Context-driven Customer Engagement (CE) is a practice that leverages AI to understand customer context and uses modeling and pattern matching of massive amounts of internal and external information to find connections (opportunity and threats) that the customer has not previously considered. This is a significant step beyond CX as it employs AI to understand the customer's context first. Customer context is vital for businesses because it is the foundation for providing personalized, unique, and continuous customer experience.
The context includes both big and historical data that gives you the timeline of the customer’s journey which can be used to curate a new experience that requires less effort to execute certain transactions. Context allows an enterprise to respond faster, reduce customer effort, and create a smooth experience that generates better customer retention.
This type of contextual personalization allows customers to select their preferred method of company interaction. The difference between traditional customer identity profiling and CE is that CE considers broader ranges of customer identification. The analysis evaluates personal and community context that helps determine the best way to engage with customers by understanding behaviors and responses. This information is then processed by AI to explore future customer engagement opportunities that can enhance the execution of sales.
This research note explores the changing customer expectations and illustrates a stepped evolution toward AI-enabled context-driven customer engagement.