A subscription publisher that specializes in international real estate expanded its product offering to include physical real estate. The customer knew that buyers of real estate generated 2-3 orders of magnitude more in LTV but did not have deep insights into this customer base and how it differed from their core customer base. As a result, buyers of real estate were difficult to acquire.
Actable built a Google BigQuery instance that combined 3rd party data the client had about its customers with site visit and CRM data. By integrating 1st and 3rd party data streams at a customer level, Actable conducted a comparative analysis of real-estate vs non-real-estate buyers and core product purchasers. The selection of the 3rd party provider, who specialized in voter demographics, was a strategic choice that gave the analysis quality enrichment points and geographical coverage of the customer base.
Actable delivered the analysis in a filterable dashboard using Looker Studio, Google Cloud’s free data visualization software. The dashboard highlighted key patterns in demographic data and displayed time-based cohorts of users, augmented by product-level data.
Actable subsequently created machine learning models based on high-value cohorts, which scored users according to their likelihood to purchase real estate. Scores were then deployed in the CDP and activated in downstream marketing systems.
The real estate buyer demographic became clearly defined – younger, more affluent, and with a lifetime value nearly 10X their core subscription customers. Actable is now working with the client to deploy triggered mailings and cross-channel marketing activations to convert more users based on the machine learning Actable developed, which is helping the client capitalize on a demand spike created by the COVID-19 pandemic.