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Insurance Meaning-Making: More than Just Analytics (Part 2)


The very size of big data can overshadow – and complicate – what's really important: meaning-making. Here’s how insurers can begin deriving key insights from big data.

Insurance is about foreseeing the future, weighing risks and betting on probabilities when underwriting policies. The same is true of analytics, which enables companies to convert big data into credible insights and foresights. The ever-growing, never-ending avalanche of data is of no use unless companies can derive epiphanies that inform revenue growth and cost reduction strategies. That's where meaning-making comes in. 

For instance, all insurable assets, including auto, home, life and the environment, have the potential to generate rich Code Halos (i.e., the revealing digital exhaust that surrounds people, places, organizations and things) to help insurers make decisions, create value and fine-tune their messages. 

But only half of insurers are using company-wide analytics, according to our recent phone survey of 100 senior-level executives. As a result, an obvious competitive advantage will emerge for insurers in the next two years that get analytics “right”– while others struggle with gut decisions.

Currently, according to our survey, an estimated 50% of insurers forgo company-wide analytics (see Figure 1). 

Figure 1

While insurers voice legitimate concerns about analytics, these challenges cannot be show-stoppers. Data growth is not only inevitable; it is also essential for creating, personalizing and improving the customer experience.  Insurers can decode and apply meaning from the metadata contained in personal and organizational Code Halos to offer specialized services across customer touchpoints, from risk management and marketing, to customer engagement and claims settlement. For example, insurers could grant policyholders the opportunity to create and modify their policy without ever speaking to a representative. 

Moreover, in our experience, failure to provide strong analytics support carries the risk of serious process degradation and reduced efficiency. This was reinforced by one of our survey respondents: “I think most of us are using analytics to evaluate underlying factors that we didn’t see before. These could be around underwriting, claims and managing losses.”

How to Act Now

Based on our conversations with insurance leaders and more than a decade of experience with analytics and meaning-making, here are our recommendations for moving toward a meaning-making capability: 

Go digital – your customer lives there. 

From mobile devices and social media, to policy lookups and online interactions, organizations can collect more data about processes, people and things than ever before. Winning companies can make meaning of this data through the use of analytics to gain a rich understanding of customers. Further, by using digital approaches to engagement (i.e., self-help portals and app-based policy administration), insurers can improve the customer experience, along with their bottom lines, specifically in areas such as policy administration, underwriting, distribution, billing and receipts, and claims management. 

An example is insurers offering camera-enabled mobile apps that let customers file a claim from the scene of the accident, eliminating the need for an appraiser. Taking mobility one step further, insurers are piloting voice-activated software to expedite claims processing and reduce the cost of serving customers. 

With analytics, insurers can better understand social and mobile behaviors, improve service, and update products and pricing. This is especially important during “moments of truth” when buying decisions are being made.

Conquer big data with meaning-making.

The amount of data that is collected from internal sources – whether it’s related to claims, sales, prospects or agents – can be daunting, especially when factoring in external and often unstructured data such as market research, mapping info or social media. However, integrating and analyzing these data sources will help improve underwriting outcomes and create efficiencies for the consumer, agent and insurer. 

Once the data is identified, integrated and mined, insurers can begin to create predictive models. Based on these models, consumers can underwrite a policy themselves, self-input information to see if their claims will be approved, or determine a hierarchy of risks to reduce their insurance premiums. This will provide them with hands-on control of their policies, claims and payments while reducing the insurer’s operational costs. 

A key goal of predictive analytics and meaning-making is to help guide and support risk managers’ decisions, whether through “pre-claim” or “post-claim” loss-prevention methods. Predictive analytics can help insurers hunt through gigabytes of data that hold clues to customer risk levels and purchasing behaviors.

Add customers and reduce churn with personalized policies.

Insurers are moving toward providing omnichannel service delivery across channels that takes into account customers’ life stages, circumstances and problem-solving capabilities. Formulating personalized policies requires insurers to collect personal data from customers, which can only happen when a trusted relationship is formed. To earn customers’ trust, insurers need to show positive intent and maximum transparency at each stage of the experience. Customers need to understand how access to their data will ensure more accurate premiums and coverage, and they need a way to easily view real-time claim status and be alerted to any actions they need to make or information they need to provide. 

This type of personalization and transparency is especially important to millennial customers, who expect the companies they engage with to sense their needs and automatically generate next-best actions, without requiring phone calls or paper-based document exchanges. 

As one survey respondent said,  “With transparently-priced policies designed for different types of customers, such as those with a new baby, a new house, or retirement planning, consumers can simply select a package designed to meet their needs and keep the option to customize their situational policy by adding or removing individual items.” 

Moving Forward 

Given its central importance to insurance, leaders need to prioritize analytics budgets — putting customer-facing processes first, followed by internal process efficiencies. By laying the meaning-making groundwork today, leaders can help their organizations succeed tomorrow. 

To learn more, read our report on future-proofing insurance, consider how insurers can skirt the ad war, or visit our Insurance business unit.

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Insurance Meaning-Making: More than Just Analytics (Part 2)