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EG57: How availability bias affects your pricing

Written by Emanuel Martonca
on July 11, 2022

It would be great if availability bias would have a positive effect on your pricing.

Unfortunately, it usually doesn’t. It’s just one of those many factors that lead to sub-optimal outcomes.

What is availability bias?

It is the tendency to overestimate the likelihood of events with greater “availability” in memory, which can be influenced by how recent the memories are or how unusual or emotionally charged they may be.

The basic working mechanism is: if something can be recalled, it must be important.

The consequence is that we are more likely to consider recent information when making decisions, even if that is not necessarily the most important or even the most relevant aspect for the given situation.

What this means for pricing decisions in software services

If a manager in a software company learns about the actual rates charged by a direct competitor, just one, on one project, there is a high probability that this will influence their thinking and the prices they set for the next projects.

If a sales team just lost a project and the potential customer mentioned the high price, they are much more likely to reduce the price on the next project proposal they make – even if the team setup, quality delivered, client relationship or any other variables might be different than the previous instance.

If a sales team has just signed a new contract, there is a high probability they will use the same rates for the next proposal they make, without taking into account the differences between the clients and the context. Sometimes they might be justified to increase the rates. Or they might have objective reasons to ask for a lower price. In any circumstance, the rate from the recent, signed project should not play such an important role in the decision. But it usually does.


We cannot easily escape our biases. Maybe we don’t even need to, as this is what allows us to function on a day to day basis.

But when it comes to price setting decisions, biases usually lead to negative consequences. 

We need to work hard to limit their influence, by using repeatable methods, rules and algorithms to set prices.

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