Using AI to Track How Customers Feel — In Real Time
The most common methods of tracking customer sentiments has a big blind spot: They can’t pick up on important emotional responses. As a result, qualitative surveys, like Net Promoter Score, end up missing critically important feedback. Even if they provide a positive score, customers often reveal their true thoughts and feelings in the open-ended comment boxes typically provided at the end of surveys, and AI can help companies make use of this valuable data to better predict customer behavior. Specifically, there are six benefits for adopting AI to analyze this feedback: It can 1) show you what you’re missing in your qualitative surveys, 2) help train your employees based on what’s actually important to customers, 3) determine root causes of problems, 4) capture customers’ responses in real time, 5) spot and prevent declines in sales, and 6) prioritize actions to improve customer experience.
In order to succeed, firms need to understand what their customers are thinking and feeling. Companies spend huge amounts of time and money in efforts to get to know their customers better. But despite this hefty investment, most firms are not very good at listening to customers. It’s not for lack of trying, though — the tools they’re using and what they’re trying to measure may just not be up to the task. Our research shows that the two most widely used measures, customer satisfaction (CSAT) and Net Promoter Scores (NPS), fail to tell companies what customers really think and feel, and can even mask serious problems.
For years, quantitative surveys have been the industry standard. They ask customers a single question: On a scale of 0-10, how likely are you satisfied with this company’s product or service? Or how likely are you to recommend this product to a friend or colleague? While these surveys are resource intensive, and customers are finding them increasingly intrusive and are becoming less inclined to participate, they’ve remained a core piece of companies’ strategy for understanding their customers.
The problem is these surveys can’t pick up important emotional responses and end up missing critically important feedback as a result. In our research, we found that customers often score firms highly in surveys even when they experience significant problems with their products or services — a vitally important response that they miss. And by masking significant customer dissatisfaction, these surveys can cause firms to lose customers without knowing why.
There is, however, a goldmine of good data if you know where to look and how to analyze it. Customers often reveal their true thoughts and feelings in the open-ended comment boxes typically provided at the end of surveys. In general, the content of these comments offers a much more reliable predictor of a customer’s behavior. Yet, these are often ignored, and if used at all, are typically used after the scores are computed.
The good news is that most companies have the power to correct this oversight relatively quickly. We developed an AI-driven approach that practitioners can use as a model to adjust their customer feedback processes accordingly.
It’s easy to see why quantitative surveys became popular: They’re a way to ask a huge number of customers how they felt. Qualitative approaches, like focus groups or manually reading and analyzing customer feedback, were too labor intensive to scale. Now, technology has changed what’s possible, and tactics need to catch up.
The first and most significant change firms should make is to flip where they’re investing in their analysis of customer sentiments. They should start with the qualitative comments, and then turn to the results of their quantitative surveys. If they have the right tools to analyze the qualitative data (e.g., customer relationship management systems, social media, customer reviews, emails, call center notes, chatbots, etc.
