Customers don’t contact their insurer to let them know that everything’s great or that they’re completely satisfied with their service. Rather, they call when they think something’s not right.
This is where predictive analytics can come into play, both by minimizing the need for customers to contact their insurer and by improving the customer experience when they are interacting with an insurer to address an issue with a claim, eligibility determination or other concern.
Insurance customers create data at every touchpoint and interaction, which can be combined with other public and proprietary data to give insurers a rich dataset for predictive analytics initiatives that help insurers understand past and present behaviors and predict likely futures.
The best way to keep an insurance customer happy is to solve their problem before they know it exists. The second best is to be addressing the problem before they have a chance to contact you. Both models benefit from predictive analytics, where problems caused by more traditional adjustment or fraud, waste and abuse investigations can be identified ahead of time as potentially in error.
When a customer does reach out, predictive analytics can play a critical role in arming your customer service representatives with information and insight that lets them immediately start work on addressing the customer’s needs, rather than trudging through complicated and annoying data gathering and storytelling.
Consider this interaction, powered in part by predictive analytics:
“Thank you for calling, sir, I have the information here on your claim with Doctor Smith’s office being challenged. We’d already identified that as a likely error on our part, as we know that you’ve been seeing her for years and there’s never been an issue, so we’ve reached out to her team just to clarify things and hope to have this resolved very shortly at no cost to you. There’s no need for you to call back, unless you’d prefer us to call and confirm we’ll only reach back out if for some unlikely reason we need to clarify some piece of information.”
Nobody wants to have to call their insurance company, but that’s the kind of response that leaves a customer far more satisfied with the outcome than many traditional customer service models.
One other powerful capability of predictive analytics is to integrate it with marketing functions to power microtargeted, personalized offers and hyper-focused campaigns. With a broad understanding of the customer, predictive analytics can identify the most worthwhile high-value customer targets for real-time marketing campaigns, including both what channels and what messages are most likely to resonate. They also, by predicting future utilization, enable segmented ROI calculations that give marketers a far more personalized engagement with their campaigns than a single, overall number can provide.
Rescue & Retention
The more you learn about your customer and track their interactions, the more predictive analytics initiatives will be able to identify the potential for relationship-ending problems. Given the general consensus that it’s seven times as expensive to attract a new customer than it is to maintain an existing one, virtually anything that powers customer rescue and retention efforts is worth consideration.
Predictive analytics can compare existing customers to a broad swath of other customers, both happy and otherwise, and look for similarities and danger signs that identify a potential need for targeted and individualized customer retention efforts. Done well, this kind of customer analysis can even identify potential pain points before the customer realizes they exist, putting the insurer in a position to do the relatively easier work of maintaining satisfaction rather than rebuilding it or earning it anew from a new customer.
Want to learn more about what predictive analytics could do to help you retain your best and most profitable customers? Contact us to find out more.