Let’s face it: no one gets excited to call customer service. You may even expect long holds and ineffective answers from certain companies. If you fix a problem in a single painless conversation, it’s a shock.
How did it get so bad? As a business, how do you create a customer-centric experience that creates happy customers?
Most customer support teams operate how they always have. They focus on the same metrics they have for decades. Average call time, support requests, case resolutions, and call volumes give you numbers to track.
But, this data doesn’t show you anything insightful.
To start transforming your contact center to a business intelligence engine, make sure you aren’t making these mistakes with your customer support metrics.
1. Focusing on the wrong customer service metrics
You need powerful data that tells you about your ability to assist customers, your quality of service, and provides guidance on improving your product or service.
Most call centers zero in on the time frame their support agents spend on each call.
This is often referred to as average handle time, or AHT. It refers to the average time each agent spends with a customer on a call.
Call centers rely on the AHT to determine how fast their agents can diagnose and treat customers’ calls. It’s important for agents to move through the queue of customers waiting to talk to them. Focusing on this arbitrary measure can hurt your customer experience and your agent productivity more than you might think.
Agents focused on meeting expectations for AHT may:
- transfer customers as they near their time limit.
- rush through explanations or troubleshooting.
- not have the opportunity to discuss their other problems, and have to call back again.
- miss out on an upsell opportunity.
Tethr gives contact centers insight into the metrics that matter. Our platform measures much more than length of call. We use conversation analytics to evaluate the quality of the call and what drove customers to contact you in the first place.
2. Making customers repeat themselves and call back for the same issue
Customer support centers should aim to answer questions and fix problems in the first call. When customers have to repeat their situation to several different agents, that adds friction to the process. They grow frustrated.
If your customer service team doesn’t ensure they fix problems for customers, they might have to call back — and repeat their story (again.)
When you integrate Tethr into your customer support system, our call tracking will show you how often your customers have to call back. It’s one of the key metrics tracked on your dashboard.
We won’t just give you that number. We also give you a playbook on strategies you can implement to reduce that number. You’ll see what issues drive repeat calls as well, so you can work to fix any issues on the business side. These types of customer support metrics, which show you which customers have to call back, matter to the overall customer experience.
3. Not giving customer support agents the power to solve problems
If you’ve already forced your customer to wait on hold and explain their situation, don’t you want to fix it for them? Some customer success teams mistakenly focus on resolution rates without understanding the reasons why some customer issues don’t get resolved during the call.
Some issues might be complicated, ingrained in business operations, or need supervisor approval for an agent to resolve. If there’s a common issue that your agents are powerless to solve, wouldn’t it be better to give your agents that power?
Tethr tracks this, too. In your customer dashboard, you’ll see how often your agents are powerless to help and which specific issues this applies to. This gives you power to make the business changes that help these agents solve their problems.
4. Implementing a quality assurance process that doesn’t give insight into agent performance
You need a process to monitor QA for customer service teams beyond tracking outdated metrics or listening to a small percentage of calls.
Monitoring your customer service agent performance requires you to check several support processes. You need to make sure agents complete a checklist of items every call should have. Without speech analytics, the QA process can take hours and only give you insight into a small number of calls.
With advanced AI-powered speech analytics, you can automatically check for things such as:
- Greeting a customer by name
- Issue diagnosis
- Transfers and wait times
- Agent responsiveness
- Empathy statements
Tethr benchmarks your company’s performance on these metrics with industry standards based on millions of calls. You’ll know what areas you need to improve. You’ll also see which of your agents meet their QA standards – based on a comprehensive scoring of all their calls, not just a few picked at random.
5. Relying on post-call surveys
We’ve talked before about why you can’t measure customer satisfaction based on post-call surveys alone. Some customers don’t respond even when they have great experiences.
With Tethr’s unique scoring method, we can tell you how your customers’ call experience went without having to ask them. We measure how much effort the customer had to exert to interact with your company.
Customers crave low-effort interactions that solve their problems without wait times, explanations about internal approvals, or transfers.
Find the most important metrics for customer satisfaction
Conversation analytics platforms like Tethr give you a comprehensive view of the entire customer experience. You’ll see
- A cumulative score we’ve developed for each call, called the Tethr Effort Index. This tells you how much effort your customers had to go through when contacting you.
- What issues drive customers to contact you
- Which agents perform best and which need coaching (and what areas they fall short on.)
- How you rank against the industry in each area
- Industry-specific benchmarking data, call metrics, and more.
Ready to see exactly all that Tethr can show you about your customer contact center? Request a demo.