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Powering Sentiment Analysis with Machine and Deep Learning
CloudCherry | February 13, 2018
“When dealing with people, remember you are not dealing with creatures of logic, but creatures of emotion” – Dale Carnegie.
Emotion plays a critical role in our daily lives. Be it in shaping our relationships or day-to-day brand choices, we look for a connect at some level. And companies that tap into this emotion and get it right are usually the ones customers flock to. They are also the ones to turn customers into loyal, lifelong evangelists.
To ace customer loyalty, however, any brand requires a deep understanding of customer sentiment. How do you make your customers feel at every interaction? Are they happy, sad, ecstatic or frustrated? And do you have the right means to decode this emotion?
The common drawbacks with most systems designed to unravel customer sentiment are that the process can be time-consuming, leading to inaccurate outcomes. Also, given the level of subjectivity involved, the technology associated with decoding emotion has to be continuously adaptable and dynamic.
And while bucketing sentiment into different themes – positive, negative and neutral – is the basic approach, it tells you very little about how customers exactly feel. How positive is the sentiment associated with website experience? Also, why is it positive and what are the drivers of this positive sentiment?
It’s about creating as detailed a picture as you can that explains how systems, processes and policies are shaping customer sentiment. And CloudCherry’s latest predictive analytics enhancements provide companies the competitive edge to unravel customer sentiment, not just more accurately, but in real-time.
Companies can now drastically reduce the time required to analyze customer feedback at scale, reveal insights and predict trends. We believe that being proactive in understanding customers and addressing their queries is the key to creating delightful experiences.
This approach has a lot to do with investing in machine learning and deep learning. Both these technologies help crunch billions of unstructured customer feedback data across a multitude of channels, in real-time, revealing key trends, a deep understanding of customer sentiment, underlying themes and drivers of customer experience. The new enhancements also provide a multi-level classification hierarchy – a quick snapshot of higher level themes, and the ability to drill down into specific aspects to look at detailed themes. The insights derived from the multi-level theme analysis can also empower employees with a prioritized list of actions they need to take in order to deliver outcomes, in addition to helping shape brand experiences.
“Brands around the globe are under ever-increasing pressure to understand and get ahead of customer needs, tackle churn and drive profitability.” says Arvi Krishnaswamy, VP Products at CloudCherry. “With the help of machine learning and deep learning, we’ve been able to achieve up to 98% accuracy in our classification of customer sentiment. This represents a significant milestone– giving companies the power to listen to their customers at scale, across a multitude of channels, mine their words for meaningful insights, and identify trends faster and more accurately than ever before.”