is essential for any organization looking to evolve and iterate in the digital world.

With Driver Analysis from AnswerRocket, users get answers in seconds so they can take action quickly. These details help users respond swiftly to performance changes with insights that fuel growth.

The post Understand KPI Changes with Driver Analysis 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.

For more than 35 years, Forrester has provided independent research that helps businesses and technology leaders accelerate growth. Forrester’s Research Reports cover a wide range of industries and do the leg work to help executives “stay ahead of change, conquer priorities and prepare their teams.”

AnswerRocket is proud to have been featured as a notable vendor in the Forrester’s Augmented Business Intelligence Landscape, Q1 2023 report.

According to Forrester:

“You can use augmented business intelligence (BI) platforms to enable last-mile intelligence, bring together signals produced from other applications and platforms, and turn data insights into data stories. But to realize these benefits, you’ll first have to select from a diverse set of vendors that vary by size, type of offering, geography, and use cases served. Technology and data leaders should use this report to understand the value they can expect from an augmented BI platform vendor, learn how vendors differ, and select one based on size and market focus*.”

AnswerRocket was highlighted for industry focus in CPG, Finance and Pharma; but also provides solutions for Alcoholic Beverage, eCommerce and more. 

The team at AnswerRocket prides itself on continuous innovation of our augmented analytics platform to provide tools that move the needle for our clients such as Panel Data Analysis and Driver Analysis.

To learn about how AnswerRocket measured up, and to purchase the full Augmented Business Intelligence report, click here. 

*Forrester Research, “The Augmented Business Intelligence Landscape, Q1 2023,” Boris Evelson, 3 January 2023

The post AnswerRocket Featured by Forrester first appeared on AnswerRocket.

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

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

What is Average Order Value?

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

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

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

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

Why Is AOV Important?

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

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

How To Optimize AOV

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

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

CUSTOMER LOYALTY PROGRAMS

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

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

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

UPSELLING

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

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

OFFERING FREE SHIPPING

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

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

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

BUNDLING YOUR PRODUCTS

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

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

The Bottom Line

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

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

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

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

How can AI and augmented analytics actually help CPGs? Many companies spend immense amounts of time and money on figuring out what happened in their previous quarter. Imagine finding the answer to all of your questions in seconds. In this webinar, we discuss how.

Speakers:

• Ryan Goodpaster, Account Executive at AnswerRocket
• Pete Reilly, SVP of Sales, Marketing, and Implementation at AnswerRocket

The post How CPGs Can Use AI to Find Growth first appeared on AnswerRocket.