The Analytical Life Insurer Profitable Analytic Strategies for Life and Annuity Carriers WHITE PAPER
SAS White Paper Table of Contents Executive Summary....1 Introduction....1 Distribution and Producer Analytics....2 Producer Segmentation....2 Territory Definition and Route Optimization....3 Lead Generation....3 Maximize Wallet Share: Cross-Selling....3 Optimize Marketing Offers....4 Compliance and Suitability...4 Profitability and Lifetime Value....5 Customer Analytics....6 Social Media Analytics....6 Customer Interaction Management....6 Product Lifecycle Management....7 Product Underwriting....7 Product Development...7 Product Profitability....8 Product Lifecycle Management....8 Operational Analytics....8 Call Routing....8 Predictive Alerts and Triggering Events....9 Conclusion....9
The Analytical Life Insurer Executive Summary In the insurance industry, the companies that focus on product lines typically under the diversified insurance umbrella (such as group and individual life, annuities, retirement plans, financial services and supplemental health products) have been slow to adopt predictive analytics within their organizations. Other industries, including property casualty insurers, continually demonstrate success in using analytics to grow their businesses more profitably and increase revenue while managing risk. Life insurance executives are beginning to recognize the need to evaluate analytics as a way to innovate, differentiate and improve their organizations. This paper will uncover business strategies enabled by analytics, and provide examples of analytic innovation that insurers can introduce in their business processes. Introduction The business drivers for considering an analytic strategy within the industry are compelling: Due to continuing low interest rates which lead to a decline in investment yields, the uncertainty of the regulatory environment, the commoditization of products, competitive industry pricing and a soft economy, insurers are feeling increased pressure on profitability. In addition, insurers are aggressively competing for awareness and wallet share among distributors, producers and customers. It s critical to identify ways to optimize their field organizations; find the most profitable agents, brokers and customers; optimize workflow while decreasing costs; and balance risk and regulatory demands. The analytic strategies that life insurers can employ are divided into four representative categories (figure 1): Figure 1: Analytical Life Insurer 1
SAS White Paper Distribution and Producer Analytics: Influencing distribution and producer behavior through loyalty and lifetime value indicators. Customer Analytics: Finding and understanding the needs of your target customers and providing them with the information they need when they want it, the way they want it. Product Management: Supplementing traditional actuarial and underwriting methodologies with predictive analytic capabilities in the product development life cycle. Operations: Identifying ways to optimize the workforce while creating a better customer experience. Distribution and Producer Analytics Many diversified insurers rely on negotiated selling agreements with broker-dealer firms and banks to sell their product lineups. In fact, roughly 92 percent of life and protection products are sold through third-party distribution arrangements. How are the insurers making sure that they re aligning their wholesaling field organizations to the right opportunities? Many insurers still see the business of wholesaling as an art, not a science, but a few innovators are introducing quantitative methods into qualitative sales practices. Insurers can apply these analytic initiatives at all levels of the distribution chain, from the external and internal wholesaling organizations to the producers. Analytic strategies include: Reducing wholesaler turnover by providing wholesalers with more qualified leads and sales. Defining sales territories based on the predicted opportunity within a geographic area. Reducing product suitability risk by positioning the right product with the right consumer. Bringing more profitable producers to the insurers. Ensuring the optimization of marketing dollars. Gaining producers loyalty by proactively helping them understand the demographics and opportunities in their potential and existing customer base. Producer Segmentation In a representative scenario, while insurers have employed basic segmentation techniques, these segments are not predictive. Some wholesaling organizations group producers into simple classifications and align sales activities with higher-value segments. The problem with this segmentation strategy is their rearview-mirror definition of value. One insurer had a campaign for producers that hadn t sold any business in the prior six months. Using predictive modeling, the organization was able to create a model that would predict which producers would stop selling before they did stop. It implemented a number of marketing campaigns targeted to the at-risk group of producers, resulting in a multimillion-dollar 2
The Analytical Life Insurer lift in sales over the course of the campaign. By implementing different campaigns, it also could determine the most effective combination of contact points to keep producers on the books. Additionally, with a segmented approach, marketing dollars are more effectively spent, so the company moved from blast to focused marketing. The campaign generated a significant lift in sales for the target group, not only increasing the bottom line by millions over a three-month period, but saving and retaining formerly at-risk top producers. Territory Definition and Route Optimization The field organization needs to be in the right place to take advantage of opportunity. Throwing a representative into a territory based purely on population is ineffective. Using demographic data and identifying target markets enables the organization to profile the population, develop products to meet specific markets, and ensure that the sales force aligns with those markets. By combining geospatial capabilities with analytics, one insurance wholesaling organization began to assist wholesalers by optimizing their routes so they could increase face-to-face coverage with their producer network. The routes could be defined by predictive segments, to ensure that wholesalers were spending time with the producers that would bring the most value to the insurer. Lead Generation The most common use of predictive analytics for captive distribution models is to provide leads to the producers. Insurers with this capability are combining their internal data with third-party database marketing solutions to mine their own books of business for opportunities. Mining Diamonds: AXA Equitable changed its business strategy from offering primarily life insurance and annuities to concentrating on financial advisory services and a broader array of products. Executives wanted to make sure that the focus remained on the customers, as product offerings and services expanded broadly. One of the biggest challenges in the field is having thousands of clients and achieving a level of data segmentation that allows us to understand our customers better; that allows us to stand back and analyze the decisions consumers in various demographics are making and the trends that exist. SAS allows us to do that type of analysis. Maximize Wallet Share: Cross-Selling Most insurers sell more than one product line, and as the producers increase the breadth of products they sell from a provider, they become stickier : They sell more, they have higher account values, and they have a longer tenure with the provider. Analysis at one insurer revealed that a producer s sales double for each additional product line sold. Max New York Life is finding success in customer cross-sales: 3
SAS White Paper Sales cycles to existing customers are faster, noted Nagaiyan Karthikeyan, PhD, Head of Business Intelligence and Analytics at Max New York Life, an Indian insurer, and the average premium amount is often 30 percent to 40 percent higher. Plus, we ve found that the retention probability for a customer goes up 300 percent to 400 percent once they make a second purchase with us. We re able to target our customer segments much more logically and granularly. We ve identified about 25 separate cells, and we see their demographics and previous transaction behaviors. That lets us tailor specific cross-sell offers and script different contact scenarios based on their value, their propensity to buy, their propensity to pay, and their propensity to lapse In the first quarter after implementing SAS, sales to existing customers jumped to more than 20 percent, Karthikeyan said. Optimize Marketing Offers Although the direct-to-consumer distribution model represents only a small fraction of industry sales, these insurers have the ability to fully realize the advantage of marketing campaign optimization, as illustrated by Transamerica Life and Protection (TLP). TLP is one of the largest direct response marketers of life, credit, supplemental health, specialty insurance and fee-based products. Considered a leader in data-driven marketing and strategic segmentation, the company uses diverse marketing methods, such as direct mail, inserts, Internet, point of sale, print, television and telemarketing. In 2009, we had more than 95 million direct marketing solicitations and 14.4 million telemarketing contacts, said Angela Williams, Director of Marketing Sciences. So you can see we would need a large and sophisticated solution to manage the offers we are making. With marketing optimization, we created a decision engine that had a single decision point. We could take all of our programs and pull them into a single campaign execution process to select names, apply contact rules, manage models, and select the best product among an expanded marketing universe. Marketing investment decisions are made in a holistic context. According to Robbie Reynolds, Manager of Marketing Sciences, You re always balancing revenue with the need for profit. Some marketing organizations believe there should be no risk; everything should be profitable. Other groups are willing to look at something that is not apparently profitable and consider the potential. With optimization, our marketing sciences team pulls all the pieces together and finds the best possible, data-driven solution within all of those dynamics. Compliance and Suitability Negative press continues to plague the insurance and financial services industry, and several recent high-profile cases of unsuitable sales to customers have surfaced in the 4
The Analytical Life Insurer past five years. Regulatory agencies have aggressively moved to protect consumers from poor or questionable sales practices at the distribution and insurer levels. An insurer s compliance department analyzes trends within the suitability guidelines provided by the regulatory bodies, establishing the appropriate baseline and monitoring activity relative to that benchmark. Predictive modeling and forecasting identify trends. Producer behavior models anticipate certain types of activity and can uncover suspicious activity. The resulting marketing and service campaigns are intended to influence or reinforce positive behavior. Information is shared by the insurer, brokerdealers, and regulators, making compliance pervasive as a business activity throughout the distribution network. Profitability and Lifetime Value The foundation for broader analytic strategies supporting product development and pricing, customer service, and risk management is customer lifetime value (CLV). Simply put, CLV is a metric that represents the customer s predicted future revenue minus costs, and is used to estimate future profitability. CLV can be calculated on an aggregate (segment) or individual basis and can include internal data sources like transaction and interaction histories, acquisition and other activity-based costs, as well as external marketing, econometric and psychometric data sources used to approximate the behavior and preferences of a prospect. One insurer using lifetime value scoring changed the evaluation criteria for its direct mail marketing campaigns. Where it used to measure success based on the number of responses and the close rates, it s moved to a model where it can differentiate based not only on the number of sales, but the quality of those sales. Going even further, it expanded lifetime value metrics to drive not only the placement of its captive agency sales force, but how those agents were compensated. Compensation now takes into account the quality of the customer relationships that the agents are bringing to the insurer and rewarding them for it. Customer lifetime value metrics help the insurer understand which producers and customers are driving greater value than others. When it identifies the differences between profitable segments, it can create programs to increase those success factors. This particular insurer increased return on investment for marketing campaigns by 14 percent and saw significant enhancements in agent performance. Even in the absence of a true CLV metric, even a simple profitability metric can drive results: A regional Canadian insurer created a segmentation strategy for its broker network that identified profitability metrics for each broker firm. By looking at the acquisition costs and quality of the book of business that the brokers were bringing to it, the insurer created treatment strategies for each segment. The insurer made additional marketing investments in the bestperforming firms, and some low-performing firms were dropped from its network. By focusing on top-performing brokers, it increased producer loyalty and per-policy profitability. 5
SAS White Paper Customer Analytics Insurance companies must meet the demands of consumers in tech-savvy younger generations as well as manage the needs and requirements of the large global population approaching retirement age. Demographic and ethnographic shifts affect these organizations internal workforce as well as distribution partners and consumers, pressuring insurers to effectively create and manage services that they can deliver at multiple levels, through multiple channels. Pervasive consumer technologies are driving change throughout the insurance industry. Social Media Analytics The insurance research consultancy firm Strategy Meets Action (SMA) performed a study in 2011 on social media in insurance. It found that more than 80 percent of insurers surveyed were studying and monitoring social media, and 40 percent had active social media strategies. SMA identified four target areas of opportunity, including: Finding market opportunity through identifying new target audiences, or inputs into product development. Executing on opportunity through campaign management to target new prospects. Brand management. Using social media data in other non-marketing activities, such as underwriting, customer service, risk management and fraud. Customer Interaction Management Pervasive consumer technologies change the way that people interact with each other. The ability to communicate instantly and share information globally is forcing companies to evolve the ways that they interact with and support their consumers and distribution partners. Insurers must support multiple delivery channels and offer a consistent experience across those channels. The challenge lies in creating the right balance between channels, from high-touch to low-touch. Consumers expect the companies they work with to understand their preferences and offer them relevant messages at the right time, requiring a deep understanding of complex customer behaviors and market segments. The growth in customer interaction channels mobile, Web, phone, mail, face-toface necessitates a careful choreography of interaction points. Some insurer industry innovators are using the mobile and Web channels to reach out to untapped market segments. AXA Equitable launched a life insurance game app for Apple s ipad and iphone mobile devices called Pass It On! The app is designed to educate consumers on the benefits of their indexed universal life products at different life stages through an interactive game (prospective customers are offered a chance to win the Pass It On! sweepstakes). Mobile applications are providing insurers with new interaction channels to engage with and educate potential customers. 6
The Analytical Life Insurer Another insurer undertook a strategic initiative to better serve its financial advisor and client networks, including service and channel segmentation to better find and grow new investors that may not have been served in the past. In order to gain the capacity needed to implement this strategy, it employed its call center to help achieve this goal. Real-time customer profiling and interaction management at the time of contact became a cornerstone of its strategy. Other initiatives included: Trigger-based outbound campaigns to producers: The insurer started with direct mail/email campaigns and then added inbound calls to take certain actions based on clients profiles. The logic was applied in the automated call system and call center desktops to provide the best interaction for the customer. Offering a combination of sale or service contact points based on the client s needs: This allowed financial advisors to focus on high-net-worth customers while allowing the call center to serve emerging investors. Product Lifecycle Management With the increased commoditization of life and protection product lines coupled with an extremely competitive market, insurers are identifying innovative ways to use analytics outside of the traditional actuarial pricing or underwriting methodologies in the product development life cycle. Product Underwriting The average time from the start of the underwriting process to policy issuance for a mid- to high-value life insurance policy is 72 days. The sales cycle is so long that many advisors don t want to deal with the complexity of the sale, and potential customers don t want the hassle of the medical reviews. With life insurance sales at a 50-year low, insurers are rushing to find ways to make the products easier to buy and sell. One of the most innovative ways that insurers are changing the underwriting process is by creating models to predict whether further medical tests are needed. The underwriters use the models as a guideline for ordering additional tests. If no tests are needed, the policy can be quickly issued. The models are developed using third-party consumer data sources to identify risky attributes. Product Development Insurers are increasingly recognizing the need to differentiate their product development approach, particularly in group insurance products. One insurer just finished the design of a new retirement product that incorporated more than 500 rating variables. In addition to new product features and compensation structure, the granularity of the rating structure allows for personalized individual plan pricing. This personalized approach makes the product a more attractive offering for both producers and plan sponsors. 7
SAS White Paper Product Profitability Other insurers are actively implementing analytic models to determine product profitability that gives a signal in advance whether adjustments should be made to product lines. Canadian insurer The Standard Life Assurance Company opted for SAS Activity-Based Management, an analytic application that models business processes to determine cost, profitability and the drivers that help organizations make informed decisions that streamline operations, deliver revenue growth and reduce costs across the organization. SAS Activity- Based Management software gives us a sense of which products we should focus on and which ones we should focus less on, says Eric Campbell, Manager of Financial Strategy and Planning, Further, we can calculate a product s efficiency and improvement over time. The solution gives insight not only into which product is most or least profitable, but also where more investment may be required over time, and is considered crucial to doing business. Product Lifecycle Management Recognizing significant opportunities within their existing blocks of business, insurers are tapping their current customers to generate incremental sales and increase their retention. One life insurer with an older book of life insurance business created a new product offering to move existing policyholders into newer, more profitable product lines. It also mined the book to identify opportunities in its distribution, including identifying attributes of top life insurance producers and influencing the behavior of emerging producers. Operational Analytics Operational analytics covers a broad spectrum of initiatives within the diversified life insurance industry, and can cover areas such as workforce and workload optimization, call center analytics and fraud detection. The following are three real-world examples of operational analytics in the life insurance industry. Call Routing The call center is the first line of defense in retention efforts. As a system for effectively matching an incoming call with the appropriate CSR, skills- and trigger-based routing have gained in popularity over the last decade. In one insurer s customer retention program, a predictive model was built to identify at-risk policyholders. If the policyholder called the call center and was deemed to be at high risk, he or she would be automatically routed to a retention specialist. 8
The Analytical Life Insurer Predictive Alerts and Triggering Events Based on a predictive model developed for customer retention efforts, one insurer identified Web-based customer transactions as a predictive triggering event, and used the event to automatically generate a follow-up interaction with the client when the event was triggered. This same insurer also allowed brokers to sign up for alerts that would let them know when clients executed a particular transaction on their own behalf. A broker who did not believe the transaction to be in the client s best interest would have an opportunity to influence the policyholder. Conclusion Insurance companies operating in their status quo business model will be at a competitive disadvantage in the coming years. Increasingly, insurers must provide unique products and services tailored to meet the diverse needs of their producer and consumer communities. To do so, they need a deep understanding of complex customer behaviors, market segments and product life cycles. The ability to capitalize on these new market opportunities will depend on an insurer s adeptness at identifying new customers and producers and retaining profitable ones, providing the right products at the right time, and giving customers top-level service through multiple delivery channels. Analytics represents the next frontier for this industry, and even with the challenges posed in shifting from a sales culture to an analytic culture, the industry is well-positioned to start down this path. 9
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