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Gathering data in your commerce solution can help you in many ways. Can it help you to predict which customers will leave in the near future? And how can you provide insight into which factors influence their departure? 

What is the churn rate?

The definition of churn differs per company: How do you determine whether a customer really has left? If the customer has not ordered anything for a year? Or if he or she no longer makes the purchase they made earlier? The definition of churn differs per product or target group, because not every product or customer has the same lifecycle. 


At our partner De Nieuwe Zaak, various companies indicated that they also looked at the degree of engagement with the customer. Does the customer still open newsletters, visit the website or click on social ads, but does not buy? Then, the challenge is to the activate them for a purchase decision. The definition of churn depends on what goal you have as a company.  

For Proforto, churn depends on the ordering behavior per category and customer. If a customer regularly orders work gloves and this glove customer does not order again, something is going on. Work shoes, on the other hand, wear out less quickly, so it makes sense that some time will pass before the customer returns to your online shop. Proforto also takes the various customer groups into account, for example students who temporarily work in the warehouse industry only order work shoes once. All of these factors need to be considered when determining churn and your marketing strategy.  


Which factors affect the churn rate?

In practice, it is often good to ask employees who have frequent customer contact, such as customer service representatives or office workers, for their gut feeling about the reasons for a customer's churn. Then you do an analysis based on data and look for the connections. Churn factors include the frequency with which the customer normally orders, the volume, and the number of product categories. 

However, factors such as who the account manager is and how complete the customer profile is filled out in your CRM system, are also relevant. When determining factors, it is more about their combination and potential effects on one another than about individual factors or KPIs. 


What can you do with e-commerce data?

If you have insight into your churn, what then? Some divide customers into segments based on the extent to which they are active. These segments (for example sleeping customers, occasional customers and active customers) can help you make choices for the use of your sales and marketing apparatus. 

Try to cross-sell some new products to occasional customers, which reduces the chance that they will leave your company. But sometimes it is also good to accept that some customers only buy something from you once. Of course it is important that you know what kind of customer you are dealing with. 


Is it always better to have more data? 

As a company, it can take a long time to dig into various factors that you need data about. But are more data and more variables always better? If you ask Facebook, the answer you would get is that you would need trillions of data. But what if you don't do a lot with data yet? Then just start and experience how well you can predict promotions based on relevant factors for your online shop. 


Your data set and the associated factors grow with your online shop. This is and will remain an iterative process. Make sure to let your data work for you, because, according to Rik Burgerdijk from Proforto: "The company with the most complete data - if it knows how to use it well - will win over the customers of the future." 

Comprehensive, interactive reports for better business decisions, supported by artificial intelligence enable you to turn future-oriented thinking into real projects. Are you interested in exchanging on this topic? Join the B2B Frontrunners!