Seth Earley

Understanding the customer experience has both qualitative and quantitative aspects. Things like customer journey maps provide a representation of what customers think, feel and do throughout their experience. These qualitative measures need to be augmented by data – the metrics for determining whether prospects and customers are achieving their objectives or abandoning the journey due to friction in the process. Metrics can be used to surface the things that slow down access to information and get in the way of a customer experience adding friction to the process. KPI’s can also determine whether investments are yielding results that justify further investment or validate assumptions or theories about how to improve the experience. In this session we will walk through how each stage of the journey can be instrumented to use feedback from course corrections to further improve the process.

AI runs on knowledge – expertise and content structured and organized for retrieval using virtual assistants, chatbots, and conversational commerce applications. But how do organizations build this capability cost-effectively and sustainably? The answer is a “knowledge factory,” an approach that solves information problems today while providing a foundation for the future methodology for analyzing business problems, breaking problems into specific information requirements, developing and structuring knowledge components, and building out models for continuous learning and knowledge curation using automated and human in the loop approaches along with process metrics to quantify business impact. Organizations not building these capabilities will be caught flat-footed as the industry progresses. Competitors build sophisticated capabilities that enable higher levels of customer service at lower costs. In this session, we will walk through examples of how organizations have developed and applied a knowledge architecture for high-functionality chatbots and virtual assistants.