Gain a better understanding of the emotions and opinions stakeholders, be it customers, potential customers or employees have towards your product and brand. Collect and use unstructured data such as customer service chats and phone calls, social media, surveys, blogs, news articles and more. Apply rule based systems or deploy automatic systems powered by ML to build a powerful system to improve your brand sentiment and product and service capabilities.
Social Listening
Customers today, are regularly interacting with brands on social networks such as Facebook, Twitter, LinkedIn and Instagram. Sentiment analysis can be used to get insights into how customers feel about the brand. Analyze interactions over a period of time to see sentiment of a particular audience and address any urgencies. Combine this with an analysis of news articles, blog posts, forum discussions, and other texts on the internet over a period of time. Route social media and other mentions to team members best fit to respond and address.
Voice of the Employee
Use Sentiment analytics to hear what employees are saying on social media platforms such as Glassdoor, Facebook and LinkedIn. Analyze employee surveys, blogs and comments on the intranet to monitor employee sentiment over time. Identify pressing concerns to be resolved immediately. The beneift Improved employee satisfaction, increased productivity and lower attrition rates.
Driving Product Innovations of your spends
Analyze surveys, customer support interactions and social media to keep a constant tab on what features of products people like and dislike about your products and tweak them accordingly. Keep a close watch during product and scheme launches to identify and fill gaps quickly. In parallel, keep a watch on your competition's social media to derive insights about new features to introduce.
Improve Customer Service
A hassle free experience with a brand is critical to happy customers and the customer success team plays a key role in this. Use Sentiment analysis to monitor trends in satisfaction levels from all incomng calls. Prioritize queries and tickets of unhappy customers for quick resolution. Route complaints to team members best suited to address these at the earliest using NLP.