Have you wondered what customer sentiment analysis really is? Let’s get to the bottom of it. But first, let’s set some context. Customers are looking for positive experiences. Brands do too!
“People will forget what you said, people will forget what you did, but people will never forget how you made them feel”
This powerful quote by Maya Angelou explains why we feel so passionately positive about some brands and so strongly negative about others. A Mckinsey study has shown that brands try hard to evoke positive emotions for a reason: Feelings are strongly correlated with profits! After a positive customer experience, more than 85 percent of customers purchased more, and after a negative experience, more than 70 percent purchased less.
You might be asking:
- How can they make customers feel positive or negative?
- Who are the customers who had particularly negative experiences? Why, and how can they help them?
- How can they engage customers who are indifferent?
What is customer sentiment analysis?
Customer sentiment analysis determines how customers feel based on their language. The basic idea is that strong feelings lead to emotionally laden words. Algorithm creators use this information to figure out two parameters:
- Sentiment polarity shows whether the feelings are positive or negative.
- Sentiment magnitude shows how strongly customers feel.
The algorithms determine customer sentiment using one of these two approaches:
- Dictionary: Look up polarity and magnitude given the word or phrase. Reverse if there is a negation.
- Categorisation: Learn from examples how to categorize any new piece of text using Machine Learning.
If you are wondering how algorithms handle sarcastic comments, such as “I love to wait hours until my dinner arrives,” the short answer is: they don’t. You can find the long answer in our related post about sarcasm in customer feedback.
How can your company benefit from customer sentiment analysis?
Both the shop itself, Dick’s Sporting Goods, and their potential customers can see an overview of what’s good, what’s lacking. The selection looks good: Customers will find it easier to find the right product, and the shop could emphasize this in their marketing. Return policy and customer service are lacking: Customers will feel that the purchase will be risky, and these are the two areas the shop should improve.
Strategic Insights Based on Customer Feedback
Modern customer sentiment analysis solutions can provide deeper insight than this. They can capture what specifically people don’t like about the return policy, and after the business has taken steps to fix the issue, or improving a process, they can track how that has improved customer satisfaction. They can also differentiate between feedback that is frequent and feedback that influences satisfaction scores.
Operational Improvements Based on Customer Feedback
Customer sentiment analysis can help brands in a more actionable way. It can pick out from large volumes of feedback which customers need extra care. They may be mentioning issues such as making a payment, repeated frustration with the call center, or intention to cancel the service. Tracking such themes and introducing required processes is the key when it comes to closing the loop: from receiving feedback to figuring out which action will result in a positive customer experience.