Industry disruptors like Netflix, Google, Capital One and Progressive share a common thread that drove their success. They were data-driven and analytical innovators that had a fresh take on how to serve their market and propel growth.
By leveraging analytics, these disruptors were able to focus on customer acquisition and segmentation to better understand their end users, build products, and present offers and pricing in real-time by leveraging their analytics.
Their competitors were too slow to catch up and had their market share stolen right out from under them. In insurance, we call it adverse selection.
Avoid Adverse Selection
Adverse selection is perhaps the most insidious threat because it’s completely invisible until well after it has infected a portfolio and the damage is done. Adverse selection occurs when a competitor undercuts the incumbent’s pricing on the best risks and avoids writing poor performing risks at inadequate prices.
A growing number of insurers are actively using adverse selection offensively to carve out new market opportunities from less sophisticated opponents. Some know that they are unwilling victims, but there’s a much larger group that’s in real trouble — they are having their lunch quietly stolen each and every day, without even realizing it.
If you don’t know which of these three groups you belong to today, then you’re already in the middle crowd. The good news is that it’s not too late to course correct by joining the exclusive club dishing out adverse selection rather than taking it.
Valen’s Decision-Making Capabilities
We always start from the premise that people make decisions. Analytics is a power tool that allows people to access the right data in real-time to make the best decisions possible.
- Art + Science – We work with every client to understand your business goals, so we can combine analytic tools with your team’s expertise.
- User Training & Adoption – We have a proven approach to make analytics accessible and demonstrate the power of human judgement working in concert with predictive analytics.
- Consistency – Our implementation approach provides a framework to improve consistency in decision-making, which leads to better and more reliable profitability results.
- Reassurance & Push Back – When a team member comes to the same conclusion as a predictive model, they are reassured in making the right call. When they disagree, it’s a flag to reconsider and ask more probing questions. Predictive models are never 100% accurate, and sometimes you have more information on hand. Either way, your team will appreciate a non-subjective element to back up their own judgement.
- Collaboration – We spell out how data-driven decision making provides a baseline standard with which to discuss specific accounts, target markets and other key business decisions that is free from bias.