What would it mean to your business if you could predict not only when customers would call, but what they’d be calling about?
Alternatives to call centers, such as chatbots and chat support, can reduce costs by almost two thirds – and our client wanted a way to make their customer service as efficient as possible.
Download our latest case study and learn how our customer genomics platform helped a major US insurer predict the likelihood and reasons behind calls, reducing costs and improving the customer experience.
Understanding customer intent is key to providing appropriate alternatives to calling customer service
The challenge: our client wanted to reduce a torrent of customer calls
Deep learning models helped our client to cut through the fog of customer intent and streamline their customer service process.
• We used digital activity to predict why customers were calling, what they were calling about and to identify alternative service offerings.
• Customer genomics made this possible, reducing model development time and offering state of the art deep learning algorithms.
• We utilized aggregated, unaggregated and sequence-based variables, providing APIs for faster model development.
The results: customer genomics provided a brighter outlook
We helped our client face an industry-wide problem with a successful proof of concept.
The top decile of predictions from the model captured 50% of all visits that led to calls
Enabled near real-time interventions for likely-to-call visitors
Established the value in using digital activity to predict the likelihood and intent of calls
Download our case study and discover the impact customer genomics can have on your business, and how prediction can lead to greater customer satisfaction, as well as real financial rewards for your business.
Download the full case study
Download our case study and discover the impact customer genomics can have on your business, and how prediction can lead to greater customer satisfaction, as well as real financial rewards for your business.
