Gearing Up for the Fourth: How Beer Brands Prepare for the Biggest Sales Week of the Year
As one of the largest weeks for beer sales, approaches, beer manufacturers are seeing increasing competition when it comes to consumers’ dollars. In this Q&A with Beverage Industry Magazine, Kristine Joji, EVP of strategy consulting at Insite AI, explores market trends and predictive pricing strategies for beer brands to successfully position themselves during some of the largest beer sales weeks of the year.
Q&A Highlights:
American consumers spent $15.8 billion for the Fourth of July in 2023, with $9.5 billion on food and $4.02 billion on alcohol.
Market trends show consumers are tightening spending due to inflation, impacting their purchasing decisions for Fourth of July celebrations.
There is a growing trend towards health-conscious and sober-curious beverage choices, including low-calorie beers, hop water, and hard kombucha.
Beer brands need to consider optimal pack sizes, hyper-localize assortments, and utilize predictive pricing to maximize ROI and cater to cash-strapped consumers.
Strategies for smaller beer brands focus on new user acquisition and brand differentiation, while national brands aim to maintain current customers and drive additional sales.
[Video] The AI Powered Future of Category Leadership
Join Vic Miles, retail and consumer goods industry leader at Microsoft and Shaveer Mirpuri, co-founder and CEO of Insite AI on this episode of “Beyond the Tech”
In this episode, of “Beyond the Tech” Mirpuri shares his journey into the field of artificial intelligence (AI) and how his experience in data science, computer science, and business led him to explore the practical applications of AI in various industries, including retail and consumer goods. He discusses the role of AI in automating data ingestion, scenario planning, optimization, and forecasting, enabling humans to focus on strategic and creative decision-making. Mirpuri emphasizes the importance of AI that provide explainable demand forecasting and insights into the drivers and constraints affecting sales for consumer brands.
The conversation focuses on the potential of AI to drive value for consumer goods companies in areas like hyper localized assortment optimization, demand forecasting, understanding price elasticities, and scenario planning. Mirpuri emphasizes the importance of explainable AI models that can break down the drivers and constraints affecting demand, sales, and pricing. He discusses the ability of AI to project accuracy into the future based on historical data and various factors, as well as the capability to run simulations starting from future time periods when business changes are planned. Mirpuri also highlights the value of scenario planning using AI to prepare for unpredictable events and macroeconomic conditions. Overall, the discussion underscores the potential of AI to provide granular insights, optimize decision-making, and drive growth strategies for consumer goods companies.
The discussion revolves around the necessity and potential benefits of investing in AI for consumer goods companies. Mirpuri acknowledges the significant time and efficiency gains AI can provide, enabling employees to be more strategic and thoughtful.
Mirpuri also underscores the exponential lead early adopters could gain over late adopters, as AI allows brands to optimize assortments, pricing, promotions, and decision-making processes. However, he stresses the importance of executive education and understanding AI methodologies to appreciate its use cases and ROI fully.
About Shaveer Mirpuri
Former executive and board member of two early stage VC backed companies (IPO and acquired), Shaveer subsequently invested in several tech companies in e-commerce, AI, consumer brands, and manufacturing, including new businesses with large corporate partners. Prior to this, he was a consultant to Walmart’s former CEO on AI. In 2019, the American Chamber of Commerce named him a top 3 in entrepreneurship, and today he is an active member of the Forbes Technology Council.
About Vic Miles
Vic Miles is the Americas Business Strategy Leader for Microsoft’s Retail Industry solutions group. Vic is responsible for go to market strategies, guidance to the client service teams and the integrated solution plan for Microsoft products in the retail industry. Vic joined Microsoft in April 2008 after over 10 years in retail as a Wal- Mart IT leader. Vic has built a specialty around retail store operations. His knowledge comes from leading application development for in-store retail systems, during his tenure at Walmart. Vic serves as an advisor to retail executives around the globe where he helps to achieve the Microsoft mission of empowering every person and organization on the planet to achieve more.
Decoding Complexity: Navigating the Intricacies of the Wine Aisle
By Capri Brixey
Inside the four walls of the retail store, few categories are more complex than wine. For consumers, there’s an array of varietals from all over the world, each sold at wide-ranging — and to the consumer — seemingly random price points. For brands, there’s getting those bottles to the shelf, working through state-by-state regulations, distributor needs, and promotional limitations. For retailers, it’s knowing what to stock.
Using emerging technology, wine brands can simplify the process through AI-powered, data-driven optimization and decision-making. Producers can leverage predictive analytics to harmonize data from retailers, distributors, third-party sources, POS data and more. This includes, but is not limited to, macroeconomic and other influencing data that tee up strategic recommendations as granular as each store in their network.
As a result, wine brands can gain more control over where their wine goes and develop stronger relationships with their distributor and retailer partners, in addition to better managing supply and demand for future planning.
Understanding wine’s journey to the store
Currently, wine brands, big and small, need to jump through plenty of hoops to get products inside retailer doors throughout the country. To start, there’s a litany of governmental regulations to comply with, particularly regarding advertising and labeling wines, and how it’s imported and sold in certain states.
Brands must adjust their strategies on a state-by-state basis, factoring in unique rules before selling into retailers through a distributor network. Wine companies often work with several distributors, each with their own regional expertise and connections. However, distributors don’t just buy wine from a producer and sell it to the retailer. Distributors build a relationship with manufacturers to leverage sales materials, displays, and most of all, data and insights to help tell a story to the retailers.
Combined, regulations and distributor requirements can complicate how a wine company moves bottles of wine. Historically, brands have leveraged a matrix for each target state, distributor and retailer, and the data can get quite dizzying. Additionally, remaining inventory and pull-through rates have been used as performance indicators, even though those often do not reflect real preferences. Rather, they could reflect default performance through pushes to reduce latent inventory, or an incentive for display. This is where AI and predictive analytics can infuse levity and strategy.
Cutting through the complexity of the category
With so many entities at play and data sources from retailers, distributors, third-party providers and much more, brands can leverage AI to harmonize the data and deliver recommended strategies on what wines will sell best in what regions. In addition, AI can be modeled by brand teams to factor in state regulations and insights such as product availability within the distributor networks.
Machine learning models can look at data to see how one varietal is performing in a region in Ohio and how another varietal is performing in Texas. The AI can go even more granular to see how tastes change at individual stores or clusters in one region, too. This is tremendously important with store locations reflecting distinct differences in preferences depending on the location’s customer profiles. Retailers want to know distributors and wine brands will stock products that fit the tastes and demographics of the shopper attributes at each store.
Another advantage AI brings to the wine category is the ability to narrow in where choice may seem endless. Machine learning recommendations can help distill assortments down to the most essential for each store, presenting a measurable value to retail and distributor partners.
This pertains to pricing, too. AI can assist brands with recommendations on optimal price points for their wines by retailer. Value brands can fall under $10, for example, while premium brands can easily be $50 and up. AI can find and explain the price in that range that will deliver the best profit gains for a retailer and a brand.
Serving brands precise recommendations
To be sure, some industries fear LLM (large language model) AI outputs, questioning the accuracy and seeing a risk in its lack of explainability; however, AI-powered insights (different from LLM AI) should be looked at purely as an asset for wine brands.
With AI/ML, brand managers can input complex data and work with it to serve reliable recommendations to explore. Brand teams can plan endless scenarios for varietals, stores and regions and make strategic decisions in near real time that are not entirely reliant on historical data.
For wine brands, predictive analytics can enhance how the category delivers the right bottles to the right shelves, meeting regulatory compliance. Brands, retailers and distributors can work better together, using a better foundation as a starting point and further refine through execution.
Consumers will likewise respond to assortments aligned to their preferences, as retailers create efficiencies and consumer delight within their existing spaces. Simply put, machine learning and predictive analytics uncorks opportunities for wine brands that they’ve never seen before.
__
Capri Brixey is EVP of strategy consulting at Insite AI, bringing extensive strategic leadership experience from both retail and supplier roles in the consumer goods industry. Most recently with The Coca-Cola Company, Capri has led small and large-store teams, across multiple routes to market, channels, categories, and segments in the industry. Recognized in 2022 as a Senior Level Leader for Top Women in Convenience, Capri has also been recognized for her leadership in collaborative/joint business planning with top retailers across multiple channels/formats.
ck unprecedented opportunities.
Four Trends That Will Drive CPGs To Adopt AI in 2024
While food retailers have been actively utilizing AI to forecast shopper behavior and streamline supply chains, the adoption rate among consumer brands lags behind. In this article, we discuss four key trends that will drive CPGs to adopt AI in 2024 and beyond.
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.
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.
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.
CPG Matters: Decoding Complexity: Navigating the Intricacies of the Wine Aisle
For wine brands, predictive analytics can enhance how the category delivers the right bottles to the right shelves, meeting regulatory compliance.
Inside the four walls of the retail store, few categories are more complex than wine. For consumers, there’s an array of varietals from all over the world, each sold at wide-ranging — and to the consumer — seemingly random price points. For brands, there’s getting those bottles to the shelf, working through state-by-state regulations, distributor needs, and promotional limitations. For retailers, it’s knowing what to stock.
Using emerging technology, wine brands can simplify the process through AI-powered, data-driven optimization and decision-making. Producers can leverage predictive analytics to harmonize data from retailers, distributors, third-party sources, POS data and more. This includes, but is not limited to, macroeconomic and other influencing data that tee up strategic recommendations as granular as each store in their network.
As a result, wine brands can gain more control over where their wine goes and develop stronger relationships with their distributor and retailer partners, in addition to better managing supply and demand for future planning.
Understanding wine’s journey to the store
Currently, wine brands, big and small, need to jump through plenty of hoops to get products inside retailer doors throughout the country. To start, there’s a litany of governmental regulations to comply with, particularly regarding advertising and labeling wines, and how it’s imported and sold in certain states.
Brands must adjust their strategies on a state-by-state basis, factoring in unique rules before selling into retailers through a distributor network. Wine companies often work with several distributors, each with their own regional expertise and connections. However, distributors don’t just buy wine from a producer and sell it to the retailer. Distributors build a relationship with manufacturers to leverage sales materials, displays, and most of all, data and insights to help tell a story to the retailers.
Combined, regulations and distributor requirements can complicate how a wine company moves bottles of wine. Historically, brands have leveraged a matrix for each target state, distributor and retailer, and the data can get quite dizzying. Additionally, remaining inventory and pull-through rates have been used as performance indicators, even though those often do not reflect real preferences. Rather, they could reflect default performance through pushes to reduce latent inventory, or an incentive for display. This is where AI and predictive analytics can infuse levity and strategy.
Cutting through the complexity of the category
With so many entities at play and data sources from retailers, distributors, third-party providers and much more, brands can leverage AI to harmonize the data and deliver recommended strategies on what wines will sell best in what regions. In addition, AI can be modeled by brand teams to factor in state regulations and insights such as product availability within the distributor networks.
Machine learning models can look at data to see how one varietal is performing in a region in Ohio and how another varietal is performing in Texas. The AI can go even more granular to see how tastes change at individual stores or clusters in one region, too. This is tremendously important with store locations reflecting distinct differences in preferences depending on the location’s customer profiles. Retailers want to know distributors and wine brands will stock products that fit the tastes and demographics of the shopper attributes at each store.
Another advantage AI brings to the wine category is the ability to narrow in where choice may seem endless. Machine learning recommendations can help distill assortments down to the most essential for each store, presenting a measurable value to retail and distributor partners.
This pertains to pricing, too. AI can assist brands with recommendations on optimal price points for their wines by retailer. Value brands can fall under $10, for example, while premium brands can easily be $50 and up. AI can find and explain the price in that range that will deliver the best profit gains for a retailer and a brand.
Serving brands precise recommendations
To be sure, some industries fear LLM (large language model) AI outputs, questioning the accuracy and seeing a risk in its lack of explainability; however, AI-powered insights (different from LLM AI) should be looked at purely as an asset for wine brands.
With AI/ML, brand managers can input complex data and work with it to serve reliable recommendations to explore. Brand teams can plan endless scenarios for varietals, stores and regions and make strategic decisions in near real time that are not entirely reliant on historical data.
For wine brands, predictive analytics can enhance how the category delivers the right bottles to the right shelves, meeting regulatory compliance. Brands, retailers and distributors can work better together, using a better foundation as a starting point and further refine through execution.
Consumers will likewise respond to assortments aligned to their preferences, as retailers create efficiencies and consumer delight within their existing spaces. Simply put, machine learning and predictive analytics uncorks opportunities for wine brands that they’ve never seen before.
__
Capri Brixey is EVP of strategy consulting at Insite AI, bringing extensive strategic leadership experience from both retail and supplier roles in the consumer goods industry. Most recently with The Coca-Cola Company, Capri has led small and large-store teams, across multiple routes to market, channels, categories, and segments in the industry. Recognized in 2022 as a Senior Level Leader for Top Women in Convenience, Capri has also been recognized for her leadership in collaborative/joint business planning with top retailers across multiple channels/formats.
About the Author
Capri Brixey (a former VP of Sales at Coca-Cola) is currently EVP of Strategy Consulting at Insite AI, which focuses on enabling sales, revenue growth and category management for large consumer brands. For more information, please visit www.insite.ai.
Unlocking the Full Potential of Walmart Luminate through AI
Walmart Luminate highlights what’s happened inside the store and online like never before. Insite AI emphasizes what to do next with the data.
Walmart Luminate is a game-changing data platform that shines a light into a deep, dark data void, where no CPG has had visibility before. The platform delivers shopper insights, product performance and channel data such as pickup and delivery orders across 140 million households — weekly.
The platform is revolutionizing how brands use data to reach their consumers, but Insite AI can help unlock even more potential within the data. For example, Walmart Luminate highlights what’s happened inside the store and online like never before. Insite AI emphasizes what to do next with the data. Insite AI’s machine learning and AI models provide a forward-looking tactical layer on top of the Walmart data. Brands can see how a change in pricing, promotions or pack sizes will alter their course in the marketplace.
Walmart Luminate delivers the data. We help you put it in motion.
Walmart Luminate and Insite AI at work
Having spent nearly 20 years of my career at Walmart, most recently as the VP of merchandising in dry grocery, as well as working within bakery, personal care and other categories, I have been fortunate to see Walmart pilot and cultivate the Walmart Luminate program.
I also have a tremendous amount of experience working with brands to grow and maneuver within Walmart. I mention this, because alongside Brooke Hodierne, who was SVP of merchandising at 7-Eleven, and Capri Brixey, who was a former leader at Coca-Cola, we add a human element to the numbers. We overlay business intuition and consultation as a result of our deep and diverse business backgrounds.
With that said, let’s look at some hypothetical scenarios where Walmart Luminate and Insite AI can work together to help brands get better results.
Product innovation. A snack food brand uses Walmart Luminate to look at larger category trends and in the process identifies a competitive product making waves. The new challenger brand delivers higher protein content and is more affordable. In response, the snack food brand has decided to create a new line of products with 10 more grams of protein. Insite AI can help that brand forecast a range of scenarios on how a certain pack size would perform against that competitor. What pricing would work better? Would a specific promotion generate more sales at launch, and how will it be performing months down the line?
Brand-switching behavior. Walmart Luminate delivers a weekly report to a detergent brand that shows the detergent was out of stock in a specific region of the country. It also shows what percentage of households switched to a new brand because of the out-of-stock product, highlights what brand the households bought and shares more in-depth analysis. Insite AI can then layer in predictive market analytics around demand forecasting in consumer segments and parts of the country to help the detergent brand see where its brand loyalty lies and how to effectively respond.
Delivery trends. Within Walmart Luminate’s data is a look at online transactions for delivery and pickup at the store, and a beer brand, for example, can narrow in to see if there was a spike in delivery during the first Sunday of the NFL football season. What brand had the highest delivery? Which brands lagged? At what time of the day did the deliveries occur most? Insite AI can layer on the tactical to see if the trend will continue and deliver efficient forecasts on what types of promotions or moves can be made to accelerate the trend and predict when out of stocks may occur.
Factor in all the categories and shopping behaviors happening daily inside a Walmart, and the scenarios facing brands are endless. Walmart Luminate works with brands of all sizes, offering a free package with limited insights. There is a monetized version which provides customer decision trees, leakage trees and real-time insights across 140 million households.
No doubt, the data platform will grow. Insite AI will grow alongside it, working with CPGs to help solve their challenges.
Unprecedented data and insights
No other data platforms deliver a view into online delivery and pickup behaviors, giving Walmart Luminate a major competitive advantage. Before Walmart Luminate, CPGs had to rely on panel data and qualitative data sets to try and spot a trend.
But the panel data is tens of thousands of households, and the data is months old. Brands have done excellent work with panel data but it can also be tricky. The results rely on how the panel responds, and sometimes people say what they want you to hear, or the subset of people in the panel isn’t very broad, in my opinion.
Walmart Luminate offers brands data at scale, and the data presents a complete picture of shopper behaviors and households. The data withholds any identifiable information of a shopper, but it represents 140 million households across income levels and ethnicities shopping at Walmart stores. CPGs have at their fingertips truly robust data.
Insite AI can help brands take this data even further. Contact us to see how we can leverage AI and predictive analytics and help you turn insights from Walmart Luminate into immediate action.
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.
Retailers frequently lean 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.
The candy category values innovation and has produced 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.
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.
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.
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.
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.
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 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.
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.
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.
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.
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