Tag Archives: predictive model

New Channels, New Data for Innovation

Distribution channels may be the most tangible part of most consumers’ experiences with insurance. While the details of the product are obviously important, once the policy is purchased, most people file it away and forget about it. Many consumers couldn’t find their policies if you asked them. And how many consumers do you think have actually read their policy?

In today’s digital world, an insurer’s success depends more on how customers interact with insurance than on the product itself. Increasingly, consumers’ expectations are being set by the Amazons, Apples and Googles of the world than by similar insurers. Insurance has the unfortunate distinction of dealing in a product that most consumers only own because they have to, not because they want to. So, insurers start perceptively behind on the product side compared with Apple, Google and Amazon. People use/shop/buy from these places because they LOVE to, not because they must. The experiences that insurers deliver through their channels are no match for digital retail giants. At least, not yet.

What can insurers do?

As insurers, we can copy pages from the Google playbook and get better at using data and analytics to improve our distribution channels – the experiences we deliver and their effectiveness. Majesco’s recent research report, A Path to Insurance Distribution Leadership: New Channels and New Data for Innovative Outcomes, provides some insights, drawing on the first-hand experiences of CIOs who shared their thoughts at a roundtable discussion this past June.

On the consumer side…

Insurers can use data and analytics to segment customers and develop the right products for their needs and, crucially, offer these products through the channels that best meet the preferences and needs of each segment. Predictive models can be used to further the precision with which to target prospects and customers for new purchases, cross-selling or increasing the stickiness of relationships. By tracking customers’ paths across channels and collecting the data they’ve provided and consumed, insurers can ensure that consumers have a seamless, connected experience, no matter what path they take.

On the insurer side…

Insurers have a wealth of data! They just need to use it like Google! Insurers have details on sales, retention, costs and profitability that they can track down to the channel and individual producer level. While most companies have always used this data to track performance, they can go even further and get additional insights on their producers by applying the same techniques we just discussed for customer data – namely segmentation and predictive modeling. Segmentation allows insurers to more efficiently apply training/development resources and match producers to markets/customer segments that best fit their potential. Predictive models can be used throughout the producer lifecycle to forecast performance and future success of individual producers as well as to anticipate future commission and incentive costs. Analytics can also be used to steer prospects and customers to the sales and service channels that optimize business outcomes like new business, retention or lifetime value.

While the benefits of using data and analytics in insurance distribution are obvious and compelling, it is easier said than done. There are at least three components that must be solidly in place for any effort to have a chance to succeed. Companies should first identify their top priorities and opportunity areas and use these to define an overall data and analytics strategy. After the strategy is secured, the focus can turn to the acquisition of internal and external data that will be needed to fuel the analytics and modeling identified in the strategy. A distribution management system can be a key enabler here, by providing rich, granular data on channel and producer performance. At the same time, a sound data governance strategy must be put in place to ensure the quality, integrity and comprehensiveness of the data.

A final important consideration is how the analytics will be operationalized. Again, a distribution management system can play a key role here by being configured to gather and track the needed data and execute business rules created through analytics and models built by using the data.

The insurance industry may currently lag behind the Apples, Googles and Amazons of the world in both product engagement and distribution experience and effectiveness. Insurance, however, has an enviable amount of data, talent and technology at its disposal. Leading companies in our industry are leveraging these assets and may very likely be the next ones pointed to with admiration by consumers and other industries for their excellence in distribution.

The 3 Ways to Customer Retention

While life insurance used to be one of many Americans’ most important financial assets, a host of changes—economic, social and cultural—have caused it to become a lower priority. Customers’ top two reasons: that life insurance is too expensive, and that they have other financial priorities.

Given the difficulty of acquiring new customers, it is imperative for carriers to focus on retaining existing ones. In fact, small increases in retention can translate to large revenue growth, and the payoff can be substantial.

Reaping the benefits of a thoughtful customer retention program requires a long-term vision. Carriers should consider the potential lifetime value of a customer (and the products he is likely to buy) that will allow a carrier to increase profitability—today and in the future.

LexisNexis recommends three steps on the road to an effective customer retention program:

  • Acquire customers with retention in mind
  • Develop a customer-focused communications agenda
  • Understand the customer experience
  1. Acquire customers with retention in mind

Effective customer retention begins with targeted acquisition. Carriers must understand their own capabilities, risk appetite and services and acquire customers that they can serve well. The better a customer aligns with a carrier’s profile and preferred market spaces, the greater the likelihood she will stay.

Segmentation and predictive models are key. Solutions available in the market include:

  • Risk classification models to help carriers optimize leads and identify the most profitable prospects.
  • Lookalike models to help carriers understand the characteristics of their best customers and attract similar prospects.
  • Lifetime value models to identify the potential long-term return of a prospect—enabling a carrier to identify prospects with the greatest future potential for growth and loyalty.
  • Prospect persistency to help predict whether a prospect will lapse within a given time.

In short, successful retention efforts begin well before a customer is acquired.

  1. Develop a customer-focused communications agenda

Having done the legwork to acquire a suitable customer, carriers should ensure they have a strategy for strengthening the relationship. Each customer touch point is an opportunity to do so, and these touch points should be outlined in a customer-focused agenda and communication plan.

The customer agenda defines customer touch points, such as:

  • Onboarding process. The onboarding process can set the tone for the carrier-customer relationship. For example, customers might receive a welcome note with contact information in case of questions; where to learn more about protecting their life, health and other assets; how to set up a holistic financial protection plan; and more. Carriers can tailor these communications for individuals and reinforce the company’s brand, nurturing a conversation from the very start. These communications are usually separate from a carrier’s requirement to deliver legal policy documents, but this is not to say that the delivery of legally required documents has to be stiff or un-tailored. Every step of the process is an opportunity to nurture.
  • Annual reviews. Many customers are either unaware of or confused about coverage options, so annual reviews are an ideal opportunity for the carrier to stay in touch with each customer and offer risk management advice. Annual reviews also help position the carrier as an adviser, not just a service provider. In addition, carrier support for annual reviews can help a sales team stay on top of its customers’ life changes—while also positioning each salesperson as a reliable and trusted adviser.
  • Cross-selling opportunities. Based on their understanding of each customer, carriers can identify opportunities to cross-sell additional products, such as an annuity or supplemental health product. Carriers should also consider cross- or multi-product purchases within a household—for example, for an insured’s spouse, child or parent.
  • Payment reminders and opportunities for automatic payments. Payment and premium reminder notices can trigger customers to lapse or switch providers, so managing these communications is critical to retaining customers. In addition, automatic payments can make paying life insurance premiums effortless for customers, minimizing the chance that they will lapse.

Carriers should also ensure that they maintain continuity across all channels, synchronizing their market messages across all digital and traditional communications channels including websites, print and radio ads, social media, email and direct mail.

Traditionally, carriers have minimized communications with their customers, believing that reminders about life insurance are a reminder of that customer’s mortality as well as a budgetary expense. As such, retention strategies were more focused on conserving customers who had already decided to cancel their policies, typically by offering less coverage and lower premiums.

  1. Understand the customer experience

The customer agenda outlines when and how a carrier will communicate with its customers but does not address an individual customer’s unique needs. To better understand their customers and identify these needs, carriers should supplement their internal data with external data sources and predictive models. This is one area where the life industry has much experience and has often excelled, but carriers have not been consistent in their pursuit of data for deeper customer insights. Exacerbating the issue, new sales have been harder to win, prompting carriers to focus heavily on acquisition—to the detriment of understanding current customers’ needs.

The Internet and social media channels have changed the way that customers make purchases—and insurance is no exception. Rather than turn to a carrier or agent for advice, many customers now begin with online research. This research may include the carrier’s website, as well as comparison sites and online reviews. Increasingly, it also includes social media, which allows positive and negative experiences to be reported and shared. In general, these channels limit a carrier’s control over its brand and the customer experience.

To better understand each customer’s individual needs, and how he experiences a relationship with the carrier and agent, carriers can work with a data partner to:

  • Tap into third-party data sources to gain insight on a customer’s life changes. External data can help carriers identify customers whose insurance needs might change: For example, people often reevaluate their finances when they move or purchase a new home. Armed with up-to-date mover and homeowner information, carriers and agents can contact customers and advise them on ways to mitigate risk.
  • Verify whether an insured has appropriate coverage. Customers may experience life changes and not think to update their life insurance provider. Working with a data partner, carriers can obtain up-to-date, accurate and validated wealth and asset information—to be certain each insured has appropriate coverage and affordable premiums for their means, and to offer alternatives if otherwise.
  • Use models to determine the risk of a customer leaving. Market solutions are available that can help carriers predict the risk of a client leaving, so that carriers can take action before she leaves.

With data, analytics and predictive models, carriers can identify customers with changing insurance needs and life events and respond appropriately. An effective response will address a customer’s specific needs—and, in an ideal situation, will deliver a tailored message at the right time. In addition, market solutions can enable carriers to establish event alerts that deliver automatic messages at the right time. For example, a carrier could establish an automated event notification when customers apply for a new mortgage. An automated process could send each customer a note outlining tips for buying a home, while reinforcing the value of the life insurance the customer already holds, in helping to protect the home for the family. The communication would also remind customers to update their life insurance policy within a suggested time of a new home purchase to ensure they have adequate coverage.

Automation ensures that messages are delivered efficiently, effectively and through the appropriate channel. It can also support a more cooperative carrier-agent relationship, as carriers can direct customer retention efforts while still empowering agents to connect with customers. In addition, automation better assures carriers that they are providing a consistent experience. Following each automated message, the carrier or agent should follow up with the customer to reinforce the 1:1 messaging and strengthen the relationship.

In a continued low-interest rate environment, customer retention must be a priority for a carrier to thrive. Customer acquisition encompasses a host of carrier activities, from advertising and marketing, to on-boarding, underwriting and policy issue. In life insurance, it can take seven to eight years to recoup the acquisition costs for one customer. Bain has estimated that it is six to seven times more costly to acquire a new customer than to retain an existing one.

Predictive Analytics And Underwriting In Workers' Compensation

Insurance executives are grappling with increasing competition, declining return on equity, average combined ratios sitting at 115 percent and rising claims costs. According to a recent report from Moody’s, achieving profitability in workers’ compensation insurance will continue to be a challenge due to low interest rates and the decline in manufacturing and construction employment, which makes up 40% of workers’ comp premium.

Insurers are also facing significant changes to how they run underwriting. The industry is affected more than most by the aging baby boomer population. In the last 10 years, the number of insurance workers 55 or older has increased by 74 percent, compared to the 45 percent increase for the overall workforce. With 20 percent of the underwriter workforce nearing retirement, McKinsey noted in a May 2010 Report that we will need 25,000 new underwriters by 2014. Where will the new underwriters come from? And more importantly, what will be the impact on underwriting accuracy?

Furthermore, there’s no question that technology has fundamentally changed the pace of business. Consider the example of FirstComp reported by The Motley Fool in May 2011. FirstComp created an online interface for agents to request workers’ compensation quotes. What they found was remarkable. When they provided a quote within one minute of the agent’s request, they booked that policy 52% of the time. However, their success percentage declined with each passing hour that they waited. In fact, if FirstComp waited a full 24 hours to respond, their close rate plummeted to 30 percent. In October 2012, Zurich North America was nominated for the Novarica Research Council Impact Award for reducing the time it takes to quote policies. In one example, Zurich cut the time it took to quote a 110-vehicle fleet from 8 hours to 15 minutes.

In order to improve their companies’ performance and meet response time expectations from agents, underwriters need advanced tools and methodologies that provide access to information in real-time. More data is available to underwriters, but they need a way to synthesize “big data” to make accurate decisions more quickly. When you combine the impending workforce turnover with the need to produce quotes within minutes, workers’ comp carriers are increasingly turning toward the use of advanced data and predictive analytics.

Added to these new industry dynamics is the reality that both workers’ compensation and homeowners are highly unprofitable for carriers. According to Insurance Information Institute’s 2012 Workers’ Compensation Critical Issues and Outlook Report, profitable underwriting was the norm prior to the 1980s. Workers’ comp has not consistently made an underwriting profit for the last few decades for several reasons including increasing medical costs, high unemployment and soft market pressures.

What Is Predictive Analytics?
Predictive analytics uses statistical and analytical techniques to develop predictive models that enable accurate predictions about future outcomes. Predictive models can take various forms, with most models generating a score that indicates the likelihood a given future scenario will occur. For instance, a predictive model can identify the probability that a policy will have a claim. Predictive analytics enables powerful, and sometimes counterintuitive, relationships among data variables to emerge that otherwise may not be readily apparent, thus improving a carrier’s ability to predict the future outcome of a policy.

Predictive modeling has also led to the advent of robust workers’ compensation “industry risk models” — models built on contributory databases of carrier data that perform very well across multiple carrier book profiles.

There are several best practices that enable carriers to benefit from predictive analytics. Large datasets are required to build accurate predictive models and to avoid selection bias, and most carriers need to leverage third party data and analytical resources. Predictive models allow carriers to make data-driven decisions consistently across their underwriting staff, and use evidenced-based decision making rather than relying solely on heuristics or human judgment to assess risk.

Finally, incorporating predictive analytics requires an evolution in terms of people, process, and technology, and thus executive level support is important to facilitate adoption internally. Carriers who fully adopt predictive analytics are more competitive in gaining profitable market share and avoiding adverse selection.

Is Your Organization Ready For Predictive Analytics?
As with any new initiative, how predictive analytics is implemented will determine its success. Evidence-based decision-making provides consistency and improved accuracy in selecting and pricing risk in workers’ compensation. Recently, Dowling & Partners Securities, LLC, released a special report on predictive analytics and said that the “use of predictive modeling is still in many cases a competitive advantage for insurers that use it, but it is beginning to be a disadvantage for those that don’t.” The question for many insurance executives remains: Is this right for my organization and what do we need to do use analytics successfully?

There are a few important criteria and best practices to consider when implementing predictive analytics to help drive underwriting profitability.

  • Define your organization’s distinct capability as it relates to implementing predictive analytics within underwriting.
  • Secure senior management commitment and passion for becoming an analytic competitor, and keep that level of commitment for the long term. It will be a trial and error process, especially in the beginning.
  • Dream big. Organizations that find the greatest success with analytics have big, important goals tied to core metrics for the performance of their business.