Using predictive analysis of customer intent to improve CX

Using predictive analysis of customer intent to improve CX and reduce costs

At a glance

To help our client improve digital customer care in their messaging channels, we leveraged machine learning in delivering a streamlined, personalized customer experience based on predictive analytics.


Customer challenge

Our client wanted to elevate their customer experience by delivering highly personalized service that anticipates user needs. They recognized that shortening the time-to-resolution for customer requests would not only improve customer satisfaction, but also reduce overall customer care costs.


Approach and solution

In the solution we designed, every customer and agent message is analyzed using Artificial Intelligence (AI) and Natural Language Processing (NLP) to continually predict the intent of the conversation and related support articles in real time. When the intent of a conversation is known, the system also looks up customer related facts applicable to that intent.


As the conversation continues, the predicted data gets pushed to the agent via a browser notification, presenting the information and tools he or she needs to quickly answer customer questions in real time. When the project was complete, our client had their first customer-facing, real-time models leveraging artificial intelligence.


Value and benefits - “the wins”

We proved that providing agents with conversation intent, customer data, and internal/external support tools in real time as a conversation continues — and consolidating it all into a single widget — reduces the number of external systems agents have to use to get the same data. This helped our client achieve their goal of decreasing the time required to solve customer issues, leading to an overall reduction in customer care costs.






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