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.
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.
Download Guide
Winning the Retail Space War with Predictive Modeling
40% improvement in assortment trade-off accuracy
What we did
One of the largest brands in the consumables sector wanted to understand how to utilize its store space within Target for optimal results. Applying innovative AI components to develop space elasticity models, we refined the brand’s planning process with calculations and forecasting that identified how space affects profit- ability and sales demand to arrive at their ideal strategy. Insite AI become an extension of their team, providing the support and resources they needed to ensure they achieved their goals through data, adoption, and guidance.
2% increase in profit with the same amount of shelf space
Analytics
We empowered the Category, Assortment, and Space planning and analytics leaders with critical insights to rapidly simulate multiple scenarios and accurately forecast effects on sales, margins, volume, and demand. Leveraging predictive modeling, our platform delivered insights for maximizing productivity and profitability so the team could determine the ideal plan for each store layout.
Reduced planning cycle time from months to days
Results
With Insite AI, this major consumer brand quickly identified the best use of in-store space. The brand can now swiftly hone its on-shelf facings, arrangements, and structures at the planogram level and defend its plan within Target. Even with competing priorities, limited budget, and resource constraints, the company priori- tized working with Insite AI. This technology has been identified as mission-critical at the executive level and the team members involved have received recognition internally for their efforts.
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How AI Helps CPG Leaders Optimize Shelf Space
The stakes for shelf space are high, and the competition for it is fierce. Optimizing space is complex and nuanced, but using the right tools to analyze space elasticity helps CPGs win big.
Your brand of tomato ketchup – does it have a high sales velocity because of its huge on-shelf presence, or would it achieve the same sales with half the space? Does it need two facings or three? These questions have perplexed retail and brand leaders for decades. When a CPG has hundreds or even thousands of products in a single retail store, spacial decisions are crucial to maximizing sales and building brands. Position on shelf can be the difference between winning and losing, and CPGs often pay significant fees to retailers to secure prime shelf positions.
Optimizing space is complex and nuanced: too many facings could be a waste of space, whilst too few could mean risk of out-of-stocks and lost sales. There are many variables to consider when it comes to space elasticity and demand, and the top considerations are both quality and quantity of space. Space quality can include factors such as in-store location, shelf height, and which other products are in proximity. According to Nielsen, there were 20,000 new product launches in the US between 2008-2013, but 85% failed and stole spaces. In 2017, out-of-stocks led to $54 billion of missed opportunity.
So what is space elasticity exactly? Put simply, it’s the relationship between rate of sale and space allocation, but it varies across different products and categories. Elastic products show a substantial increase in sales when more space is given to them. As you allocate more space to elastic products, a point of diminishing returns is reached, where the sales increase rate drops dramatically. In contrast, inelastic products show little or no increase in sales when more space is given to them. To maximize your returns, you want to hit a sweet spot, and that can be challenging.
Optimization of this across all your SKUs can represent millions of dollars in additional revenue. With inelastic SKUs, you have the opportunity to maintain sales even after reducing footprint in store. With elastic SKUs, you want to increase their space in store to the sweet-spot point.
AI and data science are helping CPG players make these decisions fast and at scale. The right platforms incorporate spacial awareness, using computer-generated, 3D representations of stores to optimize space allocation and shelf-placement decisions. Using advanced algorithms, they crunch through millions of data points and what-if scenarios in a matter of hours, using a combination of historical sales data, EPOS data, 3rd-party data, and consumer insight data. This is done together with information about product margins and the costs of various in-store locations.
The output is tangible, optimized, go-to-market recommendations. Instead of seeing countless and meaningless possibilities, you need to get to the best decisions that will build your relationship and business case with the retailer. Further, you need to be able to understand space elasticity down to the most granular level: by SKU, by store format, andby retailer. The prize is the ability to drive millions of incremental value rapidly and at scale, driving your growth in the categories that matter.
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