Getting hard data on soft outcomes

One of the reasons I love working in the insurance industry is its stubborn and relentless discipline for quantifying everything. We don’t trust and often choose to ignore what we can’t measure. For the most part, this discipline has served us and our clients well. For example, health insurance organizations typically operate at margins of 1-2%. Operating margins that thin are a remarkable achievement and speak to the state of the science and the discipline with which that science is being applied.

Where and when we can quantify the critical inputs and outcomes that drive success we generally do well. Where we can’t, we generally struggle.  One area where we have historically struggled is with the human emotion factors of our business. For example, customer satisfaction is largely a challenge of understanding when someone is “happy” with you – an emotional assessment. The recent Temkin study of C-SAT across various industries showed the insurance industry at the bottom of the list.

We also struggle in the areas where behavioral health issues impact our business. We struggle identifying where BH is a barrier to a positive outcome and we struggle helping people accept and manage these conditions once identified. This is not just limited to health, workers comp and disability lines. An auto or homeowners loss can also trigger extreme psychological distress also. Understanding when that distress is there and when a customer needs enhanced service due to their distress can be an important driver to C-SAT and retention.

The promise of advanced behavior analytics solutions such as Cogito is they bring hard measures to what have been viewed in the past as soft outcomes. The benefits are two-fold. First, by hardening these soft measures we can now add them to the hard data available to enhance our risk assessment and management tools. Second, behavior data can reveal attitude and intent – both forward looking and therefore more predictive of future risk than models built primarily from backward looking or retrospective claims data.

By capturing, visualizing and gamifying behavior data in a way that allows you to “see” how someone “sounds” in real time it opens up new possibilities for customer satisfaction, claims cost management, quality outcomes and customer acquisition.

Some questions to think about:

• Would you retain more customers if you could consistently recognize in real time during a service call if a customer was dissatisfied?
• Would you have higher C-SAT scores if you recognized which customers have been traumatized, is having difficulty coping and needed more support and hand holding during the claim process?
• Would you improve cost and quality outcomes if you recognized in real time if a customer was “distressed”?
• Would your sales teams be more successful if they could recognize and act on “buying signals” in real time?
• Would your coaches and trainers be more productive if they could see when an agent is struggling and needs additional coaching, training or support?