"Getting closer to the customer" is a response we commonly hear when asking insurance executives about their near-term strategic objectives. Because customer expectations are changing, companies need to understand their customers and change the way they interact with them. The ability to understand and appropriately influence customer behavior, notably through the strategic use of behavioral economics and predictive modeling, is becoming a critical competitive differentiator in the insurance industry because it provides new opportunities for insurers to:
• Market products and services more effectively through better targeting;
• Bridge gaps in how they address customer needs;
• Forecast future behavior with behavioral economics and predictive modeling; and,
• Create hyper-segments and new niches.
Analytics that focus on acquisition and retention can help insurers increase market share without sacrificing profitability by managing the return on acquisition and retention spend among and within marketing, distribution and service. According to JD Power and Associates' 2011 Insurance Shopping study, annual retention rates for insurance decreased from 90 percent in 2008-2009 to 87 percent in 2010-2011. This represents $12.8 million worth of premiums in motion. The same study points out that the number of personal auto insurance consumers shopping for deals has steadily increased from a low of 27 percent in 2009 to 33 percent in 2011. Consumers also are more likely to switch insurance companies: 40 percent of shoppers switched carriers in 2011, compared to 33 percent in 2010.
So, how do insurers develop a customer acquisition strategy that factors in retention and helps lead to better ROI on acquisition spend?
For starters, consumers shop for insurance and usually make purchases or renewal decisions based on price and/or value (the value they can see in the product and determination if it makes them feel safe and secure). In order to profitably acquire these consumers and then retain them, insurers must avoid the common pitfall of cutting prices to lure shoppers, only to see them leave when renewal prices increase.
Segmentation has traditionally been at the heart of customer analytics, and is used across the value chain, from helping with designing products to targeting marketing efforts, identifying cross-sell opportunities, matching agent segments with customer segments, and more. Many carriers' segmentation capabilities have matured over the last decade, but those that don't use segmentation to inform their operations are struggling to acquire customers profitably.
In order to solve the acquisition/retention problem, insurers must understand what drives their prospects' and existing customers' behavior, as well as the triggers that prompt them to shop around. They also must distinguish up-front price shoppers from value-driven shoppers during acquisition and (because price shoppers are the most likely to leave with rate increases at renewal) ensure that they do not receive discounted rates. These analytics can provide insights into what each customer segment values and help insurers meet those expectations while also achieving competitive parity/superiority. By going beyond segmentation and leveraging predictive models, among other techniques, to size the issue, quantify the underlying economic trade-offs, and develop a set of optimized customer experience tactics, insurers can significantly increase marketing effectiveness and improve their customer acquisition.
The greatest opportunity for substantially improving ROI comes from combining acquisition and retention analytics. Data-driven segmentation alone isn't enough to "acquire to retain" successfully. Moreover, factoring in additional perspectives through behavioral economics can help insurers target those customers who are least likely to shop/switch over time, even with price increases. Carriers then can redesign their acquisition strategy to support long-term retention by 1) including parameters in the rating plans for acquiring higher tenure and lower elasticity customers, and then by 2) creating scoring algorithms that identify and appropriately price for shoppers up front. For example, analytics can identify certain variables that correlate to switching, including:
• Job and residence stability correlate to less frequent switching.
• Using credit histories to determine how long people have held specific credit cards can be insightful; short times typically indicate shoppers and switchers.
It also helps to understand why people are shopping around in the first place. Insurers can investigate what is prompting people to request a quote or call, and as a result obtain better indicators on their motivations and if they are frequent shoppers. Sales people can be trained to identify kinds of customer, such as:
• Frequent switchers (the price sensitive segment), who tend to be less involved with the sales process and specifically look to drive down price, and
• The value driven segment, which tends to be more focused on benefits and coverages.
A challenge, but one insurers need to meet
While most companies have a foundational data warehouse and single view of the customer, what's missing is the right test-and-learn environment to conduct analysis and build proof of concept models that can simulate retention and shopping experiment results. Measuring results and building feedback loops into the operating model to promote continuous improvement also have proved challenging for most insurers. However, as we describe above, meeting this challenge can lead to significant ROI on customer acquisition and retention spend, increase market share and increase profitability.
About the Authors: Anand Rao, partner with PwC's Insurance practice; Punita Gandhi and Scott Busse are directors within PwC's Insurance practice.