Building a Path from QA to Performance Excellence

Hypothesize: Start with an idea or anecdotal evidence that something might be amiss—perhaps you’ve noticed a pattern in customer complaints or an uptick in escalations.

Leverage Conversation Analytics: Utilize Conversation Analytics to track behavioral trends, allowing you to generate reports that drill down from overall metrics to individual conversations.

Root Cause Analysis (RCA): Conduct RCA using MaestroQA’s RCA workflows. Dig into specific cases identified by your AI metrics and complete a Root Cause sprint that can surface insights you might not have considered.

Identify the Outlier: Begin by spotting an outlier on your performance dashboard—something that stands out and needs further investigation.

Root Cause Analysis (RCA): Conduct RCA using MaestroQA’s RCA workflow. Dig into specific cases identified by your AI metrics and complete a Root Cause sprint that can surface insights you might not have considered.

Build a Custom Metric: If your RCA reveals an issue you want to track, create a custom KPI in Maestro to track it across all interactions, helping you determine if the problem is isolated or systemic.

What’s the difference between random and targeted QA?

Random QA often checks general performance without focusing on specific insights, while targeted QA uses data to focus on high-impact areas, allowing for more analytical, in-depth evaluations.

How can AI impact headcount in a QA team?

A common misconception is that AI will directly reduce QA headcount. Instead, AI supports auditing by automating processes, which helps the team focus more on targeted, high-impact QA. While AI can streamline some tasks, it doesn't replace the need for skilled team members who can provide critical analysis and data-driven insights.

What skills are most valuable for a targeted QA team?

Transitioning to targeted QA requires team members with critical thinking and analytical skills. It’s recommended to look for people with customer empathy and problem-solving experience rather than strictly QA backgrounds.

What should be considered when evolving a QA team structure for targeted QA?

Focus on creating a structure that enables targeted QA to maximize insights and results. Seek team members with critical thinking, curiosity, and customer empathy—qualities often found in top-performing agents who understand your processes and bring a fresh analytical perspective.

How concerns about moving from random to targeted QA in BPO contracts be addressed?

Some BPO contracts rely on QA scores, which can complicate a shift to targeted QA. Consider meeting minimum contract requirements for random auditing while focusing additional resources on targeted QA for more actionable insights.

How often should AI-driven insights and metrics be validated?

Regular validation, ideally every 30 to 45 days, is recommended to ensure accuracy. As AI models can drift, periodic spot checks and adjustments are necessary to maintain reliable insights.

How long does it take to fully transition to targeted QA and build out new capabilities in MaestroQA?

Transition time can vary based on company size and complexity. For foundational setup, three to six months is typical, but targeted insights can be achieved more quickly through focused sprints on high-priority areas.

Should there be separate teams for traditional QA tasks and new targeted QA initiatives?

It’s beneficial to have one team focused on traditional QA tasks and another on developing targeted QA practices and using MaestroQA’s advanced tools. This helps balance immediate needs with longer-term goals.