If  you are familiar with GMROS (Gross Margin Return on Space)  – Calculated roughly as the (gross margin $ ÷ floor space) of the product hierarchy being measured; you may be measuring a stale snapshot of the total dollar contribution of the department, category or planogram. Digging into the metric we learn that there are sections of the department or planogram with much higher GMROS than the average. Which brings us to marginal return analysis in space optimization.

A question that I hear from the retail C-suite is “How can I succeed in a small store footprint?” Real Estate wants to know that the optimal minimum prototype size for leasing purposes. Store Planning wants to know how to shoehorn big store sales into small store footprints. Concept Development wants to know how much space is required to support baseline sales and how much to devote to experience. The key is Marginal Return on Space.

Imagine a chain of stores that average 9,000 square feet. To serve urban locations and afford the rent, you need to develop 4,000 square foot stores. If we optimize the store focusing through the lens of merchants, we are likely to develop stores with one facing of the top few hundred SKU’s in an incoherent merchandising mix. There is something called Point of Customer Relevancy that you need to reach with each category so customers “give you credit” for being in the business.

For example, you could create a 4′ household paper section.  With one SKU each of paper towels, napkins, toilet paper, paper plates, sandwich bags, aluminum foil, etc it has the top sellers. But because you have not reached a Point of Customer Relevancy in the household paper category, customers won’t put your new stores in the top of their consideration set when they purchase.  You will become a convenience purchase on par with gas station c-stores.

Still, retailers make this decision over and over again when they create small stores. No one wants to eliminate business lines and create stores that don’t offer the full range of assortment from their big stores. As a result, they get small stores with a smattering of every product line that fall short of customer expectations and revenue projections. While they may be maximizing their return on space, they are not building a relevant customer experience to draw and keep shoppers.

From a purely analytic point of view, a return on space space optimization model creates stores from the ground up using Marginal Return on Space.  To begin, parse out GMROS metrics to a level more granular than department or category.  In shelved goods, you can use the GMROS metric for each 4′ section of a planogram.  In other areas, use the GMROS metric for each apparel fixture, produce or meat counter.

Initially, the model answers: “if I only had a store that was 12 square feet (4’X 3′ – the standard space allocation for a 4′ section including aisle space), what would it be?”  The model selects the most productive segment among all available.  It subtracts that productivity from the total and ask the same question “what if I add another 12 square feet?” The model builds the store to be as productive as possible.  Users can stop the model at any point to test differing store sizes.

Years ago Best Buy realized that if they only had a 1000 square foot store, it should be nothing but cell phones and accessories.  (Mall kiosks, anyone?) Our store had to be gargantuan to justify any space at all for appliances or classical music. The Return on Space model predicted what ultimately became Best Buy’s Mobile stores. As long as  the store development team considers customer expectations about assortment, the model builds an extremely successful prototype. (Which is why Best Buy created the sub-brand Best Buy Mobile.)

This model will create optimal space allocation.  It will still require an experienced hand to take the space allocations and create the best location layout and adjacencies.  (These are the three sisters of macro space optimization.)

So if it’s so easy, why don’t more retailers use this tool?

Obstacles to Return on Space Optimization
  1. A lack of knowledge about macro space optimization and how to apply it to real world decisions.  Macro Space geeks rarely influence the strategic decisions about prototype development and store strategy.
  2. A lack of metrics in non-planogrammed areas.  Apparel, seasonal and fresh foods are often high margin areas with little to no planograms.  Retailers who rely solely on standard software tools usually become frustrated while developing metrics for non-planogrammed areas.  Without those metrics (at the fixture level) the model doesn’t work.  (for more info see: How to Make Macro Space Work.)
  3. A need to appease every business line.  Despite data that proves that customers are not shopping all departments equally, executives buy into the notion that every business needs “fair representation” – even in small stores.  Internal politics, vendor funding and promotional plan restrictions, force retailers to make the easy (and less effective) choice to include a single SKU from every business line rather than eliminate them from small stores.

The model can be used for more than creating new small store footprints. Optimal store sizes can be determined to maximize investment payback for any real estate site pro forma. Business plan forecasts can replace the historical gross margins to model what the store of the future should look like.  With store-level data,it can optimize site-specific remodel plans.

Our space modeling capability is ready to go. Let’s talk