The Failure of 5-Star Rating Systems and Net Promoter Scores
Marketing heads have started realizing that there is a problem with star rating systems even as customers rely on them to quickly judge a product or service. Companies have realized that there is a gap in customer satisfaction and the results of aggregate star ratings and Net Promoter Score (NPS) systems because they are not seeing sales numbers converting the way they should based on the insights from these methods.
In this post, we will discuss at length why star ratings don’t cut it anymore as well as the challenges of NPS. We will also see how brands can use cutting edge modern technology to overcome these hurdles and get true consumer insights.
The Advent of the Star Rating System & Net Promoter Score
The rating systems we know today evolved out of the rating systems that were used long back during the last century. For example, film ratings started during the 1920s and hotel rating systems became popular in the 1950s. At their core, all these systems rely on customers providing feedback on a product or service. In turn, this allows other customers to choose the product of service that’s right for them.
Both these systems are probably the most popular rating methods available today even though there are many challenges with star rating systems as well as the NPS. They both aim to provide an easily understandable, number-based customer satisfaction score that allows businesses to gauge how satisfied customers are with their products or services and track this score over time.
The NPS shows businesses how likely it is that a customer would recommend their products or services to other customers. Usually you would get an email or a pop-up during an online purchase that will directly ask you how likely you are to refer the product to your friends. If you said, less likely, the pop up would ask you to write a comment and specify why. The higher the score, the more a customer is likely to promote the product, and vice versa.
What Are the Challenges With the Star Rating System & Net Promoter Score?
Customers look at star ratings before they buy a product, choose a hotel or spa, eat at a restaurant, and so on. Companies too use rating systems and net promoter scores (NPS) to understand customer satisfaction levels to base business decisions upon. After all, tangible rating systems offer a simple solution to telling people how others feel about a brand, product, or service. It’s no surprise that star ratings on product websites and the net promoter score used by banks or retail outlets have become so popular. So what are these problems and challenges of these rating methods? Let’s look at them in detail.
- Ratings can be biased
The issue is that there are certain challenges of NPS and even stars because the methods behind these systems are flawed and don’t give a true aggregate reflection of how customers feel about a brand. There are many reasons for this, not least of which is that customers may give a higher rating while writing about the many issues they faced with the brand, or it could be the opposite, where the product was excellent, but a rude exchange with the product delivery team made them give a bad rating to the product on the whole.
- They don’t give useful information
A significant problem with the star rating system is that where it might give a broad indication of how customers feel, if they don’t write anything, companies don’t know what to fix if the rating is poor. Similarly, while star ratings or NPS scores can indicate that customers recommend a specific product or services, they don’t show why the customer would do so. In other words, if a star rating shows that customers like a product it doesn’t show why they feel that way. This is probably one of the most significant problems of NPS.
- Customers may not always be truthful
Although the reasoning behind star rating systems and the NPS is sound, they do present certain challenges in practice. One of the main challenges of NPS is that most people rate products or services positively, irrespective of their true experience. This is also why there is a problem with star rating systems. For example, the average star rating on Amazon for all products is 4.2 out of 5. In the same vein, more than half of the reviews of products on Amazon are 5-star ratings. Likewise, almost half of the reviews on Yelp are 5-star ratings and most of the driver reviews on apps like Uber and Lyft are 5-star ratings.
The main reason for this is that customers don’t share their honest opinions about a product or service because they worry that their feedback might damage a business’s reputation or they may be discriminated against for being difficult customers.
- Ratings are subjective
Also, another problem with star rating systems is that not all customers share the same view on what an excellent product or service is. This is because customer ratings, to a large extent, rely on subjective experiences. This creates a significant problem as customers can’t rely on these scores to determine whether a specific product or service will be right for them or not.
- Not targeted towards an audience set
These systems also have some other drawbacks. For one, these ratings don’t take customer demographics into account. For example, consumers of different age groups will prefer different products or services and as a result may give different ratings to the same product. You may find that your star ratings or NPS scores change depending on the demographics of your customer base.
A Rating System Doesn’t Need To Be Simple
The problem with not knowing why customers feel a specific way is that businesses won’t be able to improve their products or services. For example, if a customer rates a product 2 out of 5, how would the business know what part of the product the customer didn’t like? So, is the answer to use a wider range of ratings? This too doesn’t solve the problem because the core of the issue is that there is no way of knowing the real reason behind the scores unless one reads each and every customer review or comment.
This is a herculean task, and that’s why many brands shy away from looking for in depth customer insights that can be gained from social media sentiment analysis or emotion mining from surveys or reviews. Additionally, brands may be overwhelmed because there are too many variables that influence customer feedback and opinions, and trying to gain insights manually with a one-size-fits-all approach is going to be a futile effort.
What Is the Way Forward to a Better Customer Insights Tool?
Considering the above, there is a better way to get valuable insights into customers’ opinions and feelings. AI-powered customer sentiment analysis allows a business to extract meaningful information from voice of the customer analysis, regardless of the format (text, video, image) and use that to build powerful strategies for product innovation and improved customer support.
A machine learning sentiment analysis platform uses several ML tasks to analyze data and dig for insights. It uses text analytics to extract entities and features from data and proceed with granular aspect-based sentiment analysis to offer precise customer insights through a sentiment analysis dashboard. Let’s look in detail at some of the aspects of a sentiment analysis API.
- Natural language processing (NLP)
A sentiment analysis API uses NLP to extract the sentiment from videos through video content analysis, perform social media listening, and analyze content from reviews and blogs by using text analysis. This enables companies to gain valuable insights into how their customers feel about their brand, no matter where these customers are discussing it.
- The ability to deal with open-ended questions
Although ratings can be helpful in determining how customers feel about a product, as mentioned earlier, they don’t show why customers feel that way. This is where open-ended questions in surveys help because they help provide more in-depth detail into customers’ opinions. To analyze these responses and extract insights from them, an AI-enabled sentiment analysis tool is the only solution because it can analyze answers that are not from a predetermined set. Read more.
- Sentiment detection for ratings
Sentiment analysis gives you the ability to gather insights from reviews and ratings across thousands of websites quickly and easily. This, in turn, shows you how customers feel about your products, irrespective of the platform where the customer rated the product or service.
- Semantic clustering
By using semantic clustering, a sentiment analysis platform groups semantically similar words together, no matter the language used in a review, comment, or blog post. This means words like cute, pretty, beautiful, all are clubbed as semantically similar. Thus it clusters similar sentiments based on their underlying semantic meaning. This gives companies better overall insights into how their customers feel without giving double negatives or positives. This then allows you to implement the right strategies based on accurate data.
Outpace Outdated Rating Methods With AI
While there is value in using NPS or star ratings to assess how customers feel about your brand or products, these systems do have limitations. One significant problem with star rating systems is that it doesn’t show you why customers feel the way they do. To overcome this issue, and also the challenges of NPS methods, you need to collect more voice of the customer data through reviews and customer forums and then use a sentiment analysis solution to mine this data.
The AI platform can analyze this real authentic information collected from various sources including social media like TikTok or YouTube, and give you information on aspect-emotion co-occurrence, entity extraction and categorization, automatic language recognition and analysis, as well as video AI so that you know exactly what customers want from you. These are the actionable insights you need for real business growth and increased market share.