The Do’s and Don’ts of Joint Business Planning

Do not report the news. Simply reporting out category performance as up or down in volume and dollars vs year ago is ‘news,’ not insights.

In the dynamic world of retail, collaboration is key to success. During a recent panel discussion, Mastering Joint Business Planning: An Insider’s Guide, we sat down with a former VP of Sales at Coca-Cola, Capri Brixey, and a former VP of Merchandising at Walmart, Kristine Joji, to share their experience and insights into what makes a successful JBP. 

Below are some key “do’s” and “don’ts”—both practical and technical—that they shared, which can help CPGs master JBP scenarios, improve your retail partnerships, and grow your mutual profitability:

1. Understand the retailer’s plan

Entering a planning session with just the CPG’s agenda can quickly derail a conversation and lead to an unproductive meeting. Bringing a true desire to understand both parties’ needs (and being equipped for effective discovery to that end), CPGs can ensure that their own plans align with the retailer’s strategy and priorities. 

2. Use credible insights

Do not report the news. Simply reporting out category performance as up or down in volume and dollars vs year ago is ‘news,’ not insights. Data is just data. Retailers need to understand the why and the what. Indicate what happened, why it happened and what should be done next. Anyone can pull a report. The beauty is in taking numbers, overlaying and integrating insights, trends, data and analytics and helping people understand what happened and what is happening so they can create meaningful action for the future. Insights should be foundational to every phase of the planning process, from the beginning when both organizations are trying to understand priorities, to the end when tactics and solutions are being created to drive business.

3. Don’t always paint a rosy picture

Insights may reveal, for example, that while a product was projected to perform well and provide incrementality, instead performance was below expectations and demand transferred to another product or was cannibalizing existing items. Bringing objective, robust data and facts helps build trust and transparency. Being willing to collaboratively find mutual wins through data elevates partnership and strategic thought leadership. It shows that a CPG is data led and insights driven, that it understands the customer, environment and what is happening now.

4. Offer potential solutions

Use of AI tools allows CPGs to stitch together myriad pieces of data in real time, aligning them with trends and insights. This lets CPGs accurately tell retailers: 

• What their theory was.
• What the data is.
• What they know is really happening.
• What can be done about it.
• What the CPG’s recommendation is so the partners can mutually grow their business and the category.

5. Build a specific, tactical plan to deliver on what was committed

This aligned plan  should be the foundation of the partnership. It should involve everything that was agreed upon, as tactical as shelf placement, pricing and promotions, or as strategic as broad expectations for overall contribution/performance and how the organizations will engage. Then, regular checkpoints should be set to ensure the strategy remains on track, with flexibility and agility to respond to events and trends in the market. Disruptions can be small or on the scale of  the war in the Ukraine or Covid-19. In these types of cases, both organizations must ask—and answer—“Where are we now and how do we pivot to ensure we can still deliver our plan together?” 

6. Leverage AI to run “what if” scenarios

CPGs can leverage AI to run “what if” scenarios in real time. This can foster forward thinking, collaborative conversations with retailers. Data accounts for the many moves retailers can make on their chessboard. This gives them more clarity, so they can develop rich category plans. Using technology to detail the “why” and sharing explanations also helps buyers explain decisions to their leadership teams.

7. Drive collaboration

The goal of JBP is to drive collaboration. If CPGs are not weighing mutual growth, mutual priorities and planning ahead, they can be derailed by many unforeseen events. They need to be agile. Working through the process and knowing where the finish line should be and planning towards it are key components of success.

8. Deliver a better shopping experience for consumers

Retailers want to know how their CPG partners will be more consumer centric. CPGs can create a more personalized consumer experience by leveraging advanced AI capabilities. These AI-driven assortments, pricing, and promotions empower CPGs to craft shopping experiences that not only satisfy customers but also foster long-term loyalty. 

Conclusion

Thoroughly analyzing data reveals a predictive view of entire product categories, going beyond just looking at individual brands. A supplier that is driven by insights and data, promoting its own growth as well as that of the category and the entire industry, stands out as a clear trailblazer. These carefully obtained insights not only have weight but also solidify the supplier’s image as a strategic thought leader. Retailers will naturally lean toward their most developed partners,  elevated by trust and true transparency in insights and data.

The insights shared in this article were presented at a recent panel discussion featuring Kristine Joji and Capri Brixey, EVPs of strategy consulting at Insite AI. The event was moderated by Jackie Lewis, VP of content at the Category Management Association. To view the full presentation, click here. 

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.

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. 

CPG’s Guide to Walmart Luminate: Enhancing Results Through AI.

Are you getting the most out of your Walmart Luminate data? The platform offers a goldmine of shopper insights, but making the data actionable can be a challenge. That’s why we’ve created the CPG’s Guide to Walmart Luminate: Enhancing Results Through AI.

This comprehensive guide provides a deep dive into Walmart Luminate, exploring its unique benefits and how to apply predictive analytics to unlock its full potential.

In this guide:

• The key differences between the Basic and Charter versions of Luminate.
• How AI-powered solutions can harmonize Luminate data with other sources.
• Real-world examples of how brands are using shopper insights to optimize strategies.

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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

Winning the Retail Space War with Predictive Modeling

Tapping Real-Time Market Data and Performance Forecasts to Pick the Next Winners

Could AI Have Predicted the Explosive Growth of Hard Seltzers?

Hard seltzers have seen sparkling growth over the last few years, and really took hold of the alcoholic beverage scene in 2019 and 2020.

In a recent Nielsen study looking at March and April in 2020 vs. the same months in 2019, consumers decreased their share of spending on beer and wine, with beer losing 5.6 share points and wine losing 4 share points, and these spending shares show even more discrepancy in hot-weather seasons.

Mainstream beer and alcohol manufacturers who have taken on hard seltzer production represented less than 20% of the market as of June 2020, meaning that those brewers missed out on getting a piece of the action earlier and have forfeited a larger market share.

The hard seltzer segment has caught the attention of brands around the world, and they’re all eager to grab a slice of this growing market. At the beginning of 2018, just 10 brands were on the market; by the beginning of 2019, there were 26 hard seltzer brands and right now, there are more than 65 brands fighting for consumer attention:

During and early summer of 2020, hard seltzer off-premise sales in the US (i.e. supermarket and convenience store sales) quadrupled on a year-on-year basis, and showed an increase of $900 million.

Consumers now have choices that include traditional hard seltzers, cider seltzers, wine seltzers, spritzers, spirit-based seltzers, and the list goes on. There’s no shortage of options in this category.

Many CPG brands faced both c-suite and shareholder backlash for not predicting or at more quickly reacting to this global trend. Some brands have come late to the party and have been forced to play catch-up, having to forge new brands from scratch or find meaningful ways to add extensions to existing brands (a reaction seen from some of the well-known beer brands).

Retailers now have dozens of seltzer brands all fighting for.

Many CPGs saw the shift happening before them, but didn’t act sooner because they just didn’t have an accurate way to predict the potential sales or profitability of such a launch. The largest drinks companies won’t give a new brand the green light unless they can guarantee hundreds of millions of dollars in sales right off the bat. In the USA, the market is dominated by White Claw and Truly. White Claw was the brainchild of Mark Anthony Brands, the creator of Mike’s Hard Lemonade. Truly was created by the Boston Beer Company, maker of Samuel Adams. Unbelievably, the Boston Beer Company now sells more hard seltzer than it does beer!

So how could other beverage brands have spotted and acted on this trend earlier, securing a larger market share?

The problem with many beverage companies and CPGs is that data is siloed across many places: trend forecasts from strategic consultancies and futurists; shopper data from retailers (which typically is owned and guarded by the retailer); the company’s own sales and category data.

Trends like the hard-seltzer phenomenon could have been spotted much earlier by tuning in to the weak signals coming from the market. Often these weak signals are too faint for any one department to spot.

Social media, blogs, and even restaurant menus are another source of insight – the problem is, there’s such a huge mountain of data, any R&D or insights team would need to trawl through millions or even billions of data points to spot emerging trends.

It’s just not feasible for humans to do, and siloed data is too challenging, time consuming, and cumbersome to analyze, respond, and act quickly.

This is where an integrated AI platform shines. Insite AI will analyze feeds from multiple sources, joining the dots between data that would normally never be combined. It ingests internal sales and performance data, market-share metrics, and much more, then runs simulations and forecasts, combining it with data from the likes of Nielsen and Kantar and EPOS data from retailers. The AI engine has the ability to trawl through billions of social media posts and articles, too, turning this raw and disparate data into intelligence. Combining this intelligence with billions of what-if scenarios, you’ll be presented with decisions on which you can be confident, giving the CPG rigorous ammunition to prove your business case to both your company and your retail partners.

Intelligent decisions, founded on data.

Had some of the world’s largest beverage brands used this technique, they would have spotted and accurately predicted the performance of the hard-seltzer category. They would have moved faster, created new brands more quickly, and entered the market much earlier, gaining greater market share and consumer mindshare. Not only that, they would have been able to prepare their logistics and manufacturing infrastructure, ready for the huge bottling volumes required to fulfil demand.

This hard-seltzer tale is not entirely unique, and it won’t be the last. Other categories are also about to explode. Trends and changes in consumer behaviour are bubbling away.

As a CPG, imagine the gains to be had if you could anticipate now and reap the benefits of being ahead of the curve later.