I’ve thought long and hard about preserving the integrity and usefulness of customer reviews, and my thoughts have consistently circled four key elements. Each one is necessary for maintaining a reliable ecosystem—a closed review site—where customer reviews can provide a consistently accurate window into the many different establishments and products that customers are constantly deciding between.
The goal here is to produce accurate ratings that can reliably predict the experience that a customer will have when visiting a specific store location, using a specific service, or owning a specific product. This gives clarity to the customer voice and ensures that people who leave relevant reviews aren’t suffering the indignity of competing with fraudulent voices.
1. LOCATIONAL PROXIMITY
This one is a given. Of the four things I want to talk about, this is the one that current review sites do tolerably well. Simply make sure that customer reviews are tied to specific store locations, services, and product models. If a brand has more than one location or a product has multiple similar models, make sure that reviews aren’t being intermixed between them.
Review sites built their framework around this idea, and it has gotten them this far. Unfortunately for open review systems, allowing customers to drop a review on any location or product also allows for human error in accidentally posting reviews to a restaurant down the street, a hotel in a neighboring city, or the deluxe product model instead of the basic model they actually used.
With open systems, it still comes down to reviewers accurately recalling and entering the location or product. With a closed system, validation codes automatically direct the review to the right location or product model.
2. TIME PROXIMITY
Since locational proximity is so ingrained in the current review system, I’m surprised how overlooked this element is. To me, a reliable review site must have the ability to keep reviews as relevant on the timeline as they are on the map. Give me the option to look at reviews from the last 30 days, 90 days, even 180 days. That type of trend tells more about the experience I can expect to have today than an average of ratings over a location’s entire history—especially when there are very few reviews to average.
Sources estimate that the average annual employee turnover rate for U.S. restaurants is near 100%.
Ok, so what? In the restaurant business, the service is the people. The time proximity of a review is crucial. Old reviews (more than 3 months old) should not be equally weighted with recent reviews because many of the staff that delivered those results (good or bad) have likely left the establishment.
And yet, many review sites still list the “Best” review first, regardless of how old it is. They obviously don’t get it. I am using the review site to buy a meal today—not two years ago. I don’t really care how it tasted then. I want to know what my experience will be like when I go there!
3. VOLUME
Customer reviews are the lifeblood, the meat, the main attraction of review sites. Having a substantial volume of reviews is the foundation for having a relevant and representative destination. Without volume, all the proximity in the world won’t do you any good. And, frankly, a lot of sites don’t have it. That’s because open review sites are volunteer gigs. It’s a struggle to enlist volunteers in the numbers required for predictive accuracy.
Review sites need to attract more reviews, and they need to attract them from all types of customers. Right now, they are running on social fumes that can only power the lucky few stores or products with the popularity and social following required to create a representative volunteer presence.
At Mindshare, we’re able to collect millions of customer reviews for our clients by using best practices for inviting and incentivizing customers. Not only does this create the necessary volume for valid customer representation, it gets more types of customers involved in the sampling—including those who aren’t polarized to the top or bottom of the ratings scale.
In the end, how accurate do you want your review-based decision to be? With 100 reviews in the last 30 days I would say you could predict your experience within +- 10%. With 10 reviews over 6 years… Yeah, I don’t think I could get a statistician to even guess.
4. VERIFICATION & FRAUD PREVENTION
If the review comes from a real customer, then it is representative. If it doesn’t, it’s fraud. We’ve got to eliminate fraud and verify that customer reviews are actually coming from customers.
This is the strength of the closed review system. Only closed systems have the ability to guard the doors against impostors. Open systems have an open-door policy that will ultimately kill them.
Unique validation codes are imperative for a useful customer review system. They allow only verified customers to post reviews based on actual brand interactions.
