What Is Customer Service Operations?

How do you increase revenue, decrease costs, and maintain the best possible customer experience — all at the same time? It’s a delicate balancing act. And it’s only possible when your data is comprehensive, meaningful, and easy to understand.

Unfortunately, that’s pretty rare. Our research shows that 94% of business leaders feel their organisation should be getting more value out of their data.

But here’s the good news: there’s a clear way forward. Let’s take a few minutes to review the principles of great customer service operations — and what you can do right now to take your organisation to the next level.

First, the basics. Customer service operations is everything a company does (its day-to-day activities, processes, and strategies) to assist customers. Does that definition sound general? That’s because it is: customer service operations can impact everything in your organisation, so every improvement goes a long way. With the right strategy in place, you can:

• Boost customer satisfaction and retention: When you have a complete view of the customer, you have more ways to help them feel valued and understood.
• Grow revenue: Analysing customer purchase history and behaviour helps identify opportunities for cross-selling and up-selling.
• Cut costs: Data-driven operations can identify repetitive tasks suitable for automated customer service.

Many customer service organisations lack relevant and timely data to make informed business decisions and identify key areas of focus. Here’s how to turn that around.

You might already track traditional customer service analytics like customer satisfaction score, Net Promoter Score, and lifetime value — but it can be hard to connect those metrics to meaningful business outcomes. Get deeper insights by adding these analytics to your toolkit:

• Case type
• Speed to answer
• Average handle time
• First contact resolution
• First response time
• Customer effort score
• Total work items
• Accepted and declined work items

If pulling this data feels overwhelming or challenging right now, try selecting three metrics to focus on for the next few months, then go from there.

Many “a-ha!” moments are hidden within your customer data, but how do you find the signal in the noise? Enter AI for customer service. You can use this technology to answer the following questions by analysing customer calls, emails, and chatbot conversations:

• What are the signs that a customer is likely to escalate an issue?
• How much time will it take to resolve an issue?
• Which issues could be resolved with a knowledge article?

These insights can help you create new paths to improve the customer experience. For example, if customers often ask for an agent when they want to change their address, a chatbot can proactively share a relevant knowledge base article to minimise escalation.