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How do you benchmark your own store locations? And why should you?

You are bound to know which of your stores has the best turnover. And you know where the cost/benefit ratio is best. But do these stores also perform to their best advantage, or could there have been more to it? Are other stores perhaps doing relatively better?

How do you make a good comparison?

You have a wealth of data to analyse your store network. But in order to make a good benchmark, you also need to be able to accurately interpret this information. Which external factors play a role? In order to determine this, it is essential to structurally go through a number of steps:

benchmark retail locations

An example

Suppose you have two outlets of approximately 200 m².  Outlet A in area A and outlet B in area B both have had virtually the same turnover for a few years now. However, you pay substantially more rent for outlet A than you do for outlet B. Does this mean that outlet A therefore performs worse than shop B? Or is there a more nuanced answer?

In order to fully answer your question, it is useful to map out the competition and the area as well. Are they similar or do we see differences? You can retrieve the following information from the Locatus database:

benchmark retail locations

By combining the external data (Locatus) with the internal information, an entirely different picture is suddenly created. The external data indicate that the conditions in A are more favourable. Therefore, it should be possible to run a higher turnover here, which leaves you with even more net revenue than in B.

Why isn’t this happening?

Is the layout of the store more inconvenient? Is customer loyalty in B much higher? If so, why? Does this store have better staff? Or perhaps a different assortment? Do you have the wrong impression of what your ideal neighbours would be?

Combining data allows you to benchmark all your stores against each other. And, thus, learn so much about the success factors for your business and the turnover potential.

During the Covid-19 period

What have been your best branches for a long time, may have turned out to be the worst branches in recent months. And vice versa. Will it stay this way? What will change? This may seem like trying to look into a crystal ball, but doesn’t have to be. By consistently placing your data next to Locatus’, you’ll soon be able to see certain patterns. From there, you can also express projections about the future.

What type of shopping area are your stores in?  Which sectors are represented in this shopping area and how Covid-19 proof is the segmentation of these sectors? In this day and age, a centre with a lot of everyday supply and little hospitality is in better shape than a centre with a lot of fashion and hospitality.

Thus, you should also include the corona-sensitivity in your benchmark. If you have all these data aligned, you will quickly see:

  • Where most potential lies in ‘normal’ times;
  • What shopping areas were already weak beforeCovid-19;
  • Where you can expect the biggest impact from the Covid-19 corona era;
  • Which outlets are most likely to survive the Covid-19 era.

How can Locatus help you?

Locatus has both the data and the required expertise to take a close look at your store network. We can undertake the entire analysis for you or help you set up a monitoring plan. Because we have been collecting and analysing data for 20 years, we can also put the figures in a historical perspective. The past and present can tell us something about the future. Even in these uncertain times.

Locatus data driven insights

Gertjan Slob

Gertjan Slob is the Director of Research at Locatus. He is responsible for the entire data course. During his work, he is constantly analysing data, and frequently flags interesting trends and developments.