Stop Making Customers Learn Your Org Chart
Source: Nick Mehta post: “we have to stop making clients learn our org charts”
When a customer contacts you, they know they have a problem, and they want you to solve it. They may or may not have a dedicated contact point at your organization, and chances are they would like an answer quickly.
Nick Mehta’s post above elegantly states the customer’s expectation: “they shouldn’t know how our companies are structured” to get help.
What’s the best way to understand what the customer wants while also maintaining a high level of service and answering quickly? One answer is to use the Team-led growth approach where collaborating between customer and client teams happens in a shared communication channel instead of in a direct message vacuum.
But getting that first response is also really important, whether it happens in a ticketing system like Zendesk or a collaborative platform like Slack or Teams. One of the key functions in the playbook to understand the customer’s issue is to classify the inbound message.
The goal of that playbook is to first understand why the customer is contacting you, quickly identify whether this is a first or repeat contact, then research if the issue can be immediately resolved.
You’ll want to know:
- Has the customer contacted you before about this issue?
- What’s it about, generally?
- Is there an instant response to provide that is likely to answer the question?
- Who else should be alerted to what’s going on (a seller, a technical support agent, a CS rep, or other) with what kind of summary?
- When will they expect their next response, depending upon the contents of the message, their past history, or the severity of the issue?
That’s a lot to determine quickly, so it would be helpful to have a few tools at your disposal.
Getting to neutral
Looking at the customer’s past tickets - both closed and open - will help you to know if you need to consider an existing issue or if you can start from a neutral standpoint. If there is open communication, acknowledging that effort and assessing if it was the correct response needs to lead your response to the customer.
What does that customer expect? They expect a consistent response from the team whenever they ask for help. Generating that response requires a summary of the issue, a statement of troubleshooting, and acknowledgement of any difficulty. One way to get an effective summary of an issue is to use WorknetGPT, which produces a contextual summary of the issue based on your own help content.
AI is a copilot, not a people replacement
Summarizing a message in a template does not replace the need to respond to the customer in a human way. Nick Mehta’s comment above reminds us that people want to be heard. Responding to them with bland text doesn’t help them to know that there’s a person on the other end of the message.
But that co-pilot aspect of AI can help you get to the right answer much faster, enabling you to serve the customer and direct them to the next effective action.
How do you measure whether this is working?
Wow, that’s a lot. Fortunately we can break down the efficacy of this approach using data. Let’s do it this way:
- For first responses to Zendesk tickets, how many can be answered effectively without another message by a person along with a high satisfaction level and strong sentiment?
- What percentage of messages can be accurately classified and summarized?
- For teams that use summarized responses to assist in their tickets, are they able to answer more customer queries at a higher level of service?
Consider the outputs to these data questions when you’re assessing how well things are going.
A Blueprint for Team-Led Growth in Support
When they have a problem, customers or prospects should not have the burden of figuring out which team to contact and solve their issue. When this goes well, a customer detailing a novel issue kicks off a process where – if there is an easy fix – another team can put a permanent fix in place that prevents more customers from hitting the same issue.
When things are more complicated, summarizing the issue deftly and sharing with another team as part of the knowledge base or a standard template makes it easier for the team to identify, respond, and empathize with the customer.