6 cognitive biases that get people to buy online

6 cognitive biases that influence online shopping

Google's consumer insights team wanted to "understand how consumers make decisions in an online environment of abundant choice and limitless information."

They found that "people deal with scale and complexity by using cognitive biases encoded deep in their psychology."

As consumers cycle through exploration and evaluation phases of their decision, they rely on the following six cognitive biases:

1. Category heuristics - Short descriptions of product specs can help, like how many megapixels a camera has. You therefore need to know what characteristics your customer associates with what you're selling.
2. Power of Now - People love immediate gratification. The longer you make them wait, the less keen they'll be.
3. Social Proof - What do others think about it? Ratings and reviews.
4. Scarcity bias - As Australia experienced during the great toilet paper crisis of 2020, a product becomes more desirable as it becomes less available. However, in Google's simulations this was the least effective strategy (best used at final evaluation phase) and can even be a turn-off.
5. Authority bias - Authority figures (e.g. spokesperson) or trusted sources (e.g. impartial publications like Choice) can be persuasive.
6. Power of free - A free gift with purchase can win people over.

Google found ramping up a fictional cereal brand's profile with five-star reviews (social proof) and a 20% bonus offer (power of free) generated 28% of consumer preferences. A fictional car insurer won a massive 87% of consumers by using techniques across all six biases.

As this research demonstrated through experimentation, behavioural science can dramatically shift consumer buying behaviour.

So why doesn't everyone run more experiments like this?

Experiment aversion

Just get on with it. That's what I took from a new paper by Heck, Chabris, Watts & Meyer (2020) which delved into aversion to A/B testing (aka Randomised Control Trials).

Most people preferred to go with any idea (ie. Policy A or Policy B) rather than bother testing whether A or B was best. "In four out of five domains, people tended to prefer direct implementation to rigorous evaluation of untested policies, even when they judged one policy to be superior to the other."

To encourage testing, the authors suggest perhaps "describing these policies as what they often are: a shot in the dark based on the highest-paid person’s opinion."

My advice if you want to run experiments but are getting push-back is to flush out reasons for resistance. Use my free Behavioural Analysis tool to work through your scenario and identify possible solutions.