QA and conversation analytics

Quality assurance in support means reviewing conversations to check that answers were right, complete and in the tone you want, then using what you find to coach agents and fix processes. Conversation analytics looks across many conversations at once to show what customers contact you about and where they get stuck.

Traditionally a team lead would grade a small sample of tickets by hand with a scorecard. The tools here make that faster. MaestroQA organizes grading, calibration and coaching for support teams. Observe.AI and Level AI score conversations automatically, which suits contact centers with large volumes of calls and chats. Medallia and Qualtrics collect feedback and analyze survey text and speech at company scale. Chattermill groups themes from tickets, reviews and surveys to show what customers raise most.

Automated scoring covers far more conversations than a person can, but it is only as good as the criteria behind it. Write your scorecard first, test automatic scores against human grades on the same conversations, and adjust before you rely on the numbers for coaching or performance reviews.

Analytics are most useful when they lead to fixes outside the support team. If a theme such as delivery delays or a confusing setting keeps growing, share it with the product, operations or marketing team with examples. Support data is often the earliest warning a company gets.

Be open with your team about how QA works and how scores are used. Agents accept reviews more easily when the criteria are clear and when feedback comes with examples and time to improve.

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5 AI support tools in qa and conversation analytics

Observe.AI

Contact center AI for conversation analysis, automated QA and real-time help for agents on calls and chats.

Level AI

Contact center QA and analytics that scores conversations automatically and surfaces trends for coaching.

Medallia

Customer experience platform that collects feedback and analyzes text and speech across surveys and contact centers.

Developer API

Qualtrics

Survey and experience management software, used by support teams for satisfaction surveys and text analysis.

Developer API

Chattermill

Feedback analytics that groups themes from support tickets, reviews and surveys to show what customers raise most.

Start small if you are new to QA. A shared scorecard, a weekly session where reviewers grade the same few conversations and compare notes, and short coaching notes for each agent will do more than an elaborate system nobody keeps up. Add automated scoring once you know what good looks like for your team.

Combine QA with customer feedback. A conversation can score well on process and still leave the customer unhappy, and the reverse happens too. Looking at both shows whether the scorecard measures what customers care about.

Keep an eye on bias in automated scores. Check that scores are fair across shifts, channels and languages before you use them to compare agents.

Decide early who owns the findings. Without an owner, themes pile up in a dashboard nobody opens. A short monthly summary of the top issues, with examples and a suggested fix, gets more attention than a long report.

Questions about qa and conversation analytics

How many conversations should we review for QA?

Small teams often review a few conversations per agent each week by hand. Automated tools can score many more, but a person should still review a sample to check the automatic scores and to give useful coaching.

What should a support QA scorecard include?

Common items are whether the answer was correct and complete, whether the agent followed the process, tone and clarity, and whether the customer had to write back. Keep it short enough that two reviewers would score the same conversation the same way.

Can these tools analyze phone calls?

Several can. Observe.AI, Level AI and Medallia work with recorded calls by transcribing them first. Check that your call recording practices follow the rules where you and your customers are.

Who else in the company should see support analytics?

Product, operations and marketing teams usually benefit most. Share themes with real examples, so other teams can see the problem in the customer's own words and act on it.