When you run a local business, it is easy to collect feedback on the spot. But what can you do when you have thousands or even millions of customers scattered around the world? Listening to customer voice is still important, especially when your business spreads over a few continents.
However, you’ll need the right tools for that. Hoping that you can achieve this only by having excellent customer support is not enough anymore. When the scale is an issue, automating as much as possible is the right answer.
Before AI and machine learning, the automatic analysis of feedback focused more on quantitative indicators such as product reviews by stars or ratings. Looking at what people were saying about products on forums or in complaint emails was usually done individually or by searching for specific keywords.
The advancement of natural language processing (NLP) offers a new universe of listening to what customers have to say. We are moving toward a world where companies compete on the grounds of customer experience. In this new landscape, the customer wants 24/7 assistance and availability of self-help tools, with as little waiting time and friction as possible. If the company can foresee and prevent future problems, that would become the next step in customer service.
What Can AI Do for Customer Feedback Analysis?
AI works best in pattern detection and classification problems. This is great news for customer support. If you can identify the issues and group them together, you can provide classes of appropriate solutions.
The advantage of using AI for customer feedback problems is that this method helps you dig into the stack of customer feedback expressed as sentences in our human language. This is far more revealing than simple star ratings because it gives insights about the nature of the problem.
Before NLP, the only way to improve services was to record customer interactions with call agents and replay them to get to the root of the problem, or to scan heaps of complaint emails. Now there is a better and faster way based on text analytics.
The outcomes of looking at customer feedback this way include learning the topic of the complaint, the scale of the problem and the customer’s dominating sentiment. TAI software does that by looking at the frequency of certain words and grouping them.
The easiest way to understand a text is to look at the frequency of keywords and their intensity indicators. A functional analysis can be involved to identify the relationships between words, as word pairs or groups are usually much more expressive than simple words. This works similarly to longtail keywords giving more insight into what exactly is the problem. If the same group of words appears in more comments with related intensity indicators, that shows a situation worth investigating.
Customers usually take the time to provide feedback when they have a strong (negative) opinion about a certain topic. Sentiment analysis offers a way to measure how intense the customer’s sentiments towards the brand are. The only problem with this method could be the use of sarcasm, which the AI system usually does not detect accurately.
Since AI works on pattern detection, it has a great ability to create groups of issues and tag them automatically. Based on the tags, the software can then classify an individual complaint and place it in the respective categories. Sentiment analysis shows how important the user thinks the problem is. This can assign a priority to the ticket. It should be applied carefully though, as most customers tend to be very biased and exaggerate their problem when in distress.
Automatic classification of customer tickets in predefined categories is also the option that allows either solving the tickets automatically or passing them to human agents. The good news is that the classification algorithms can also work with synonyms.
The AI system can use a combination of qualitative and quantitative analysis. By looking at the frequencies of each set of problems and comparing it with other parameters such as tdays or weeks, it can trigger alarms when a particular issue is trending.
Such anomalies could signal that a specific product or feature is causing more trouble than others and thus needs particular attention. Because AI operates dynamically and can adopt to new input as it comes, it can identify anomalies which the system designers didn’t took into account in the first place.
AI Use Cases for Customer Feedback
Social listening can help a company to stay on top of their customers’ problems by looking at the feedback in real time.
An excellent example of such a system in use is feedback analysis for gaming communities. Some online gaming platforms amass millions of users who constantly communicate their opinions on the product. They use dedicated forums or even a chat integrated into the game. Here, AI can help with automated feedback collection. NLP models can also be trained to understand the slang of a specific game, thus immersing into the community the same way a player would do. This is an excellent tool to evaluate how new releases are performing as well as keeping them bug-free and up-to-date.
Another application is to analyze the information retrieved from chatbots. Machine learning algorithms can automatically classify problems and intelligently route calls either to an automatic system or to a human call agent. Also, by looking at the successful communication patterns from previous interactions, a chatbot could even assist a new agent by suggesting the best responses on the spot. This feature can also be used in email marketing automation software, which would help to create a more personal message and improve customer communication.
Automated Social Listening Is On the Rise
Although it sounds like Big Brother news, social listening is already a reality. As NLP and machine learning systems are improving, this will become the norm in customer service. AI will impact almost all aspects of running a business, making a real difference in competition.
Author Bio: Marta Robertson has over 7 years of IT experience and technical proficiency as a data analyst in ETL, SQL coding, data modeling and data warehousing involved with business requirements analysis, application design, development, testing, documentation, and reporting. Implementation of the full lifecycle in data warehouses and data marts in various industries.