Quality assurance (QA) done well influences agent behaviors, motivates the team, and pinpoints CX issues that would otherwise go unnoticed.
Yet, across many contact centers, the initiative has fallen flat, with stale scorecards, limited buy-in, and time-pressed supervisors.
In many cases, these supervisors still randomly pick out five or six contacts per agent each month and – in a rush to hit targets – focus on those with low handle times.
By doing so, supervisors miss many weighty issues where customer retention can be won or lost.
As a result, agents rarely receive meaningful feedback. Meanwhile, many lose faith in the process, deeming “lucky-dip” QA deeply unfair, particularly if tied to performance incentives.
All of this has led to disengaged supervisors, unmotivated agents, and – ultimately – lost time for everyone involved.
Sound familiar? If so, automated QA (Auto-QA) may offer hope.
However, it’s no magic wand. Contact centers must get their ducks in a row and build an awesome QA strategy first.
The Awesome QA Strategy: Where Everything Is Connected
QA is a team effort between agents, supervisors, analysts, and coaches. Each stakeholder should collaborate to set shared team standards.
Afterward, analysts can focus on identifying performance gaps via various initiatives, like outlier analysis.
Through outlier analysis, they target contacts with high handling times, multiple transfers, and long hold periods to extract key learnings.
Leveraging these learnings, coaches can then offer personalized training – targeting performance gaps – and supervisors may reinforce those lessons.
From there, analysts can track the success of the coaching, learning over time how to optimize the agent’s performance through training and reinforcement.
That connected learning strategy is powerful, and – with this thinking – contact centers can secure much more value in their Auto-QA investments.
Layer Over Automation
When integrated into contact center solutions, Auto-QA analyzes each customer’s interactions against criteria derived from shared performance standards.
Contact centers can also attach metadata to categorize contacts by intent, channel, and customer segment, enabling analysts to prioritize high-risk conversations.
For instance, analysts may cherry-pick the best and worst of customer interactions, aiming to cancel their subscriptions for praise and improvement.
Meanwhile, contact centers may attach post-contact survey data to assess how agents drive positive and negative feedback.
These examples exemplify how Auto-QA can focus analyst intervention and spot the issues that impact business performance most.
That reinforces the connected learning strategy. Yet, it also paves the way for continuous improvement. As Carl Townley-Taylor, Product Manager at Enghouse Interactive, told CX Today: “We enable this in two ways: standard reporting and promoting self-learning.
“So, we send automated performance insights via email or a platform like Microsoft Teams to agents, giving agents daily feedback and inspiring continuous improvement. As a result, they don’t have to wait for monthly training sessions.
“By the time formal coaching occurs, agents are on the path to success,” he continued.
Finally, the latest QA solutions leverage conversational analytics, enabling deeper analysis of agent performance and contact center pain points.
As such, contact centers can leverage Auto-QA as the foundation for ongoing improvement.
3 Best Practices to Maximize QA’s Impact
While that’s a quick sweep of what an awesome QA strategy may look like, there are several other best practices to account for. Consider the following three examples.
- Don’t Confuse Strategy with a Scorecard
Many contact centers consider the QA scorecard their strategy, but it’s only a tool.
The strategy should encompass standards, coaching practices, and clear roles and responsibilities. The scorecard comes later. It’s just part of the picture.
- Stay Connected Via Collaborative Calibration Sessions
Calibration sessions drive consistency, ensuring everyone views QA standards similarly. But they shouldn’t stop there.
Participants should share insight, refine standards, and strengthen their alignment.
Encourage participation from all key QA stakeholder groups to ensure these calibration sessions are impactful.
- Ditch the Reports for Dashboards
Reports are an old method. Today, forward-thinking brands leverage live dashboards, like Power BI, to track progress in real time.
“The dashboard complements AI service, making it easy to see the value of the technology and track performance without manual reporting,” said Townley-Taylor.
Now Is the Time to Reimagine QA
Contact centers are undergoing a great transition thanks to AI. It all started with generative AI (GenAI). Now, AI Agents are the latest eye-catching development. These AI Agents automate individual tasks and collaborate to mechanize workflows.
The promise is significant. Envision this: an AI Agent analyzes how reps successfully resolve particular queries. Another then leverages that intelligence to create new and update existing knowledge articles. Finally, a third customer-facing bot leverages that knowledge content to automate more customer queries.
That loop is not far from becoming a reality.
But, before racing towards such a future, contact centers must first step back and consider the implications of implementing such AI innovations.
Most pointedly: how will this impact the team?
Ultimately, live agents will face complex contacts much more regularly, without those lighter contacts they use to take a breath.
As such, workforce engagement management (WEM) must take precedence, starting with – you guessed it – an overhaul of the quality assurance (QA) process.
Ready to overhaul your QA program? Enghouse Interactive can help by meeting your team where it is and transforming your WEM strategy.