The Old 1-5 Star User Review Process is Being Transformed for Better Customer Experience

Image - Pixabay

The advent of the internet was accompanied by the golden opportunity for businesses and companies to advertise as well as offer their products and services online. In this information and communication age, it is almost unheard of to have a business without a website or some other form of online presence, and the reason for this is easy to see - one can reach a wide range of audience/potential customers on the internet at a faster and cheaper rate than without using the internet.

However, as with every form of innovation, this development came with its disadvantages. While it is easy to showcase one's business online, potential customers/users had no way of knowing which businesses to trust (as in the case of ordering products online where there is a risk of being defrauded), which applications to use for the best user experience (as in the case of social media), and so on.

This difficulty birthed the 1-5 star review process, which allowed users to rate the quality of products or services they received from a particular business so that others could use this as a guide in deciding whether or not to patronize that business.

Image: Amra Lear

This was a welcome development as customers could now make informed decisions before opting for a product or brand. Statistics have shown that 94% of customers mentioned that online reviews gave them a reason to avoid some businesses and 63.6% said they would Google reviews before visiting a business.

Image: ReviewTrackers

However, this review system was fraught with a number of problems. To start with, using this review process, there is a general lack of incentive for the customers to give reviews and where customers have chosen to give one; chances are these customers have had extreme experiences - either a really bad one in which case they are livid, or a really good one in which case they are elated; thus prompting them to leave a review on the website.

It has been found that those who had average experiences with a product or service are generally less likely to leave reviews. This tendency sometimes ends up creating selection biases where the reviews about a business are either extremely good or bad.

This is where tech startups like Revuze, Aspectiva, Spirable and so on come in. Revuze, for example, is a New York-based company that uses AI-enabled solutions to provide a new way of rating which uses Customer satisfaction score (CSAT). The company focuses on transforming Customer Experience (CX) Insights for users and they plan to carry out this transformation by providing self-service platforms, eliminating the need for experts and also eliminating the need for IT.

Image: revuze

The above-mentioned tech startups make market research as simplified as possible so that any product, marketing, and consumer insights experts can make better, well-educated business decisions faster.

These startups use an autonomous artificial intelligence (AI) solution which turns any type of data (eCommerce, call center, emails, social, restaurant, etc,) into market insights about a brand, as well as its products, competitors, and important features.

Their AI-enabled solutions promise to be multidimensional compared to the simple one-to-five star rating system which does not provide sufficient information on different product attributes. 

Because the one-to-five star review process provides the average positive and negative opinions of customers, its effectiveness has to be questioned because it hardly improves consumers’ decision making. However, this AI-enabled multidimensional rating system will improve the quality of product information conveyed in its ratings.

When customers conduct a search, the solution provided by Revuze, Aspectiva, Tact, Spirable and so on identifies the attributes of the products from the product search.

For instance, an online search or review for a diaper will display attributes such as “softness”, “sensitive skin”, “overall quality”, “absorbency” or “wellness indicator” while a search or review for a wireless soundbar for televisions may display attributes based on several factors including "quality," "sound," "bass," "Bluetooth specifications," "remote capabilities," and "overall features." These attributes are suggested based on the analysis of user-generated content such as reviews and previous shopping behavior.

Asides helping businesses in monitoring their products/services and their performance in the sales market, these startups are transforming the way reviews are done and improving customer experience in the following ways:

1. Provision of Efficient Self-Help Services for Help-Seeking Customers

The average customer would rather navigate their way around a website or search for what they need themselves rather than through interaction with support agents or a chatbot. However, this self-service process can get a bit tedious when the customer has to try several keywords on a search engine and also navigate through several web pages before they find what they are looking for.

This setback is now being taken care of as recent improvements in AI has proven to be a useful tool in ensuring that customers are given the right information to solve their problems when carrying out searches online.

Through algorithms developed using AI, machine learning and natural language processing (NLP), businesses are capable of learning which help articles can best solve a customer's problem and then recommend that article to the customer.

2. Better Tailored Contents to Suit Customers' Needs and Requirements

Search/content personalization is an invaluable part of customer experience. Customers are always pleased to find products and services that seem to be tailored to suit their needs, and as a result of this, businesses are continually seeking ways to make customer's experiences more unique and personalized.

One of the telltale signs of non-tailored content is when customers keep going back and forth quickly between various help centers or FAQs, or the articles on a site; also if they give feedback stating that they did not find the help they were looking for.

Based on data gathered from reviews and customer activities, AI can be used to create more tailored content for a specific customer base through the use of deep learning models which can catch the common words and phrases used and then make useful recommendations. This, in turn, will help customers in having a better search experience which is tailored to their needs.

3. More Efficient Customer Support Agents

Research has shown that customer care agents generally spend 20% of their time searching for product information requested by customers because they themselves may not know what the customer is talking about, and this can have a negative effect on customer satisfaction.

AI is used to generate automated self-service suggestions from user data to provide businesses/agents with the insight needed to serve their customers better.

4. Enhanced Customer Engagement Using Data-Driven Suggestions

Our activities and interactions online accumulate into large amounts of data that can be used by machine learning algorithms which in turn help in AI's predictive proficiency. The data recorded from customer service interactions online through ratings and other such factors can also be used to improve customer experience.

By assessing the details of previous experiences and ratings, an AI tool can predict whether a current interaction will result in a positive or negative evaluation by the customer.

5. Organizations Have More Time to Improve Other Aspects of Customer Satisfaction

The recent innovations in AI have created a situation where humans are not needed to handle as many tasks as they had to previously. AI can perform a plethora of functions in very little time. Consequently, businesses can channel their manpower into other areas that cannot be handled by AI and machines. Some of these areas include phone calls (i.e. customer care lines), live chats with business personnel and so on.

Conclusion

The use of AI has revolutionized the way customers and business owners interact online with the use of data gathered from several people as well as those gathered from a user's online activities (as in the case of personalized data).

The use of various reviews on several interfaces of a company's products and services has helped in creating a more user-friendly environment where feedback is used to make improvements and deliver features that are better tailored to suit the needs of customers. This goes beyond the old 1-5 star review process which left many other important areas of businesses untouched.