Refine Wine: Navigating Regional Preferences With Precision

The intricacies of the wine category make it one of the most complex to manage. AI and machine learning can help brands cut through the complexities and get the right wine onto the right shelves at the right time.

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.

The result was a 3% lift in sales for the overall category in those retailers, growing annual revenue by $20 million.

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?

“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.”

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 21st 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.

The CPG’s Guide to AI

Empowering Consumer Brands with Clear and Actionable AI Insights

Research confirms leading consumer brands who harness the value of consumer insights and artificial intelligence (AI) better predict the needs of their customers, improve category performance, accelerate growth, and outpace the competition.

72% of executives consider AI as a business advantage

But how can you get started? With data overload, an abundance of options and unclear direction, many companies opt to do nothing. This is no longer an option. You will be left behind. Armed with the right data, AI-driven CPG brands are working hand in hand with their retail partners to better meet consumer demand. By turning mounds of overwhelming data into actionable intelligence, these CPGs are scoring big with retailers and end consumers alike.

In this guide:

  • Demystifying AI
  • How consumer brands can leverage AI today.
  • Top 5 AI/ML Use Cases in CPG
  • Going beyond Power BI and advanced analytics
  • Making the case for AI in your organization
  • Top questions to ask for a fruitful AI journey

Harness the power of AI to ensure you have the right products on the right shelves at the right time. Download this guide to begin your AI journey toward becoming an AI-driven, category-leading consumer brand.

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Taming the Data Tsunami for Better, Faster Decision Making

Won category “Captaincy” at top specialty retailers

What we did

A leading brand in the craft beer sector wanted to elevate its category advisement
at Whole Foods and Sprouts. Applying powerful predictive and AI elements, we
brought together multiple previously unstructured data sources to surface insights
that enriched and accelerated the brand’s planning and decision-making processes.

Create multiple assortments within seconds.

Analytics

We deployed assortment capabilities in the brand’s environment to ingest and
analyze key data sources. Utilizing a flexible implementation strategy, our platform
quickly delivered critical insights on demand transference so the team could
understand and act upon the incrementality associated with product additions,
deletions, and resulting effects on demand.

Drive value from insights with speed and agility

Results

With Insite AI, this leading craft beer brand swiftly developed clear forecasts on
how their category—and others in the category—are performing within Whole
Foods and Sprouts. The brand can now create multiple assortments within seconds
and recommend the best one.

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Mastering Joint Business Planning: An Insider’s Guide

When it comes to joint business planning (JBP), Category Advisors bring a source of truth to the table, fostering credibility and trust in the JBP, yet oftentimes they take a backseat in the planning process. Join us for a panel discussion with guests and former sales and merchandising leaders from Walmart and Coca-Cola, as they share their perspectives on how Category Advisory can lead JBP to improve outcomes. Gain a deeper understanding of retailers’ data challenges and find out how you can become their go-to partner by bringing the right data and insights to the table.

Key topics addressed in the webinar:

  • Understand the pivotal role of category advisory in Joint Business Planning.
  • Discover how to establish credibility and trust as an objective third-party advisor.
  • Empower sales growth through actionable and clear insights.
  • Employ emerging technologies to stay ahead of the competition.

Don’t miss this opportunity to elevate your advisory skills and drive mutual success.

Panelists:

  • Kristine Joji, Executive Vice President, Strategy Consulting, Insite AI
  • Capri Brixey, Executive Vice President, Strategy Consulting, Insite AI.

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Winning the Retail Space War with Predictive Modeling

40% improvement in assortment trade-off accuracy

What we did

One of the largest brands in the consumables sector wanted to understand how to utilize its store space within Target for optimal results. Applying innovative AI components to develop space elasticity models, we refined the brand’s planning process with calculations and forecasting that identified how space affects profit- ability and sales demand to arrive at their ideal strategy. Insite AI become an extension of their team, providing the support and resources they needed to ensure they achieved their goals through data, adoption, and guidance.

2%
increase in profit
with the same amount of shelf space

Analytics

We empowered the Category, Assortment, and Space planning and analytics leaders with critical insights to rapidly simulate multiple scenarios and accurately forecast effects on sales, margins, volume, and demand. Leveraging predictive modeling, our platform delivered insights for maximizing productivity and profitability so the team could determine the ideal plan for each store layout.

Reduced planning cycle time from months to days

Results

With Insite AI, this major consumer brand quickly identified the best use of in-store space. The brand can now swiftly hone its on-shelf facings, arrangements, and structures at the planogram level and defend its plan within Target. Even with competing priorities, limited budget, and resource constraints, the company priori- tized working with Insite AI. This technology has been identified as mission-critical at the executive level and the team members involved have received recognition internally for their efforts.

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One of the Largest Consumer Brands Achieves Category Leadership at Walmart

Became category leader at the world’s largest retailer

What we did

One of the world’s largest brands in the consumables area wanted to elevate its
category advisement at Walmart. Applying powerful predictive and AI components
across multiple data sources, we enriched and accelerated the brand’s planning
and decision-making processes to present Walmart with the best assortments at a
planogram level for all 4,600 stores.

Reduced planning cycle time from months to days

Analytics

We deployed assortment capabilities in the brand’s cloud environment to ensure
data never left. Merging multiple data sources, our platform delivered insights on
demand transference so the team could understand the incrementality associated
with product additions, deletions, and resulting effects on demand.

5-15% sales improvement

Results

With Insite AI, this major consumer brand developed very clear forecasts on how
their category—and others in the category—are performing at a planogram level
within Walmart. The brand can now create multiple assortments within seconds and
recommend the best one.
As a result of their adoption of AI, they have delivered a better shopping experi-
ence for their consumers, growing their joint profit pool, and achieving category
leadership. They are now expanding into other retailers.

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