How Likely Am I to Recommend You? Why This Question Makes No Sense

Yet another survey arrived in my inbox. It was one of those familiar ones that open with the same predictable question: “How likely are you to recommend us to a friend or colleague?” If you have ever paid for a product or service, you have almost certainly seen it. It appears after phone contracts, utility bills, software subscriptions, and customer support interactions.

The question always feels oddly misplaced. I paid for a service. I used it. The transaction ended. Instead of a simple acknowledgment, I am asked to rate my enthusiasm. One to five stars. Sometimes a sliding scale that pretends satisfaction can be measured with scientific accuracy. The language sounds important, but the meaning feels vague.

What really gets missed is the assumption built into the question itself. Take a basic example. I use a telecommunications provider. Why would I recommend my phone or internet service to a friend? They may already have a provider, either the same one or another they are perfectly happy with. Recommending it assumes that everyone needs the same product, in the same way, at the same time. It is the logic of “I like this company, so you should too,” which is not how real life works.

The same logic applies to banks, utilities, insurance, and other everyday services. These are not personal endorsements. A few years ago, I discovered that several of my friends and I used the same bank. I stayed with it. Others left because they were unhappy, even though we were using the same products. The question sounds meaningful, but it often has little relevance to how people actually make decisions.

Star ratings make the problem worse. Anything less than five stars is treated as failure. A lower score triggers follow-up questions, requests for explanations, and written feedback. Suddenly, the burden shifts to the customer to justify dissatisfaction, often without any indication that the response will lead to real change. In many cases, the outcome is a polite automated message and nothing more.

There is also quiet pressure attached to honesty. A poor rating may affect an employee who was simply following procedures. In some cases, it may even put their job at risk. Alternatively, the customer may be flagged as difficult for expressing dissatisfaction. Over time, this discourages genuine feedback and encourages inflated scores or silence.

If companies want insight, they should ask better questions. And if they want honesty, they should stop assuming that every service deserves to be recommended. Sometimes a service is simply used. That should be answer enough.