Using CRM data to harness clients' irritating experiences feedback thanks to machine learning enrich outcomes with a social data deep dive into customers' pain points and related emotions.
Using CRM data to harness clients' irritating experiences feedback thanks to machine learning enrich outcomes with a social data deep dive into customers' pain points and related emotions.
Many vendors and clients are adopting AI tools to drive effectiveness and efficiencies of market research, most of the utilization is centered around speed, data integration and analytics and lowering cost. The study presented here, demonstrate the partnership with a tech start-up, leveraging their AI/NLP tool to discover the insights, using inputs from both primary research and syndicated information, while at the same time to capture the emotion of the audience. The key learnings have been fundamental as the brand team is on the journey of position the brand and integrate all communication in an ever-evolving and dynamic sector.
We know that 70% of our communication is non-verbal, while verbal communication adds another 7%. Yet, interpreting non-verbal communication by humans is a time-consuming and highly subjective process. For this presentation, we show how machine learning is making qualitative concept testing more efficient, more scalable, and more objective. We demonstrate how the latest cloud computing and machine learning technologies of emotion analytics and text mining were applied in the process of qualitative in-depth interviewing. The combination of the two methods help observe nuanced consumer responses that never before were capable of being observed and compared by humans.