Why data can be deceiving at times …
Here’s how.
Imagine you want to buy a chocolate from a grocery store. Let’s say 1 chocolate costs $1 (Sorry, chocolate enthusiasts it’s a really cheap chocolate!)
Now, if someone asks, how much would 100 chocolate cost?
Data nerds will tell you $100.
However, in reality, the answer in most cases would never be $100.
Why?
In real world, you would probably either ask for a discount for buying in bulk (buyer power) OR if supplier realizes he’s the only one in town who can meet your demand in the situation he might charge you a premium (supplier power).
So, you’ll end up paying either <$100 or >$100 but probably not $100.
Again, how much would 100 chocolate costs, if one chocolate costs $1?
Appropriate answer should be somewhere in the range say $90 – $110.
Same basic principle applies to more complex metrics such as customer acquisition costs (CAC), average order value, etc.
Product owners should always keep in mind the extrapolation discrepancy bias, while deciding their KPI targets.
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