“User trust is a finite resource”.
I love this sentence from the UX collective’s article of 2024 trends.
It’s even more true for online reviews: their very purpose is to generate trust about businesses. But for this trust to be well-funded, people also need to trust the reviewing process; and that’s were a few cracks started appearing.
95% of people look at online reviews before making a purchase (Spiegel Research Center, 2022), but only 50% of people left a review for one of the 10 last products they purchased, and only 2% did it on every purchase (PowerReviews, 2018). Reviews are massively used, but it seems like only a small sub-set of customers participate to it, not to the same extent, and often with very few context and information.
So, what’s wrong?
<aside> 🔑 This is a comprehensive overview of the online reviews principles, what’s working or not, and an exploration of potential solutions. It’s based on books, articles, research studies and statistics- and of course personal interpretation.
This document is fully open and ongoing: please comment, react, share your opinions, confirmations or contradictions, and any other articles that can add to the reflection. The idea is to make it an evolving exploration, to be used for reference.
I’ll use the 5-star rating system as the default system throughout this document. Other systems exist that we’ll talk about (NPS, CSAT, Like/Dislike…) but the 5-star system being the most common especially on public platforms, it makes more sense to focus the analysis on it.
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Before deep diving in what needs to be fixed, let’s understand how online reviews are being used today and in what context. This will help figuring out if the existing systems and explorations comply with those ‘principles’.
In the “Why do we leave online reviews” section, we exposed the reasons and motivations for leaving a review. This chapter basically covers all the reasons why people don’t leave reviews, despite having a tangible reason for doing so.
Let’s first list the issues at the individual review level, from a reviewer perspective. We’ll later cover the agglomeration level and what readers see. Traditionally, a reviewer would be invited to choose between 1 and 5 stars and leave a comment. So, what’s wrong with that?