Tech Guest Viewpoint: Avoid Merchandise Optimization Confusion
Executing merchandise optimization, or at least performing it so the end game is won, can be confusing and convoluted since “optimization” seeps into almost every retail activity. Are we talking assortments, space, price, promotion, categories, merchandise or some combination?
Terms are irrelevant. Results are not. Merchandise optimization must deliver and reverberate success among every store and online channels. It must drive shopper loyalty, and must be executed to maximize margins, beat competitors and draw in your shoppers.
Clearly Defined, Clearly Delivered
The mission and definition of merchandise optimization is to leverage data to create knowledge about pricing, products, assortments, space and channels that is used to attract shoppers and get them to buy from you, again and again. You collect data, analyze data and execute against that data. You manage demand and it must be strategic, dynamic, automatic and systematic.
Here are the three must-knows for effective merchandise optimization:
1. Validate what really impacts pricing. There are so many variables that touch products and pricing. Retailers can’t assume that traditional pricing models and tactics will yield optimal results. New ways, new science and technologies are being used.
For example, online and mobile technology have expanded the number of attributes that affect consumers’ decisions to buy. In order to recommend prices and forecast, and therefore shopper behavior (demand), retail science must shift from modeling demand at the SKU-level to the attribute-level. Attributes include product specifications, price/promotion, competitor prices, channel, product placement and fulfillment (e.g. reserve online – pick-up in-store).
2. Start with rigorous merchandise intelligence and demand analysis. This combines traditional price elasticity for retailers’ own products, competitive cross-elasticity with competitors’ products, substitution effects from a retailer’s own assortment (across channels) and discrete demand impacts associated with own product attributes. The results will be more precise and repeatable competitive intelligence that drives optimal assortment, prices and promotional planning.
3. Don’t underestimate the value of online data. What’s happening online has a direct impact your stores. In one recent case, online sales created in-store lift by 30%-plus by offering in-store pick-up. The Buy Online Pick Up in Store (BOPIS) phenomena adds a bit more complexity but is intensely relevant to shopper demand, in store and beyond. Any optimization practice requires the sophistication to maximize your online data.
There’s at least one other key ingredient: The “solution.” There are too many cases in which a vendor delivers a solution that gums up what works. The world is moving too fast for mistakes and mayhem. Every vendor must prove results, be it a quantitative proof of concept or otherwise. Don’t assume you’re avoiding risk by going with some “standard.” The good news? Solutions are increasingly easier to implement and apply quickly, thanks to cloud and SaaS adoption.
Bottom line, optimization of any type must be practical and prove it can extend retailer success.