Furniture Retailer Explores Customer Value
An upmarket UK furniture retailer uses customer value analysis to determine whom to recruit and how best to cross-sell.
The Client’s Business Goals
Initially the client, who had historically mainly monitored store and product performance, just wanted to understand the longer term value of customers.
We proposed extending the brief to looking at the characteristics of high and low value customers, and also the typical customer journey in terms of second and subsequent purchases so as to inform crm activity.
- Step one was to assemble customer level records from very detailed transactional history, and in particular to join together multiple transactions into one sale when they were closely linked in time
- Step two was then to count longer term value by recruitment time period (the data going back almost ten years)
- We then matched the customer data to external demographic and lifestyle data in order to see if there were any characteristics that differentiated high from low value customers, or whether that was influenced by the store they used to make their first order
- Next we looked the time between first and any subsequent orders
- And finally we looked at patterns in the sequence of purchases to see, for example for all purchasers of product category A, what were their most likely subsequent purchase categories
- An initial and surprising finding was that it was not possible to differentiate high from low value customers by their demographics or lifestyle; however there was a major scale difference between high and low value customers at first order, which was not challenged by subsequent orders. This enabled the client to focus from the start on cross selling and retaining those with initial high value purchases
- Our next discovery was that nearly all second orders came in the first year since first order, except for a small number of people buying replacement covers for sofas and chairs after a few years. This implied that follow up CRM activity needed to be initiated immediately after first order, and that it was not productive after the first year had elapsed.
- The third key benefit was that the analysis showed clearly what to cross-sell; we were able to find very distinct patterns in the types of product purchased at second order compared to first order.
UniFida is the trading name of Marketing Planning Services Ltd, a London based technology and data science company set up in 2014. Our overall aim is to help organisations build more customer value at less marketing cost.
Our technology focus has been to develop UniFida. Our data science business comes both from existing users of UniFida, and from clients looking to us to solve their more complex data related marketing questions.
Marketing is changing at an explosive speed, and our ambition is to help our clients stay empowered and ahead in this challenging environment.