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The global fashion business journal

21 Mar 201902:52

Online big data to analyze stores: the new KPIs in the omnichannelity era

The geospatial analysis will be one of the new indicators that will measure the probability of stores in the new digital era, according to the study Perspectives on retail and consumer goods, prepared by Mckinsey.

05 Mar 2019 — 09:00
C. Juárez
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Online big data to analyze stores: the new KPIs in the omnichannelity era

 

 

Do all the stores that do not sell have to shut down? How is profitability measured in a business that does not invoice, but instead has customers that come to do showrooming? Omnichannelity is radically changing the way to consider whether a store should remain open, forcing the development of new KPIs that are starting to be measured online.

 

Among these new indicators are, for example, geospatial analysis, as reflected in the study Perspectives on retail and consumer goods, prepared by McKinsey. The consultancy explains that, of the more than 11,000 stores that closed in the United States between 2017 and the first half of 2018, many made economic sense, but were not measured with the appropriate indicators.

 

According to the report, the first step to assess the profitability of a store is to define its role: is it a space to meet with the client, a showroom or a pick up point for online shopping?

From there, the companies have to assess the significance of each of their stores for the overall value of the company and how it influences the rest of the channels. In this regard, McKinsey recommends combining traditional demographic data with new metrics such as the use of mobile phone tracking and the consumer’s route through the web.

 

These new variables are not only means to decide whether or not to close a business, but also to design strategies that improve their performance or determine if it its location should be changed.

 

 

 

 

Through different variables, the consultancy came to the conclusion that retailers can improve their network of stores and increase their ebitda by 20% when closing, relocating or renovating a store. For example, turning a point of sale into a showroom depending on the type of client that resides in the area where a business is located.

 

The document states that a company with a high percentage of online sales in a specific geographical area often has a flagship store close to the area where young consumers gather, is far from other stores of the same company and has not much tourist traffic.

 

In the study, McKinsey drew a diagram with two variables: the impact of a store on a group’s ebitda and potential sales. In the first case, if opening an establishment means that the ebitda grows but the sales potential is negative, the consultant suggests proceeding with the opening. In the second case, where the two variables are positive, the consultant suggests using the opportunity to prepare more openings.

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