Using Mad-Libs to Fuel Your Data Capabilities

Madlibs for data capabilitiesThere is little question that the availability of new and extensive amounts of data is changing our world. Nearly everyone has a smartphone, which acts as a sensor, collecting new data about nearly every action we perform. As processes become increasingly digital, new data can be collected about users’ behaviors. Furthermore, the Internet of Things is making use of sensors on our bodies, cars, appliances, homes, equipment, planet, and nearly everything else. This rush of new data will accelerate change and force insurance companies of all kinds to adapt.

Alan Kay said in 1971, “the best way to predict the future is to invent it.” The big question for insurers is, “how can I invent it?” This is especially challenging given the pace of change insurers are facing. They are asking, “How can I find the right data to change processes, broaden service capabilities, increase sales, and bring more value to my customers?” The following simple brainstorming technique will help insurers explore the possibilities related to data that they may or may not have today. If you don’t have the data today, you may need to find a source that can deliver it, or look for data that is only now beginning to become available.

The key to this brainstorming exercise is the question “What would I do if I knew?” This will help you broaden your thinking and move past your current constraints. You may recall the Madlibs you completed as a child. This exercise is a little like a Madlib. Replace the “I” with a department or group. So it reads, “what would [claims] do if it knew [ ______ ] ?” The first bracket could contain any department or group. So for example, you could fill in sales, service, training, agents, marketing, underwriting, and on and on.

In the second bracket after the word “knew,” fill in the data that you already have, or wish you could get access to. Have a list of data you already have, and a list of data you could likely obtain to begin to fill in the second bracket. Using the same example, “what would [marketing] do if it knew [the income bracket of the beneficiary]?” Once that is filled in, explore what possibilities it opens up. In this example, outcomes might be the following:

  • We could send targeted offers about reinvesting the claims money rather than simply sending a check.
  • We could have the check delivered by an agent (even for orphan policies) for wealthier beneficiaries to build a relationship rather than mailing it.
  • We could prioritize our engagement efforts and offer additional information, services, or other value to the beneficiary to establish a relationship with those beneficiaries who would make ideal customers.

Some additional examples could include:

  • What would sales do if they knew which agents were not illustrating our products?
    Possibility: We could send out additional training or offer to do illustrations for them.
  • What would service do if they knew how much their customers used mobile devices to interact with their life/annuity insurer?
    Possibility: We would know what services would provide the most value to high-value consumers. What would sales do if they knew the buying triggers of prospects?
    Possibility: We might send them targeted offers that align to the life stage of the consumer.

As the availability and use of data continues to expand, insurance companies will need to find ways to successfully leverage that data to their advantage. This “what would you do if you knew” brainstorming technique can help insurers open up their thinking about what is possible, and how their customers can be better served.

We would love to hear how this went with your teams. Did you generate some new ideas? Did you wish you had data that you don’t currently have? Do you have a list of great ideas that you have no capacity to implement? Let us know in the comment section below.

To achieve data objectives, insurers should apply the three types of data (diagnostic, decision support, and alerts) to improve their services to their distribution partners and agents. Read the full whitepaper.