What is Survivorship Bias How Does Survivorship Bias Apply to Marketing

What is Survivorship Bias? How Does Survivorship Bias Apply to Marketing?

Also Known As: Survival bias


Survivorship bias occurs when we concentrate on people or companies that were “selected,” while overlooking those that were not selected because of their lack of visibility.

Survivorship Bias Examples:

Listening to Startup Survivors. The world of startups is full of stories of who made it, how, and why. We don’t often hear about startups that failed following the exact path of a startup that was successful. If you were to study car rideshare startups, to avoid survivorship bias, you should not only study the difference between Uber and Lyft, but also Sidecar, Split, and Bridj.

Market Research. Let’s say that I am doing market research for a car company. I am trying to determine if my product should include a sunroof. When I analyze all car models that were profitable over the past year, I see that only 10% have a sunroof. This data point indicates we should not include the sunroof.

However, what if in the unprofitable segment of car models, only 1% have sunroofs? If I know this data, it may make more sense to include a sunroof on the car we are designing. Looking only at the winners has made us blind to the reality of the situation. We must asses both the winners and losers for the most realistic conclusions.

Marketing Tactics. The problem with most marketing tactics written about online is that they are successful. Not many authors choose to write about marketing tactics that failed. The problem with this is that we are constantly chasing the next greatest tactic. This is otherwise known as shiny object syndrome.

Instead of truly understanding the context of the situation, we are looking for a quick fix to our problems. It’s akin to a get rich quick scheme, rather than letting our tactics flow from a well-thought-out strategy. The latter takes a lot more effort, which makes it a less appealing decision in the short term.

See also: Selection bias, Cherry picking, Econometrics, Fooled by Randomness, Meta-analysis, Multiple comparisons problem, Selection principle, Texas sharpshooter fallacy

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