Predictive analytics can dramatically improve retention, prime growth

According to the Insurance Information Institute, the combined ratio for the P&C insurance industry deteriorated last year, increasing from 99.5% to 105.6%.

It is clear that the industry and individual insurers must find better ways to select, underwrite, and price risks to improve profitability. Insurers are continuously challenged with making the right risk selection while balancing the pressures of pricing, retention, and customer acquisition.

Too often customer retention is undervalued in this mix — and in terms of its potential to contribute to profitability and growth. Whereas the fact is that retention has a particularly strong correlation with higher profits and growth.

While the average insurer has a retention rate of just 84%, top-performing insurers can achieve in the range of 93% to 95% retention rates.  And consider that acquiring a new customer can be anywhere from 10x to 25x the cost of retaining an existing customer.

According to Bain & Company, one in 10 P&C insurance customers in the US will shop around this year. And more than a third of those will ultimately end up switching insurers. With the competition just a click away these numbers can be expected to increase in the future.

Insurers surely want to improve their retention rates. So, what is holding them back?

Well, traditional methods no longer suffice. The practice of simply engaging with the customer at the time of renewal is insufficient. And insurers often:

• Struggle to access the appropriate data needed to drive retention rates upward.
• Lack the analytics to empower frontline personnel to make optimal retention decisions.
• Fail to measure the effectiveness of their retention actions and processes – and adjust course.

That need not be the case. Insurers can leverage new data sources and predictive analytics to decrease churn and discover opportunities for cross-sell and up-sell. To improve your customer retention, we recommend a four-step process:

1. Leverage insights from internal and external data. Insurers should start by ensuring that they are maximizing the internal data available to them. Start with core and adjacent systems such as, claims, underwriting, policy, billing, CRM, and other customer interaction sources. Supplement this with external third-party data. There is a wealth of data readily available now that was not available just a few short years ago. Look to data sources for consumer purchase behavior, spending habits, prior insurance or traffic violations — and then go a step further with firmographic information. Upping your data game will enhance your overall customer intelligence, predictive capabilities, and ultimately your retention.

2. Implement a predictive model. The next step is to leverage the data in a predictive model. There are a range of models available on the market – and some larger insurers build their own models. When selecting a modeling tool, be sure that it can support machine learning languages such as R and Python. The modeling tool should also be able to ‘operationalize’ the model results into the underwriting processes thereby creating a system of insight.

3. Leverage analytics to take customer-specific retention actions. Once the modeling solution is put into production, frontline personnel can take customer-specific retention actions (Figure 1), that properly balance retention likelihood and customer profitability. These actions will also help improve customer satisfaction as the insurer is engaged with the customer throughout the policy lifecycle and not just at renewal.

4. Measure business impact and adjust your model, as necessary. Best-in-class practices call for insurers to continuously monitor and improve their predictive models and processes with real-time audits. Best-in-class solutions will monitor key metrics in real-time and offer the capability to compare model performance prior to rollout with A/B testing.

A small improvement in retention has a significant impact on revenue and margins. To illustrate, consider a $400 million auto insurance portfolio — managed by an average insurer versus a top-performing insurer. Let’s say the average insurer has a 3% growth rate and 93% retention rate as compared to a top-performing insurer with the same 3% growth but a 95% retention. Over just one year’s time, which equates to a $412 million portfolio for the average insurer versus a portfolio of $420 million for the top-performing insurer.

Over the course of five years, presuming the same growth rates and the differing retention rates, the “average” insurer would build their portfolio to $464 million in value versus the top-performer who will build their portfolio to $512 million in value. That is a difference of $48 million between the two insurers in just five years based on only a 2% difference in retention.

That is the power of analytical insights to improve retention and prime growth.

Satyen Paneri is a seasoned technology professional with over 20 years of experience developing innovative technology solutions across various industries. Currently, he works at Guidewire, a leading provider of technology solutions for the property and casualty insurance industry, where he specializes in predictive analytics solutions. 

Opinions expressed here are the author’s own.

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