Data & Analytics

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Anand S. Rao, Punita Gandhi, Scott Busse and Fred Cripe, <a href=
Anand S. Rao, Punita Gandhi, Scott Busse and Fred Cripe, Commentary

Information as a Game Changer: Analytics for Improving Insurance Customer Retention

Insurers grappling with high attrition rates should invest in developing shopper/switcher insights for their customer base via data analytics.

The head of product development at a top 10 personal lines insurer has been pondering an increasingly worrisome dichotomy: new business continues to grow, but retention of existing customers — particularly in auto — has been decreasing over the past several years. He is far from alone alone experiencing this. Customer behavior in the auto insurance industry really has been changing, to the understandable concern of many insurers.

[For related insights from PwC, see: Keeping the Customer: Analytics Driven Acquisition and Retention .]

Figure 1: At Risk Segment Shoppers PWC Analysis, JD Power Shopping Studies

While the likelihood of people shopping for insurance appears to have stabilized over the years, the percentage of shoppers that actually switch is inching upwards, and is presently the highest that the industry has ever seen. As figure 1 shows, 44 percent of shoppers in 2012 switched, compared with 33 percent in 2010. This trend is alarming for insurers, because it has resulted in aggressive price wars, increasing product commoditization, erosion in profitability, and diminished combined ratio performance. Recently observed shopping trends include the following:

Nearly 80% of shoppers who find that they can save money switch carriers.

Over 60% of defectors start shopping because of a premium increase.

Defection is exacerbated when carriers fail to offer policyholders advanced notification of rate changes, reasons for the increases, or options to adjust their policies to manage the cost.

On the positive side, insurers have observed that when they provide policyholders with advance notification and reasons for imminent rate increases, they are more likely to retain them. Also, customers report significantly higher satisfaction scores if their agents get involved in the claims process, and therefore are less likely to switch carriers.

Our analysis from work across a range of large insurers reveals that there are three major interacting causes of currently high rates of attrition:

1. Higher than expected auto price elasticity: Auto policy customers have become increasingly more price sensitive in recent years (most likely in response to the sluggish economy), and more consumers are choosing to switch for a small amount in savings; in fact, between 2009 and 2010, defectors increased from 43 to 62 percent for the small savings they had passed up the year before.

2. Decreasing emotional cost of switching: The continued growth of the online sales channel, coupled with growing customer use of data-driven internet transactions is resulting in the depersonalization of the carrier-customer relationship. Some carriers who have been able to maintain strong agent-customer relationships in fact have seen fewer defections as a result of the personal connection between agents and policyholders.

3. Increasingly voluntary marketing of renewal policies by sales force/agents at renewal: Many carriers focus primarily on increasing new business and incentivize their sales force heavily for acquisition. In fact, sales commissions for renewal business tend to be low or even absent in order to offset high acquisition costs and low prices. This, alongside the growing margin-squeeze felt by most distribution channels, is increasingly leading to “rogue” behaviors among sales reps and agents. These include automatically re-marketing customers at renewal to earn higher commissions, even without any prompting from these customers.

The Vital Role of Analytics in Retention

In order to effectively combat this apparently "new normal" customer trend, there is an increasing need for insurers to more proactively identify and then effectively retain the segments that are likely to churn. More granular predictive analytics can help insurers do this by:

Proactively identifying customer segments that are prone to switching;

Recognizing patterns that indicate customers are starting to shop; and

Driving innovation on ways to retain these customers, particularly those that are high-value.

However, analytical insights alone will not help to reverse the switching trend. An organization wide, cross-functional focus on the customer experience needs to support all retention efforts. For example, a poor claims experience often is not reported to the relevant customer service / sales force teams, remain unaddressed, and as a result ultimately undermine retention investments.

Insurers can obtain significantly more predictive insights into their customers if they combine their proprietary customer information with external data from 3rd party sources. Through close behavioural and attitudinal analysis of customer data across several personal lines insurers, we have identified four unique customer segments; understanding their respective characteristics can help carriers pre-emptively identify customers at risk of churn, and to specifically target them with relevant retention initiatives. In addition, this segmentation also provides extensive insight into the lifetime value and profitability of the individuals in these segments. All of this can help insurers determine just how much to spend on retaining them.

PWC Shopping Switching Insurance

A) Price Sensitive Segment: These customers frequently shop for better prices, and are most likely to switch when they can obtain a lower price (even if their savings are minimal). They are typically not very profitable for insurers because they switch so frequently. Therefore, insurers who target this segment should base product prices on the true contract value and not build pricing algorithms on longer-term expected customer life-time value.

B) Value Driven Segment: These customers shop frequently to validate that they are paying a reasonable price for their insurance, but switch only if they perceive that they get significantly less value from their current insurer than they would be from an alternative. They generally have a clear sense of the value they receive for the price they pay. However, this segment has shrunk somewhat since the financial crisis, as more of its members have been willing to switch for smaller savings.

C) Inertia Segment: These customers are generally not prone to shop unless they have a compelling reason. This could be the result of a bad claims experience, poor customer service, or an especially compelling offer from another insurer.

D) Company Loyalists: These customers, the most profitable segment, tend to have the longest tenures with their carriers and would prefer not to make any decisions about their coverage once it’s already in place. They stay with insurers even through negative experiences.

Once an insurer segments its customer base into these four categories, rich simulation models can test and predict the potential impact of various retention initiatives without the need to actually launch them. This approach can help insurers gain significant economic value by improving retention, while saving millions in averted marketing efforts.

Rentention Rates at Different Customer Tenures with their Carrier Insurance

The End Result

The effective application of analytics (alongside well-synchronized business functions) to get customers through only a couple of renewal cycles can have a dramatic impact on overall retention across the book of business. Our research reveals that customers who stay through one to two renewal cycles are most likely to do so for the long term. Accordingly, getting them past this point is paramount (figure 2), and the first renewal is the most important time to focus on retention. As a result, the approach to retaining the newest portion of the book of business will have the biggest impact on retention; however, designing additional approaches for various potentially vulnerable stages across the customer lifecycle (figure 3) also can gradually reduce the number of switchers over time.

Figure 3: Switching Curve Over Customer Life Cycle PWC Analysis

A continuous feed of insightful analytics can help insurers identify situations in which customers are likely to shop more actively (rate increase, claims incident, etc.), as well as proactively help influence retention initiatives at these inflection points. For example, the simulation model has shown that, with the effective market implementation of these initiatives, an insurer with a $6 billion personal auto book of business can increase income by $50 million with just a single percentage point improvement in retention.

Therefore, it is highly advisable for insurers to invest in developing shopper/switcher insights for their customer base via data analytics. Doing so will help them identify "at risk" customers early and prevent them from shopping. If insurers track only defection rates, then they could miss out on understanding the variables that ultimately lead to switching.

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