The new update would reduce the time required to analyze customer feedback at scale, reveal insights and predict trend
CloudCherry, Customer Experience Management, CX platform, predictive analytics engine, machine learning, deep learning, real-time, customer sentiment, data analysis
SALT LAKE, US: CloudCherry, a leading Customer Experience Management company, recently announced significant enhancements to its CX platform’s predictive analytics engine, reducing the time required to analyze customer feedback at scale, reveal insights and predict trends.
“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.”
CloudCherry’s advanced analytics engine uses machine learning and deep learning to crunch billions of unstructured customer feedback across a multitude of channels, in real-time, revealing key trends, a deep understanding of customer sentiment and the underlying themes and drivers of customer experience.
The new enhancements provide the ability to create a multi-level classification hierarchy for key driver analysis -- a quick snapshot of higher level themes, and the ability to drill down into areas to look at detailed themes.
These insights not only reveal how customer conversations are shaping brand experiences but empower employees with a prioritized list of the actions they need to take in order to deliver outcomes.
To cater to their growing global customer base, CloudCherry has expanded its international coverage with data analysis that supports over 50 languages.