Crowd-sourced rating systems like Yelp and TripAdvisor have become part of many of our lives. Many of us rarely venture out to a new restaurant, or stay at a hotel, without first checking the rating on a crowd-sourced review app or website to see what others think about it. But the evolution of ratings has started to reveal a dark side.
The evolution of rating systems was bumped up with the advent of the internet and social media. Almost overnight, any human with a data package could be a critic of, well, anything—businesses, books, hotels, restaurants, taxi drivers, and the list goes on.
Though rooted in new technology, these rating systems serve two purposes simultaneously—guidance for customers and information for businesses. They also give “the crowd” tremendous influence. The mere fact that any anonymous user can have power over a business is where it starts getting scary.
One low rating too many for an Uber driver and he can be deactivated. For every star a restaurant loses on Yelp, revenue falls by between 5-10%; reach one star, and you can pack up your pots and pans because your restaurant will likely go out of business.
With the quantity of data and our ability to store and process it growing every minute at a phenomenal rate, and open source algorithms democratizing data processing, the possibilities are endless. The data exists. What we choose do with it can ultimately be either Orwellian or Utopian.
When all you have are ratings numbers, your take-away is by definition, reductive. While these ratings may be useful to users, they are of limited value to companies. For complex human behavior, such as interactions on a web or mobile site, you need more complex summary data on which to base corrective action.
Experts think long and hard about how customer data can be used as a force for good, while protecting individuals and helping the businesses they frequent improve. Here are some lessons that others could benefit from. The ideal is to use automated, unbiased method (with objective criteria) that can measure user satisfaction levels on digital channels. The best way to accurately measure satisfaction is by not mentioning that this is what you are measuring. In other words, focus on what people are doing- their behavior on your site or app - and not only on what they are saying.
For example, take all the customers who left your sales funnel and then go back to the data you have, and using machine learning or other methods, try to find patterns. Let the data teach you why it happened, instead of asking questions of your data.
By literally seeing what makes customers happy, and what makes them frustrated, you’ll gain a far deeper understanding of what’s working (and what isn’t), far better than any rating could express. This is not to say that ratings are obsolete—for people looking for the best workout or restaurant in an unfamiliar city, resources like Yelp and TripAdvisor are crucial. But as a business, there is very little to learn from ratings scores. As the saying goes, talk is cheap—companies should pay more attention to what their customers are doing than to what they are saying.
The author is head of data science at Clicktale
Published by Globes [online], Israel business news - www.globes-online.com - on July 18, 2017
© Copyright of Globes Publisher Itonut (1983) Ltd. 2017