Customer sentiment analytics

What is customer sentiment analytics?

Customer sentiment analytics is the process of analyzing and understanding emotions behind customer feedback, support calls, and other messages. Sentiment analytics turns all these millions of categorical data into chunkable reports, making it easier to understand for marketing, sales, and customer support teams.

Imagine a brand trying to understand how their audience reacts after a new product launch. There is a large bunch of text messages, feedback form data, and customer support interactions lying around. With the help of customer sentiment analytics, they could get a summarized version and make sense of it all—are they frustrated, are they happy, what exactly is their problem, and more.

Example of customer sentiment analytics

An example of customer sentiment analytics would be a retail or an eCommerce brand, who extracts what their customers talk about them, all over the internet (product reviews, social media posts & comments, brand tags, etc). As someone who always launch new collections and run global operations, this helps them improve their design, experience, and feel of the product, getting and filtering out real-time feedback.

Why is customer sentiment analytics important?

In today’s experience-driven world, understanding customer sentiment is more important to fix problems proactively.

To not let feedback become clouded and noisy: Whether it’s user messages or feedback, the more it accumulates, the more it becomes noisy.

Real-time insights from feedback to act on instantly: Modern customers expect instant actions. They want to be felt and heard. Through customer feedback sentiment analysis, you can get real-time insights that’s useful, if applied instantly.

Customer voice is becoming paramount: With online shopping and products being more comparable with one another, customers rely on feedback and sentiment analysis exactly helps with that—understanding what exactly they want from the product.

To make data-driven decisions based on the voice of the customer: KPIs are good. But what if you could drive better, well-rounded decisions by considering how people actually feel about the product? That’s what sentiment analysis does.

Identifying bad experiences as soon as they happen: One bad experience or comment could ruin your brand reputation. Customer sentiment analysis senses it and allows you to deal with it before it escalates.

Proactive cx: Predict what your customers are about to experience and prevent any setbacks and dissatisfaction they might go through.

How does customer sentiment analytics work?

Customer sentiment analytics require data integration, AI, and NLP to turn massive unstructured data into understandable feedback.

Collect data: This is scraping data from all data points and pooling them in one place.

Process the text: This involves cleaning up the text and removing noise, tokenizing and breaking down large chunks of texts into words & phrases.

Score emotions: Detecting emotions can be either rule-based, using a predefined structure of dictionaries for positive, negative words, and neutral words, or NLP in sentiment analysis, which analyzes the tone of every word, does speech tagging, entity recognition, and categorization through advanced ML models.

Aggregated insights: Based on the assigned score and weightage, aggregated feedback is obtained, which explains the results. For example, 80% positive, 10% neutral, and 10% negative comments, with relevant keywords and highlights for each category.

Presenting them in easy-to-understand formats: The insights obtained from the above information could be presented better for quick understanding. For instance, dashboards and automated reports sent to respective stakeholders.

AI in customer sentiment analysis

Text classification: Segmenting words that fall under each category: positive, negative, and neutral, using clustering ML models.

Understanding context beyond words: What if, ‘I really love this product that I wasted 20 minutes on it.’ is written out of sarcasm? AI models could be trained to read between the lines and detect the exact sentiment behind.

Detecting emotions beyond happy and sad: There are more than 1000s of specific reactions an average human can show: frustration, blissfulness, surprise, disgust, fear, and more. Finding that out clearly helps brand react the right way, than what they could with generic sentiment analysis.

For feedback that’s multi-lingual: Some international users may include multi-lingual comments. Incorporating AI could retrieve accurate translations, thereby, propelling proper sentiment analysis.

Improvising feedback models: Fixed, rule-based systems find it difficult to improvise and refine themselves so that it could detect complex sentiments.

Trend prediction: A sudden rise in certain sentiment detection could be picked up by a model and alerted before the team could recognize.

AI based sentiment analysis for customer feedback is the great way to automatically capture and understand what people are talking about your business. If you are becoming customer-centric and need to build the right foundation, customer sentiment analysis should be your priority.