This page is part of a global project to create a better online reviews system. If you want to know more or give your feedback, write at [email protected] and we’ll grab a beer ;)

As with any statistical analysis, the specific question posed to the panel significantly influences the responses. There's a subtle but important distinction between asking, "Did you like it?" and "Would you recommend it?" The way the question is framed can affect the answers provided.

The response can also vary based on who is requesting feedback: the platform for sorting options, the business for performance measurement, or other potential customers seeking confirmation?

Here’s a list of common questions customers encounter:

• "Would you recommend it?" - This straightforward question anticipates a binary "yes or no" response.

  When a friend asks me, "Would you recommend it?" my answer might differ from what I tell the company. In real life, the question "Would you recommend it?" usually elicits three types of responses: "Yes," "No," or a hesitant "Hmm, it depends," which often leans negative but might not apply to everyone.

  In NPS (Net Promoter Score) surveys, the standard question is: "On a scale of 1 to 10, how likely are you to recommend?" This introduces a spectrum of sentiment to a question that is fundamentally binary, adding an element of ambiguity.

• "What did you think of it?" - This question invites descriptive answers, asking users to express their judgments about their experiences. "What" prompts a detailed and relatively objective response. Despite referring to verbal feedback, this question is accompanied by a rating scale alongside a comment section, which leads to confusion. “What” doesn’t ask for a numerical rating. Also, the question is not requesting a recommendation, but businesses still use it in that purpose.

• "How was your experience with it?" - While similar to the previous question, this one focuses on the user's feelings rather than their judgment. It seeks an emotional and personal narrative. "How" encourages a subjective, story-like response.

• "Leave a review," without a specific question - Platforms like Google Maps use this approach, with a placeholder in the comment section stating: "Share details of your own experience at this place”. Despite everything, this might be the clearest way to ask for a rating.

Even when we aim to obtain clear feedback, the framing of the question can influence the user’s response, as we aren’t always asking the exact same thing.

In practice, people are so accustomed to the 5-star (or 10-star) rating system that they often overlook the specific question. Instead, they tend to respond to an implied query:

<aside> ❔ "Did your experience meet your expectations entirely, or were there specific aspects that fell short of your satisfaction?”

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If the rating is 5 stars, the customer was entirely satisfied. Ratings below 5 indicate some level of dissatisfaction. But some users still refer to 5 stars as “excellent”, not just “satisfactory”. This will be further discussed in the "Unclear Scale" section.

<aside> 💡 Exploration

Going further, we can think of other questions to ask:

• "Would you come back? / Would you purchase again? / Will you keep using the service?" - These questions refer directly to the user's experience. Recurrence is a mark of loyalty, which is a form of satisfaction.
• "Tell us what you liked and what can be improved" - This question expects words for an answer. It may come as an additional question to "Would you recommend?" because even though things might be improved, the customer may have enjoyed the experience. We’ll cover more about this in the “Nuances” section.
• "How does this compare to other services you’ve tried?" - Reviewing a product or service often involves comparisons to standards based on previous experiences. Comparison is a significant part of the purchase flow and this information would be valuable to potential customers when applicable.

There are many ways to ask for feedback. Some companies are trying to innovate, but they are blocked by industry habits: people expect similar standards to compare options on the market, making it harder to change the system.

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