A customer sentiment analysis solution uses advanced language processing and machine learning to evaluate feedback from different sources like reviews, social media, and support interactions. It categorizes sentiments as positive, negative, or neutral, giving businesses valuable insights into customer satisfaction and preferences.
We’ve helped these brands shape their business and future
It analyzes customer feedback from diverse sources, providing businesses with valuable insights on sentiments expressed in reviews, social media, and support interactions.
Real-time monitoring capabilities enable businesses to identify and address customer concerns promptly. This allows companies to mitigate potential issues before it’s too late.
By categorizing sentiments as positive, negative, or neutral, businesses can pinpoint areas for improvement and tailor their offerings and operations to meet the customer expectations.
Identifies areas of innovation within a business and helps organizations identify needs that were not met, emerging trends, and potential areas for product or service enhancements.
Our client, a global leader in food processing, specializes in crafting delicious, nutritious breakfast and cereal products tailored to diverse age groups. Renowned for its globally distributed ready-to-eat cereals, the company has expanded its product line to offer a variety of flavors and nutritional profiles, catering to the diverse dietary preferences of customers.
The client wanted to compare their existing and new food products with their competitors' products. From taste, quality, packaging, and nutritional value, they wanted to know every opinion of their customers.
Social media was the best place to get answers to all these questions. However it was challenging as customers were on different platforms, and data was scattered.
We gathered around 2000 messages about our client and their biggest competitor from different social media platforms (Twitter, Instagram, etc) over a month to understand how their customers feel about both brands. We also looked at publicly available data to get a complete picture of their experience and sentiments. Positive or negative feedback from the text-based words and emojis were analyzed using natural language processing. All the data was then visually represented using AI algorithms to show the most frequently used words by their customers.
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