In 2009, YouTube famously abandoned its star ratings and reviews system after it discovered that the majority of videos were being given 5 stars while those who loathed videos gave a 1 star – with very few ratings in between. They deduced that ‘’the ratings system is primarily being used as a seal of approval, not as an editorial indicator of what the community thinks about a video.’’ (source)
Fast forward to mid-2015 and Google Music abandoned their 5 star system in favor of a thumbs up/down rating. More recently, Netflix’s CPO has made it clear that the entertainment streaming service wants to ditch their five star system.
The 5 star ratings system has become a ubiquitous presence in the online shopping experience. Its influence is significant and can make or break the success of products and services. In a recent study, Entrepreneur Magazine noted that 35.3% of survey respondents said that a 3-star rating (or below) would dissuade them from choosing to eat at a particular restaurant. (source)
There does, therefore, seem to be a disconnect between the power that a rating has over a product or service, and the value that retailers are increasingly placing in 5 star ratings.
Here is a roundup of the top 5 complaints that retailers have with the star rating system:
Every shopper is different and even those looking for similar products might be motivated by different reasons. The key question here is intent.
Take laptops for example. You might be looking to buy a laptop for a number of reasons – gaming, education, software development, design etc. A list of laptops with 4 stars is therefore not going to be a useful indicator of whether it is the right one according to your shopping intent.
Shoppers are forced to spend additional time researching each product and wading through the mountain of reviews hoping to find one that matches their intent.
- Reviewer motivation
One of the main criticisms of 5 star reviews is that the motivation to select between an arbitrary 1-5 lends itself most to those that either love or hate the products they are reviewing. Looking at the YouTube example, people tend to not leave ratings of 2 or 3 about ‘meh’ experiences. Text reviews on the other hand, allow reviewers to go into more detail about the specifics of their experiences. Opportunities to write about these ‘meh’ products and the particulars about their 2 or 3 star experiences provide a richer, more useful review.
- Range compression
This is the technical term given to the phenomenon where most people give a similar positive or negative answer leaving your average at around 4.5 out of 5. Shoppers generally give high ratings for the products they like and then abandon the products they don’t. This results in many categories having similar 4.5 stars for products that have been reviewed by a certain mass of people.
- It’s a tie!
With so many products settling on a similar score, the process of referring to the star ratings then becomes less useful. It everything is tied then you can’t differentiate between items being rated. This then prevents shoppers from moving forward with their shopping experience. Retailers are finding that many abandoned cart experiences are due to this ‘paralysis by analysis’ when shoppers are forced to carry out extensive research and assessing the reliability of ratings.
Finally, a common complaint about star ratings is that people use subjective scales and so the averages don’t mean much anymore. It is easy to have two people who give an item the same four-star rating but actually feel differently about it. Because a star rating scale itself is so ambiguous and uncertain, so too are the ratings submitted to it. Many users will not use this scale as intended even when retailers provide explanatory text. Other reviewers will use the scale as intended, but that usage is always based on their subjective ability to understand the way the scale should be used. Smaller websites or recommendation systems with fewer ratings will suffer due to the subjective nature of its small rating sample.
Aspectiva analyzes massive volumes of consumer opinions from across the web, turning them into comprehensive and valuable insights. Based on Artificial Intelligence and Natural Language Processing technologies, we leverage User Generated Content to help online shoppers search for the products they want and provide the recommendations to enable them to make informed purchasing decisions. Supporting eCommerce sites across any type of product or service, Aspectiva significantly increases shopper engagement and conversion rates.