Customer Sentiment Analysis

Customer Sentiment is how customers feel different emotions when they interact with brands. The emotions could be positive, negative or neutral when customers are engaging with the brand, it can be also measured as anger, sadness, happiness, elated and others.

It is important to understand the journey and the emotions that the customers go through during the buying process, as it helps the brand understand how they can have repeat customers and make their experience worthwhile.

Customers' perceptions of a brand, service, or product have the power to make or break an e-commerce business. Because of this, it is imperative to measure client satisfaction. Finding recurring themes in consumer feedback helps brands better understand the pros and cons of their products or services so that changes may be made for the Perfect Product Market Fit (PMF).

Customer sentiment analytics is a data-driven approach to measure the positive, neutral, or negative feelings expressed through texts such as emails, chats, texts, social media posts, and online reviews from customers. Powered by artificial intelligence technologies, mood analytics models can learn to automatically categorise customers' opinions as negative or positive, saving the brands significant time and energy required to manually handle those responses.

Customer Sentiment Analysis can be used for:

1. Enhance Customer Service: Having a customer service response team that understands the pain points of customers is one the most crucial aspects of growing a brand. Around 80% of customers are likely to switch to a different brand when their experience is not good after 2 or more times.
2. Having survey responses where customers can rate their experiences, helps the brand in realising the exact point in the customer journey where they are unsatisfied with the service.
3. Improve Products and Services: Performing sentiment analysis on reviews, social media posts, surveys, and more, can shed light on Issues/bugs that need to be fixed.?
4. Optimise Marketing Strategies: Companies can get powerful insights to boost their marketing strategy. For example, marketers can keep an eye on industry trends by analysing sentiment towards new features or products on social media.
5. Segment Customers: Targeting customers based on how they feel towards the brand, brands can create hyper-personalised experiences. A brand might want to target Detractors in Net Promoter Score (NPS) responses by offering them discounts or free trials.
6. Customer sentiment analysis can also be used to identify the competitor’s strengths, weaknesses and issues that they are facing to reduce any such issues when the brand launches its own product or service.

In order to analyse and classify customer contacts, consumer sentiment analysis algorithms generally use two parameters

Customer sentiment analytics helps companies derive value from product reviews, social media, and NPS responses, among other data, and leverage these insights to make more intelligent decisions that improve customer satisfaction. The machine learning of the numerous tools used in sentiment analysis allows users to collect qualitative data, which in turn, can be used as a powerful tool for improving brand recognition, improving reputation, and improving customer experience. Sentiment analysis, also known as sentiment mining, is a process that measures the tone or emotions of a set of words, whether they are positive, negative, or neutral across social media, in customer feedback forms, in online surveys, and more. Sentiment analysis gives you a birds-eye view of people's feelings towards your brand, products(s), advertisements, or even competitors.

Sentiment analytics tools will break down the feedback to be positive, negative, or neutral, according to different phrases of language and tones used by customers in an interview. Sentiment analytics tools monitor responses from surveys, conversations with customer care representatives, customer reviews across various online portals, etc. Then, sentiment analytics tools will analyse the collected data and identify words used in feedback. This usage of sentiment categorization gives organisations a sense of whether customers are giving positive, negative, or neutral feedback.

Example: When a customer calls the customer care service centre the calls are recorded for training and quality purposes. The recorded calls are dissected and used to train other employees to understand why the emotions of the customers affect sales and how they can turn a negative emotion into a positive one.

By engaging customers who are feeling strong negativity towards the brand’s product or service, their customer support team can address the customer's concerns in-depth. With Live Chat, one can also actively engage in outreach to learn about customers' feelings about customer service or product experience.

With bonafide resources, companies can correctly determine both negative and positive sentiment scores and turn insights into an enhanced customer experience down the line. To improve customer experiences, they can take sentiment scores--positive, negative, and neutral--from customer reviews and identify gaps and pain points that might not have been addressed by a survey.

In today’s times, every company desires to be customer-centric and is using CSA to capture customer opinion in some or the other form, CSA organisations are also continuously working towards building and improvising the tools which can deliver maximum accuracy with efficiency to refine the customer experience.