Predicting Consumer Trends: How AI Forecasts Shopping Behaviors

From chatbots to inventory management, the use of AI in retail continues to grow, but how can brands use AI to understand the desires of shoppers? In a recent guest post with Consumer Goods Technology, Gopal Tadiparthi, Head of ML/AI at Insite AI discusses how AI-driven insights can uncover what consumers want today and what they will be buying tomorrow.

CPGs (Consumer Packaged Goods companies) have access to extensive shopper data from their own sources, third-party panels, macroeconomic reports, social listening insights, and retailer purchasing and loyalty data. AI can analyze this wealth of information to deliver strategic findings on shopper behavior. By leveraging AI, brands can enhance customer segmentation, forecast future shopping behaviors, personalize shopping experiences, conduct sentiment analysis, predict customer churn, and map customer journeys. These capabilities allow brands to optimize their marketing, sales strategies, and product assortments, ultimately leading to improved performance and stronger collaborations with retailer partners.

Key Points:

  • AI and platforms like Walmart Luminate are revolutionizing shopper research by providing deep insights into consumer behaviors.
  • Brands can uncover data on BOPIS usage, brand-switching, pricing, and promotions to predict future shopping trends.
  • CPGs have access to extensive data sources, which AI can analyze to deliver strategic findings on shopper behavior.
  • AI helps brands enhance customer segmentation, personalize shopping experiences, and conduct sentiment analysis.
  • Leveraging AI in shopper research allows brands to optimize marketing strategies, improve product performance, and strengthen retailer collaborations.

[Virtual Q&A] Decoding AI — Where and How to Get Started

Do I want to predict? Or do I want to prescribe? You can do all of this with a crawl, walk, run approach.

Many shopper insights professionals are stuck when it comes to meaningfully incorporating AI into their workstreams. In fact, during a recent panel discussion with the CMA, the vast majority of polled attendees said they “don’t know where or how to start.”

The survey results revealed that despite the hype, consumer brands need help adopting AI solutions. 

The webinar panelists included:

  • Capri Brixey, EVP, strategy consulting (former VP of Sales at Coca-Cola Company)
  • Kristine Joji, EVP, strategy consulting (Former VP of Merchandising at Walmart)
  • Marsha Shapiro, SVP of client solutions (Former Senior Product Leader at Nike)

Here are a few key takeaways from this panel discussion, sharing thought-provoking tips on where to start with AI and why it’s vital for category management.

When a CPG begins the process of interviewing solution providers, an important step is to ask the provider to “open the black box.” 

“One of the biggest constraints in early AI adoption was that end users didn’t trust the results and I don’t think that’s changed a lot today,” Shapiro said. “We have the ability to open that black box and very easily answer why [AI works the way it does].” 

Solution providers must explain results and how the technology works throughout the entire AI adoption journey, not just during the sales cycle. Shapiro also shared tips when interviewing prospective providers such as:

  • Ask AI companies to provide case studies or references.
  • Evaluate a provider’s team by asking where the data scientists and machine learning engineers previously worked and how they describe their skill sets.
  • Request the company conduct a proof-of-concept project.

Before implementing AI, CPGs need to know what type of results they want to get from the technology. For example, two potential approaches to consider:

  • Automate. This is a scenario where a brand analyst may be seeking to automate tasks to save on manual workload time or gain a more accurate perspective of what’s happening right now in the business. 
  • Elevate. Looking farther down the road, a brand team may want an AI solution to elevate the business by predicting future performance results and prescribing strategies to grow the business. 

Of course, these aren’t the only ways to use AI and some companies may want to automate and elevate.

“You need to know, do I want to give perspective? Do I want to predict? Or do I want to prescribe? You can do all of those — and along that spectrum — and you can create a crawl, walk, run with your provider,” Brixey said. “So ask them those questions.

How does this [AI solution] help give me perspective? How does this predict for me and how does this help prescribe for me?

Drawing on her years of experience in merchandising at Walmart, Joji presented that retailers are looking to CPGs to be the experts in their respective categories. Brands should know if the category is growing, which customers are trading in and out of the category and how their retailer partners are performing against competitors.

“Retailers are looking for the ‘why’ and the ‘so what.’ This is where AI technology solutions come into play. AI can harmonize disparate sources of data that when streamed together create a single source of truth that provides a more holistic view into the business,” Joji said.

Several ways AI helps brands improve collaboration and category management including:

  • Generating micro and macro space optimization insights. AI provides an output of store item-level recommendations with forecasted performance explanations and item-level demand transference.
  • Optimizing a modular review of assortments and updated financials. While this is a common task for brands, AI provides a detailed explanation of where volume is coming from, where it’s going and who are the new customers being acquired.
  • Keeping pace with pricing habits. Over the last three years, customer spending habits have been volatile. AI monitors constant shifts between national brands and private brands, high-end brands vs. mid-tier brands and tracks price elasticity trends that are timely, meaningful insights for the revenue growth management team to consider when managing trade spend and more.

Brixey, Joji and Shapiro shared a tremendous amount of knowledge during the CMA webinar. The session guides consumer goods brand teams and their IT colleagues on what questions to ask when interviewing AI solution providers, what to consider when getting ready to start an AI journey and how AI will ultimately transform how they do business.

To watch the full conversation, stream the on-demand video here.

Gearing Up for the Fourth: How Beer Brands Prepare for the Biggest Sales Week of the Year

As one of the largest weeks for beer sales, approaches, beer manufacturers are seeing increasing competition when it comes to consumers’ dollars. In this Q&A with Beverage Industry Magazine, Kristine Joji, EVP of strategy consulting at Insite AI, explores market trends and predictive pricing strategies for beer brands to successfully position themselves during some of the largest beer sales weeks of the year.

Q&A Highlights:

  • American consumers spent $15.8 billion for the Fourth of July in 2023, with $9.5 billion on food and $4.02 billion on alcohol.
  • Market trends show consumers are tightening spending due to inflation, impacting their purchasing decisions for Fourth of July celebrations.
  • There is a growing trend towards health-conscious and sober-curious beverage choices, including low-calorie beers, hop water, and hard kombucha.
  • Beer brands need to consider optimal pack sizes, hyper-localize assortments, and utilize predictive pricing to maximize ROI and cater to cash-strapped consumers.
  • Strategies for smaller beer brands focus on new user acquisition and brand differentiation, while national brands aim to maintain current customers and drive additional sales.

[Video] The AI Powered Future of Category Leadership

Join Vic Miles, retail and consumer goods industry leader at Microsoft and Shaveer Mirpuri, co-founder and CEO of Insite AI on this episode of “Beyond the Tech”

In this episode, of “Beyond the Tech” Mirpuri shares his journey into the field of artificial intelligence (AI) and how his experience in data science, computer science, and business led him to explore the practical applications of AI in various industries, including retail and consumer goods. He discusses the role of AI in automating data ingestion, scenario planning, optimization, and forecasting, enabling humans to focus on strategic and creative decision-making. Mirpuri emphasizes the importance of AI that provide explainable demand forecasting and insights into the drivers and constraints affecting sales for consumer brands.

The conversation focuses on the potential of AI to drive value for consumer goods companies in areas like hyper localized assortment optimization, demand forecasting, understanding price elasticities, and scenario planning. Mirpuri emphasizes the importance of explainable AI models that can break down the drivers and constraints affecting demand, sales, and pricing. He discusses the ability of AI to project accuracy into the future based on historical data and various factors, as well as the capability to run simulations starting from future time periods when business changes are planned. Mirpuri also highlights the value of scenario planning using AI to prepare for unpredictable events and macroeconomic conditions. Overall, the discussion underscores the potential of AI to provide granular insights, optimize decision-making, and drive growth strategies for consumer goods companies.

The discussion revolves around the necessity and potential benefits of investing in AI for consumer goods companies. Mirpuri acknowledges the significant time and efficiency gains AI can provide, enabling employees to be more strategic and thoughtful.

Mirpuri also underscores the exponential lead early adopters could gain over late adopters, as AI allows brands to optimize assortments, pricing, promotions, and decision-making processes. However, he stresses the importance of executive education and understanding AI methodologies to appreciate its use cases and ROI fully.

About Shaveer Mirpuri

Former executive and board member of two early stage VC backed companies (IPO and acquired), Shaveer subsequently invested in several tech companies in e-commerce, AI, consumer brands, and manufacturing, including new businesses with large corporate partners. Prior to this, he was a consultant to Walmart’s former CEO on AI. In 2019, the American Chamber of Commerce named him a top 3 in entrepreneurship, and today he is an active member of the Forbes Technology Council.

About Vic Miles

Vic Miles is the Americas Business Strategy Leader for Microsoft’s Retail Industry solutions group. Vic is responsible for go to market strategies, guidance to the client service teams and the integrated solution plan for Microsoft products in the retail industry. Vic joined Microsoft in April 2008 after over 10 years in retail as a Wal- Mart IT leader. Vic has built a specialty around retail store operations. His knowledge comes from leading application development for in-store retail systems, during his tenure at Walmart. Vic serves as an advisor to retail executives around the globe where he helps to achieve the Microsoft mission of empowering every person and organization on the planet to achieve more.

Decoding Complexity: Navigating the Intricacies of the Wine Aisle

By Capri Brixey

Inside the four walls of the retail store, few categories are more complex than wine. For consumers, there’s an array of varietals from all over the world, each sold at wide-ranging — and to the consumer — seemingly random price points. For brands, there’s getting those bottles to the shelf, working through state-by-state regulations, distributor needs, and promotional limitations. For retailers, it’s knowing what to stock.

Using emerging technology, wine brands can simplify the process through AI-powered, data-driven optimization and decision-making. Producers can leverage predictive analytics to harmonize data from retailers, distributors, third-party sources, POS data and more. This includes, but is not limited to, macroeconomic and other influencing data that tee up strategic recommendations as granular as each store in their network.

As a result, wine brands can gain more control over where their wine goes and develop stronger relationships with their distributor and retailer partners, in addition to better managing supply and demand for future planning.

Understanding wine’s journey to the store

Currently, wine brands, big and small, need to jump through plenty of hoops to get products inside retailer doors throughout the country. To start, there’s a litany of governmental regulations to comply with, particularly regarding advertising and labeling wines, and how it’s imported and sold in certain states.

Brands must adjust their strategies on a state-by-state basis, factoring in unique rules before selling into retailers through a distributor network. Wine companies often work with several distributors, each with their own regional expertise and connections. However, distributors don’t just buy wine from a producer and sell it to the retailer. Distributors build a relationship with manufacturers to leverage sales materials, displays, and most of all, data and insights to help tell a story to the retailers.

Combined, regulations and distributor requirements can complicate how a wine company moves bottles of wine. Historically, brands have leveraged a matrix for each target state, distributor and retailer, and the data can get quite dizzying. Additionally, remaining inventory and pull-through rates have been used as performance indicators, even though those often do not reflect real preferences. Rather, they could reflect default performance through pushes to reduce latent inventory, or an incentive for display. This is where AI and predictive analytics can infuse levity and strategy. 

Cutting through the complexity of the category 

With so many entities at play and data sources from retailers, distributors, third-party providers and much more, brands can leverage AI to harmonize the data and deliver recommended strategies on what wines will sell best in what regions. In addition, AI can be modeled by brand teams to factor in state regulations and insights such as product availability within the distributor networks. 

Machine learning models can look at data to see how one varietal is performing in a region in Ohio and how another varietal is performing in Texas. The AI can go even more granular to see how tastes change at individual stores or clusters in one region, too. This is tremendously important with store locations reflecting distinct differences in preferences depending on the location’s customer profiles. Retailers want to know distributors and wine brands will stock products that fit the tastes and demographics of the shopper attributes at each store. 

Another advantage AI brings to the wine category is the ability to narrow in where choice may seem endless. Machine learning recommendations can help distill assortments down to the most essential for each store, presenting a measurable value to retail and distributor partners.

This pertains to pricing, too. AI can assist brands with recommendations on optimal price points for their wines by retailer. Value brands can fall under $10, for example, while premium brands can easily be $50 and up. AI can find and explain the price in that range that will deliver the best profit gains for a retailer and a brand.

Serving brands precise recommendations

To be sure, some industries fear LLM (large language model) AI outputs, questioning the accuracy and seeing a risk in its lack of explainability; however, AI-powered insights (different from LLM AI) should be looked at purely as an asset for wine brands. 

With AI/ML, brand managers can input complex data and work with it to serve reliable recommendations to explore. Brand teams can plan endless scenarios for varietals, stores and regions and make strategic decisions in near real time that are not entirely reliant on historical data.

For wine brands, predictive analytics can enhance how the category delivers the right bottles to the right shelves, meeting regulatory compliance. Brands, retailers and distributors can work better together, using a better foundation as a starting point and further refine through execution.

Consumers will likewise respond to assortments aligned to their preferences, as retailers create efficiencies and consumer delight within their existing spaces. Simply put, machine learning and predictive analytics uncorks opportunities for wine brands that they’ve never seen before. 


Capri Brixey is EVP of strategy consulting at Insite AI, bringing extensive strategic leadership experience from both retail and supplier roles in the consumer goods industry. Most recently with The Coca-Cola Company, Capri has led small and large-store teams, across multiple routes to market, channels, categories, and segments in the industry. Recognized in 2022 as a Senior Level Leader for Top Women in Convenience, Capri has also been recognized for her leadership in collaborative/joint business planning with top retailers across multiple channels/formats. 

ck unprecedented opportunities.

AI Washing: Top 5 Questions To Separate Substance From Hype

Interestingly, the AI washers tend not to be small technology companies but rather larger consultancies

A concerning trend is emerging: Some large companies are engaging in “AI washing” – promoting themselves as having powerful AI capabilities when they actually have little to no real AI software or knowhow.

As this Forbes Tech Council article, Insite AI CEO Shaveer Mirpuri explains, AI washing is deceptive marketing akin to greenwashing. Companies capitalize on the AI buzz and hype to mislead customers into thinking they offer “AI-powered” services, when in reality they are using openly available tools like ChatGPT or have minimal AI intellectual property.

The article outlines 5 key questions companies should ask potential AI software partners to avoid getting AI washed:

  1. Can they demo a real working AI solution?
  2. How does their solution work without AI capabilities?
  3. Can their AI be integrated into your internal cloud?
  4. Who trains and monitors the AI’s performance?
  5. Can the platform explain the reasoning behind its outputs?

With so much investment pouring into AI applications, companies must be diligent to separate the AI hype from actual robust AI solutions. Doing your due diligence is critical to avoid missed outcomes and wasted resources on overhyped, underdelivering “AI” products.

About the Author

Shaveer Mirpuri is cofounder and CEO of Insite AI, an AI and strategy partner for large consumer brands.

The Innovation Conversation: Data Illuminated

As featured in the Morning News Beat

In this episode of “The Innovation Conversation,” former Amazon executive Tom Furphy, now the CEO of Consumer Equity Partners, and the Morning News Beat’s Kevin Coupe explore the impact of Walmart’s Luminate data on retailer-manufacturer collaboration. Luminate enables the sharing of detailed, yet anonymous, customer information with suppliers. They discuss the significance of filtering and harmonizing data to turn it into a strategic asset, emphasizing the competitive advantage it provides to major players like Walmart, Amazon, Kroger, and potentially Albertsons. The conversation highlights the imperative for other retailers to leverage technology in order to establish closer connections with shoppers in the dynamic, technology-driven retail landscape.

Decoding AI for CPGs: A Path to Category Management Success

Hosted by the Category Management Association

Curious about integrating AI into your category management practices? Join us for this panel discussion with retail industry veterans and former category and sales leaders at Coca-Cola, Walmart and Nike as they discuss AI adoption in the CPG world.

Our panelists will explore critical topics such as generative AI, strategic starting points on your AI journey, and the nuances of outsourcing AI solutions. Equip yourself with the knowledge to thrive in an AI-driven marketplace and stay ahead of the curve.

  • Identify the best opportunities for AI integration in your category management practices
  • What to look for in an AI partner and how to identify AI white washing
  • Receive expert guidance on where and how to initiate your AI journey, tailored specifically for CPG companies.
  • Benefits and challenges of outsourcing AI talent.
  • Explore the potential of generative AI for CPGs

Get actionable steps and practical advice on how to execute an AI project, both with partners and gain alignment and support internally. Gain clarity and confidence in embracing AI to outpace your competitors in the dynamic CPG landscape.

Presented by:

  • Capri Brixey, EVP, Strategy Consulting at Insite AI
  • Kristine Joji, EVP, Strategy Consulting at Insite AI
  • Marsha Shapiro, SVP of Client Solutions at Insite AI

Outsourcing Software Development: It’s Changed With AI

As featured on Forbes

According to the IBM Global AI Adoption Index, the leading barrier to AI adoption is talent. More than a third of the IT executives surveyed said they are hindered by limited AI skill sets, which has led to an exponential increase in the outsourcing of AI talent.

In this Forbes Technology Council article, Shaveer Mirpuri, shares insight to help CPGs as they consider staff augmentation partners to accelerate AI initiatives.

About the author

Shaveer Mirpuri is cofounder and CEO of Insite AI, an AI and strategy partner for large consumer brands.

Paralysis by Analysis: Is too much data bogging down brands?

[Video] CPG brands’ AI ambition might be weighed down by ‘digital debt’

As featured in Food Navigator, 29-Feb-2024 by Ryan Daily.

CPG brands increasingly rely on AI technologies to crunch large data stores and find insight to win in the market, but many are falling short of their digital transformation goals due to outdated technology, Capri Brixey, EVP, strategy consulting at Insite AI, told FoodNavigator-USA in a video interview.

Key Insights:

  • Digital Debt: Outdated systems are stalling progress, requiring significant investments to stay competitive.
  • Data Overload: Brands struggle with decision-making due to an overwhelming amount of data and market volatility, leading to “paralysis by analysis.”
  • AI Advantages: AI can streamline administrative tasks, aid decision-making, and improve ROI by optimizing resource allocation.
  • Strategic Implementation: Brands should adopt AI incrementally, ensuring new systems are adaptable and aligned with current needs.
  • Starting Small: Begin with small, manageable changes rather than an extensive overhaul to effectively leverage AI capabilities.

For a deeper dive into these insights, watch the full interview with Capri Brixey on FoodNavigator-USA.