When it comes down to it, a huge majority of the moves brands make, are to increase customer satisfaction. Customers are the key to ensuring success, and data analysts spend a huge amount of time measuring and tracking their behaviour.

It is one thing to satisfy your customers, but to ensure your customers are receiving the highest quality experience when interacting with your brand is paramount when it comes to loyalty and retention.

Why is sentiment analysis important?

To ensure customers are happy, sentiment analysis is essential. It enables you to understand what consumers are saying when they talk about your brand and services. It allows you to understand what makes them happy, and in doing this, improve your strategy.

The digital world is booming, and it is now deemed unusual when a brand doesn’t have a heavy online presence. We can only assume that the future will bring with it an increasingly digitised market. This means that the conversations customers have are going to take place in the same way.

Twitter and LinkedIn are great places for customers to share their experiences, and with social listening becoming a technique which is growing in popularity for brands to understand their customers, the two naturally go hand-in-hand. Twitter is, in fact, a common platform for sentiment analysis to take place. Customers flock to tweet about their experiences, and brands can educate themselves and upgrade their services to suit this.

What exactly does sentiment analysis measure?

Sentiment analysis is simply the measure of whether a piece of text – a comment or tweet, for example – is positive or negative. Data analysts can use AI, as well as their knowledge, to identify the tone of a customer’s feedback. This can then be used to influence the techniques which are then implemented in business and marketing strategies.

There are three main categories of sentiment analysis:

  1. Knowledge/rule-based techniques: Assessing text according to unambiguous adjectives such as sad, happy, bored, pleased et cetera.
  1. Statistical methods: Using machine learning (ML) techniques for deep learning. This enables much more accurate information.
  1. Hybrid approaches: Using elements from ML and knowledge representation to detect semantics that have been expressed in a more subtle way.

The different algorithms used in sentiment analysis work to show data analysts exactly what and how their customers are discussing a brand. These techniques are only going to become increasingly important as more and more brands move online.

Using this technique will allow you to truly understand your customers, in turn resulting in a much more positive brand-customer relationship. It should enable you to identify the causes of the problems you may not have been able to solve, meaning you can work on improvement, ultimately boosting your brand reputation.