Many retailers are closing the wrong stores, says McKinsey
By Marjorie van Elven
6 Aug 2018
More than 7000 stores closed in the US in 2017, and the trend continues into 2018. As traffic in US shopping malls decreases, investment bank Credit Suisse forecasts 25 percent of all American malls to close by 2022. However, some companies may be choosing the wrong shops to close, according to a recent study by management consultancy firm McKinsey & Company. “Many retailers are primarily taking into account the sales and profits that the store generates within its four walls, without considering its impact on other channels”, reads McKinsey’s report.
The consumers of today shop across different channels. “Showroomers” visit brick and mortar stores to look for ideas and inspiration, only to buy the products online later on. “Webroomers” do the opposite, as they prefer to see and touch the product before committing to a purchase. There are also many consumers who prefer to pick up or return their online purchases at a physical store. Considering all of these practices, the usual methods to measure a store’s success are outdated, according to McKinsey. “The most sophisticated retailers are now closely examining the interplay between offline and online customer decision journeys”, the firm says. A store that doesn’t sell much may still be beneficial for the company in terms of brand awareness, or by driving sales in other channels.
How to find out which shops to close, then?
First, McKinsey advises retailers to take a close look at consumer behavior data, to which companies have more access nowadays than ever before. Opt-in e-receipt programs and anonymized mobile-phone location data were mentioned by the consultancy firm as two efficient ways to shed light on the quantity and quality of customer traffic.
But there’s more retailers can do in that regard. According to McKinsey, the more advanced retailers are using a combination of geospatial data and machine learning to obtain insights on who’s shopping where.
The report mentions the example of an American retailer which hired a team of data scientists to identify the factors that most affect a zip code’s sales potential. Some of the variables they examined: high weekend foot traffic, high online spend in their product category, and the proximity of a competitor store.
After finding out which variables influence a store’s sales positively, the company was able to predict the potential sales of each of their stores, and compare the estimates with the actual sales. “Then, using geospatial stimulation, it estimated each store’s impact on wholesale and online sales”, reads the report. They found out, for example, that stores located in areas with a high proportion of young, urban professionals tend to drive more online sales. Based on this data, it became a lot easier for the company to decide which stores to close, which ones to optimize and which ones to turn into digital showrooms.
"In our experience, retailers can quantify performance gaps, uncover growth opportunities, and reap early wins from advances geospatial analytics within 6 to 12 months -- particularly when an empowered, cross-functional team is leading the charge", concludes the consultancy firm.