Chatbots need to have contextual awareness if they have to adequately resolve a query. This contextual awareness leads to intelligence over time, by handling millions of queries over significant periods. Conversational UX relies on effective contextual intelligence to create more meaningful relationships with customers. From banking to health services, each industry has unique requirements from contextual chatbots that work with large data sets.
Designing a contextual chatbot
Designing a contextual chatbot requires strategically planning out key characteristics and use-cases for the technology. This includes any critical data points that it needs to analyze first, as well as any customer-based interactions it can start having early on. When designing the right chatbot, embedding contextual analysis is important from the get-go.
Planning is a critical component that needs to be fully optimized if a company is to leverage contextual intelligence. This is done by analyzing existing features & scope and mapping out future requirements. Through this process, various technology integrations can be put in place to ensure that there is congruence.
Additionally, the right resources must be put in place if you want to design a contextual chatbot. From the right teams to the right talent, a contextual chatbot requires an integrated approach when it comes …