Editor's note: This is the first article in a series
In 1870, legendary retailer John Wanamaker quipped, "Half the money I spend on advertising is wasted; the trouble is, I don't know which half." That challenge is no less true today. Last year, the personal lines auto and home insurance industry is reported to have spent a jaw-dropping $6 billion on advertising. For most insurance companies, the normal marketing approach is to allocate a budget to this task and blast the company's brand everywhere they can. Marketing campaigns, often wasteful and inefficient, are typically conducted in this untargeted way. Insurance companies, in particular, are failing to focus the lens on identifying and marketing to their most desirable customers. Instead, they are reaching many undesirable customers alongside the optimal ones.
Enter Marketing Analytics
Though it is a commonly heard term, marketing analytics takes on specific characteristics when applied to the insurance industry, in contrast to how it's used more broadly by consumer products companies. With the latter, as long as a paying customer is won over, marketing has done its job. In that world, marketing typically has a one-way direction around an instantaneous cost of goods sold and a known return on that investment where the product is offered and somebody buys it. In the insurance world, by contrast, the cost of goods sold can be an unknown until much later, potentially presenting enormous liability. As insurance companies see it, most customers are less-than-preferred risk. Marketing becomes a two-way street in our world: First, point me toward the customer who is going to spend money on my product; second, point me toward customers who are less likely to file claims.
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Who is the right insurance customer, and how can auto and home insurers find more of them? The reality is that customers don't choose the supplier; insurance companies, to a certain extent, pick customers, given their right to turn down insuring them.
While insurance companies are using data analytics for many of their functions -- underwriting, claims management, and even agent selection -- nearly no insurance companies to date are using "Big Data"-type analytics to identify the right customers. Underwriting analytics is a well-oiled machine that has already been built to do the heavy lifting for the insurance industry. Now the marketing end of the insurance industry needs to do the lighter-weight job of using data analytics to get better, lower-risk customers to come to them.
More than reinventing what has already been built, marketing analytics just needs to learn to access and analyze the data that will weed out high-risk prospects. Ideally, marketing analytics will help channel marketing spend toward lower-risk people who are more likely to fit the ideal profile. For example, most insurance companies find young men aged 16 to 22 to be undesirable risks. The obvious move would be not to advertise in places that are likely to draw that crowd. They'll apply for insurance, be denied, and proceed to smear the insurance company on Twitter or Facebook. That's a modern day marketing disaster, on many levels.
The Role of Data
Where does the data come from, and how can it help the industry? Data for marketing analysis comes both from the insurance company's internal data sources as well as external data sources. This needs to be supplemented with data from social media channels, such as Facebook, Twitter and YouTube, to track leads and customer sentiment. Also, the marketing analytics platform should have data feeds from other business platforms -- such as new business, policy administration, claims and billing -- to obtain a complete view of each customer, encompassing demographics, shopping behavior, credit history and life events.
Companies don't need to house all of this customer information in their internal databases, but they should be able to access it and use it for predictive analysis. Surprising as many insurance marketers may find it, there are real answers to the questions they should ask, such as number of leads generated per campaign, number of prospects for every 100 leads, number of quotes generated for every 100 prospects, and number of policies issued for every 100 quotes. And those answers can set the companies on the path to getting more and better customers, more smartly and cost effectively.
What will change by using marketing analytics? Advertising placements of insurers will be better targeted to the bull's eye. They will reach the people they want to reach -- the right customers. Possibly, their media mix will change -- right now they blast everywhere, but the percentages of TV, online and print advertising may need adjusting for a more positive result. Best of all, word-of-mouth advertising begins to take effect. They will now spend less on advertising because they will let the right customers do the marketing for them.
By using marketing analytics, insurers will get a clearer handle on their ideal customers, find more of them, and develop a smarter and more cost effective approach to marketing.
Michael Kim is Vice President, Insurance Consulting, and Agil Francis is Senior Manager, Insurance Consulting, at Cognizant.