Customer service agents tried to build rapport with customers. It didn't work like they thought.
October 10, 2022
July 21, 2020
Are you ready to reimagine quality assurance? Customers deserve a spot on your QA scorecard, but they’re often forgotten in the QA measurement process. Using Tethr’s Agent Impact Score (AIS) for quality assurance, businesses can start connecting agent quality to effort reduction, while holding agents accountable for customer experience.
Customer care teams have been using QA scorecards to measure agent performance for decades. Traditional scorecard metrics include criteria such as whether or not an agent used a proper greeting, how many times they said the customer’s name, if they thanked the customer for their loyalty, if they followed compliance scripts and several other factors that while might be important to the company, have minimal impact on improving the actual customer experience. AIS represents a new way forward.
Based on the Tethr Effort Index, AIS is a machine learning-based score that isolates the specific variables attributable to an agent within an interaction with a customer. By focusing on the actions that agents take to increase or reduce customer effort, AIS can be used to measure quality, improve rep performance, and track changes in how agents handle difficult customer situations.
With AIS, every customer interaction - 100 percent of them - receives a separate, objective score with no bias. Agent performance can be looked at across a spectrum from poor or below-average performance to superior behavior. Companies can use these scores to:
A Fortune 1000 hospitality company is using AIS as a way to get back to what truly matters—the customer. While their old QA scorecard provided a checklist of traditional agent measures, adding AIS to their scorecard enables them to track at an individual agent level the percentage of all of their interactions that were low-performing. And, by adding the level of effort as tracked by the Tethr Effort Index (TEI), the company is able to understand how agent performance changes when customers are frustrated or upset, how agents respond in those situations, and how an agent's actions compare to their peers.
By putting AIS to the test, they were able to color their perspective on quality assurance in their organization with objective data and understand the context associated with the agent behaviors that are connected to the business outcomes that the company cares about. This company found that adding AIS to its QA scorecard gives them the customer-centric focus they were searching for. They also discovered that it drives more constructive and results-driven coaching enabling them to be better equipped to measure agent performance in their company.
Innovative CX and customer service leaders can now use advanced machine learning-based techniques to effectively listen for and score the specific contributions agents make to improve the customer experience. By using AIS for quality assurance, leaders can overhaul contact center quality assurance so that it delivers what it was originally intended for: higher quality customer interactions and an improved bottom line.
Rather than a checklist of low-value items like that on a traditional QA scorecard, AIS measures the important things such as what is driving sales conversion, customer churn reduction, upsell/cross-sell, positive word of mouth and how to achieve a lower cost of service. Moving beyond traditional quality assurance practices can improve the customer experience and align agent performance to business outcomes. By measuring the agent performance metrics that actually matter to their business, companies see tangible results.
Stay tuned for the next post in our series on aligning agent performance to business outcomes. In the meantime, uncover the drivers of good and bad Agent Impact Scores here. You can also watch a webinar on AIS for quality assurance here.
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