Facebook advertising activity was delivering decent volumes of leads, however the data provided by our client showed that more of the leads could be converted to policy, allowing us to significantly bring down the policy cost per acquisition.
We implemented a custom campaign tracking system that allowed us to see which campaigns were driving quality leads right through to policy and analysed the profile of these leads. We then grouped these learnings into different combinations of include and exclude quality targeting and deployed iterations of it across campaigns. Winning elements were kept, combined with new targeting tests, and launched iteratively. This meant that over a period of time our Facebook adverts were incrementally appearing to a better-quality audience that was more likely to convert to both quote and policy issued.