Have you considered what switching pack sizes and shifting products from cold to ambient or warm (ambient) planograms could do for your revenue? We recently conducted an experiment involving ten stores to assess the revenue potential of making these simple changes, and the results were surprising. Acting on a hunch, our client, acting as a Category Captain, had an intuitive sense of the behavior of a certain shopper segment; the "domestic premium shopper" was primarily focused on seeking the lowest prices and was less concerned with whether the product was a warm or cold shelf. In addition, it was their hunch that premium shoppers preferred immediate consumption. If the hunch were true, it would open up new ways of preparing assortment plans and overcoming space constraints. To put these insights to the test, we applied data and AI to validate these beliefs.

We began the experiment; and assessed the feasibility and potential impact of shifting larger pack sizes of those domestic premium brands from the cold to the warm (ambient) planogram. However, we quickly discovered that moving all distribution points from warm to cold was not feasible. After several trials, we could successfully move about 40 percent of the distribution points, specifically the larger pack sizes. The resulting shift opened up a promising revenue opportunity while maintaining a feasible plan for store layouts.

The results from our data-driven exploration echoed the client's initial hunch. Indeed, the domestic premium shopper was price-driven, indifferent to product temperature, and preferred immediate consumption. As a result, we strategically decided to keep the import and premium products in the cold planogram. This understanding, derived from combining our client's intuition with data-driven validation, enabled us to flex our items from cold to warm without a drop in sales.

We implemented several rules for the experiment to ensure we obtained meaningful and actionable results. We put certain rules into place to ensure the buyer could action our recommendations and deliver on turning the store. For instance, we set the minimum days supply to two, implemented orientation rules, and ensured no duplication of products in the warm and cold sets. We also made sure to respect the buyer's original decisions and maintained at least 90% of the base assortment in the store.

Implementing these rules had an interesting outcome: we could create substantial space without completely reinventing the entire set. In essence, we didn’t just open up space; we also maintained the integrity of the buyer's initial decisions and overall vision.

A standout finding from this exercise was that, out of the ten stores in the experiment, we identified 185 points of distribution that existed in both the warm and cold sets. With careful calculation, we split sales revenues between warm and cold sets, contributing to an overall revenue increase.

Our experiment resulted in an astonishing 13.7% increase in overall category revenue and an amazing 9.4% increase in revenue for our client's brand. Essentially, every manufacturer in this category benefited from an increase. It's a win for all - suppliers, retailers, and shoppers.

In addition to the revenue gains, Curate reduced out-of-stocks by improving days of supply across all items and stores. To put it simply, for this category, using Curate's Space-aware assortment optimization engine, we were able to reduce instances of out-of-stock items to zero. We analyzed ten stores and found 162 PODs with Days of Supply (DOS) less than 2 in different stores and 25 PODs for clients. After conducting space-aware assortment optimization analysis in Curate, there are now zero instances of this problem. This leads to revenue growth while also improving the effectiveness of in-store operations, ensuring that category plans are always locally relevant, effectively merchandised, and operationally efficient.

Retail Fearlessly: Harnessing AI to Validate Hunches and Drive Category Growth

What does this mean for you? It demonstrates that rethinking your assortment strategy, testing your hunch about certain shoppers, and making minor adjustments can yield major results. Shifting larger pack sizes and adjusting your distribution points can open up substantial revenue opportunities, all while respecting the initial decisions made by the buyer.

As Category Captain or Category Advisor, you can bring new thinking to the retailer without fear but with confidence. With AI as your co-pilot, you can challenge old assumptions, test shopper insights, and present new perspectives for increasing category growth. This case is a powerful demonstration of how data and AI can turn intuitive hunches into actionable strategies for retail success.

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