How Retailers Can Work With Consumer Brands To Fit Their Private Label Strategies
In this StoreBrands guest post, we explore how retailers can collaborate with consumer brands using predictive analytics to customize assortments, optimize shelf sets, and predict sales trends. Private label sales are growing, and by leveraging AI-driven insights, retailers and consumer brands can work together to build assortments that align with shopper preferences, enhancing the overall shopping experience and boosting category sales.
The Candy Aisle Renaissance: From Impulse Buys to Strategic Category Growth
Insights help arm a brand with the knowledge that can lift an entire category for a retailer, earning them category advisor roles at coveted retailers.
The candy category is much more than the Halloween season and the impulse rack at the checkout lane. It’s an innovative $43 billion business, increasing sales annually, partly due to inflation, but also due to consumers seeking an affordable way to treat themselves. Retailers too are developing a larger in-store presence for the category.
I know this from my days as SVP of merchandising at 7-Eleven, where we frequently leaned on candy to deliver innovative opportunities for sales growth inside the stores.
For candy brands, however, navigating the shifts in consumer behavior can be difficult, knowing that the products are largely bought on impulse. How do you effectively target a consumer who may not know they want that sweet treat yet?
On top of that, brands need to understand behavior shifts at a wide range of retailers such as mass merchants, club stores, traditional grocers, c-stores, drugstores, dollar stores and hard discounters — and even sporting goods and apparel are in the game.
Factor in consumer trends such as seeking smaller pack sizes and using social media for inspiration, and predictive analytics and machine learning can become major tools to help craft a winning candy strategy.
Uncover candy trends
According to a report this year from the National Confectioners Association (NCA), annual sales in both non-chocolate candies and the gum/mints categories increased by nearly 14%, comparing 2022 annual sales to 2021.
At 7-Eleven, I saw my fair share of wild flavors in novelty non-chocolate candies, sour chewy offerings and hard candies driving these sales and reaching a growing interest among millennial and Gen Z markets. But there are other behavioral trends and product trends to take note of, too:
Portion control. According to the NCA report, eight in 10 consumers seek smaller pack sizes to help curb how much they eat. The report also noted that consumers are seeking guidance from brands on appropriate portion sizes, factoring in calories, sugar and the impact of natural ingredients.
Healthier and functional candy. Consumers are increasingly looking for candies that offer health benefits, such as low-sugar, sugar-free, organic and fortified options. Functional candies with added vitamins, probiotics or other health-promoting ingredients are gaining popularity.
Nostalgia brands. Nostalgic or retro brand candies from the past continue to make a comeback, appealing to consumers who want to relive childhood memories through their favorite sweets.
Multi-channel purchases. The NCA report found nearly 60% of consumers said they buy candy at checkout in the impulse section, but they’re also buying in three to four different channels. Less than 10% of consumers exclusively buy candy online, but a third said they buy in-store and online.
Social presence. Candy consumers are active on social media, with nearly 60% of consumers surveyed in the NCA study saying they access their networks for inspiration on products to buy or use with recipes, and to simply engage with the brand. Candy brands are increasingly leveraging social media platforms and partnering with influencers to expand reach and engage with their target audiences.
Considering these trends, candy brands have a lot of shifting behaviors to wade through. However, this is where predictive analytics, AI, and machine learning can help them figure out which trends to pursue, at which retailers and in what ways. Brands can develop smarter strategies around pricing, promotions, and where to put products at checkout and in the candy aisle.
Brands can feed AI-powered engines a mountain of varying data: social listening, POS, shipment data, third-party global trend forecasts, loyalty information and more. The AI model reads the data and directives from the brand teams on price elasticity, promotions strategies, assortment optimization and other inputs to recommend decisions for their brand goals and category growth overall.
Let’s repeat that last part: the insights help arm a brand with the knowledge that can lift an entire category for a retailer, earning them category advisor roles at coveted retailers.
Sweeten sales for retail partners
As noted from the NCA data earlier, most consumers still rely on the checkout lanes for their impulse candy purchases. However, the data also states nearly 80% frequent the candy aisle, where retailers have been expanding assortments to bring more excitement to the category.
Major players like Walmart and Kroger have constructed expansive in-aisle sets for candy that push the retailers to become candy destinations, perhaps challenging c-stores and drugstores that have been often associated as a primary purchase destination for the category.
Part of this pivot from large format retailers is also to compete with value chains and general merchandise retailers also carrying candy. What’s more, the consumer behaviors around candy are much more than the impulse buy at checkout. Consumers are adding candy to their shopping lists as more wholesome ingredients make it a more acceptable indulgent treat.
Candy brands can help retailers make sense of consumer behavior changes by bringing AI-powered, robust data-driven insights such as:
Should chocolate continue to receive the amount of space it’s getting based on its space elasticity?
Is there room for expandable consumption inside stores, meaning can the store offer more candy even when they don’t need it, lifting a retailer’s bottom line?
How much play should mini-size packages get, and should they be in bulk packages for consumers buying for extended at-home consumption?
Fine-tuned predictive analytics can answer these questions, helping brands develop the right products for their company’s success and sweeten sales for their retailer partners.
AI-powered platforms can change how candy brands work with retail partners, elevating a category from checkout lane to major players with grand merchandising sets and powerful growth strategies.
Dig deep with data
In today’s dynamic and highly competitive market, candy brands are constantly seeking innovative ways to stay ahead. With predictive data, brands can accurately forecast consumer demand, anticipate market trends and tailor their assortments accordingly. Not only does this enhance profitability but it fosters a more personalized and satisfying experience for candy enthusiasts.
AI-powered platforms can change how candy brands work with retail partners, elevating a category from checkout lane to major players with grand merchandising sets and powerful growth strategies.
Sweeten your brand’s success and elevate your brand with AI-powered insights, contact us to learn how.
Connect at Groceryshop
Connect at Groceryshop
Accelerate Your Sales, Revenue Growth, and Category Management Initiatives
Groceryshop | September 19-21, 2023 | Mandalay Bay, Las Vegas
Connect with Insite AI at Groceryshop and find out how our revolutionary approach can accelerate your top initiatives. Our team of AI and strategic consulting teams have walked in your shoes, giving them unparalleled insights into your industry-specific hurdles. Our Strategic Advisors are consumer brand and retail veterans from Coca-Cola, PepsiCo, Mars, Anheuser-Busch InBev, Walmart, Target, 7-Eleven, Kroger, among dozens of others.
Let us guide you in tackling your organization’s distinct challenges head-on. Through our collaborative approach, we craft a tailored solution to elevate your product assortment, pricing strategies, trade promotions, and demand forecasting.
Don’t miss this opportunity to expedite your success and lead your organization toward a more efficient and profitable future.
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.
Navigating Uncharted Waters: How I’ve Used AI To Help Brands Prepare for Unexpected Events
AI can not only produce plans A, B and C, but also plans X, Y and Z.
Having spent roughly the last seven years in data science at leading global brands, including Bacardi and Anheuser-Busch InBev, I can tell you that brands are too often working in “react mode.” Even at leading brands that already have an army of data scientists and AI-enabled sales and commercial teams, the organization can be challenged by events or market shifts it didn’t see coming.
During my time in the large CPG world, I saw how the brands I worked for — and competitors — reacted to economic shifts, technological changes, unexpected category product trends and more. There’s a lot that can throw a brand off track.
For any global brand looking to scale-up results or that is newer to AI and machine learning, there’s room to improve when it comes to preparing for the unexpected. There are learnings to gather that better manage product assortment, pricing and demand forecasts.
Unexpected Events Impacting Retail
The greatest and most timely example of an unexpected event is the COVID-19 pandemic, which upended all industries, but especially retail. At AB InBev, being a global company, we were able to prepare for shifts in the market and jumped to work with professors at MIT. Understandably, not all brands prioritized using data and technology to help, which has left many of them still learning from what happened.
Natural disasters are also events that can ravage operations regionally and have impacts on a global scale. But there are several other examples that CPGs may not immediately consider such as:
Economic crises. Sudden economic downturns, financial market crashes or currency devaluations that squeeze consumer spending and purchasing power.
Technological disruptions. An emergence of a new disruptive technology — or the obsolescence of an existing one — can throw a wrench into business models. Think of the advent of streaming services impacting traditional media consumption.
Geopolitical events. Unforeseen trade disputes, political instability or international conflicts can greatly disrupt supply chains, sourcing and trade.
Social and cultural shifts. A large cultural movement that causes a shift in consumer values and preferences can impact brands. It can be a sudden reaction to a brand or larger changes like consumer attitudes towards sustainability and ethical sourcing.
Regulatory changes. Unexpected changes in policies or legislation can disrupt business. Product label changes and safety standards, taxes and tariffs can impinge on production costs and market access.
The analysis of these events happening around the world simultaneously can greatly complicate brand strategies. Brands need the right talent in place to understand every shift occurring and how it impacts the total value chain.
Where My AI Efforts Helped Brands
It is my belief that AI’s role isn’t so much to unequivocally predict an event, but the technology can better prepare brands for unexpected scenarios. Here are five ways I’ve used AI to help protect and better manage brands during unexpected events.
1. Enable teams to run endless test-and-learn scenarios (mimicking the many events listed above). AI can not only produce plans A, B and C, but also plans X, Y and Z.
2. Analyze historical data, current market trends and external elements to identify patterns and indicators that may precede such events. This analysis helps alert brands to the potential of unexpected events.
3. Monitor economic indicators, social media sentiment, news reports and even weather patterns, to identify any warning signals. For instance, predictive technology can detect sudden shifts in consumer sentiment or emerging global risks, such as geopolitical tensions or economic instability. By incorporating this information into predictive models, the CPGs were in a better position to anticipate and prepare for unexpected events.
4. Use technology to make snap decisions in real time to chart a new course of action for a brand and make effective moves immediately to limit any damage incurred. For example, if there is a sudden surge in demand for certain products due to panic-buying (like the toilet paper scare of the pandemic) or a shift in consumer needs (like shelf-stable foods during a storm), predictive technology recognizes patterns and quickly predicts behaviors going into and out of the trends.
5. Analyze customer behavior, such as the current downward turn in online grocery shopping or shifts in pricing sensitivity. These real-time insights empower consumer brands to adjust their production, distribution and marketing strategies accordingly.
The power of predictive technology isn’t so much to be a crystal ball, but to aid brands in delivering a collaborative and communicative relationship with retailers during challenging times.
Why Technology Is a Table Stake
The power of predictive technology isn’t so much to be a crystal ball, but to aid brands in delivering a collaborative and communicative relationship with retailers during challenging times.
I saw this firsthand through the brands I worked with, and the technology developed much stronger relationships with retailers. Brand teams can utilize retailer data and run daily reports — especially since constant communication during major events is extra important. These insights can help retailers optimize their inventory management processes and place core, valuable products onto shelves and maintain stockouts.
Data-driven capabilities and AI can be a necessary assistant during unexpected events. The tools can support how crisis teams manage unforeseen events.
The ability to provide data-driven recommendations and support helps build trust inside and outside an organization, and improve operational efficiency. The learnings also guide brands to weather any storm and better expect the unexpected.
To learn more about how Insite AI can help brands mitigate unexpected events, contact us.
Why Are CPGs Still Making Multi-Billion-Dollar Decisions Using Spreadsheets?
“Mainstream media and technology companies have made the topic of AI so confusing to the point that it now seems too conceptual and risky to adopt.”
Many CPGs still rely on the trusted yet limited capabilities of spreadsheets as primary tools for assessing and taking action on assortment, trade spending, space and promotions planning. While effective for certain applications, spreadsheets were invented in 1979; Excel was invented in 1985. These are not intuitive or enabled tools that can provide the timely, precise details required to make multibillion dollar decisions. With the retail landscape moving faster than ever, it is time to break free from the constraints of spreadsheets and leverage the transformative power of 21st century technologies. The future belongs to those who embrace innovation and adapt to the evolving industry landscape.
Limitations of Spreadsheets
Relatively easy to learn and use, spreadsheets are a popular option for conducting data analysis among CPGs. They offer a familiar and accessible interface for handling data, performing calculations, and creating visualizations. However, when it comes to fast decision-making in the dynamic world of consumer brands, spreadsheets reveal their limitations. While they can handle considerable amounts of data, they can be slow and unstable, particularly when data is complex. Spreadsheets further struggle to efficiently process and consolidate diverse data sets, leading to manual efforts (heavily reliant on already limited human resources) and potential inconsistencies. Excel can also impede collaboration and sharing at a time when there is more data than ever before to leverage. The bottom line is that spreadsheets are not intuitive and they require human intervention for use and to create value.
Market Volatility
Organizations are constantly trying to evaluate market volatility, competition, emerging markets and channels, and consumer behavioral shifts to assess where to allocate resources. Not having the right products and package sizes in the right place at the right time with the right price results in lost sales opportunities. If performance data shows gaps to targeted objectives, the organization will spend the year working to re-assess remaining planned actions and investments. This makes dependence on historic data troublesome. The gap between the “look back” and the “look forward” is a missed opportunity, especially in light of the market and supply chain volatility of the past three years in the CPG industry in particular.
Modern Approaches
Today, purpose-built CPG-tailored software can ingest billions of data points from disparate sources to assess category maturity, predict future performance and assess the value of investments, allowing brands to appropriately allocate resources. It can also make more precise financial predictions. If resources are not allocated properly, expected results are not achieved. The resulting “gaps” can take a long time to close. Spreadsheets simply indicate what those gaps are; they do not indicate how to solve them. They can only hold data.
CPG-tailored technology uses timely data to project into the future, reducing dependence on historical data alone. Unlike spreadsheets, CPG focused software can “learn” from repetitive patterns and algorithms; it does not simply report data.
CPG-specific software with modeling capabilities uses multiple data sources in real time, incorporating everything from product sales and gas prices to labor department data and demographics. Because their models (accelerated by different prediction, product and pricing engines), are continuously finding data points and learning, they are able to provide forward-looking and prescriptive insights. It can signal package optimizations–e.g. whether there should be more gallon sizes of milk in a particular store versus single-serve cartons. The technology also finds those “needles in the haystack” that can be key differentiators from one store’s assortment to the next. By allowing all data to work together, teams can respond swiftly to market changes and adapt strategies dynamically, providing a competitive edge in a fast-paced industry.
Collaboration & Pinpointed Goals
Moving beyond spreadsheets enables greater collaboration and agility. Cloud-based platforms and data-sharing technologies have begun to facilitate seamless communication across departments, breaking down silos and fostering a collaborative culture. As part of that evolution, good software can facilitate better annual business planning, factoring in supply chain, labor and other costs into input assumption fields. The beauty of this is that it gives visibility to everyone in an organization and makes highly accurate predictions. This elevates target-setting, breaking out targets by function. It measures and compares achievements and lets retailers and suppliers work together to meet goals. Retailers and CPGs can then enable the Joint Business Planning process with these same powerful tools and more collaboratively agree upon a set of metrics and activities that will achieve aligned business objectives that are very specific to categories, investments or activities. Progress against all objectives is part of the modeling, constantly assessing and improving accuracy of predictions, reducing or eliminating the replanning that results from gap closure and volatility.
Lack of Trust & Familiarity with AI
Mainstream media and technology companies have made the topic of AI so confusing to the point that it now seems too conceptual and risky to adopt. Despite evidence to support the use of AI, its effective application to broad data sources and existing processes is still nascent in the CPG industry. Just 11% of CPG organizations have adopted ML/AI tools. This stems from various factors, including concerns about the accuracy and reliability of AI algorithms, and a lack of clarity on how to apply the forms and functions of AI models to existing business processes.
There is tremendous efficiency to be gained using technology over spreadsheet, regardless of whether it incorporates a little AI or a lot of AI. Good software does not necessitate adding people (nor replacing people) to make that happen. It’s a small investment compared to what the returns can be when technology is used to augment teams and enable them to act with exponential speed and precision. Any returns can be high with clearly measurable objective-setting and ROI.
Conclusion
The move away from spreadsheets is not just a call for change; it is an opportunity for growth and innovation. By embracing cutting-edge software and analytics, the full potential of data can be unlocked, allowing CPGs to make informed decisions and drive sustainable business growth. The time to act is now, as the CPG landscape continues to evolve rapidly. Those who adapt to change will be the ones to thrive and capitalize on the transformation opportunity.
To learn how you can evolve to be a more agile and AI enabled company, contact Insite AI.
Seeing AI Through a Practical Lens (Featured on C-Store Dive)
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.
Weathering Retail M&A: How CPGs Can Ride the Waves With AI (Featured on CSP Daily)
With AI, CPGs can weather the storm and gain some control during the stressful M&A process. CPGs can use AI and bring thoughtful insights to the table that ease any tension in the process and give them more control at the same time. CPGs can look to AI to support difficult conversations and arm the newly formed retailer with accurate predictions around store space, total units, unique demand, loyalty and more.
About the Author: Brooke Hodierne currently serves as an EVP – strategy consulting at Insite AI, an AI and strategy partner for larger consumer brands. She joined the company following her time as SVP of merchandising for 7-Eleven. In the role, she drove category management teams that developed, implemented and communicated merchandising strategies for vault, packaged goods, tobacco and services. Before joining 7-Eleven, Brooke held multiple positions at Giant Eagle, serving as VP of own brands, senior director of strategic sourcing and own brands, and director of prepared foods merchandising. She supported brand marketing at Del Monte Foods and held analytical roles with financial investment firms Wilshire Associates, Federated Investors and the Vanguard Group.
The CPG’s Guide to AI
Empowering Consumer Brands with Clear and Actionable AI Insights
Research confirms leading consumer brands who harness the value of consumer insights and artificial intelligence (AI) better predict the needs of their customers, improve category performance, accelerate growth, and outpace the competition.
72% of executives consider AI as a business advantage
But how can you get started? With data overload, an abundance of options and unclear direction, many companies opt to do nothing. This is no longer an option. You will be left behind. Armed with the right data, AI-driven CPG brands are working hand in hand with their retail partners to better meet consumer demand. By turning mounds of overwhelming data into actionable intelligence, these CPGs are scoring big with retailers and end consumers alike.
In this guide:
Demystifying AI
How consumer brands can leverage AI today.
Top 5 AI/ML Use Cases in CPG
Going beyond Power BI and advanced analytics
Making the case for AI in your organization
Top questions to ask for a fruitful AI journey
Harness the power of AI to ensure you have the right products on the right shelves at the right time. Download this guide to begin your AI journey toward becoming an AI-driven, category-leading consumer brand.
Download Guide
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/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.
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