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Over the past several years, a small group of insurance carriers have discovered a powerful new tactic for optimizing customer acquisition efficiency: serving their competitors’ ads on their own website quote pages.
Sounds crazy, right? After all, why would an insurance carrier direct their own website visitors to go get a quote from someone else?
The answer is that this new revenue stream can make a huge difference on a carrier’s bottom line. Already, several of the industry’s leading advertisers are using this method to generate incremental revenue equivalent to 20-30 percent of their digital marketing costs.
These additional funds can be put back into the funnel to acquire new customers (thus increasing scale), or kept as pure profit. Indeed, while a policy sale bears the costs of potential claims payouts, this revenue stream does not. And whereas premiums get paid in monthly installments, ad revenue is paid upfront after the user clicks.
For insurers, the calculus becomes fairly simple: How many additional policies would you need to sell to generate the millions of dollars in profits you could earn simply by serving ads to the people who are likely to leave your website without buying a policy?
At this point, you may be wondering: Won’t these ads just chip away at the carrier’s policy sales? Website visitors might click a competitor’s ad and never come back.
The answer is no, and the reason is predictive analytics.
By using data science to analyze their conversion history, carriers can predict how likely a visitor is to buy a policy. From there, they can make intelligent decisions about which customers to monetize with an ad and which ones to serve an ad-free page that unambiguously pushes them toward buying a policy.
In essence, predictive analytics gives carriers the power to make smarter user experience choices and maximize the value of every customer interaction.
Very few website visitors actually convert. Predictive analytics helps insurance carriers maximize the value of the ones who don’t.
As you might expect, many carriers were skeptical of this tactic when it first came on the scene. After all, insurers have spent decades building their businesses, and they’ve done it by selling policies—not ads.
Ultimately, they were won over by the fact that the data just makes too much sense.
Think of it this way: Each year, leading carriers spend hundreds of millions of dollars on sports sponsorships, branding campaigns, and digital marketing, all to entice customers to come to their site and request a quote from them. But once those customers show up on their websites to get a quote, the percentage of people who wind up buying a policy is typically in the single digits.
Rather than letting the overwhelming majority of site visitors skitter away without contributing to the bottom line, carriers can present competitive offers to recoup some of the money that was spent to bring these visitors to the site in the first place.
Predictive analytics is the key ingredient that makes the whole thing work. Without this crucial application of data science, carriers would have no way of predicting how valuable a visitor was likely to be for them, and no way of tailoring the user experience to the ideal business outcome.
Certainly, no right-minded carrier would sell ads on its website if it thought it might persuade someone who was likely to buy a policy to click on an ad, instead.
Predictive analytics allows carriers to serve each site visitor the right user experience.
The most advanced carriers are using predictive analytics to serve consumers a variety of user experiences, based on what their data science and conversion data tells them about their customer base.
For instance, if a carrier finds that a certain group of customers is exceedingly unlikely to purchase a policy from them—say, married California homeowners with two cars—they can serve those users an offer from a carrier that might be a better fit, in a prominent location on the quote page where the visitor is likely to see and click on it.
What’s great about these competitive offers is that they create real value for consumers by presenting them comparison options that are highly relevant to their shopping search. Precisely for this reason, competing carriers are willing to pay a premium to reach shoppers in this high-intent environment.
On the other hand, if a certain group of site visitors is somewhat likely to purchase a policy, a carrier could instead decide to serve them a large, prominent quote with a small, less noticeable ad beneath it. This way, the user can seek out an additional offer once they’ve decided not to purchase a policy.
And if a carrier identifies a cluster of site visitors who are its highest converting shoppers, they might choose to serve them a quote page with no ads at all.
Through the power of predictive analytics, carriers have the flexibility and the insight they need to serve each user the site experience that will maximize profits. The key is for carriers to make sure they’re tracking how each change impacts their performance, so that they can adjust their strategy accordingly.
This is especially important given that a carrier’s results can sometimes be surprising, particularly when it comes to the way advertising influences a user’s likelihood to convert. For instance, the data tells us that consumers sometimes request a quote from one carrier, click an offer to receive a quote from another brand, and then return to the original carrier to purchase a policy.
One carrier even saw an increase in conversion rates after it started serving ads to consumers who its data predicted would be unlikely to convert.
The only way to discover these valuable insights is for carriers to consistently test new strategies and analyze their results.
As customer acquisition efficiency becomes more important, carriers will increasingly rely on predictive analytics to stay ahead.
For all the success cutting-edge carriers have had selling ads these past few years, it’s likely that predictive analytics will only play a more important role in the coming months, particularly in the realm of auto insurance.
With COVID-19 reducing the amount of time drivers spend commuting to and from work, auto insurance carriers have been able to save money during the pandemic due to a lower frequency of driving accidents. Sensing an opportunity, carriers are putting the money they would have spent on additional payouts toward growing their businesses by acquiring new customers.
As Dowling & Partners put it in a November report covering this trend, “When the margins are good, everyone wants to grow.”
This means that the already cut-throat customer acquisition marketplace is somehow becoming even more competitive, with carriers willing to pay top dollar to reach their next potential policyholder. As marketing costs rise, the carriers who will be able to continue growing profitably will be the ones who are using data science to acquire customers as efficiently as possible.
In this environment, carriers simply cannot afford to pass on the 20-30 percent of their marketing costs they can recover by selling ads on their website. And the ones who come out on top will be those who are effectively using predictive analytics to maximize the value of each customer interaction.