It’s old news by now that Google is shutting down Compare, its financial services and insurance comparison site. It wasn’t open long – less than a year. When Compare was first announced, the industry reacted with warnings that this was a major disrupter in insurance distribution. With the massive audience that Google has, the industry expected that Google was going to swoop down and capture the online insurance market – which by the way is pretty big – typically 75% of prospects research online and 20-25% of all new auto policies are purchased on line according to those who track this type of metric.
So what happened? Well, the fundamental idea of capturing the online market is a sound idea. And Google was pretty smart at avoiding all the hard technical costs of building out the aggregator engine by partnering with those who had already done the hard work – like Compare.com, Coverhound and Bolt.
But the business model of an online aggregator is hard. There are three models – online agents – who earn full commissions. That wasn’t really Google’s deal. They weren’t interested in any of the after service or ongoing relationships. A traffic generator – sending a potential lead to another site and being paid for the eyeballs. Well, that’s not very lucrative either – and frankly, Google can make money through their own advertising and search capabilities. Spending the money to build an online quoting front end only adds cost to something they already do quite well, thank you.
So why would Google have invested the money in an online quoting front end? To take advantage of a lead model. With a lead model, the aggregator collects data, processes a request for quote and sends a highly qualified lead to be fulfilled. The price per lead is significantly higher than the price for traffic. But there’s a fundamental challenge with this model. For the lead to be valuable to a carrier, the lead has to actually purchase insurance. And because a lead is sold to multiple carriers, the acquisition costs rise for a carrier.
Let’s say a lead is sold for $5 to ten carriers. The aggregator makes $50 for that lead. But only one carrier actually writes the lead. If ten leads are sold, and each carrier writes one, the aggregator makes $500 but the carrier has spent $50 for that lead. Play out a competitive situation where the leads aren’t equally distributed, and you can see that the acquisition costs can rapidly rise. If I only get one lead out of twenty, I’ve spent $100 for that lead. If I only get one lead out of $30 I’ve now spent $150 for that lead – which now is pretty close to what I’d probably be paying an independent agent.
And what if the customer NEVER buys – and simply goes in looking for prices so they have a comparison to an off line model? The numbers rise rapidly. Remember those numbers above – 75% shop on line and 25% purchase on line. That means that only one in three leads actually results in a sale. Assuming leads are distributed evenly, an aggregator will distribute 165 leads before I close one. That brings this $5 lead fee up to $82.50 –, which is pretty expensive. The way to make those economics work is to increase the conversion rate so that more of the leads a carrier purchases actually ends up buying a policy.
So while carriers are very interested in participating in the online marketplace, they really want to work with those aggregators who are successful at converting traffic to leads that will convert to policyholders. The online agent model is attractive as the carrier doesn’t pay until the policy is written. The traffic model is similar to online advertising, so that works as well. But the success of a lead model is a combination of the price of the lead and the likelihood of closing that lead – which is dependent on the number of carriers the lead is sold to and the propensity to buy.
So here’s where Google lost an opportunity with Compare. They thought they could convert relatively low paying traffic into high paying leads simply by putting a quoting front end on and didn’t think through what they could have done to improve the conversion rates.
With their analytical power, Google could have created a truly disruptive experience by providing consumers with a powerful recommendation engine. Google is a master at finding out information about individuals from social media and other publicly available data. They could have created an algorithm that used the information about the lead to tailor and target recommendations.
Personal auto isn’t that hard. If we were talking about commercial, it’s a much harder set of algorithms. But honestly, it’s not that hard to create something that tells a customer that given their location, the value of their home, the type of vehicle and their driving record, 64% of people like you choose this limit/deductible/additional coverage etc. And getting a personalized recommendation drives conversion. When people trust that the advice is good, they’re willing to buy. We’ve seen many examples of how inserting advice and recommendations into the quoting process drives conversion.
When I personally go to get an online quote – it’s part of my job – I enter information that shows I own a home in California and I drive a luxury car. So why oh why do the aggregator sites today recommend minimum limits coverage to me? My car is worth more than that. Today, trusting the advice from an aggregator site is dicey. And that is why policyholders continue to rely on the advice of an agent.
Does this mean the role of aggregators is dead? No.
But Google missed a major opportunity to truly disrupt by providing a powerful recommendation engine that could use their ability to easily find information about individuals and combine it with their powerful analytical abilities. They ended up creating just the same thing we had back in the 90’s. Kudos to them for killing it quickly – but they missed an opportunity to use their capabilities to make the model work.
This blog entry has been reprinted with permission from Celent.
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The opinions posted in this blog do not necessarily reflect those of Insurance Networking News or SourceMedia.
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