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.

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.

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

Four Trends That Will Drive CPGs To Adopt AI in 2024

While food retailers have been actively utilizing AI to forecast shopper behavior and streamline supply chains, the adoption rate among consumer brands lags behind. In this article, we discuss four key trends that will drive CPGs to adopt AI in 2024 and beyond.

About the Author:

Brooke Hodierne serves as EVP of strategy consulting for Insite AI. She previously worked at 7-Eleven as SVP of merchandising for the leading c-store. Before joining 7-Eleven, she held multiple positions at Giant Eagle, notably as VP of own brands.

Unlocking the Full Potential of Walmart Luminate through AI

Walmart Luminate highlights what’s happened inside the store and online like never before. Insite AI emphasizes what to do next with the data.

Walmart Luminate is a game-changing data platform that shines a light into a deep, dark data void, where no CPG has had visibility before. The platform delivers shopper insights, product performance and channel data such as pickup and delivery orders across 140 million households — weekly.

The platform is revolutionizing how brands use data to reach their consumers, but Insite AI can help unlock even more potential within the data. For example, Walmart Luminate highlights what’s happened inside the store and online like never before. Insite AI emphasizes what to do next with the data. Insite AI’s machine learning and AI models provide a forward-looking tactical layer on top of the Walmart data. Brands can see how a change in pricing, promotions or pack sizes will alter their course in the marketplace.

Walmart Luminate delivers the data. We help you put it in motion.

Walmart Luminate and Insite AI at work

Having spent nearly 20 years of my career at Walmart, most recently as the VP of merchandising in dry grocery, as well as working within bakery, personal care and other categories, I have been fortunate to see Walmart pilot and cultivate the Walmart Luminate program.

I also have a tremendous amount of experience working with brands to grow and maneuver within Walmart. I mention this, because alongside Brooke Hodierne, who was SVP of merchandising at 7-Eleven, and Capri Brixey, who was a former leader at Coca-Cola, we add a human element to the numbers. We overlay business intuition and consultation as a result of our deep and diverse business backgrounds.

With that said, let’s look at some hypothetical scenarios where Walmart Luminate and Insite AI can work together to help brands get better results.

• Product innovation. A snack food brand uses Walmart Luminate to look at larger category trends and in the process identifies a competitive product making waves. The new challenger brand delivers higher protein content and is more affordable. In response, the snack food brand has decided to create a new line of products with 10 more grams of protein. Insite AI can help that brand forecast a range of scenarios on how a certain pack size would perform against that competitor. What pricing would work better? Would a specific promotion generate more sales at launch, and how will it be performing months down the line?

• Brand-switching behavior. Walmart Luminate delivers a weekly report to a detergent brand that shows the detergent was out of stock in a specific region of the country. It also shows what percentage of households switched to a new brand because of the out-of-stock product, highlights what brand the households bought and shares more in-depth analysis. Insite AI can then layer in predictive market analytics around demand forecasting in consumer segments and parts of the country to help the detergent brand see where its brand loyalty lies and how to effectively respond.

• Delivery trends. Within Walmart Luminate’s data is a look at online transactions for delivery and pickup at the store, and a beer brand, for example, can narrow in to see if there was a spike in delivery during the first Sunday of the NFL football season. What brand had the highest delivery? Which brands lagged? At what time of the day did the deliveries occur most? Insite AI can layer on the tactical to see if the trend will continue and deliver efficient forecasts on what types of promotions or moves can be made to accelerate the trend and predict when out of stocks may occur.

Factor in all the categories and shopping behaviors happening daily inside a Walmart, and the scenarios facing brands are endless. Walmart Luminate works with brands of all sizes, offering a free package with limited insights. There is a monetized version which provides customer decision trees, leakage trees and real-time insights across 140 million households.

No doubt, the data platform will grow. Insite AI will grow alongside it, working with CPGs to help solve their challenges.

Unprecedented data and insights

No other data platforms deliver a view into online delivery and pickup behaviors, giving Walmart Luminate a major competitive advantage. Before Walmart Luminate, CPGs had to rely on panel data and qualitative data sets to try and spot a trend.

But the panel data is tens of thousands of households, and the data is months old. Brands have done excellent work with panel data but it can also be tricky. The results rely on how the panel responds, and sometimes people say what they want you to hear, or the subset of people in the panel isn’t very broad, in my opinion.

Walmart Luminate offers brands data at scale, and the data presents a complete picture of shopper behaviors and households. The data withholds any identifiable information of a shopper, but it represents 140 million households across income levels and ethnicities shopping at Walmart stores. CPGs have at their fingertips truly robust data.

Insite AI can help brands take this data even further. Contact us to see how we can leverage AI and predictive analytics and help you turn insights from Walmart Luminate into immediate action.

Connect at Groceryshop

Connect at Groceryshop

Accelerate Your Sales, Revenue Growth, and Category Management Initiatives

Groceryshop | September 19-21, 2023 | Mandalay Bay, Las Vegas

Connect with Insite AI at Groceryshop and find out how our revolutionary approach can accelerate your top initiatives. Our team of AI and strategic consulting teams have walked in your shoes, giving them unparalleled insights into your industry-specific hurdles. Our Strategic Advisors are consumer brand and retail veterans from Coca-Cola, PepsiCo, Mars, Anheuser-Busch InBev, Walmart, Target, 7-Eleven, Kroger, among dozens of others.

Let us guide you in tackling your organization’s distinct challenges head-on. Through our collaborative approach, we craft a tailored solution to elevate your product assortment, pricing strategies, trade promotions, and demand forecasting.

Don’t miss this opportunity to expedite your success and lead your organization toward a more efficient and profitable future.

The Leading Partner for Large Consumer Brands

• Comprehensive Scenario Planning
• Goal-Driven Optimization
• Decompose the “Why”

Know the precise impact of your decisions.

We’re the only partner that lets you dial in multiple scenarios, and confidently predict how they would perform on a forward looking basis against multiple KPIs, with details down to the most granular level, regardless of complexity. Make confident decisions at either the big-picture strategic or tactical level involving commercial aspects such as assortment, pricing, trade, space, and planning. In one click, foresee the results of exactly what will happen in any given scenario. Our unique capabilities take in multiple conditions and assumptions; alternatively, decision makers can rely on us to leverage the technology on their behalf. Act with extreme certainty, speed, save significant time, and ensure your actions will achieve commercial results.

Learn More

Define your specific objectives, and receive new and creative ways to reach them.

Are you seeking to grow volume? Maximize prices? Grow shelf space? Improve trade effectiveness? Outperform a competitor? Rationalize spend? Our capabilities “goal seek” the exact new strategies or tactical outputs to achieve this, taking into account all of your business dynamics, beliefs, and nuances. Get multiple novel strategies that are truly implementable and actionable. Fuse your vision with our technological levers that incorporate an incredible number of factors. See the forward looking and granular articulation on the recommendation’s performance. This is something any large team of experts aren’t capable of.

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Explainable assortment, space, pricing, and trade promotion decisions.

Harmonizing data and searching it for insights is old news, and few companies see value from it. We provide internal and external narratives that are defensible and truly differentiated. In one click, our capabilities explain and decompose the “why” on a forward-looking basis; and the data is presented in a powerful, immediately understandable manner. Incrementality, demand transference, price elasticities, cross elasticities, attributions, shifts, patterns, and factors affecting your existing or recommended actions are clearly articulated.

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Meet our Team:

Brooke Hodierne

EVP, Strategy Consulting

Former SVP of Merchandising at 7-Eleven, Brooke brings nearly 20 years of grocery and convenience retail experience to Insite AI. She understands what it takes to build valuable partnerships with retailers, and in her role as EVP of Strategy Consulting, she advises consumer brands on ways to elevate strategic business planning, achieve category leadership, and create optimal shopping experiences for their consumers.

Capri Brixey

EVP, Strategy Consulting

Former leader at Coca-Cola, Dr Pepper Snapple, and Delhaize, Capri brings extensive strategic leadership experience from both retail and supplier roles in the consumer goods industry. She was recognized as a Senior-Level Top Woman in Convenience in 2022 and has also received recognition for her leadership in collaborative/joint business planning with top retailers across multiple channels and formats.

Kristine Joji

EVP, Strategy Consulting

Kristine is a highly accomplished retail executive and former VP of Merchandising at Walmart.  Kristine led strategic initiatives that resulted in substantial revenue growth for the company across Grocery and prior to that Personal Care.  Widely recognized as a visionary leader, she played a pivotal role in optimizing Walmart’s merchandising with large CPGs.

Why Insite AI?

A Consultative Approach

Our team becomes an extension of your team. Our Strategic Advisors are consumer brand and retail veterans from PepsiCo, Mars, Anheuser-Busch InBev, Walmart, Target, 7-Eleven, Kroger, among dozens of others. Our top priority is ensuring you have the guidance and support you need to achieve your goals and maximize the value of your investment.

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Most Mature, CPG-Proven Capabilities

Everyone else starts from scratch, yet Insite AI has already invested over eight figures of capital and several years into building leading edge technology; creating unmatched advantages for tackling your top initiatives.

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Deeply Tailored to Meet Your Goals

We deeply tailor our engagements and fully configure our solutions to meet the unique needs of your brand. Insite AI is a true innovation partner providing CPGs with fully customizable solutions built to solve their unique challenges, enabling them to adapt quickly to changing market conditions and outperform their competition.

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Navigating Uncharted Waters: How I’ve Used AI To Help Brands Prepare for Unexpected Events

Having spent roughly the last seven years in data science at leading global brands, including Bacardi and Anheuser-Busch InBev, I can tell you that brands are too often working in “react mode.” Even at leading brands that already have an army of data scientists and AI-enabled sales and commercial teams, the organization can be challenged by events or market shifts it didn’t see coming.

During my time in the large CPG world, I saw how the brands I worked for — and competitors — reacted to economic shifts, technological changes, unexpected category product trends and more. There’s a lot that can throw a brand off track.

For any global brand looking to scale-up results or that is newer to AI and machine learning, there’s room to improve when it comes to preparing for the unexpected. There are learnings to gather that better manage product assortment, pricing and demand forecasts.

Unexpected Events Impacting Retail

The greatest and most timely example of an unexpected event is the COVID-19 pandemic, which upended all industries, but especially retail. At AB InBev, being a global company, we were able to prepare for shifts in the market and jumped to work with professors at MIT. Understandably, not all brands prioritized using data and technology to help, which has left many of them still learning from what happened.

Natural disasters are also events that can ravage operations regionally and have impacts on a global scale. But there are several other examples that CPGs may not immediately consider such as:

• Economic crises. Sudden economic downturns, financial market crashes or currency devaluations that squeeze consumer spending and purchasing power.
• Technological disruptions. An emergence of a new disruptive technology — or the obsolescence of an existing one — can throw a wrench into business models. Think of the advent of streaming services impacting traditional media consumption.
• Geopolitical events. Unforeseen trade disputes, political instability or international conflicts can greatly disrupt supply chains, sourcing and trade.
• Social and cultural shifts. A large cultural movement that causes a shift in consumer values and preferences can impact brands. It can be a sudden reaction to a brand or larger changes like consumer attitudes towards sustainability and ethical sourcing.
• Regulatory changes. Unexpected changes in policies or legislation can disrupt business. Product label changes and safety standards, taxes and tariffs can impinge on production costs and market access.

The analysis of these events happening around the world simultaneously can greatly complicate brand strategies. Brands need the right talent in place to understand every shift occurring and how it impacts the total value chain.

Where My AI Efforts Helped Brands

It is my belief that AI’s role isn’t so much to unequivocally predict an event, but the technology can better prepare brands for unexpected scenarios. Here are five ways I’ve used AI to help protect and better manage brands during unexpected events.

1. Enable teams to run endless test-and-learn scenarios (mimicking the many events listed above). AI can not only produce plans A, B and C, but also plans X, Y and Z.   

2. Analyze historical data, current market trends and external elements to identify patterns and indicators that may precede such events. This analysis helps alert brands to the potential of unexpected events.

3. Monitor economic indicators, social media sentiment, news reports and even weather patterns, to identify any warning signals. For instance, predictive technology can detect sudden shifts in consumer sentiment or emerging global risks, such as geopolitical tensions or economic instability. By incorporating this information into predictive models, the CPGs were in a better position to anticipate and prepare for unexpected events.

4. Use technology to make snap decisions in real time to chart a new course of action for a brand and make effective moves immediately to limit any damage incurred. For example, if there is a sudden surge in demand for certain products due to panic-buying (like the toilet paper scare of the pandemic) or a shift in consumer needs (like shelf-stable foods during a storm), predictive technology recognizes patterns and quickly predicts behaviors going into and out of the trends.

5. Analyze customer behavior, such as the current downward turn in online grocery shopping or shifts in pricing sensitivity. These real-time insights empower consumer brands to adjust their production, distribution and marketing strategies accordingly.

The power of predictive technology isn’t so much to be a crystal ball, but to aid brands in delivering a collaborative and communicative relationship with retailers during challenging times.

Why Technology Is a Table Stake

The power of predictive technology isn’t so much to be a crystal ball, but to aid brands in delivering a collaborative and communicative relationship with retailers during challenging times.

I saw this firsthand through the brands I worked with, and the technology developed much stronger relationships with retailers. Brand teams can utilize retailer data and run daily reports — especially since constant communication during major events is extra important. These insights can help retailers optimize their inventory management processes and place core, valuable products onto shelves and maintain stockouts.

Data-driven capabilities and AI can be a necessary assistant during unexpected events. The tools can support how crisis teams manage unforeseen events.

The ability to provide data-driven recommendations and support helps build trust inside and outside an organization, and improve operational efficiency. The learnings also guide brands to weather any storm and better expect the unexpected.

To learn more about how Insite AI can help brands mitigate unexpected events, contact us.

Fueling Growth: AI Unleashed in the Energy Drink Industry

The energy market has evolved considerably over the last 30 years. While many suppliers have disappeared, the number of new entrants continues to rise. With increased competition and what seems like constant innovation, consumer preferences and shopping patterns continue to evolve with the category. Without infinite space,  retailers are under pressure to design the optimal assortment with the best combination of SKUs, flavors, brands and package sizes to maximize selling space and win the most consumer dollars. 

In six months, the landscape for this rapidly evolving segment may look dramatically different. Whether its volume, shifting brand loyalty or cross category migration, retailers need to know what will happen next—not yesterday–to maximize profits in valuable selling space. 

A Profitable Segment

Constant category changes can make it challenging to stock the right SKUs. In addition to the continual bombardment of new products and suppliers, Covid-era shopping patterns have shifted back to normal. With only a handful of brands having long-term staying power, retailers continually ask themselves, “How do I balance my assortment of core brands/flavors with new ones?” and “How many cooler doors do I utilize for energy drinks versus other beverages?”

Choosing the right size cans and multi-packs is another dilemma. During Covid-19, purchasing of multi-packs and larger pack sizes increased as more consumers drank beverages at home. Today, retailers must meet the needs of changing consumer mobility patterns, with increased shifting purchasing behaviors between immediate consumption to at home consumption. While in-line square footage is a bit flexible, cooler space is fixed.

Demanding More Space

The growing energy drink industry continues demanding more square footage. But brands must “prove” their entitlement. Traditionally, suppliers used historic data to make predictions. But, more so than most other CPG categories, the energy drink market is in a constant state of flux, making this data seriously unreliable. AI tools use real time data, allowing vendors to more accurately predict how many items will sell in what space and which will perform best moving forward versus looking backwards. 

AI can provide a granular level of clarity around purchase decisions and consumer preferences. As the energy segment evolves with added need states, functionalities, and more, understanding the ‘why behind the buy’ becomes crucial for achieving long-term category success

Consumers’ preference for new brands and formulations versus legacy brands varies by store, channel and demographics. AI can indicate which shoppers stick with tried-and-true labels and which ones are more adventurous. Using data points to form useful insights, AI can help indicate what flavors, package sizes and formulations will resonate with certain groups.

AI’s ability to track shifting purchasing patterns yields data that can dramatically impact suppliers’ go-to-market strategies. When brand is not the primary motivator, it can help retailers avoid duplicating similar flavors, package sizes and formulations. Consequently, space becomes more profitable.

The Right Price

Over the past 18 months, cost increases have impacted almost every consumer category. While products must be profitable, brands must clearly understand the impact raising prices can have on shopper demand. AI can help. If the everyday retail price increases from $2.49 to $2.69, for example, AI can clearly project new sales volume. AI can also compare pricing to that of competitors. This helps retailers assess consumers’ sensitivity to particular prices, including associated risks. This is a win for both supplier and retailer.

Technology and modeling strategies can cross categories. The increased consumer need-state for Energy has caused a surge of other cross- category entrants in the added caffeine space. Where typically, brands focused on hydration benefits, we see more brands entering the space. But some newer items contain high caffeine levels, blurring the lines between categories and threatening energy drinks’ market share if energy drink prices go too high. 

AI can predict which shoppers will remain loyal to energy drinks and which will not. It can also determine what this means to the vendor’s base business and how the vendor can protect and defend its energy space. 

Immediate Consumption vs. Planned Purchasing

As consumer household penetration for Energy drinks continues to grow, developing broad brush pricing strategies for all channels from a historical POV will limit opportunities. Some retailers are losing traffic. Higher prices are prompting many consumers to plan purchasing online or in discount, club and grocery channels. These purchases frequently involve more economical multi-packs. Data and predictive modeling can track cross-channel migration and associated purchasing behavior. And it can do so down to the individual flavor, ingredient profile, brand and package type level.

Conclusion

In the dynamic world of energy drinks, AI holds the key to success. With the market ever-evolving and competition intensifying, the optimal assortment is crucial to attract customers and maximize profits. AI offers real-time data to predict consumer behavior, helping brands to make informed decisions on SKUs, flavors, and pricing. By discerning brand preferences, tracking shifting patterns, and unlocking cross-category insights, AI elevates strategic planning to new heights.

Embrace the AI advantage to secure lasting success in this dynamic landscape. Contact Insite AI.