What is client sentiment analysis?
Client Sentiment Analysis plays a critical role in understanding the Voice of the Customer.
Client Sentiment about a product or service is the lifeline that helps organizations create and maintain their reputation. Sentiment analysis, or opinion mining, is a priceless tool available today. Client sentiment analysis refers to the method in which information is collected and analysed to understand the opinions and responses of customers by researching the social media sources. This otherwise complex process can be made easy by utilizing predictive and prescriptive AI and ML techniques.
Performing sentiment analysis helps you:
- Get an outside in view of user experience
- Get Industry/Peer comparison
- Drive continuous improvement
- Prioritise backlog
- Understand client feedback across social channels to improve digital experience & service quality
What is sentiment analysis used for?
Organizations capturing data about a segment of customers that is happy or unhappy with discount sales or returns policies is a good sentiment analysis use case. Another application of sentiment analysis in real life is that it helps organizations understand whether their clients liked a newly launched product with newer features – or if they are upset that the existing features have been dropped! Based on this critical information, organizations may then plan for changes to the products.
An effective enterprise sentiment analyzer can help track genuine client sentiment continuously. Social media platforms play a critical role in gathering authentic information by capturing reactions, comments, frequency, whether a post is trending etc. These platforms give users an opportunity to be naturally expressive. A lot of times these inputs help promote community buying – for example using the Amazon feedback platform.
Sentiment analysis helps organizations to position and represent their products by means of creating a personal connect with their users. It helps organizations to responsibly understand the emotions of their clients. By providing genuine, continuous product listening, along with the correct interpretation of the amassed information, organizations can convert the sentiments of their clients to actionable steps and plans. These steps can then be utilized across development, testing, and operations to improve the products.
What are the common challenges with which sentiment analysis deals?
With customer experience requirements at their peak, organizations need to wow them by continuously adding the demanded features to the products, without impacting quality or security. However, most organizations face the challenge of not being able to collect, understand, and analyze the voice of their clients – their end-users.
Organizations must be able to understand and predict the needs, demands, and even imagination of their end-users and future clients. Organizations thus need to shift-left the process of analysing the features expected by their clients, so as to implement the same in their products.
Add to this the fact that while 99% of times the users are not able to express their requirements, even the feedback received as part of surveys conducted is confusing at times. This makes it very difficult for organizations to really understand the sentiments in general for their product.
Organizations thus need access to sentiment analysis applications using machine learning and AI to overcome most of these challenges.
Cigniti Enterprise Sentiment Analyzer (CESA)
CESA identifies and categorizes feedback expressed by end-users to determine (objective and subjective) usability from a product and service quality perspective. The primary source of information is from app store reviews, Twitter and Facebook conversations. The analysis will help qualify topics, sentiment, feature requests, what works, what doesn’t work, quality issues, regression issues from the conversations.
CESA crawls and captures end-user feedback from publicly available sources and provides insights for improving end-user engagement, emotion, and experience. This helps them gain AI-based insights on Customer Sentiment & Opinion Mining to maximize their customer experience (CX). It also helps them prioritize business decisions effectively based on direct user feedback analysis.
Sentiment Analysis Steps
Here’s how we use CESA to analyse customer experience:
- Understand the user applications
- Identify the nature and source of downloads
- Analyze the ratings, reviews, conversations, communications across social media
- Profile the users under defined segments
- Create region-specific reports
- Share downloadable links
To achieve runtime automation, contact us.