Price heavily influences a customer's expectations, which in turn shape their experience. A discounted product, like a painkiller, may be perceived as lower quality, leading to a measurably lower placebo effect and reduced effectiveness for the user. The actual experience deteriorates with the price.
Since AI can deliver results instantly, customers may perceive the output as low-effort and thus low-quality. To combat this, shift the focus from the speed of delivery to the immense effort, experience, and investment required to build the underlying AI system in the first place.
Customers often rate a service higher if they believe significant effort was expended—a concept called the "illusion of effort." Even if a faster, automated process yields the same result, framing the delivery around the effort invested in creating the system can boost perceived quality.
After establishing competence, admitting a minor flaw or making a small blunder (a "pratfall") can significantly increase appeal. This humanizes a person or product, making them seem more relatable and trustworthy. It works because it proves honesty and makes other claims more believable.
Generic social proof like "1 million customers" is minimally effective. The key is to tailor the message to the user's identity. We are most influenced by people like ourselves, so messages like "other doctors in Sydney" or "your neighbors" have a much stronger impact.
Instead of only testing minor changes on a finished product, like button color, use A/B testing early in the development process. This allows you to validate broad behavioral science principles, such as social proof, for your specific challenge before committing to a full build.
Contrary to the belief that big B2B decisions are purely rational, they are more susceptible to biases. With infrequent, high-stakes purchases like enterprise software, decision-makers face greater uncertainty and are more likely to rely on mental shortcuts and biases like social proof.