In contrast, policy events are specific to individual customers and need higher intelligence to integrate them into the conversation flow. Depending on whether an all-inclusive, holistic view of customers is available, events suitable for persuasion could either be limited to the policy that forms the basis of the conversation or extend to other policies in the customer’s portfolio. Two scenarios could emerge when the event pertains to the same policy. First, where the customer intent is about the same event and a normal dialogue is adequate to handle it. For example, when a customer initiates a query on the reinstatement of a lapsed policy. Second, where the customer initiates a different query, and the event suitable for persuasion must be introduced by the chatbot. Taking the above example, if the customer inquires about a change of address, the chatbot will be required to proactively introduce the event of policy reinstatement into the conversation.
The efficacy of introducing a proactive persuasive gambit will have to be reassessed when an ongoing conversation is about an event requiring reactive persuasion. The underlying implication of events suitable for persuasion—positive or negative—and the logic for pairing them must also be factored in. For example, a customer inquires about surrendering a policy for its cash value, which is a negative event necessitating reactive persuasion. Introducing a proactive persuasive element into the conversation by trying to sell a new product may be inappropriate and expose the insurer to moral hazard risk. Similarly, proactively persuading a customer to reinstate a lapsed policy or repay a long-pending loan after reactively persuading against withdrawal would be unwise.
Where a holistic customer view is available, many scenarios arise. Customers’ policies could have various events amenable for persuasion, which could be the same or different from the intent of the ongoing conversation. For example, a customer may have more than one lapsed policy but inquires about changing the beneficiary for an in-force policy. In contrast, the customer may inquire about reinstating only one of the lapsed policies. The chatbot will need to be designed to handle such scenarios as sub-optimal pairings will be inappropriate, ineffective, and may expose the insurer to conduct risk and consequent regulatory scrutiny.