Why You Want AutoQA and Things to Consider

3 Most Common Reasons & Biggest Lessons Learned

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Embracing the Future with AutoQA

AutoQA is a cutting-edge technology that promises transformation. But remember:

It's a feature, not a strategy.
For maximum impact, embed AutoQA within a broader strategic approach unique to your business needs.

We surveyed our CX community and identified the three most common reasons Quality teams seek AutoQA:

42%

Optimized QA Team

33%

Voice of Customer Insights

25%

Agent Performance Excellence

Three Most Common Reasons CX Teams Want AutoQA

Reason 1

Optimizing Your QA Team

Goal: Prevent the need to scale your QA team directly in line with your Support Team.

Key Insights:

While AutoQA can grade part of the scorecard, the hardest questions that take the most time require human touch.
Even with AutoQA's efficiency, the whole call still needs reviewing to capture its intricate details.
Though soft skills can be graded automatically, a whopping 94% feel the entire call must be listened to for accurate evaluation.
The ideal QA solution seamlessly merges technology with human discretion.

Noteworthy Point: Regardless of AutoQA's assistance, the full call's evaluation remains necessary for tasks like identifying the root cause & issue resolution.

Poll

If AutoQA did soft-skills 100% accurately, would I still need to listen to the full call to identify if we identified the issue and resolved it properly?

Yes 94%

No 0%

Other 6%

Poll

Do you need to check back-end systems to see if we identified and issued the resolution accurately?

Yes 84%

No 6%

Other 10%

Poll

Do you expect AutoQA to do the back-end system checking?

Yes 21%

No 68%

Other 11%

Reason 2

Voice-of-Customer (VOC) Insights

Goal: Extract deeper insights about products, operations, customers, and more from customer conversations.

Key Insights:

VOC is essential for cross-functional collaboration and extracting intelligence from customer interactions; however, 64% of people find AI challenging to leverage, even though such insights are key to achieving ROI.
Current AI struggles to capture nuanced VOC insights, leading many to doubt its precision.
A significant 73% don't trust AI alone and prefer manual review for genuine depth in insights.
An effective VOC solution combines AI's analytical capabilities with the subtlety of human review.

Poll

Do you expect someone to still read / listen / QA the conversations based on the AI-tagged VOC conversations?

Yes 73%

No 13%

TBD 13%

Poll

Has anyone had great success getting AI to identify nuanced VOC insights?

Yes 16%

No 64%

TBD 20%

Reason 3

Agent Performance Excellence

Goal: Elevate agent performance and ensure holistic optimization across the organization.

Key Insights:

AutoQA in a silo will not drive Agent Performance.
Integrating insights from AutoQA with KPIs (e.g., CSAT, AHT, FCR) offers quantitative analysis of conversations, but it's only a piece of the performance puzzle.
Enabling visibility into these metrics for Agents and Team Leads is mission-critical to drive improvements in your team.
The true value of insights is realized only when they're both effectively communicated and utilized.
Performance excellence extends beyond mere quality assurance, demanding a proactive approach where insights are not just gathered but actioned.

Challenge Thought: Is it worth implementing AutoQA if you're not addressing the holistic challenge? Ensure that AutoQA insights are not just collected but also communicated and actioned upon—otherwise, it's like a tree falling in a forest with no one around to hear it.