How to Manage QA Across Different Channels

Today’s customers engage with contact centres across voice, email, chat, messaging, and social media.

While each channel requires a different approach, customers still expect a consistent experience every time they interact with an organization – but consistent Quality Assurance (QA) isn’t always underpinning it.

So how do you get this right? To find out, we asked our panel of experts for their top tips and advice for managing QA more effectively and consistently across multiple channels.

Look Beyond Individual Scorecards to Spot Recurring Issues Across Channels

Ask how to handle QA across channels and the talk turns straight to scorecards. Which criteria carry over from voice to chat, what needs rewriting for email, how to weight the scores. That’s fair enough, and you do need to get it right. But it leaves a bigger problem sitting untouched.

Your scorecards mark one interaction at a time, as they should. Agents can only be held to what they controlled. The trouble is your customers gave up working one interaction at a time years ago.

They open on chat, follow up by email a couple of days later, then ring you when neither got them anywhere. Score those three separately and every agent involved can pass comfortably. But the customer still spent a week chasing a resolution, and none of your scores will tell you it happened!

So, stop asking the scorecard to answer that. Put a layer above it instead. Classify conversations by the reason behind them, run those topics across voice, chat, and email alike, and the same issue starts showing up three times in a week under three different labels.

When one reason keeps returning across channels, you’re looking at a process problem, and no amount of coaching your agents will shift it.

Contributed by: Derek Corcoran, CEO, ScorebuddyCX

Be Mindful That Shared Metrics May Require Different Timeframes for Different Channels

Managing QA across channels means creating a joined-up strategy that respects each channel’s differences while creating consistent scoring.

Shared metrics like Average Handling Time, First Contact Resolution, and self-service containment rate apply across voice and digital, though timeframes for each metric may vary.

Digital channels also require unique measures: average response time, total responses to close an interaction, and unmet demand (measured as abandonment across channels).

The key is devising equivalence between metrics, for instance, linking customer effort score to total responses in written channels, so scoring stays consistent regardless of channel.

Traditional QA sampled a percentage of interactions per agent. Modern QA uses automated tools to score every interaction, flagging cases needing human review so auditors spend time actually auditing, not searching.

Looking ahead, agentic AI introduces containment as a bigger FCR: measuring how many interactions were fully resolved by AI alone within pre-defined guardrails. 

Contributed by: Martin Taylor, Co-Founder and Deputy CEO, Content Guru

Run an Inventory on Every Channel – Including its Volume and Current QA Coverage

QA coverage in most contact centres is badly lopsided. Industry research shows only 36% of contact centres rate their telephony QA as “very effective” – and that’s the best channel. For email it drops to 17%, web chat 14%, and social media just 13%.

Meanwhile most centres manually review six or fewer calls per agent per month. The practical consequence: your highest-growth channels are getting your shallowest review, and some are getting none at all.

Start with an inventory – every channel, its volume, its current QA coverage. A useful rule of thumb: any channel carrying more than 10-15% of your volume with zero QA is your most urgent risk and your fastest win.

AI-assisted evaluation can then lift coverage towards 100% of interactions, freeing human reviewers to focus on coaching and the flagged, high-risk cases where judgement matters most.

Don’t Forget Your Bot Is a Channel Too (So Make Sure to Include it in Your QA!)

Most contact centres can quote their chatbot’s containment rate. Far fewer can say whether its answers were actually correct.

That gap is the newest quality risk: high containment can quietly mask unresolved issues, hallucinated answers, and handovers that dump customers on a human agent with no context.

Treat automated conversations as a channel within your QA programme. Grade bot answers against your knowledge base as the source of truth.

Monitor containment alongside accuracy, customer satisfaction, and repeat-contact rates – never containment alone. Test the escalation triggers, and score handover quality: did the human inherit the full conversation?

Apply the same outcome-based rubric to bot and human conversations, and you can finally answer questions like, “Is the bot more accurate than the team on billing queries?” with data rather than assumption.

Contributed by: Chris Mounce, Product Training & Enablement Specialist, evaluagent

Pinpoint Exactly Where High-Value Customers Experience Friction

The biggest barrier to consistent QA isn’t a lack of standards – it’s limited coverage. Traditional sampling leaves massive blind spots, forcing each channel into its own disconnected rules.

The fix is shifting from sampling to total coverage. AI now enables automatic, 100% analysis of customer interactions across every channel.

Crucially, pairing this coverage with customer specific metadata – like customer tier, product line, or resolution outcome – transforms generic scores into targeted insights.

It pinpoints exactly where high-value customers experience friction or which issues repeatedly slip through. Ultimately, QA consistency doesn’t come from harmonizing scorecards channel by channel; it comes from unifying data and context in one place.

Contributed by: Matthew Clare, VP, Product Marketing, UJET

Track Whether or Not Information is Being Passed Clearly Across Channels

Customers do not think in terms of channels. They think about whether their issue was understood and resolved. If a customer starts with chat and later calls, they should not have to begin again.

QA should assess whether information was documented accurately, passed along clearly, and used effectively so the customer did not have to repeat themselves. This means looking beyond isolated interactions and evaluating the whole journey.

When the same problem appears across multiple channels, the root cause may not be employee performance.

It could indicate a process gap, disconnected systems, or unclear internal communication. Evaluating QA across the full customer journey helps leaders identify those broader issues.

Set Aside Time to Review Real Interactions and Compare Scoring Decisions

Quality Assurance only creates value when the findings lead to meaningful improvement. Regular calibration helps reviewers and managers agree on what good performance looks like across different channels.

Teams should review real interactions, compare scoring decisions, and discuss where expectations may be interpreted differently. Technology can help analyse more interactions and identify patterns, but generating more scores should not be the end goal, it should be accurate insights.

Leaders should use QA insights to find recurring behaviours, channel-specific challenges, and coaching opportunities.

When employees receive clear and consistent feedback, QA becomes more than a monitoring process. It becomes a practical tool for development and better customer experiences.

Contributed by: Jonathan Kenu Escobedo, Customer Success Manager, MiaRec

Develop Channel-Specific Scorecards With Relevant Adjustments

Create a consistent QA framework based on what matters most to your business and customers. Channel-specific scorecards can then be developed with relevant adjustments.

For example, voice assessments may focus on greeting, active listening, tone of voice, empathy, resolution, and call closure. For written channels, these translate into personalization, rapport building, response speed, spelling, grammar, and communication style.

The right QA technology enables automated evaluation of interactions at scale, providing valuable insights into customer behaviour and agent performance.

Instead of analysing only a small sample of contacts, businesses can review 100% of interactions, identify trends, improve coaching, and make data-driven decisions that create meaningful service improvements.

Contributed by: Shaunna Ruddick (Wilson), Technical Consultant, Route 101

Move Beyond Random Sampling to Identifying Trends and Highlighting Outliers

A fragmented approach to QA inevitably leads to inconsistency, inefficiency, and missed insight.

An omnichannel QA strategy therefore ensures that customer experience is measured consistently regardless of channel, agents are assessed fairly, and organisations gain a true, end-to-end view of performance. Without this, quality remains channel-dependent rather than customer-focused.

The most effective organisations are moving towards unified platforms with common agent interfaces across all channels, enabling standardised, in some cases automated, evaluation frameworks across interactions.

Combined with AI and machine learning, this allows teams to move beyond random sampling, instead, identifying trends, highlighting outliers and predicting emerging issues.

Contributed by: Lewis Gallagher, Senior Solutions Consultant, Netcall

Don’t Forget to Monitor the Handoff Between Virtual and Live Agents

As organizations deploy virtual agents alongside live agents, their Quality Assurance must evaluate the entire customer journey rather than individual touchpoints. An important QA focus is the handoff between virtual and live agents.

Quality teams should review whether the virtual agent accurately identified customer intent, collected relevant information, and passed context to the agent handling the interaction.

AI-powered QA tools help by analysing both automated and agent-assisted conversations so supervisors can identify common transfer reasons, escalation trends, and opportunities to improve AI/bot workflows or agent readiness.

The goal here is not simply to maximise automation, but to ensure that virtual agents and live agents work together effectively to resolve customer issues with minimal effort.

Treat QA as a Continuous Improvement Program – Not a Monitoring Activity

AI-powered QA platforms can evaluate large volumes of interactions and generate scorecards that highlight strengths, weaknesses, and areas for improvement. However, the real value comes from analysing these recommendations over time and channel.

If similar actions are being suggested across multiple channels, organizations can identify broader operational trends that may be affecting customers and employees alike.

With AI, instead of just treating QA as a monitoring activity, leaders can now view it as a continuous improvement program.

By tracking recurring recommendations and measuring their impact, contact centres can improve service consistency, strengthen agent performance, and deliver better customer experiences across every communication channel.

Contributed by: Jeff Lear, Sr. Solutions Engineer at Enghouse

Flag Sentiment Shifts and Surface Patterns That Random Sampling May Miss Entirely

Good multichannel QA starts with shared evaluation dimensions (issue understanding, empathy, resolution quality) applied consistently whether the interaction happened over the phone or in a messaging thread.

But the scorecards themselves should be channel-specific. A great email response looks nothing like a great phone call.

The real shift comes when you stop sampling and start scoring everything. AI-powered Quality Management is designed to analyse 100% of interactions across voice, chat, email, and social, flagging sentiment shifts and surfacing patterns that random sampling may miss entirely.

That moves QA from a compliance exercise to a coaching tool – one that can help improve how agents perform on every channel, not just the one you happened to review.

Contributed by: Ben Neo, Head of CX EMEA, Zoom

Do You Manage QA Well Across Different Channels?

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For more great insights and advice from our panel of experts, read these articles next:

• Smarter Ways to Give Agents Better Work/Life Balance
• 23 Questions to Ask When Choosing Your Next CCaaS
• Are Your Virtual Agents Escalating Far Too Many Queries?

Author: Megan Jones
Reviewed by: Jo Robinson