Substitution Effects on Retail Products
Olga Pak, Ani Tekawade, September 2022
When building predictive and prescriptive models, retail managers often struggle to capture the substitution rate among products they carry. This happens when products that share similar attribute features each other are easily substitutable, which allows the customer to easily replace the functionality of a preferred product with an alternative. For example, if two different snacks are highly substitutable (potato chips vs. veggie chips), in the absence of potato chips on a shelf (e.g., a temporary stockout), the customer who loves potato chips will buy veggie chips instead. In this case, the manager will be able to capture the demand through the sale of another product. In contrast, if the two snacks - potato chips vs. veggie chips - are not substitutable, the absence of potato chips on the shelf will cause the customer to turn away, and the potential sale will be lost. Unfortunately, managers rarely know the substitution rates of each SKU and often resort to heuristics and intuition.
Here is another example. Let’s assume a manager is looking to reduce their product assortment and seek to remove a single SKU from its assortment while choosing between SKU 1 and SKU 2, where each is selling ten units a week and generating the same profit margin per unit. At face value, the decision-maker is indifferent about which of the two SKUs to remove, as both SKUs would seemingly lead to the same unit loss of 10 units. However, in the table below, we illustrate how, unbeknown to the decision-maker, SKU 1 has a higher substitutability rate (0.7 vs. SKU 2's 0.2), which leads to the recapturing of a larger portion of SKU 1's lost sales. Here substitution rates capture the percent of customers that would switch, should an SKU not be available, to an alternative SKU that is available. Once the substitution rates are accounted for, it is clear that the decision-maker should eliminate SKU 1 and keep SKU 2.
Measuring this effect requires a deep understanding of the dynamic relationship among products’ attributes. And the outcome of this analysis has a significant impact on the bottom line of a retailer. Correctly measuring the substitution effects among products will influence the overall supply chain strategy, including inventory planning, assortment selection, and revenue-cost analysis, to name a few.