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AI Can Have a Lot to Say During Category Planning

Convenience store chains can let machine learning power their category plans.

For both brands and retailers, the category planning and review processes can be stressful. This is particularly true for convenience stores, where shelf space is limited so retailers need to make the most of their store footprints.

However, for c-store retailers that want to construct strong, long-term relationships with brands, leveraging artificial intelligence (AI) powered data analytics can make category planning much more productive and results-oriented, and keep category reviews honest.

During planning, AI-powered insights can forecast an item's performance and generate predictive models for how an item is likely to perform against unit, revenue and margin goals. During reviews, insights can help chart new plans of action.

This way, the data can do the talking, delivering precise estimations for both retailers and brands to leverage, and enabling much more productive collaboration between suppliers and category managers.

Retailers with AI-powered platforms that help manage inventory, handle demand forecasting, automate planograms and produce customer-centric assortments based on shopper insights can lean into learnings that set the tone for a modernized process informed by end-to-end visibility and awareness.

The Challenges of C-store Category Planning & Reviews

Before brands and retailers sit down for a category planning or review session, it's helpful to note the nuances and specific challenges impacting the convenience channel.

For one, the average store size of nearly 3,500 square feet creates the onus of optimizing every inch of high-rent locations. In addition, many operators are focusing more on producing high-turnover foodservice items, which may reduce space available for long-shelf items. Also impacting shelf space is the trend of c-stores developing more private label products to drive margins in an unstable economy.

C-stores are also struggling with less foot traffic in their stores due to volatile gas prices and steep credit card transaction fees, something characterized by former NACS Chairman Jared Scheeler as "a broken system" in his address at the 2022 NACS Show. The high prices ultimately get passed onto the consumer, he said.

Fortunately, AI can help c-stores review the best scenarios in categories across the store and recommend a plan forward. The retailer can bring the data to the category planning meeting, work with their brand partners and their data to flesh out a plan together, and agree upon plans that will achieve defined business goals with greater confidence.

AI-Powered Category Planning in Action

C-stores with a fully connected store can run AI-powered insights and modeling throughout the organization, all feeding into thorough category planning.

AI helps shine a light on products moving through the supply chain, forecasts product demand, proactively identifies product replenishment needs, and works alongside computer vision technology at the shelf to flag planogram noncompliance, out-of-stocks and more.

Across the retailer, AI is working to run a more efficient business. C-stores that leverage these AI insights can use them in reporting for categories. Here are a few examples at work:

Sales Forecasting Through high-powered AI insights, a large c-store chain can fully optimize assortments at an individual store level to determine what will drive defined business goals in units, revenue or margins. The chain can target specific types of customers at each store and align what items will work best at which stores.

Store Intelligence Powered by AI and computer vision, store intelligence with 24/7 shelf monitoring can help instantly identify labeling, pricing or promotion errors, out-of-stocks and planogram concerns to help categories perform to their full potential. In this sense, a c-store operator can have staff review a candy section and enable the AI to record direct performance issues, addressed in near real time, to bring to a category review discussion.

Promotion Strategies With shoppers feeling jittery amid inflation and economic uncertainty, it's important for brands and retailers to make informed decisions to set the best promotion strategies for their products. AI can support rapid if-then scenario planning to identify which promotions will drive the greatest success at the store-item level.

AI-Powered Answers

C-store retailers have much to gain from AI and machine learning providing category and item-level insights by location, a cluster of stores or across an entire chain. AI-driven insights lead to more efficient business decisions — and that includes category planning and reviews.

Operators that use AI daily to keep an eye on inventory and trends impacting sales have a leg up on competitors in terms of keeping their shoppers engaged, and they can also level up category reviews by backing conversations with strong data.

AI can have a voice in the conversation and lead to stronger strategies for a channel that needs new ways of looking at growing the business.

Julian Miller is global head of retail solutions success at SymphonyAI Retail CPG, where he’s been for nearly a decade. Throughout his time in the industry, he has developed expertise in category management and has been responsible for the design and vision of highly innovative software solutions. Before joining SymphonyAI Retail CPG, Miller worked as a development manager at Aldata and as a developer for Cosmic Solutions Ltd.

Editor's note: The opinions expressed in this article are the author's and do not necessarily reflect the views of Convenience Store News. 

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