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.

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

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

Limitations of Spreadsheets

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

Market Volatility

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

Modern Approaches          

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

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

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

Collaboration & Pinpointed Goals

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

Lack of Trust & Familiarity with AI

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

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

Conclusion

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

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

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

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

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

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

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

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

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

Understanding the Complexity of Craft Beer

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

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

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

Driving Revenue and Market Share

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

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

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

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

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

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

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

Building Better Assortments

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

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

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

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.

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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How AI Will Revolutionize Annual Business Planning

Annual business planning is one of those constants, like taxes and change, that nearly every organization can count on each year. It is enormously important to consumer goods organizations, and is a complex and ongoing process throughout a fiscal year where brands continuously shift priorities and strategies to meet performance gaps and adjust to fluctuating business conditions.

And this is all still largely done on spreadsheets.

Planning tool evolution (or lack thereof) aside, CPG organizations typically inform their annual planning decisions with historical sales trends and year-over-year performance data to paint a predictive view of how the year ahead might play out.

It is a strategy built on looking backward to go forward. This model has been reliable; learning from history has always been a competency, rather than a liability, and the consumer goods industry has typically been one of stability and predictability. However, history also tells us what worked before is not always going to be what works going forward (just ask Blockbuster Video).

CPGs (as most of us do) often miss black swan events, those rare sea changes in the market, because they are repeating what was done before. In our current environment of ever-advancing artificial intelligence and machine learning capabilities, we can now more accurately look ahead, better preparing brands for what may seem unpredictable. Further, the benefit of AI is continuous learning and an ongoing, realistic view of the direction in which a brand’s portfolio is heading, providing predictive outcomes against which to work and to plan.

The application of AI to annual business planning is a tipping point in organizations’ operations, resourcing, and capabilities. With smarter, evolved predictive market analytics, CPGs can lead the market in making the annual business planning process more manageable, and more importantly, more accurate.

It All Begins With Reliable and Relevant Data

The last few years may have produced some of the most historically unreliable data on consumer behavior. The COVID-19 pandemic, inflation and record-high costs resulted in brands facing highly unpredictable situations. Across the board, supply, labor, health, and macroeconomic trends created one hurdle after another for the production and delivery of goods of any kind.

When it comes to annual business planning, brands working backward to look forward aren’t fully armed to make the best decisions about what part of history will repeat itself. AI-powered predictive analytics integrate multiple sources of data, stabilizing volatility and creating a continuous learning model, enabling it to constantly import new data, test, learn and readjust to only deliver the most relevant information.

Produce Actual Insights on Category Futures

AI capabilities, when applied to annual planning, shift mindsets on portfolio investments. With predictive analytics at its heart, the future performance of categories and product classes/packs informs the most appropriate growth targets and levels of investment, optimizing profitability and effort. Imagine the efficiencies that could be attained through knowing, before hindsight is available, which categories are shifting in maturity? The cycle of growth and decline in any category (and the creation of new categories), based on consumer behavior and sentiment, is the moving target within which brands bet on growth investments and performance, all of which begins with the annual business planning process.

• Emerging / Growth categories. These categories are where new entrants, or even evolving established products, begin defining new niches within an existing category. At one time, ‘energy’ was not a category, but is now one of the largest categories in any cold vault, with most trend data pointing to continued growth ahead. Winning in newly defined space is both potentially a higher risk and a bigger reward. This is a category that will see many new competitors enter the category, but there is a big growth potential, and AI can help brands identify where to invest and take advantage of the white space in the market.
  
  
• Mature categories. These more developed categories face limited incremental space availability and more competition within existing space. But small amounts of growth in these categories can be worth more dollars in totality, since household penetration is likely higher in a mature category. Here, AI can enable brands to appropriately optimize strategic goals and investments to maximize potential.
  
  
• Declining categories. In these categories, space is often shifted to emerging categories as a result of sustained declines overall. Which is not to say that a category will eventually be eliminated, but sized appropriately, it could eventually evolve into a growth category with new entrants and evolution of offerings. AI can help brands optimize portfolios, but the technology can also help identify how to disrupt a declining category to bring back growth trends.

Shifting from Setting Targets to Closing Gaps

Annual business planning is just getting started once the targets are set. This continuous cycle on which nearly all business routines are anchored is one of measuring progress and performance against targets and plans, closing gaps, adjusting strategies and solving challenges that arise. AI can quickly help teams optimize strategies to focus on the best opportunities to shift resources and priorities to achieve plan goals. Further, if teams are using AI continuously in this process throughout the year and make it an ongoing part of reporting and performance measurement, trends could be better predictive and prescriptive analytics can used to take the most efficient and effective action possible.

AI Is Annual Business Planning

It’s important to note that AI doesn’t remove the human in the middle of the data. AI helps find the most impactful needles in the haystack for teams to consider and around which to develop strategies.

AI/ML never stops learning, so organizations and teams can be prepared for fluctuations and changes in near real-time, removing inefficiency in guesswork, creating options for action, and ultimately, enabling plan achievement. At its core, AI technology is annual business planning. Customized solutions are designed to look at where a brand / organization is sitting relative to the category and market, identify where the consumer / trends will go, harmonize data streams to inform financial deliverables, and then manage to and against those targets in aggregate through continuous learning.

Put the Spreadsheets Away

Establish leadership in the industry by shifting the paradigm on annual business planning. Free up resources currently mired in planning and re-planning to get back to the business of thought leadership. Take advantage of what innovative technologies offer and evolve dynamically beyond the complexity of a static spreadsheet. Enabling the future means finding better ways to work smarter: the thoughtful application of AI in your data environment is the best way to do that now.

To learn more about how AI can create efficiencies in resources and accuracy in both macro and micro-trend planning, click here.

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

How Brokers Can Lead the Way in AI Adoption

Positioned between brands and retailers, brokers can leverage AI and precise data to find a common truth — and pave the way for CPGs to adopt AI 

Brokers play a crucial role in the CPG and retailer community. They possess extensive knowledge of the market and categories across every store, including consumer preferences, trends, and pricing dynamics. Brokers effectively represent brands and lead as strategic partners in navigating the retail industry. Leveraging their expertise, brokers help retailers streamline their supply chains, expand their product offerings, and ultimately enhance customer satisfaction. Their ability to effectively bridge the gap between suppliers and retailers as a total solution makes brokers essential in optimizing retail operations and driving business growth.

By sitting between the CPG and the retailer, brokers hold a unique position, with an opportunity, or even a responsibility, to become leaders in how CPGs of all sizes adopt AI. The technology is currently in its infancy for effective adoption, with limited clarity on exactly how CPGs will allow AI to change ways of working. However, brokers can shape the ways this technology creates efficiencies, reduces the digital overload, and pioneers its broad application to the industry overall. In doing so, they differentiate themselves and fulfill their promises to their CPG partners in helping them gain a competitive edge in this dynamic retail landscape. 

Through business intelligence and predictive analytics, brokers can ascend to new heights among CPG partners. They can also strengthen their standing among retailer partners. Moreover, brokers can be a bridge between both, using high-powered AI to uncover common data truths and drive growth across the store.

Here are top ways brokers can lead the way in AI:

1. Present Accurate Demand Planning and Predictive Market Analytics

In 2023, retail sales are expected to grow more than 4%, generating nearly $5.23 trillion, according to the National Retail Federation. NRF also said more than 70% of those sales will be inside physical stores.

How close to reality will that forecast of 4% growth turn out to be? Brokers can provide a precise view of what’s happening in the market and what is likely to happen through AI-powered demand planning and market-level trend forecasts. These data and insights help inform forecasting from the highest level. Brokers can help predict future buying behavior across channels and subcategories. They can inform retailers of trends and shifts in the marketplace, and they can provide the most granular store-level view into inventory and click-and-collect service. All of these efforts, powered by AI, continuously learn, adapt, and create an enterprise environment enabling strategic decision-making, rather than an increased digital workload. Brokers can become a single source of truth in developing a precise view of enterprise market and demand planning.

2. Assist With Store Execution and Assortment

At a store-by-store level, across retail channels, brokers can leverage AI to customize insights for CPGs in any category. AI can be custom-tailored to each of the brands with which brokers work, to build the most impactful product mix and decision-enabled portfolio. 

Further, they have the unique perspective of working with brands at all points in their journey of scaling and growth. For larger brands, some brokers have a responsibility to effectively build a mature portfolio with multiple opportunities in the retail environment. In that role, they fill gaps where large CPGs lack visibility and provide solutions where larger CPGs cannot internally manage the need for additional capabilities. For emerging, growth, and niche brands, brokers have a different, more targeted set of responsibilities to deliver that those brands might not be able to generate themselves. 

All brands are looking to achieve category thought leadership and mutual growth with retailers they serve. AI application to assortment optimization, demand transference, and predictive analytics can help them achieve a greater share of the category and effective increases in visual inventory. Smaller brands aiming to get a stronger foothold in a category can tap into brokers and their ability to lead with AI-driven insights to bring retailers data-informed strategies on how they’ll grow a category overall.

3. Optimize Promotions and Trade

Even without a robust services suite, as sales partners to CPG brands, brokers, enabled by AI, can boost acumen in understanding elasticities of price, space, and market. AI modeling shows how the interconnected dynamics in availability, leakage, allocated category space, pricing and promotions impact sales and profitability. 

Brokers that embrace this technology will lead by using learning models to predict the most effective promotional outcomes, optimized for their partners’ established goals and the current macroeconomic environment. 

The technology allows for actionable insights on how to execute the best overall plan, and the best use of promotions, in the most impactful locations, and in the most deserving regions. The technology backs brokers with the unique and differentiating capability to plan efficiently as partners and lead with the optimization of portfolios, brands, and categories, in ways CPGs are currently not leveraging themselves. Brokers can align a pricing strategy that maximizes sales, revenue, and profits for their partners.

4. Become a Bridge to a Common Truth

Possibly the greatest strength a broker can leverage through AI is an ability to lead the data capabilities that solve problems and enable more efficiencies for CPG clients, in addition to relieving their own ‘digital debt’ that continues to grow for the industry overall.

Digital debt is costing us innovation. According to a recent Microsoft study, 64% of people struggle with finding time and energy to get their work done, and those workers are 3.5x more likely to say they struggle with innovation. 

Common truth, or insights driven by the integration of multiple sources of data, narrow the focus to that with the greatest impact on the outcome. And those that excel at — or adopt these integrated models to find the common truth — will be the bridge-builders and the leaders in the industry. This becomes a powerful position for brokers, solidifying them as intelligence-driven category advisors.

Brokers have a tremendous opportunity to enhance their offerings to CPGs through the adoption of AI. AI and machine learning solutions can enable brokers to analyze vast amounts of data, including market trends, consumer behavior, and competitor insights down to the store level. By harnessing these insights, brokers can further establish themselves as thought leaders and strategic advisors, providing CPGs with valuable market intelligence, helping them to be more agile, make more data-driven decisions, and outpace the competition.

For more on how Insite AI can help brokers become innovation leaders in the industry, contact us here.

CPGs & Joint Business Planning: A Retailer’s POV

A former executive at 7-Eleven and Giant Eagle, Brooke Hodierne, EVP – Strategy Consulting, discusses where CPGs can evolve joint business planning and take more control

Joint business planning (JBP) is mission critical for retailers and their consumer goods partners. It’s a months-long process that runs from the starting line, through various checkpoints and past the checkered flag. JBP is when retailers address goals, category strategies and marketing initiatives, and CPGs bring insights, innovation and investment in the pursuit of growth.

After going through various stages of the process to see where the parties’ strategies align, they then settle on product assortment, pricing, promotions, shelf space, marketing and e-commerce decisions. The process is deliberate, but generally powered by old data and slide presentations. It needs a boost.

In my view as a former retailer, CPGs can light that fire and revamp JBP through new data and near real-time data and insights. CPGs can leverage more accurate and intelligent predictive analytics to chart a better course at the beginning of JBP, maintain their efforts throughout the year, collaboratively work to “gap close” and, frankly, drive more of the conversation. 

This is the type of intelligence that will keep CPGs at the top of a retailer’s list. 

Where CPGs Can Level Up During JBP

No matter the technology or industry advancements, a part of JBP will always be like playing three-dimensional chess. Both retailers and CPGs hold back just enough information for competitive reasons while being as transparent as necessary to drive win-win and mutual benefit.

It’s understandably complicated, but within that chess match, there are ways CPGs can help improve the process overall. Here are tips to gain a better standing in JBP: 

Be Insight Rich

You’ve heard the saying, “data rich but insight poor,” and this can pertain to many CPGs. The companies might be swimming in data but often they either don’t have access to it, can’t digest and harmonize it, or can’t synthesize it quickly enough to make it actionable. This often occurs during JBP and it can be obvious to a retailer when a CPG purchases data for the sake of saying yes but doesn’t shape it to a specific retailer’s customers or goals.

Honor the Deadline

A retailer’s internal planning deadlines need to be taken seriously. For years, retailers granted extensions to certain brands while negotiations continued, but in a world where teams are under-resourced or in the middle of reorganization, CPGs going into a JBP thinking there will be an exception will miss the boat. If a deadline isn’t met, the decision will be made for you. The retailer, with or without you, will make a final decision on the brand plan, space, pricing and promotional strategy. It can even come down to a retailer not including a brand’s new item introduction. Instead, they’ll choose the competitor’s new item because they followed the process.

Fair Share Isn’t Always Fair

Every category is different — especially when it relates to  allotted shelf space in stores. For CPGs entering a JBP with a retailer, they need to know their place in the category and be prepared to not always earn their “fair share” of space. From the retailer’s view, there always will need to be extra space reserved to make room for private brand introductions and innovations that excite a category from smaller, emergent, challenger brands. A CPG can’t merely expect to receive their fair share, so plan ahead and prioritize the brands and products that will deliver the most growth and differentiation.

Keep Stakeholders in the Know

Perhaps the single biggest issue to disrupt a JBP is when Sales teams don’t bring key decision makers along for the journey. In large, matrixed organizations, it’s especially important to expand discussions early and often with members of finance, revenue growth management and marketing.

CPGs that can improve processes around these tips can come to a JBP with better expectations and an understanding of a retailer’s priorities and constraints. But there are also ways CPGs can win over a JBP meeting.  

Where CPGs Can Shine During JBP

Lest we forget, retailers also have a lot of room to improve in how they handle JBP meetings. But, with technology like AI, CPGs are in a grand position to rewrite the game. They can change the tone of meetings with precise, accurate, forward-looking data. They can earn more control over how their products are received by bringing rich insights that help grow an overall category. Here’s where CPGs can win in JBP:

Bring Clear-Eyed Data

Retailers look to CPGs for data and insights. CPG organizations can leverage AI to run “what if” scenarios in real time that foster forward-thinking, collaborative conversations with retail buyers. The data accounts for all the ways retailers can play on their chessboard, which helps them develop rich category plans with more clarity. Using technology to detail the why, and share the explainability factor goes a long way with a buyer, and helps them also explain their decisions to their leadership teams.

Invest Toward Category Growth

Rich data that can present a predictive view of the entire category — not just how your brands sit within it — ultimately will win over a retailer. Data that highlights an investment in overall category growth and that arms a CPG to be the smartest person in the room when it comes to their product and the total category can be a massive game changer.

Forget the Rear View

For years, JBP relied on CPGs coming to the table with insights based on historical data. Brands looked backward, referencing what happened a year ago to predict what will happen in the year ahead. It’s simply not accurate. Do you behave the exact same you did last year? I know I don’t so why would we believe a customer would? There is so much change in shopper behavior and macroeconomic trends that it can’t be relied upon. The windshield is bigger than the rear view for a reason. Let’s all start looking down the road. 

AI Can Power Your JBP

Just as AI and machine learning can revolutionize how CPGs perform annual business planning, CPGs can leverage the technology to vastly improve how they meet with retailers and master JBP. 

To learn more about how AI can create smarter scenarios for an in-depth view of business planning, click here.