Big Data and Analytics Help Life Insurers Pinpoint Customer Concerns

Using Big Data to improve analytics can provide life insurers with broader understanding of their customers and prospects, as well as insight into the profitability of individual customers, specific products and producer segments.

"Know your customer" is one of the oldest maxims in marketing. However, the rapid development of data analytics — accompanied by new technologies for dealing with what has come to be known as "Big Data" — have given the phrase new meaning for life insurers. As insurers seek to grow profitably in an environment characterized by less-than-optimal investment opportunities, new competitors and rising customer expectations, analytics offer insurers the chance not only to learn more about their customers (and the producers who serve them) but to identify the factors that lead to profitable, multi-level relationships.

Using Big Data to improve analytics can provide life insurers with broader understanding of their customers and prospects, as well as insight into the profitability of individual customers, specific products and producer segments. Life insurers, in particular, can benefit from the ability to analyze huge amounts of data from both structured and unstructured sources. These carriers can then concentrate their efforts upon successful initiatives, improving the effectiveness and lowering the cost of the overall program. In particular, analytics derived from Big Data and from traditional sources can help insurers in these four areas:

1. Actively identifying and targeting underpenetrated markets. Life insurers can use analytics to help identify gaps in the customer base caused by changing demographics and cultural shifts. Female heads of households, for example, represent a potentially rewarding area for exploration. Analytics can help pinpoint such households and can also help in designing potentially appealing products and features for such groups.

2. Micro-segmentation and better penetration of the cultural, diversity and middle markets. While this is closely related to the identification of underpenetrated markets, micro-segmentation uses insights into the preferences and behaviors of clearly defined market segments (Hispanic families, for example, or small business owners) to develop appropriate products and distribution strategies.

3. Identifying retention risks and cross-selling opportunities. Data analytics can help life insurers know when customers have reached a level of frustration that may cause them to switch providers. Similarly, analytics can identify opportunities not only to preserve but to expand relationships — for example, by highlighting customers nearing retirement age who might benefit from private pension or annuity products.

4. Moving from a transaction model to a true relationship. Analytics can guide the customer contact team toward a "lifetime" relationship with the customer. Rather than beginning and ending with one transaction, analytics can help insurers serve customers at multiple points in the relationship including starting a family, paying for higher education and planning for retirement.

Big Data-related technologies that enable life insurers to draw upon structured data – such as information on existing customers – and marry it with unstructured external information such as social media feeds and zip code demographics can make it easier to concentrate efforts on high-potential geographic areas. This can help producers in their search for customers who not only need insurance products but who have the ability to pay for them.

With information supplied by analytics, life insurers put themselves in a better position to deliver tailored, relevant experiences to their customers. Companies that collect, organize and analyze this information properly – and then route it to the right producer – can gain tremendous leverage in the critical first contacts with the customer prospect.

[For more of Brian DeMaster's industry analysis, see Retirement Income Market is the Growth Engine of the Future for Life Insurers.]

Life insurers also need to be aware of the importance of continuity in dealing with customers, especially when communications span multiple channels. No matter what the channel, customers need to be able to pick up where they left off – and their experience with the insurer should be consistent in terms of brand and quality whether they are working through a call center, a company website or with an agent.

Finally, life insurers need to catch up with retailers, airlines and other service providers in rewarding client loyalty. It costs much more to acquire a new customer than it does to keep an existing one, but life insurers have been slow to introduce client loyalty programs. Effective use of analytics can help design a system of incentives that reward loyalty and encourage adaptation of other products offered by the insurer.

As we have seen, the challenges facing life insurers are significant, but insurers do have options and resources available to them. We believe that successful life insurers – using analytics and Big Data solutions as a foundation – will speed up product development, tap into new and underserved markets, and ultimately make the purchase of life and retirement products a richer and more individualized experience for the customer.

About the Author: Brian DeMaster is a senior executive in Accenture's insurance practice.