Online reviews changed the game forever, making the importance of customer experience increasingly relevant. Research shows that 91% of people read online reviews while 84% trust them as much as personal recommendations.
But online reviews don’t have to be always public. Many successful companies use them to collect feedback from customers at scale to measure their satisfaction and discover opportunities for improvement. Metrics such as Net Promoter Score have been proven to accurately measure the satisfaction of customers as well as their willingness to refer.
Whether the reviews are public or private, there is a lot of data distortion. We are emotional human beings and evaluating something on a simple 5-star scale will never be objective. Here are the 3 main biases that occur when people leave reviews and what you can do with them.
Bipolar review distribution
Emotions drive people into action. The majority of customers review when they either love or hate the product, service or experience. If they have moderate views, they simply don’t care enough to go that extra mile and leave a review.
This creates a bipolar distribution of online reviews with the majority of them on the two ends of the scale; a phenomenon well observed by many big reviewing players such as Amazon or Yelp
To collect reviews even from less excited users, it helps to ask for them right after the delivery of the product or service when emotions are strongest. Uber, for instance, pushes a notification requiring feedback right after the ride ends. Aliexpress first asks customers to confirm the delivery of product through email confirmation and then sends another email asking to rate the experience (including both, an actual product and its delivery). Foursquare tracks users by GPS and sends notification about restaurant they are likely sitting in.
Some services even gamificate the reviewing process. Tripadvisor promotes reviewers into several levels on the basis of how many reviews they wrote and how many likes they received for them. This is a great way of making more loyal reviewers on, let’s say, e-commerce websites with a wide product portfolio.
Lastly, it pays off to filter fraudulent and low quality reviews. This way, Yelp claims it improved the balance of review distribution significantly. If reviews are public, some websites also have an opportunity for users to like or dislike them (like in case of Tripadvisor), which helps to determine their relevance.
A well known Halo effect occurs when people evaluate the whole experience based on just one highly emotional aspect; whether positive or negative. But as we already know, most people leave reviews when they are emotional. So the key question is — how to lower the impact of Halo effect?
One way is to ask for both — pros and cons of the experience. This will make people think about good things even though they are upset because of the bad ones and vice versa. A good example is reviewing products at Heureka.sk, which even displays hints to advice the reviewer what aspects to focus on.
Another way is to ask people to review different aspects separately. This way, companies can collect more structured data that makes the overall rating more objective. This is also helpful for customers who care about a certain aspects more than others.
For instance, one traveler might prioritise the location of the accommodation while another might consider spacious rooms to be the main criteria. The multi-aspect reviewing is used especially in case of more complex services such as travel (Uber), accommodation (Airbnb, Booking) or gastronomy (Tripadvisor, Foursquare).
The problem is that the more questions companies ask, the less people will answer, so it’s important to find a compromise between quality and simplicity. A great example is Airbnb, which first sends the customer a simple email asking “What was your experience like?” on a five-star rating scale. After the customer rates the overall experience, he is redirected to the Airbnb website where he receives more questions in several steps. Each steps is a little bit more demanding but the reviewer can skip questions that he doesn’t want to fill at the moment.
Fear of personal evaluation
We first observed this bias when we did customer research for a taxi marketplace app — Hopin. People were leaving 5-star reviews even when they weren’t fully satisfied because they didn’t want to get the driver into trouble. “Everyone can have a bad day, I don’t want the driver to lose a job because of me” said one of the respondents.
To avoid this bias, it helps to soften the questions and make them more about the service, not the people behind it. Instead of “Rate the driver”, we asked “Were you satisfied with the ride?”. Instead of “What was bad?”, we asked “What could be better?”. Instead of telling people to write reviews, we simply gave them several options they can highlight to specify what was good or bad. Interestingly, the number of received feedback doubled after the redesign even though we asked for more data than before.
What can’t be measured, can’t be managed
Even though there is a lot of data distortion whenever we try to quantify humans’ emotions, the online reviewing represent a powerful research method that can be used to evaluate the customer experience on many channels. It’s useful for both — companies, as a source of feedback, and customers, as a source of objective information about the products and services.
Online reviewing is just one of many research methods but because of its simplicity and efficiency, it would be shame not to use it.
What about you? Let us know about your experience with online reviewing in the comments or at [email protected] Also, if you’d like to receive more articles like this, sign up for our newsletter here.
Written by Milan Tibenský, Lighting Beetle