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

More than Halloween: Maximizing consumer trends in candy

A recent Advantage Solutions survey of Halloween shopping habits found that more than a third of shoppers reported price as their main purchase driver—the top ranking influence within the survey.

In this article, Brooke Hodierne (former SVP of Merchandising at 7-Eleven), shares recommendations for candy brands can make more informed pricing decisions, maximize their presence in stores, and collaborate with retailers to foster continued growth in the candy category beyond Halloween.

View full article featured on Candy Industry.

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. 

The Candy Aisle Renaissance: From Impulse Buys to Strategic Category Growth

Insights help arm a brand with the knowledge that can lift an entire category for a retailer, earning them category advisor roles at coveted retailers. 

The candy category is much more than the Halloween season and the impulse rack at the checkout lane. It’s an innovative $43 billion business, increasing sales annually, partly due to inflation, but also due to consumers seeking an affordable way to treat themselves. Retailers too are developing a larger in-store presence for the category.

I know this from my days as SVP of merchandising at 7-Eleven, where we frequently leaned on candy to deliver innovative opportunities for sales growth inside the stores.

For candy brands, however, navigating the shifts in consumer behavior can be difficult, knowing that the products are largely bought on impulse. How do you effectively target a consumer who may not know they want that sweet treat yet?

On top of that, brands need to understand behavior shifts at a wide range of retailers such as mass merchants, club stores, traditional grocers, c-stores, drugstores, dollar stores and hard discounters — and even sporting goods and apparel are in the game.

Factor in consumer trends such as seeking smaller pack sizes and using social media for inspiration, and predictive analytics and machine learning can become major tools to help craft a winning candy strategy.

Uncover candy trends

According to a report this year from the National Confectioners Association (NCA), annual sales in both non-chocolate candies and the gum/mints categories increased by nearly 14%, comparing 2022 annual sales to 2021.

At 7-Eleven, I saw my fair share of wild flavors in novelty non-chocolate candies, sour chewy offerings and hard candies driving these sales and reaching a growing interest among millennial and Gen Z markets. But there are other behavioral trends and product trends to take note of, too:

• Portion control. According to the NCA report, eight in 10 consumers seek smaller pack sizes to help curb how much they eat. The report also noted that consumers are seeking guidance from brands on appropriate portion sizes, factoring in calories, sugar and the impact of natural ingredients.
• Healthier and functional candy. Consumers are increasingly looking for candies that offer health benefits, such as low-sugar, sugar-free, organic and fortified options. Functional candies with added vitamins, probiotics or other health-promoting ingredients are gaining popularity.
• Nostalgia brands. Nostalgic or retro brand candies from the past continue to make a comeback, appealing to consumers who want to relive childhood memories through their favorite sweets.
• Multi-channel purchases. The NCA report found nearly 60% of consumers said they buy candy at checkout in the impulse section, but they’re also buying in three to four different channels. Less than 10% of consumers exclusively buy candy online, but a third said they buy in-store and online.
• Social presence. Candy consumers are active on social media, with nearly 60% of consumers surveyed in the NCA study saying they access their networks for inspiration on products to buy or use with recipes, and to simply engage with the brand. Candy brands are increasingly leveraging social media platforms and partnering with influencers to expand reach and engage with their target audiences.

Considering these trends, candy brands have a lot of shifting behaviors to wade through. However, this is where predictive analytics, AI, and machine learning can help them figure out which trends to pursue, at which retailers and in what ways. Brands can develop smarter strategies around pricing, promotions, and where to put products at checkout and in the candy aisle.

Brands can feed AI-powered engines a mountain of varying data: social listening, POS, shipment data, third-party global trend forecasts, loyalty information and more. The AI model reads the data and directives from the brand teams on price elasticity, promotions strategies, assortment optimization and other inputs to recommend decisions for their brand goals and category growth overall.

Let’s repeat that last part: the insights help arm a brand with the knowledge that can lift an entire category for a retailer, earning them category advisor roles at coveted retailers. 

Sweeten sales for retail partners

As noted from the NCA data earlier, most consumers still rely on the checkout lanes for their impulse candy purchases. However, the data also states nearly 80% frequent the candy aisle, where retailers have been expanding assortments to bring more excitement to the category.

Major players like Walmart and Kroger have constructed expansive in-aisle sets for candy that push the retailers to become candy destinations, perhaps challenging c-stores and drugstores that have been often associated as a primary purchase destination for the category.

Part of this pivot from large format retailers is also to compete with value chains and general merchandise retailers also carrying candy. What’s more, the consumer behaviors around candy are much more than the impulse buy at checkout. Consumers are adding candy to their shopping lists as more wholesome ingredients make it a more acceptable indulgent treat.

Candy brands can help retailers make sense of consumer behavior changes by bringing AI-powered, robust data-driven insights such as:

• Should chocolate continue to receive the amount of space it’s getting based on its space elasticity?
• Is there room for expandable consumption inside stores, meaning can the store offer more candy even when they don’t need it, lifting a retailer’s bottom line?
• How much play should mini-size packages get, and should they be in bulk packages for consumers buying for extended at-home consumption?

Fine-tuned predictive analytics can answer these questions, helping brands develop the right products for their company’s success and sweeten sales for their retailer partners.

AI-powered platforms can change how candy brands work with retail partners, elevating a category from checkout lane to major players with grand merchandising sets and powerful growth strategies.

Dig deep with data

In today’s dynamic and highly competitive market, candy brands are constantly seeking innovative ways to stay ahead. With predictive data, brands can accurately forecast consumer demand, anticipate market trends and tailor their assortments accordingly. Not only does this enhance profitability but it fosters a more personalized and satisfying experience for candy enthusiasts.

AI-powered platforms can change how candy brands work with retail partners, elevating a category from checkout lane to major players with grand merchandising sets and powerful growth strategies.

Sweeten your brand’s success and elevate your brand with AI-powered insights, contact us to learn how.

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.

Learn More

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.

Learn More

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.

Learn More

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.

Learn More

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.

Learn More

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.

Refine Wine: Navigating Regional Preferences With Precision

Unlike the beer category, where summer sales can make or break a brand, wine experiences less seasonal volatility. Instead, brands are tasked with answering cyclical purchasing trends among different varietals year-round. Further, consumers have very distinct preferences across geographic regions that brands need to keep in mind.

Throw in heavy state-by-state regulations, pricing compliance, and managing product availability through distributors, and wine is one full-bodied, complex category.

Even though there are a lot of variables to consider for wine brands, the ultimate mission is the same: optimize product, placement, and pricing. AI and machine learning can help brands cut through the complexities and get the right bottles (or boxes, cans and any other emerging packaging style) onto the right shelves at the right time.

Here’s how AI-enabled technology can refine the wine category and accelerate the most critical opportunity metrics.

AI harmonizes massive amounts of data

One thing wine does have in common with beer is there are a lot of brands competing in the space, and it can be tough for consumers to decide what to buy. For brands, it can be equally confusing to strategically assess what products should go where — and which ones can’t go where based on distributor input and state-by-state regulatory compliance.

The level of data that brands are working with is a highly complex stew, overwhelming in its diversity and complexity, that AI can harmonize and distill to deliver simplified, optimized, and precise product recommendations at a granular, even store level. Each state and distributor network has a matrix of stores and retailers they serve, sliced by state line, retailer requirements, and wineries and distilleries.

Just navigating excellence in execution within this complex web is difficult. Brands are often short on resources and capacity thresholds to properly elevate strategic assortment optimization based on consumer behavior and preferences.

With AI-enablement, that overwhelming digital debt can be managed, breaking the silos between data and functions, regulatory compliance, and complex distributor networks and availability of inventory. Using billions of data points, AI/ML delivers pinpoint forecasting and recommendations on where products should go.

For example, one retailer may have several distributors delivering wine to one store. It presents a tremendous amount of data overlays to manage. Brands can help by harmonizing the data they have from their distribution partners and more to create models for that buyer, factoring in sales and distinct distributor data to deliver highly intelligent strategic assortment plans that the industry has not seen before.

Product and Placement: Identify preferences across regions

As mentioned above, where wine gets tricky is getting products out to the right regions. For example, there are nearly 7,500 wineries in the U.S., up nearly 4% from 2022. Each winery sells better in different parts of the country.

Wine is very regional by nature, so it’s not surprising that wine drinkers in Michigan may prefer wines produced nearby vs. in northern California, where consumers may prefer other wine attributes.

But AI allows for more granularity than that. AI can recommend products to place by store clusters or at individual stores, knowing what customers like and what distributors are able to deliver to precisely place the best options available. 

Insite AI worked with a national beer brand to optimize the craft beer assortment inside two leading grocery chains to leverage the peak summer season. The result was a 3% lift in sales for the overall category in those retailers, growing annual revenue by $20 million.

While the wine category can have additional variables in play, it doesn’t mean brands and retailers can’t see similar optimization and granular-insight-driven success.

Pricing and Promotion: Optimize premium to value brands

Another issue in wine is the wide-ranging price points in the category. AI helps brands understand elasticities of price across a diverse category. Again, what price points work best across varietals and regions can be very different.

The economies of scale are not a benefit or a factor in wine; to drive incremental and sustainable growth, brands need to take a localized approach. It can be very time-consuming using legacy models to devise pricing and promotion strategies. AI/ML can recommend precise moves in seconds.

There’s the saying that wine gets better with age. That may be true in a home cellar, but retailers need to move product, know trends and what sells better during certain seasons. Distributors play a key role here in revenue growth management by deploying similarly effective AI/ML strategies to predict what will generate the greatest returns.

Uncork efficiencies with AI

The intricacies of the wine category make it one of the most complex to manage. Regulations by state, in addition to working through multiple distributors to get as much product coverage as possible is a challenge.

There’s an increasing benefit for larger wineries to partner with specialized distributors that have a wider network across states, too. Recently, several wineries have formalized more exclusive distributor partnerships to gain simpler, and broader coverage.

While the complexity can be high, the benefits of new technology in managing it makes the possibilities for incremental growth in addition to significant advancement in efficiencies across multiple business functions considerable.

Wine brands can leverage AI to forecast demand and predict strategies that better meet the needs of the value chain. Contact Insite AI to learn how AI-powered solutions can get more granular than ever imagined in a category where granularity is a must.

Why Are CPGs Still Making Multi-Billion-Dollar Decisions Using Spreadsheets?

Many CPGs still rely on the trusted yet limited capabilities of spreadsheets as primary tools for assessing and taking action on assortment, trade spending, space and promotions planning. While effective for certain applications, spreadsheets were invented in 1979; Excel was invented in 1985. These are not intuitive or enabled tools that can provide the timely, precise details required to make multibillion dollar decisions. With the retail landscape moving faster than ever, it is time to break free from the constraints of spreadsheets and leverage the transformative power of 21^st century technologies. The future belongs to those who embrace innovation and adapt to the evolving industry landscape.

Limitations of Spreadsheets

Relatively easy to learn and use, spreadsheets are a popular option for conducting data analysis among CPGs. They offer a familiar and accessible interface for handling data, performing calculations, and creating visualizations. However, when it comes to fast decision-making in the dynamic world of consumer brands, spreadsheets reveal their limitations. While they can handle considerable amounts of data, they can be slow and unstable, particularly when data is complex. Spreadsheets further struggle to efficiently process and consolidate diverse data sets, leading to manual efforts (heavily reliant on already limited human resources) and potential inconsistencies. Excel can also impede collaboration and sharing at a time when there is more data than ever before to leverage. The bottom line is that spreadsheets are not intuitive and they require human intervention for use and to create value.

Market Volatility

Organizations are constantly trying to evaluate market volatility, competition, emerging markets and channels, and consumer behavioral shifts to assess where to allocate resources. Not having the right products and package sizes in the right place at the right time with the right price results in lost sales opportunities. If performance data shows gaps to targeted objectives, the organization will spend the year working to re-assess remaining planned actions and investments. This makes dependence on historic data troublesome. The gap between the “look back” and the “look forward” is a missed opportunity, especially in light of the market and supply chain volatility of the past three years in the CPG industry in particular.

Modern Approaches          

Today, purpose-built CPG-tailored software can ingest billions of data points from disparate sources to assess category maturity, predict future performance and assess the value of investments, allowing brands to appropriately allocate resources. It can also make more precise financial predictions. If resources are not allocated properly, expected results are not achieved. The resulting “gaps” can take a long time to close. Spreadsheets simply indicate what those gaps are; they do not indicate how to solve them. They can only hold data.

CPG-tailored technology uses timely data to project into the future, reducing dependence on historical data alone. Unlike spreadsheets, CPG focused software can “learn” from repetitive patterns and algorithms; it does not simply report data.

CPG-specific software with modeling capabilities uses multiple data sources in real time, incorporating everything from product sales and gas prices to labor department data and demographics. Because their models (accelerated by different prediction, product and pricing engines), are continuously finding data points and learning, they are able to provide forward-looking and prescriptive insights. It can signal package optimizations–e.g. whether there should be more gallon sizes of milk in a particular store versus single-serve cartons. The technology also finds those “needles in the haystack” that can be key differentiators from one store’s assortment to the next. By allowing all data to work together, teams can respond swiftly to market changes and adapt strategies dynamically, providing a competitive edge in a fast-paced industry.

Collaboration & Pinpointed Goals

Moving beyond spreadsheets enables greater collaboration and agility. Cloud-based platforms and data-sharing technologies have begun to facilitate seamless communication across departments, breaking down silos and fostering a collaborative culture. As part of that evolution, good software can facilitate better annual business planning, factoring in supply chain, labor and other costs into input assumption fields. The beauty of this is that it gives visibility to everyone in an organization and makes highly accurate predictions. This elevates target-setting, breaking out targets by function. It measures and compares achievements and lets retailers and suppliers work together to meet goals. Retailers and CPGs can then enable the Joint Business Planning process with these same powerful tools and more collaboratively agree upon a set of metrics and activities that will achieve aligned business objectives that are very specific to categories, investments or activities. Progress against all objectives is part of the modeling, constantly assessing and improving accuracy of predictions, reducing or eliminating the replanning that results from gap closure and volatility.  

Lack of Trust & Familiarity with AI

Mainstream media and technology companies have made the topic of AI so confusing to the point that it now seems too conceptual and risky to adopt. Despite evidence to support the use of AI, its effective application to broad data sources and existing processes is still nascent in the CPG industry. Just 11% of CPG organizations have adopted ML/AI tools. This stems from various factors, including concerns about the accuracy and reliability of AI algorithms, and a lack of clarity on how to apply the forms and functions of AI models to existing business processes.

There is tremendous efficiency to be gained using technology over spreadsheet, regardless of whether it incorporates a little AI or a lot of AI. Good software does not necessitate adding people (nor replacing people) to make that happen. It’s a small investment compared to what the returns can be when technology is used to augment teams and enable them to act with exponential speed and precision. Any returns can be high with clearly measurable objective-setting and ROI.     

Conclusion

The move away from spreadsheets is not just a call for change; it is an opportunity for growth and innovation. By embracing cutting-edge software and analytics, the full potential of data can be unlocked, allowing CPGs to make informed decisions and drive sustainable business growth. The time to act is now, as the CPG landscape continues to evolve rapidly. Those who adapt to change will be the ones to thrive and capitalize on the transformation opportunity.

To learn how you can evolve to be a more agile and AI enabled company, contact Insite AI.

Beers & Sunshine: How Brewers Can Lead the Season Through Assortment and Space Elasticity

How a national brewer optimized and cultivated a successful assortment in the craft beer category, increasing revenue and market share

The most important months for alcohol sales are underway. From Memorial Day to Labor Day, retailers see the largest percentage of sales in beer and alcohol. To capitalize on that demand, however, retailers rely on their beer partners to deliver the most profitable assortment available.

The craft beer sector can be one of the most complex categories, requiring a retailer to choose among hundreds of unique breweries local to their stores; then there are thousands of regional breweries and national craft companies. Next, how many IPAs should be carried? What about sours, stouts, pilsners, maybe a gose with hibiscus or a near-beer pale ale? How many four-packs, singles or cases? Cans or bottles? The options are seemingly endless.

Factoring in the importance of the summer — the National Beer Wholesalers Association ranks the top three beer holidays as Independence Day, Memorial Day and Labor Day — retailers need powerful insights that deliver reliable visibility into seasonal trends like summer.

Beer brands need to help retailers measure the price elasticity of craft beer in the summer and perfect assortments to take advantage of summer habits, trends and taste profiles. Insite AI and precise predictive modeling can set brands up to lead the way. 

In this blog, we explore further through a national brewery client that optimized an assortment for two national specialty retailers, resulting in an increase of sales of nearly 3% for the craft beer category, growing annual revenue by $20 million.

Understanding the Complexity of Craft Beer

Year after year, the total number of craft breweries entering the U.S. market continues to climb. But is there enough space for them? Or, better yet, how can we truly expect a retailer to know what to put on the shelf or in the cold vault? 

In the problem of too much beer, we worked with a national brewer to optimize assortments at two national specialty retailers, one with 300 stores and another with 500. As a result, it was the first time the national brewer had been awarded a category captaincy to help rein in and optimize assortments. 

For both chains, the brewer helped make the most out of the retailers’ craft beer shelf space, something incredibly important for the big four months. The National Beer Wholesalers Association has reported summer beer sales represent anywhere from 20-40% of a company’s sales.

Driving Revenue and Market Share

Working with the national brewer, we leveraged AI-powered predictive analytics at a store-by-store level, as opposed to store clusters.

Every store came with unique space constraints and localized options to consider. With Insite AI, the brewer was able to take multiple sources of unstructured data and deliver granular insights to help the brand forecast performance over a two-year period. Insite AI deployed targeted assortment capabilities inside the brand’s cloud environment to analyze key data points across multiple retailer accounts. Insite AI then applied sophisticated models that delivered visibility into areas of growth and decline, and predicted innovation trends. 

They quickly delivered critical insights on demand transference. The platform highlighted the incrementality associated with new products to add to an assortment, suggested what to remove, and looked at other market factors.

For the national brewer, the modeling led to huge success for the two retailers, generating:

• A 3% lift in sales above “business as usual.”
• $20 million in annual revenue increases for the retailers. 
• Significant market share gain.

These numbers are significant for a category where retailers are looking to pull back on inventory and players in the space. It is more important than ever for brands to become trusted and credible thought partners to their retailers with business planning and decision-making.

With Insite AI, the brewer can now create multiple assortments within seconds and recommend the best one for its retailer partners. Alongside the consulting engagement, they provide an AI model that is continuously learning so brands can deliver updates to optimally run the category and maintain a captainship.

Building Better Assortments

Brands across the CPG space can refine and optimize assortments, space planning and trade promotions through AI modeling. Machine learning distills the months of manual work required to understand movements and trends within a category to minutes. AI serves as an accelerant to internal teams and ways of working.

Insite AI can help brands create stronger relationships with retailers through predictive and precise technology that brings clarity to complex categories like craft beer.

To see how AI can lift your brand, contact us here. 

How CPGs Can Move Beyond Price (Featured on Path to Purchase)

Guest article originally featured on Path to Purchase Institute website. See original article here.

Amid the current inflation cooldown, retailers and consumers are over price hikes. It’s now on brands to implement strategies that drive organic volume growth. Retailers are seeking brand partners with knowledge and data that lifts a total category and moves products. Consumers want prices back to normal.

About the Author: Brooke Hodierne currently serves as an EVP – strategy consulting at Insite AI, an AI and strategy partner for larger consumer brands. She joined the company following her time as SVP of merchandising for 7-Eleven. In the role, she drove category management teams that developed, implemented and communicated merchandising strategies for vault, packaged goods, tobacco and services.

Before joining 7-Eleven, Brooke held multiple positions at Giant Eagle, serving as VP of own brands, senior director of strategic sourcing and own brands, and director of prepared foods merchandising. She supported brand marketing at Del Monte Foods and held analytical roles with financial investment firms Wilshire Associates, Federated Investors and the Vanguard Group.

Seeing AI Through a Practical Lens (Featured on C-Store Dive)

Guest article featured on C-Store Dive. See full article.

There’s a lot of noise around AI and what it can or cannot do. In this article, Brooke Hodierne, former SVP of Merchandising at 7-Eleven explores the practical applications and challenges of implementing AI in the convenience store industry. She discusses the potential benefits of AI technology and emphasizes the importance of aligning AI initiatives with actual business needs and objectives rather than pursuing AI for its own sake. She also addresses the obstacles and skepticism faced by businesses, highlighting the need for realistic expectations and understanding AI’s limitations.

About the Author: 
Brooke Hodierne currently serves as an EVP – strategy consulting at Insite AI, an AI and strategy partner for larger consumer brands. She joined the company following her time as SVP of merchandising for 7-Eleven. In the role, she drove category management teams that developed, implemented and communicated merchandising strategies for vault, packaged goods, tobacco and services.

Before joining 7-Eleven, Brooke held multiple positions at Giant Eagle, serving as VP of own brands, senior director of strategic sourcing and own brands, and director of prepared foods merchandising. She supported brand marketing at Del Monte Foods and held analytical roles with financial investment firms Wilshire Associates, Federated Investors and the Vanguard Group.