Imagine I ask you to predict the life expectancy of a healthy male living in the Western hemisphere. You don’t need to be an actuary to say 80-90 years. And your answers will likely be right.
Now let’s try a different experiment. Try and predict the life expectancy of something non-perishable. Let’s say a new book which is a New York Times bestseller. Or even the survival rate of a company like Google or Uber. This is clearly much harder to do. Most of us will vacillate and the answers will vary widely in range.
It turns out there is a general theory to think about survival rate for nonperishable items like these. Named the Lindy effect, it suggests that the longer a nonperishable good, i.e. an idea or technology has been around, the longer it is expected to around.
Originally conceived by Albert Goldman, the theory has been further expanded by Benoit Mandelbrot and Nassim Taleb in recent decades. Here’s Nassim Taleb explaining the idea in his recent book:
If a book has been in print for forty years, I can expect it to be in print for another forty years. But, and that is the main difference, if it survives another decade, then it will be expected to be in print another fifty years. This, simply, as a rule, tells you why things that have been around for a long time are not "aging" like persons, but "aging" in reverse. Every year that passes without extinction doubles the additional life expectancy. This is an indicator of some robustness. The robustness of an item is proportional to its life!
The theory has relevance for everything ranging from design patterns in everyday life to general stock-picking rules. However, it is important to remember that the theory is not a scientific law but merely an observation around power law distributions.
In the insurance context, the Lindy effect poses a couple of interesting questions:
Could the effect explain away the retention behavior of a certain class of customers. It is a popular cliche that large insurers have best-in-class metrics because they have an excellent ‘portfolio’ of risks. For a motor insurance company, these are often customers who’ve stuck around for 10 years, have never visited a PCW and have 10 years of no-claims discount or bonus. And what’s amazing is that they are quite likely to stick around and make no claim in the ensuring ten years. So could there be a Lindy effect at work here? I.e. is the predicted survival rate of a customer directly proportional to their current tenure with the insurer. A customer acquired 1 year ago might thus be predicted on aggregate to have a life span of 1-2 years. On the other hand, a customer acquired (and retained) 5 years ago, might be predicted to survive another 5 years. This could present an interesting challenge to insurtechs entering a new market. The hypothetical CAC of these customers is much higher than what any standard LTV/NPV analysis might predict. Thus, these companies should be willing to pay much higher than the $100-300 to acquire general insurance customers. One suggestion might then be to build a business around investing heavily upfront in promotions to acquire such customers.
Intangibles now constitute a majority of market value for most major indexes. See the graph below which often appears in presentations:
Such intangibles include things like brand, data and goodwill. If the survival rate of such intangibles only increases with time, shouldn’t risk-modeling teams more explicitly account for the age of these intangibles in their valuation models?
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