Much Insurtech is about data that enables cross-sales. The example that follows is from ANT, the ginormous Chinese financial company/insurance company that, for those unfamiliar with ANT, is also a payment system. They (and all big data users) are constantly checking data for correlations. With massive computing power, one does not have to be a statistical genius anymore to theorize about relationships.
Just dump the data into sophisticated (and expensive) analytical software run by powerful computers, and let the software find the correlations. ANT discovered that women who wore skinny jeans broke their phones more often. Whether this was from their jeans being too tight or the pockets not actually being designed to hold phones was a moot point. They discovered this because the data showed a high correlation between skinny jean purchases and new phone purchases/phone repair charges. So, ANT began selling these women phone insurance.
I am not sure how many different Insurtech press releases I have read that promised that this firm or that firm now had software that could time the sale of ancillary insurance purchases perfectly based on some factors discovered by that firm's algorithms. This may be one reason Pet Insurance is catching fire.
I have no doubt some of this will work. Such technology provides great solutions to consumers who are not sophisticated, educated, or bright enough to realize their pockets are not designed to hold phones.
Making consumers think they need a product when they do not or creating a financial service of convenience that does not really fit the consumer's need is an exciting trick. The sellers get to think they are doing a great service and the consumer thinks they are getting a great service. This is what happens when consumers lack education -- something the industry has failed miserably to provide.
The industry for that matter is not emphasizing education for people selling insurance either. Uneducated agents are relics, expensive relics, because the computing systems are more powerful and with scale, far cheaper and they don't complain to their employers.
If one is ethical, and ethical is defined as working with clients to sell them the coverage they need for their exposures rather than opportunistic selling (i.e., specific, but definitely not all algorithm selling), which means educating clients as to what their exposures really are, then this technology is not the central point of your future. As an agent, how you sell and how you are paid will definitely change and you will need far better data than you have collected in the past, but your future will be bright.
The reason is that a conversation with a client regarding what their exposures really are and helping them discover their exposures, remains the best method for assisting clients to buy the coverages they truly need. You get more data in a conversation. People need better pockets, not insurance for their phones. They need the right coverage for their lives because one cannot create any other pocket to hold them securely.
Mike Edwards, a retired coverage guru with whom I had the opportunity to travel for a full week once, would say, "Selling insurance isn't the same as the clerk at a drive-up window asking, 'Would you like fries with that?'" ANT has shown that cross-selling today really is more like cross-selling French fries and hamburgers -- but only relative to small insurance policies. The proof does not exist that cross-selling in this manner for large sales exists. The best way to do this is by analogue, by talking to your client and learning their needs.
Insurtech is currently mostly focused on very small commercial and low limit personal lines accounts. Another advantage this target market possesses from the sellers' perspective is that the buyers have even less insurance knowledge than normal. Agents have heard small contractors say they only want insurance so they can provide a certificate a quadrillion times. These contractors truly, at least when buying, do not care about the insurance itself. They could not care less about the coverage. Agents trying to help, trying to educate clients as to why they should care eventually just give up. They give the client the best policy they can or they tell them to buy from the agent down the road. However, for Insurtech, these are good candidates because the buyers do not care and do not know the difference. This makes them even better candidates for cross-selling.
Again, large scale cross-selling insurance works when taking advantage of uneducated consumers and/or uncaring consumers. Scaling cross-selling, even personal lines, at a high-quality level remains problematic for many reasons. One simple reason is a carrier may have a great form for one line but not the other line.
The differences in cell phone forms is not likely to be important. The difference in a quality commercial auto form and a GL form, that is totally different. Even the difference between a carrier's homeowners form and auto coverage is often too wide. Even if the forms are close, are the rates for both close?
If the rates are good and the forms are good, are the underwriters onboard?
The best way for agents to cross-sell is to sit down with their clients, learn about their clients' needs and exposures, and then offer them good coverage solutions. The best tool is a coverage checklist or an exposure checklist. These tools are usually recommended for E&O protection but my clients who use them well also happen to cross-sell much more insurance. Competition is less for agents using this tool because it requires solid coverage knowledge, and most agents will never make the effort to learn coverages deeply. Furthermore, this is not massively scalable, so it is not of interest to Insurtechs.
Another competitive advantage and reason why it is not scalable is because the clients who want the right coverage and who will simultaneously give agents the time to offer the right coverages are not likely to be the same consumers who insist on putting their phones in jean pockets not designed to hold phones.
NOTE: The information provided herein is intended for educational and informational purposes only and it represents only the views of the authors. It is not a recommendation that a particular course of action be followed. Burand & Associates, LLC and Chris Burand assume, and will have, no responsibility for liability or damage which may result from the use of any of this information.
None of the materials in this article should be construed as offering legal advice, and the specific advice of legal counsel is recommended before acting on any matter discussed in this article. Regulated individuals/entities should also ensure that they comply with all applicable laws, rules, and regulations.
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