What are the key performance indicators (KPIs) that should be monitored to assess the effectiveness of a WhatsApp chatbot in resolving customer support issues?

Several key performance indicators (KPIs) should be monitored to assess the effectiveness of a WhatsApp chatbot in resolving customer support issues. Firstly, resolution rate is crucial. This measures the percentage of customer support issues that are fully resolved by the chatbot without requiring human intervention. A high resolution rate indicates that the chatbot is effectively handling common customer inquiries. 'KPIs' are measurable values that show the progress of a business activity. Secondly, first response time is important. This measures the average time it takes for the chatbot to respond to a customer's initial message. A fast first response time improves customer satisfaction. Thirdly, customer satisfaction score (CSAT) should be tracked. This measures customer satisfaction with the chatbot's performance, typically through a post-interaction survey. A high CSAT score indicates that customers are happy with the chatbot's service. Fourthly, conversation length measures the average number of messages exchanged between the customer and the chatbot to resolve an issue. A shorter conversation length suggests that the chatbot is efficient in addressing customer needs. Fifthly, escalation rate monitors the percentage of conversations that are escalated to a human agent. A low escalation rate indicates that the chatbot is effectively handling most issues on its own. Sixthly, containment rate measures the percentage of customer interactions that are handled entirely within the WhatsApp chatbot channel, without requiring the customer to switch to a different channel (e.g., phone, email). Seventhly, error rate measures the percentage of chatbot responses that are incorrect, irrelevant, or fail to address the customer's needs. For example, if a chatbot consistently misunderstands a specific type of query, this will show as a high error rate for that query type. Regular analysis and monitoring of these indicators will highlight ways to improve the chatbot's responses.