How the company cut campaign spend by 78% and grew purchases by 50%

The cash buyer had built a solid we-buy-any-house operation. Twelve people, a clean process from enquiry to exchange, and a marketing machine built entirely around letter drops. Every month, they'd pick a postcode, print 5,000 letters, drop them through letterboxes at £0.80 each, and wait. It was the industry standard. It was also costing them £4,000 a campaign and returning a 0.8% response rate.

The maths were uncomfortable. Of those 5,000 letters, roughly 40 would generate a response. Of those 40, the team would arrange viewings for most of them — burning the MD's time, the acquisition manager's time, and the fuel budget, on properties that failed basic financial qualification checks on arrival. The response-to-offer conversion was low. The cost per completed acquisition was high. And the wasted viewing time was the operational cost nobody had properly quantified.

Ryan Foster, the MD, had been thinking about targeting for years. The ideal scenario was obvious: identify the specific properties most likely to have motivated sellers — long on the market, recently reduced, distressed ownership signals — and focus every pound of letter campaign spend on those addresses. But without the data infrastructure to do it systematically, it remained a manual exercise that never quite reached the level of precision that would make it transformative.