The company’s purchase prediction model has been able to identify groups of readers three to five times more likely than average to buy a subscription, and advertise offers to them differently.
Of all news site readers, only a small number typically bother to register an account. And of all registered users, only a small number typically buy a subscription. So Scandinavian publishing house Schibsted is trying to use data to saving its marketing efforts — and subscription deals — for the readers who are more likely to pay up.
Schibsted’s subscription-purchase prediction model, developed by the company’s data science team, has been in use at four of the group’s Norwegian sites since last year: national newspaper Aftenposten and regional titles Bergens Tidende, Stavanger Aftenbladet and Fædrelandsvennen. The model predicts how likely readers who are already registered and logged into one of these sites are to buy a subscription, based on their browsing behavior and other activities.
So far, the efforts seem to have paid off. Across the sites where it’s in use, the model has identified groups of readers 3× to 5× times more likely than average to buy a subscription. Sales staff at these news sites are then able to, for instance, target these specific registered users on Facebook with special subscription deals.