Insights
AutoQA gives you the data. Here’s how to turn it into action.
Most conversations about AutoQA focus on coverage. After all, moving from manually reviewing 3% of interactions to scoring every single one is a significant leap. But coverage is just the starting point.
The teams getting the most out of AutoQA aren’t the ones with the highest percentage of scored calls. They’re the ones who walked in with a clear answer to a deceptively simple question: once we have all this data, what are we actually going to do with it?
As Xander Freeman, Digital Content Director at Call Centre Helper, put it on evaluagent’s ‘From Scores to Signals’ webinar: “I can’t remember a time in my lifetime where contact centre professionals have had more leverage over the wider business than they do right now.”
The data exists – is your team best positioned to use it?
More data doesn’t automatically mean more action
There’s a version of AutoQA adoption that looks great on paper: 100% coverage, consistent scoring, dashboards full of insight. That all sounds good – but it often doesn’t change how the business operates at all. The QA team has more information than ever, and less time to act on it than they’d like.
When AutoQA frees up capacity, that capacity needs somewhere to go. Without a deliberate plan, it tends to get absorbed back into the team’s existing workload rather than redirected toward the work that actually moves the needle – deeper coaching conversations, cross-functional insight sharing, pattern analysis that informs process change.
As Matt Jones, Head of Product at evaluagent, framed it: “You’ve really got to think about what you want to do with that capacity… otherwise it’s just more data at the end of the day.”
The technology can create the space; only the team can decide what fills it.
The ROI question worth asking before you go live
The most useful exercise your QA team can do before, or shortly after, implementing AutoQA is to map out the problems they want to solve with the time they’re getting back. Not in abstract terms, but specifically. Answer the following:
- If scoring is automated, where does that effort go instead?
- What would better coaching conversations look like in practice?
- Which business questions could quality data finally help answer?
These aren’t questions the platform can answer for you. But having clear answers to them is what separates the teams that see measurable business outcomes from the ones that see impressive-looking reports.
It’s also what makes the ROI conversation with senior stakeholders far easier to have. Boards and leadership teams aren’t moved by coverage statistics. They want correlations between quality scores and the metrics they’re already accountable for: customer satisfaction, repeat contact rates, resolution times.
Xander elaborated: “From a data perspective, you can craft the narrative you want, win or lose. You have the tools to do that, you just need to learn how to use them in the right way, and again, translate it into the metrics that matter for that particular stakeholder.”
When QA teams can draw that line, they stop being seen as the quality police and start being seen as one of the most valuable sources of business intelligence in the organisation.
Starting small is fine. Staying small isn’t.
One thing worth saying clearly: you don’t need to automate everything at once.
Starting with a subset of interactions, a single channel, or a specific team is a perfectly sensible approach – it lets you build confidence in the outputs, bring agents along with you, and refine your setup before scaling. The important thing is that even a small-scale rollout should have an end goal in mind.
As Xander noted, “Accountants didn’t go extinct at the invention of a calculator. If you embrace this technology and learn how to use it in the right way, you’re setting yourself up really nicely to succeed.”
What does success look like in six months? What will you do differently as a result of having this data? If you can answer those questions now, you’re in a much stronger position to demonstrate value to your team, your leadership, and the agents whose trust you’ll need to bring on board.
Quality data is only as good as what you do with it
AutoQA is genuinely transformative for contact centre teams, but the transformation isn’t automatic. The technology handles the heavy lifting of scoring and consistency. The strategic thinking about what to do with that foundation is still very much a human job, and arguably a more interesting and impactful one than what came before.
The question isn’t whether you have enough data. At 100% coverage, you almost certainly do! It’s whether you’ve got a plan for it.
Watch the full From Scores to Signals webinar with Matt Jones and Xander Freeman on YouTube.
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