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by Jaimie Pickles on October 13, 2016

Can Consumer Shopping Journey Data Predict Lifetime Value?

BLOG-11-12-16-predict_insurance_lifetime_value_1.pngAs I spend the majority of my day speaking with insurance marketers, interesting topics developing across the industry often bubble up across my conversations. One topic, in particular, has been a mainstay since I began working in the online insurance leads space nearly 20 years ago – how to drive profitable growth with online leads (and more recently clicks and calls).

 

Given the current environment where insurers are raising prices, more consumers are shopping for insurance—see our recent Trends in Insurance Customer Acquisition infographic.  While this is all playing out, many carriers shut down in markets where they do not yet have adequate prices. Any carriers that have the ability to move more quickly than others are well positioned to gain market share. Those that are actively seeking new business are focused more than ever on making sure they do so in a profitable manner. In some cases, insurers have focused principally on marketing their Homeowners Insurance products even though acquisition costs may exceed targets. They can justify this “loss leader” approach by aggressively cross-selling auto insurance, at little if any incremental cost, once the customer is in the door, thus adding a customer that has a better chance at having an acceptable lifetime value.

Lifetime Value Qualifiers

Before selling a consumer a policy, those that care about lifetime value want to answer three basic questions:

  • Will she be a loyal customer or will she shop again in six months and leave for a $20 savings?
  • Will she buy additional products from me?
  • What is the likelihood she will file a claim?

In addition to the initial cost to acquire her, these are the fundamental drivers of ultimate profitability.

 

Auto insurance pricing is geared to reflect the risk of the consumer and typically does not take into account the unique characteristics of marketing sources. Today, the aspects that insurers base this risk assessment upon include demographics, risk characteristics, and third party data like credit score and accident/violation history.

 

When insurers started using credit in the 1990s to determine the likelihood of a consumer submitting an insurance claim, it was a silver bullet for the industry. More recently, new technology has helped better refine those predicted outcomes by generating new, verifiable data points such as distance driven, time of day, place, and driving behavior.

Behavioral Data: the Next Silver Bullet

More than ever, insurers have the ability to access new data that may provide the insights they need to help them better predict lifetime value. Behavioral data captured during the consumer’s considered purchase journey could be that next silver bullet for the insurance industry—and other industries, as well. The theory is that by looking at the behavioral data of new customers where a profitability or lifetime value measure can be appended, we may be able to ascertain behavioral characteristics that drive various levels of profitability.

 

When you are able to determine patterns in behavior like this, you can then use those characteristics as indicators for future consumers that hit the acquisition funnel from your company’s website or third party source and price, prioritize and treat those leads accordingly.  This consumer shopping journey data can be leveraged in real-time, so insurers can make better marketing decisions.

 

A great article from McKinsey & Company about how new data sources are reshaping the way insurers unleash value states that: “The key for insurers is to motivate their highly skilled experts to adopt the newest tools and use them with creativity, confidence, and consistency.”

 

I am personally looking forward to working with my insurance marketing clients to help them leverage the behavioral data of their consumers’ shopping journeys to unearth insights into the performance of their new business sources to start testing these hypotheses. In fact, I see this as a call for marketing and carrier partners who want to engage in analyzing this issue to join us now in the lab and be part of a potential great discovery.

 

For more information on how you can leverage behavior data today, visit our website.

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