How BCLC automated QA and improved agent performance

The customers support division of the British Columbia Lottery Corporation used to devote 3 full-time customer experience positions and 6 team leaders to auditing customer support phone calls and chats. Yet despite the high effort, they rarely uncovered specific feedback that would help their entire team improve. Agents often explained away individual problems detected on calls and managers had no way of knowing if behaviors were isolated or systemic.

In addition, despite capturing and gathering massive amounts of raw data about its customers, BCLC didn’t know how to distill the data into information they could act on.

A new approach to QA

In late 2021, BCLC implemented Tethr, a conversation intelligence platform, to analyze all of their phone calls and chats. The first major change: they eliminated manual call and chat auditing by QA agents.

“Our traditional way of conducting QA ended the minute we got Tethr,” said Martin Lampman, BCLC’s Director of Customer Support Operations. “We were going to have to hire two more QA agents. We didn’t have to hire anyone. The application has paid for itself three times over.”

Under their old QA process, they audited less than 1% of customer conversations. Each QA analyst had between 15 and 17 agents to monitor. The resulting information gave them plenty of data points but was difficult to pin to specific actions or needed improvements. The results were prone to human errors, lacked statistical validity, and created confusion on what was the right course of action to see immediate improvements.

“We were doing high-effort work for what we perceived was high value,” Lampman said. “In the end, it wasn’t high value at all.”

That’s not the case anymore.

With Tethr, they automatically score each customer interaction - both on phone calls and chat - with a custom QA scorecard. It shows supervisors patterns in agents’ behaviors as well as trends for the team as a whole.

Before, constructive feedback depended on whatever calls the QA team audited, picked at random. The dashboard they review now shows them how each agent performed on key areas for every conversation.

“Instead of just gathering information, we’ve taken Tethr and used it to dramatically change QA’s function and role,” Lampman said. “It’s a training tool and an insight tool for agent performance, instead of just gathering information that we can’t use.”

Isolating + eliminating problem behaviors

Instead of focusing their coaching on the low performers who were failing QA tests, managers are now able to isolate types of calls that lead to difficulty and frustration for customers. Then, they can give their agents practical advice on how to handle those situations.

For example, they noticed that registration calls tended to have high customer effort overall. They could then compare registrations with chronic effort to those that resolved quickly, noting what agent responses and behaviors worked best.

It’s that type of data that Lampman finds most useful. BCLC always collected plenty of data, but they didn’t have a way to transform it into information they could use to improve the business.

Tethr also offers a score called the Tethr Effort Index (TEI), which rates how difficult every conversation is. BCLC’s QA team regularly examines the difficult interactions that received suboptimal scores. This helps them in two ways: It shows them which agents might need more coaching, but it also signals which processes and products may need business improvements.

“We come into those conversations equipped with data, which has made them much easier.” - Kristin Galan, CX and Accounts Manager

Actionable insights for each audience

BCLC organizes the information in a few different ways, using Tethr’s in-platform dashboards that automatically populate based on recent calls and chats. They created an executive summary that summarizes high-level trends such as NPS scores, the breakdown of calls compared to chats, and issue resolution rates.

Supervisors in the contact center receive detailed team analytics dashboards that show individual agents’ performances across a variety of behaviors.

“Those team leaders are taking insights from that dashboard and telling their agents how they can fine-tune their conversations,” Lampman said. “We are able to be more surgical in our approach and really zero in on coaching specific actions. It’s making training better and easier.”

Measuring QA’s value

The metrics from Tethr also helped the QA team quantify their contributions. Before, it was difficult to measure activities, results, and improvements, especially with so small of a sample size.

They couldn’t confidently say, “We worked with these specific agents on these three behaviors and as a result of that coaching, we were able to reduce average handle time.”

Now that we have Tethr and we are able to track behaviors across 100% of interactions, we can easily see and measure how our QA team, our team leaders, and our process improvements move the needle,” Galan said. “We can show cost reductions and value.

Using feedback for business improvements

Customers contact BCLC for a wide range of reasons: on one end of the spectrum, a person who just won $70 million might contact customer support. On the other, it may be someone battling a gambling addiction.

BCLC uses Tethr to glean insights from every type of interaction so they can also improve products and processes. For example, they found ways to make it faster and easier for customers to claim winnings.

Customer response

Customer support departments all aim to provide seamless, easy service. Based on customer feedback, BCLC has made significant strides in improving their overall experience. Since they implemented Tethr and started their focused agent coaching key customer experience metrics improved, including:

• 220% increase in NPS scoring
• 7 pt increase in CSAT score
• 56% decrease in difficult calls
• ‍16% increase in advocacy
• ‍21% increase in expectation setting