How To Leverage AI and Language Processing to Analyze Customer Reviews

Do you remember “Dell Hell”? It’s the story of how one customer review took down the brand reputation of a multinational technology company almost overnight. 

A journalist wrote an article about their poor experience with Dell’s customer support team and it quickly spread within their friend group before it got picked up by the New York Times. Although Dell eventually came back from the incident, it’s a learning lesson for us all: monitor and respond to your customer reviews or risk ruining your brand.

The echoes of Warren Buffet’s famous quote, “It takes 20 years to build a reputation and five minutes to ruin it” has never been louder. The social sharing power of the digital age has an immense power to publicize online reviews, help consumers make an informed purchase, and keeps brands on their toes. Here is the lowdown on its impact on consumers:

Not only do these ubiquitous reviews impact a potential customers’ impression of your business, but also have the power to seriously damage your brand image and bottom line. Reviews contribute to SEO, which accounts for approximately 10% of rank factors on search engines. So, no matter how much effort you put into being on the first page of search results, poor reviews could cause you to land on the second page or worse.  

Luckily, there are a couple of different forms of AI technology that can keep your brand on top of reviews and at the top of search results.

 

Machine Learning and Natural Language Processing Open the Gates to Understanding Customer Sentiment 

Machine learning (ML) and natural language processing (NLP) are the two most commonly used AI technologies that help make sense of large sums of customer review data. This data process enables teams to analyze the collected customer input and generate metrics from all reviews to provide deeper, more actionable insights. As a bonus, it can also sort feedback into categories based on topics such as price, quality, and effectiveness to help your team organize sentiment.

Sentiment analysis leverages ML combined with NLP for processes such as monitoring opinion, gauging user feedback, or reactions to brand and products on social media. This allows your company to track what is being said about your product or service and make improvements based on customer feedback. It also serves as a huge competitive advantage, especially if your competitors aren’t listening to their audience.

 

Employing the Right Type of AI 

Although ML and NLP are sometimes used together, there are differences between when to use one versus the other. 

ML can be utilized to make predictions based on historical feedback data such as sentiment trends concerning a specific aspect of your product or service. This provides marketers with valuable insights into the future actions of site visitors, such as the likelihood of churn or how to increase customer loyalty. ML is also used in text analytics to detect patterns in large quantities of feedback including sentiment, entities, parts of speech, and other text developments to categorize it, making insights more accessible. 

NLP on the other hand, determines the sentiment of individual reviews by grouping strings of text together to discern their meaning. It extracts entities and topics such as people, places, and company names from text. 

Determining which AI technology is best for your business should be based on your company’s goals and specified applications. For example, NLP techniques are used to extract emotions from raw text. They are often used when trying to determine if customer reviews on social media posts are positive or negative and why. Machine learning is more focused on generating deeper insights and stats like the number of positive or negative reviews about a specific topic. 

 

5 Benefits Of Using AI for Customer Reviews

Leveraging AI to analyze your customer reviews offers many benefits to your organization including:

 

ROI and Cost Effectiveness 

An AI solution to monitor customer reviews and generate valuable insights is significantly more cost-effective than trying to handle the process manually. The results speak for themselves by improving your customers’ experiences while giving them what they want from a product or service.

 

Agility to Your Operations 

In today’s competitive marketplace, it’s critical to stay aware of customer sentiment and respond to it quickly. Companies who fail to do this will pay the price through customer churn and reduced sales caused by unaddressed customer issues and preferences.

 

Making Customer Feedback More Actionable

AI translates massive quantities of customer input into actionable insights so your company can make changes and adjustments more quickly and stay ahead of the competition.

 

Voice of the Customer

Leveraging AI to analyze customer reviews enables your organization to truly hear your customers’ needs, expectations, feelings, and product improvement requests. Being in tune with this makes it possible to plan changes to your product and services accordingly. If you don’t have access to this information, how else will you know what they want?

 

Insights to Drive New Strategies

Hearing what your customers have to say also makes it easier to prioritize your company’s next steps. Taking the mystery out of which is the best direction for your company in the future increases the odds of success.

So, if you aren’t already taking advantage of all the benefits of using AI and language processing to analyze customer reviews, what are you waiting for? It’s time to get started. 

Want to know more? Drop us a note here and let’s chat about how to streamline your customer review process. Contact info: sales@taskus.com

TaskUs

November 26, 2019