How Customer Data Turned Store Closure Into Opportunity

September 19, 2019 Sam Malissa


One of Custora’s clients is an omnichannel retailer that decided they needed to shutter some underperforming brick-and-mortar stores. While using customer data in an after-action analysis, they discovered a problem they didn’t know they had, and an opportunity to turn that problem around. 

The stores they closed were bringing in less than the cost of keeping them operating, so it seemed prudent to cut losses and regroup. The thing is, based on what they were measuring at first, they weren’t getting the full picture.

They were primarily using the financial lens, going off of sales numbers — which is of course important, but it’s not the whole story. 

Too many retailers don’t take into account customer metrics like retention rate and customer lifetime value, which account for differences in customer behavior. If they did, they would see a whole other landscape, with new opportunities and root-causes for problems.

So What Did Customer Data Reveal?
Our client decided to apply these customer-focused metrics to an analysis of their store closure. Taking stock of where things stood, they saw that they had lost 31% of their existing customer base in the previous year, which outstrips a more typical (though still not ideal!) customer attrition rate in retail of 20 - 25%.  

This retailer’s assumption prior to the customer behavior analysis was that the bulk of that 31% loss was coming from stores closed in the previous year. Makes sense; stores close down, so fewer customers are shopping.

But once they applied the lens of customer analytics they made a crucial discovery: it turned out only 8% of customer loss was directly attributable to store closure, and the other 23% was an in-general loss associated with other aspects of the customer experience. More in line with retail standards, but naturally they wanted to do better. 

They were able to determine this number based on variations in how frequently different customers were shopping, where they were shopping, and whether or not they were coming back to shop again.

Some customers were only purchasing once at one of the now-closed stores and not coming back, but others were shopping at more than one brick-and-mortar location as well as online, making a few purchases before they stopped shopping. 

This represents a missed opportunity: the retailer wasn’t focused on these higher-value customers who were more open to a continuing relationship with the brand, and didn’t make special efforts to retain them. 

So by attributing the whole 31% drop in retention to store closure, this retailer was missing the fact that they had an in-general customer churn rate of 23%. While this across-the-board churn rate of 23% wasn’t good news, it did give them a much more focused picture of what they needed to do and where they could concentrate their efforts.  

Identifying these potentially high-value customers — the ones who made several purchases and then stopped shopping — gave the marketing team a great target segment for renewed outreach to recoup losses.

It also highlighted the importance of differentiating between customers as a way to determine who is the best bet for driving repeat sales.

Finally, focusing on the behavior of customers gave them a more robust model for evaluating the necessity and impact of future store closures. 

How Are You Using Your Customer Data?
The case outlined above illustrates how customer data and customer-focused metrics work like X-ray vision. They reveal what’s behind the top-line financial numbers, and they uncover which customers are your best customers, so you can set your strategy around them. 

But a lot of retail marketers aren’t using customer data to its full potential. 

The basic use cases are for marketing campaigns: grouping customers into personas to better target email; making smart recommendations for next purchases based on lookalike segments; personalizing communication. 

And the basic metrics applied to customer data typically track incremental results: return on ad spend; cost per click; cost per acquisition. 

Of course marketing applications for customer data are the basic tools of the trade nowadays, and incremental results add up to real money. We’re not saying to forget about these. 

What we’re saying is that these uses of customer data are now table stakes. And, more than that, using customer data as a basis for measurement can uncover problems you didn’t know you had and opportunities you didn’t know you were missing. 

When you use customer data for email triggers and marketing campaigns, that’s tactics. When you use customer data as an analytical lens, that’s strategy. You need both to win the day. 

Here's some more info on the customer-focused metrics that can guide strategic decision-making.


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