Knowing how valuable someone will likely be to a brand over time lets the growth team calculate how much it’s willing to spend to attract that customer, and create targeting segments that are better pegged to overall ROI.
For the last two years, Madison Reed has been working with a startup called Retina that helps companies build look-alike audiences based on their predicted LTV, by crunching a brand’s first- and third-party data on a daily, weekly or monthly basis to score customer relationship profitability and what types of customers are most likely to retain.
Look-alike audiences are often modeled off of lists that include customers who are about to churn or maybe only purchased a single product and then never returned, which doesn’t make a lot of sense, said Retina CEO Michael Greenberg.
Madison Reed is running tests using LTV data to power its look-alike modeling on Facebook, and the results have been encouraging, Kalinowski said, with a roughly 50% increase in ROI in some cases. The company plans to keep scaling its tests.
Read the full article: Madison Reed Enriches Its Data Strategy With A Focus On Lifetime Value (AdExchanger)
