A national home and auto insurer was experiencing a consistent increase in the acquisition cost per lead for their digital marketing strategies. The leads were also of lower quality as a high percentage of them were outside the bounds of their risk tolerance. EQ was tasked by their actuarial team to train LOCUS’ Cognitive machine learning model to segment the market by risk profile and identify households that were qualified leads meeting risk KPIs.
EQ’s data science and marketing teams worked with the client to uncover new data opportunities in the EQ marketplace. A list of human movement attributes available in LOCUS was used for the existing marketing campaigns and incorporated into the machine learning models for enhanced risk and lead scoring. LOCUS Notebook and Query Lab were used to generate powerful reports and insights that led to extremely successful marketing and customer acquisition strategies.
Results were well above expectations. The new formulated criteria led to a 68.2% decrease in the customer acquisition cost while significantly increasing exposure to the highest LTV customer segments. The results were material to the business with LOCUS occupying the same cohort as Google and Facebook when it comes to volume and efficiency. Based on an extremely successful pilot, the datasets were then rolled out more broadly with similar efficacy and results.