Customer satisfaction (CSAT) scores are an indicator of customer loyalty and confidence. It is reasonable to assume that CSAT scores play an important factor in reducing customer churn, increasing recurring revenue, and increasing customer lifetime value.
We analyzed customer and agent chat interactions for factors that impact customer satisfaction (CSAT) scores. Negative CSAT scores are directly correlated with four main factors, three of them specifically around wait time:
- Customers are put in a queue to wait to speak to an agent.
- Customers wait for agents to respond.
- Customers need to be transferred to another agent after speaking with an initial agent.
- Customers using digital communications are timed-out from their chat due to inactivity and must start over
The results show: Wait time significantly impacts CSAT scores
We reviewed CSAT scores against the customer experience for more than 17,000 interactions. As CSAT scores are broken down into a 5 point scale, scores between one and three were consolidated into the negative class, whereas the top two scores were consolidated into the positive class.
Negative CSAT rates (scores between 1 and 3) only occur 20% of the time, but when a customer is timed out, the negative CSAT rate jumps up to 80%.
How ASAPP provides opportunities for higher CSAT scores
Directed automation features such as “agent autosuggest” and automated conversation summary notes reduce agent response times. And, AI-driven knowledge base article retrieval models help agents streamline the troubleshooting process. This has the benefit of reducing current customer wait times for agent response, but also improves throughput, reducing queue times as well.
It is important that customers get to an agent that can actually solve their problems. ASAPP intent classification sorts conversations into different types based on the initial set of utterances by the customer. This classification helps match each customer with an appropriate agent and reduces the need for multiple transfers.
A queue check-in feature checks to see if a queued customer is still available before routing to an agent. This eliminates having the agent spending time to connect when a customer has vacated the line.
As agents gain efficiency and communicate on asynchronous channels they’re able to handle multiple issues at once, further reducing enqueuement times. Small gains in efficiency on an individual conversation level add up to larger effects on throughput—for each agent and for the whole CX team.
Denton Zhao is a data scientist at ASAPP. His work focuses on opportunities to support agents using directed automation and analyzing feature impact via AB tests. Prior to ASAPP, Denton spent time in the financial services and financial tech industry.