Does the Length of a Google Review Matter?
In our last blog post, “Is Google Looking for Diversity in Reviews,” we presented a case study about reviews and diversity in review scores. In that study, we concluded that Google is not holding one-star reviews at the top of a listing any longer than five-star reviews. But, we also determined in that study that it is also not the most recent reviews that stay at the top of the listing. So, the next thing we looked into was the length of the reviews. We wanted to determine if a longer review (a larger word count) would stay at the top for more time than a shorter review.
ICYMI:
In case you missed our last blog post, we started this case study by tracking reviews on a lawncare site. After six months, we decided to add more industries to this test (first a jeweler, then a marketing company, and finally a handyman). See the image to the left.
What We Noticed:
In the image below, you will see that the average one-star review was 244 words, while the average five-star review was 74 words.
And this makes sense because we all know that people who leave one-star reviews usually like to go on and on for paragraphs because they are upset, while people who are leaving five-star reviews often only write a sentence or two.
We found a very strong correlation between the length of a review and how long it stayed at the top of the businesses’ review profile. In the image below, you will see that the longer a review was (the more words included in the review), the longer it stayed in the top 10 (or 11 in the case of the lawncare site) positions.
TL;DR:
Encourage customers to write longer reviews (100+ words). Since longer reviews tend to stay at the top of a listing for a longer amount of time, this is a good way to ensure that your best reviews stay at the top of a listing for the longest amount of time.
Rather disappointing since, as you mention, longer reviews are more likely to be an attribute of negative-sentiment reviews. This indicates there is an unfair GBP bias toward visibility of negative reviews. Were there enough datapoints for you to generate a statistical significance value to make it even more concrete?
No, this was simply a case study. You’d have to look at a much larger data set if you wanted something more concrete. I tracked it for a little over a year and based this on the number of days the review stayed in the top 10-11 before falling out so I’m not sure of a tool that currently tracks review positioning.
Have you guys done a study to see what benefits reviews w/ added pictures have?
Yes, that one is being published in a few weeks.