Ratings have been an important part of human decision making for as long as we have been comparing people, places, and things. The emergence of the internet has provided us with a constant influx of new information in amazing quantities. The need for ratings would appear to be more vital than ever, but the need to evolve how we rate is even more paramount.
The internet is a world of reviews. You can find out how many stars a restaurant has on Yelp, see a percentage rating for a movie on Rotten Tomatoes, and even evaluate a professor with RateMyProfessors.Com. If it can be rated, the internet has a place for it.
Not too long ago, you would get your ratings and reviews from two sources: your personal network or from experts. These one-to-one or one-to-many forms of communicating views were ideal for rating systems. One person expressing their view to another person(s) in the form of a simple score and evaluation. People were free to pick and choose the reviews they wanted to trust based on the individual they got them from.
The web changed this. The collaborative power of the internet invited people from across the globe to share their feelings about anything they wanted. What started out as blogs or newsletters quickly coalesced into forums and then larger reviews sites. Being a restaurant critic perceptibly moved from the realm of the professional to that of the hobbyist.
With the floodgates of opinion open, the next question was how to organize all of this feedback. The solution was twofold: create a centralized space to cluster reviews so that they could be searched by anyone and aggregate all ratings into a single super rating.
At the time, structuring reviews this way made sense. Times have changed.
The days of the static aggregate rating have ended. Just as Web 2.0 brought an end to an unchanging web experience, the time has come for ratings and reviews that reflect the personal interest of the reader and not some hive mind aggregate of reviewers.
Everyone can recall a time that a rating didn’t match up with your personal experience. A favorite restaurant turns out to have a low rating on Foursquare or a one-star score for an Amazon product you can’t live without. Even Snapchat has a three-star rating (at the time of writing) on the App Store and I’d be surprised if their 200 million users shared an equally lukewarm opinion of the app. Simple aggregate ratings just don’t add up.
This doesn’t mean Snapchat is really a five-star app for everyone. Its average of three-stars suggests that it’s probably pretty polarizing. It’s either a one or two-star experience for some or a four and five-star for others. The problem is how do you tell which camp you fall into without having to test for yourself?
You can always dig deeper. Many rating and review sites allow users to check all of the underlying information that has been received about a place or product. Unfortunately, with so many people submitting their opinions online, you’ll usually find yourself lost amongst the tens or hundreds of pages of reviews. Even if you had the time, how do you know who to trust? What if someone ranks a restaurant with five stars because it’s the best Mexican spot they’ve ever been to? How many Mexican spots have they really been to?
This isn’t limited to food, plenty of products have one-star reviews for reasons unrelated to the product itself. Amazon even has to put ‘verified purchase’ next to reviews because people were abusing the rating section to enact personal vendettas. Yes, you certainly will find useful reviews buried amongst the pile of bad and useless ones, but do you really have the time?
We don’t know who to trust and we don’t know what ratings will truly match our experiences. Fortunately, we now have the technology to solve this problem. We can now collect so much more metadata on user behaviors. We know what people enjoy, what they don’t enjoy, and even who their friends are. Most importantly, we can match users with this data.
The old aggregate rating no longer needs to consider every single review. Now, when a user sees a rating for a product they see the average across all users most like themselves. When they dig deeper for more meaningful information they don’t have to sift through mountains of irrelevant prose, but instead selected feedback from peers who care about the same things they do.
Technology gave us the ability to source knowledge from the world in ways once thought impossible. Today it has the ability to connect us with more meaningful (and useful) insight than ever before.
It’s time we review the way we’ve been doing ratings.
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