Customer demand for categories in grocery, drug and general merchandise changes constantly. Ten years ago, retail store shelves were stacked with CDs and DVDs. Supermarkets sold many cases of standard lager. Drug stores had photo processing departments. Today, subscription streaming is king, craft beer is mainstream and online photo printing services dominate.
As a merchandiser, how do you maximise value from your real estate as competition continues to heat up from the endless aisle served by Amazon?
In the brick-and-mortar world, the scarcest commodity is shelf space. Companies that manage the shelf most effectively will be more likely than their competitors to survive and thrive in the ever-changing tide of retail.
Ironically, retail spacing strategies haven’t kept up with assortments. A decade ago, the typical US grocery store carried 30K SKUs. Today, these same stores merchandise more than 50K SKUs. And while product proliferation is rampant, store space has remained relatively static.
While the art of the assortment will always be important, identifying how much finite store space to allocate within and across each level of merchandise is essential. Successful retailers understand that they must take a strategic approach to space planning, with shelves allocated to deliver maximum sales return for the store overall.
Demand analytics has a key role to play in retail space optimization. As the “umpire” between category managers squabbling over who should forego space, data analytics can help you determine the optimal space distribution among departments—and the fair share of space that should go to categories and sub-categories to best meet customer demand.
The store that does the best job in allocating space, from bikes to car parts, gas grills to plumbing supplies, is the store that is best positioned to make more granular sub-category and SKU-level allocation decisions. This retailer will grow basket value.
How do you get there? We use data analytics on customer catchment, competition and market trend data to cluster stores into groups. These store clusters help retailers make strategic decisions about new services like concession space for a coffee shop, dry cleaners or mobile phone counter: In addition to data the stores have on customer demand, clustering incorporates external, contextual data to help determine which stores are a good or bad fit for certain services.
The insights gleaned through analytics should guide the rollout of new offerings, and help you build a clear business case to support the capital required to change your store layout—and create your exit plan for dying categories. We compile a ranked list of stores that offer the best opportunity for sales improvement through space reallocation, allowing the COO to invest where returns will be greatest.
In any change management program, it’s vital to measure incremental sales through test and learn to prove value and support a wider rollout. We typically see sales lifts of 1% to 3% versus control stores in retailers who implement this approach to strategic space planning.
Your sales space is your most valuable asset. Use data analytics to make the most of it.