Sentiment Analysis Is One Way To Make Sure Customers Aren't Falling Out of Love With You

Many companies are using sentiment analysis primarily to manage risks.


Imagine for a moment that you had all of your customers in one place. If half of them suddenly got up and walked out on you, that'd be a pretty clear signal that they weren't happy with your product or service.

In real life, you can't always get such a direct signal from your customers about their perceptions of your brand and what you're doing. Subtleties of language and culture, combined with ambiguities borne of logistics and within traditional feedback strategies, can leave you uncertain about what people actually think and want. Sentiment analysis is a technological tool that can erase some of this confusion and enable stronger decisions toward a good customer experience.

How Sentiment Analysis Works

Sentiment analysis is an AI- or deep-learning-based method of determining the meaning or opinion behind what people are saying in their feedback. It usually involves some degree of natural language processing (NLP), which is a linguistic science that seeks to apply the way people actually communicate in real life to computers. NLP, in turn, is built on specific vocabularies or "books of words," with each word or phrase in the lexicon being scored for positivity, negativity or neutrality. There are many lexicons available for engineers to apply to sentiment analysis tools, such as AFINN, TextBlob and VADER.

No matter which lexicon stands as the heart of a sentiment analysis solution, sentiment analysis is a two-part affair. First, the sentiment analysis tool must understand the relevant categories involved. Secondly, the tool must attribute a sentiment to those categories. In a typical analysis, the tool aggregates all of the responses you have and then runs an algorithm to sort the data into the appropriate categories and assign sentiments.

Sentiment analysis can involve many types of data, such as information from chats, voice recordings, videos, reviews and more. But it works especially well with the data customers leave in free-response text boxes on satisfaction surveys. You can also do sentiment analysis at different levels, such as by line, paragraph or over an entire document.

Assigning sentiments to data with sentiment analysis tools is relatively simple. The real difficulty with sentiment analysis processing is ensuring that the tool is capable of understanding how sentiment changes over time and navigating the often-complex nature of responses. A customer might like your coffee but not your brownies, for instance, or feedback might start out with a customer feeling or thinking one way and then becoming more satisfied or upset over time.

Applications for Sentiment Analysis

Many companies are using sentiment analysis primarily to manage risks. They generally use sentiment analysis to track the response to campaigns, assess customer satisfaction, get out ahead of problems before they spread through social media or other channels, and offer customized resolutions to prevent customer churn.

Call centers are a more specific place to apply sentiment analysis. Representatives can use real-time sentiment analysis to keep track of which interactions demand the most immediate attention, monitor how an individual session is progressing, or decide whether it's time to escalate an issue. They can also use sentiment analysis to ensure they use specific words or phrases that have demonstrable potential to alter a customer's mood and experience for the better. Businesses can also look at agent engagement to detect whether specific representatives are operating efficiently and representing the company properly.

Although most companies focus on the potential of sentiment analysis to address what is or could go wrong, you can also employ sentiment analysis when customers are happy. On this side of things, your goal would be to turn your satisfied customers into brand advocates who are willing to spread the word about you. You could use sentiment analysis to figure out who really enjoys specific products and then offer customized incentives for them to get others to buy or sign up.

Another positive opportunity for sentiment analysis consists of identifying unmet customer needs. Let's say you have a database with reviews of restaurants in your area. You could run sentiment analysis on the information in the database and see that people are talking about coffee shops a lot. You might see that people seem to have a really negative view of the coffee shops around you — that is, that there's a need for a good coffee shop. You could fill this gap by improving the coffee at your current store, or by starting an entirely new shop with a higher-quality, tastier coffee menu.

Sentiment analysis is growing increasingly sophisticated. Contextual semantic search, for example, can filter and analyze data based on concepts. This means the tool would still be able to tell what the feedback was about and how it was intended, even if the customer didn't use specific keywords or phrases in their response. It also allows business leaders to more easily filter out information that's not relevant to obtain deeper, more accurate insights about what's going on. As the technology improves, use cases will continue to expand, but options that are customized to your unique needs rather than out-of-the-box have the greatest potential to provide a competitive edge.

Customers Are Eager To Express Themselves, Sentiment Analysis Can Help You Listen

It's important to note which companies would and would not benefit from sentiment analysis. Smaller companies would not necessarily benefit from this kind of analysis. But as larger companies invest in customer experience programs, they often deploy surveys to gather feedback. It's not unusual, in this context, to receive thousands of new text responses per month. Companies receiving thousands of text responses and wishing to unify them efficiently can use sentiment analysis to understand the root cause behind a low score. Such high volume can justify the investment because they would otherwise spend an inordinate amount of time sifting through responses without being able to attend to the real problems underlying any criticism.

Sentiment analysis can be a powerful tool for many elements of risk management, such as reducing customer churn. It can also help companies fill market gaps and turn satisfied customers into strong brand advocates. With so many potential applications, and with technologies and opportunities for customization only getting better, explore how to integrate it into your operations in a way that matches both your budget and vision.

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