Using data patterns to deliver personalised customer support for DIY-ers.
Home improvement projects outside of key seasonal drivers, are irregular and unpredictable. Homebase's direct marketing strategy had been built around monthly high volume event mailings with blanket discounts. Where targeted communications were used, it was taking eight weeks to go from data point collection to comms delivery. Homebase had fallen into a cycle of promotion and discounting, with a focus on improving the 9% response rate - ignoring the 91% of non-responders and overlooking the fact that they obtained less than 50% share of wallet for
To turn the old approach on its head and put the customer at the heart of its thinking. Understanding a customer's need (or intent) for any given home or garden improvement task meant Homebase could initiate, maximise and acknowledge the projects that customers went through and grow customer lifetime value. After over 750 hours analysing different product, purchase and non-purchase behaviours, web browsing and other indicators, 10 project behaviours were identified.
By finding patterns in shopping behaviours in both transactional and non-transactional data it identified customer needs as close to spending opportunities as possible. The ability to make unpredictable behaviour predictable, led to the plan to spot specific behaviours that indicated a future need, then act quickly to prompt further spend, growing what was called 'Share of Project'. Responding quickly enough to be relevant was critical. By evolving data management and fulfilment processes Homebase was able to get direct mail delivered within six days and email
The strategy has been transformational. The evolved CRM programme has delivered incremental sales four times greater than it did three years ago - ROI up 350% to 4.3:1. The new CRM strategy smashed the profit target by +48%. All ten programmes exceeded targets - sales 17%, profit 20% and ROI 22%. The focus on timely and relevant communication has created a more engaged and valuable customer in terms of both total spend and activity for example DM response increased from an average of 9% to a maximum of 46%, up 500%, ATV increased by 13.5% and repeat visits up 6%.