The current state of retailer pricing sophistication is all over the map. Some, with little investment and low organizational adoption, stay in the low-value early stages perennially. Others develop advanced analytic capabilities which, when paired with the broader strategic context, drive huge financial impacts. Setting aside why these disparities exist, there’s value in exploring how they progress from naive to best-in-class.
In the evolution of pricing analytics there are six core capabilities that retailers must master to not only broaden the scope and applicability of the analytics, but also increase the value delivered. The rewards, however, are large: pricing is one of the single most important levers for immediate bottom-line impact.
Capability 1: Basic Elasticity Measurement
The basic core of price elasticity is an understanding how price changes tend to move unit velocity. While the concept is fairly straightforward, the devil is in the details of scoping. In the early phases of development it’s often measured at the sum of the assortment- i.e. measuring price’s impact on the unit velocity of the sum of the SKUs in the line. It’s often executed at the national or total footprint level, with price recommendations applied just as broadly.
Retailers further along are able to measure at the very granular SKU-Store level and then properly roll up to assortments and categories so that the merchants and buyers can make optimal pricing decisions. The recommendations, if approved, must be disaggregated back to the SKU-Store level to be executed.
Capability 2: Unpacking Assortments
The next level of sophistication involves unwinding the interactions between SKUs in sub-assortment groups- like the “good” in the “good-better-best” lineup. This answers questions like whether you can bring up the bottom end of the assortment while leaving the rest of the line intact, etc.
Another key to the assortment is to identify and focus on Key Value Items (KVIs), which drive a higher-than-expected response to price changes due to visibility, extreme promotion, or existing reference prices in the mind of the consumers. In grocery, for example, the typical KVI SKUs are things like the 2 liter bottle of Coke, the dozen eggs, and loaf of bread.
Capability 3: Store & Regional Breakouts
Retailers looking to set prices store by store often encounter two challenges: IT system constraints and the resistance to pricepoint proliferation in the eyes of the merchant or buyer. There is a real “tyranny of averages” in store-level pricing because the differences between any single store and the broader average can be quite large. The enormous value of being able to set a store’s price for its location, customer base, and competitive environment is what drives most of the returns of increased pricing sophistication.
Capability 4: Integrating Competitive Price
Moving from ad-hoc rumors from the field toward systematic data feeds from web-based monitors is only the first, and often easiest, step in the process. The second is finding a way to match the external SKU data to their identical (or at least equivalent) SKU internally. And once that is accomplished, many retailers end up building exception reports to notify the category owners when prices get out of a certain range to the market, with push-button response options.
While those are important, the value comes from measuring gap elasticity, or the impact on your velocity from a competitor’s price change. Robust data and calculated gap elasticity is necessary but not sufficient to drive the long-term price strategy, as best-in-class retailers then leverage game theory concepts to determine how they set price relative to the market landscape to maximize their own performance.
Capability 5: Leveraging Basket Data
The challenge with basket data is always how to allocate the credit to that product that was responsible for bringing the customer into the store. This item can be called the basket starter, or trip mission SKU, or driver product. The correlation of candy bars from the checkout with other products in the basket does not imply that the candy bar caused the sale of those other items. It gets much harder when there is more than one SKU that could have plausibly caused the trip in a basket. Retailers facing this challenge end up either simply reporting the basket data as context information (meaning it is never part of any formal calculations) or develop advanced analytical approaches for allocation of the basket value to the SKUs within.
Capability 6: The Strategic Overlay
Incorporating a category’s Role and Intent into pricing analyses is the most important, yet often last accomplished capability. Naïve pricing teams often begin optimizing on profit alone, but topline sales performance matters in retail, which presents diverging analytic goals. A product’s role and intent assignment can inform the best balance between sales and profit performance. For example, a product with a “Destination” role and “High Growth” intent should often target topline sales exclusively. A “Traffic” product like our candy bar should optimize on profit.
Fitting it all Together: Value Delivery
While there is no single path forward, retailers generally progress in similar fashions as they grow in capabilities. The capabilities are not just simply intellectual: there is tremendous financial and strategic value in becoming best-in-class in pricing: