Upgrade How You Listen to Customer Feedback

Customer feedback is one of the most valuable inputs for improving service and experience. Still, many contact centres continue to rely on outdated, survey-driven methods, and only skim the surface when it comes to tuning into what their customers really need.

But there are now far smarter ways to do this! That’s why we asked our technology panel for their best advice on how to upgrade how you listen to customer feedback.

Replace the “Death Certificate” With a Live Friction Score

Too many contact centres learn about customer feedback only as autopsies: detailed, expensive examinations of a customer relationship that is already dead, driven by the need to discover what went wrong.

The shift to AI-powered analysis of 100% of interactions detects friction, frustration, and churn signals in real time, before the patient flatlines.

But modernizing the listening delivers only half the diagnosis. Every insight needs to leave the CX team as a financial case: “This contact driver costs £X per call, affects Y% of volume, and is generating Z% churn. Fix it and recover £N in revenue.”

Replacing the death certificate with a live friction score – a single AI-generated metric updated daily from real interactions – gives the C-suite the simplicity of NPS without the uncertainty of what generates it. Stop studying the ghost of customer experience. Start monitoring its vital signs!

Contributed by: Taoufik Massoussi, Product Manager, Enghouse Interactive

Use Technology to Amplify Critical Thinking and Empathy

Upgrade how you listen by empowering agents to respond meaningfully. Train for emotional intelligence, reward great service, and most importantly: when customers express disappointment, change something!

Technology can support this by highlighting where issues have occurred previously and suggesting what may be of value to a customer, but it should amplify, not replace, critical thinking and empathy. Use surveys intelligently and close the feedback loop so customers feel the impact.

Where technology truly excels is in deciphering implied feedback. Changes in behaviour and habits can reveal issues or opportunities that may otherwise go unnoticed.

Contributed by: Ben Willmott, Principal Solution Consultant at Route 101

Introduce a Regular Cross-Functional Review of What Your Conversations Are Telling You

Genuinely upgrading how you listen means building deliberate routes for conversation intelligence to travel – to operations teams who own processes, to product teams who shape self-service journeys, to leadership who set policy.

If the insight loop closes only within the QA function, the investment in listening rarely translates into visible improvement for customers.

A practical first step here is to introduce a regular cross-functional review of what your conversations are telling you. Not a QA meeting – a business meeting! That single change creates shared ownership of what customers are experiencing and genuine accountability for doing something about it.

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

Move From Objective Questions to Asking for the Unknowns

Thanks to advances in generative AI, speech analytics has evolved. No longer are we asking objective questions such as “Was a particular compliance statement read out word for word?”

Now we can be much more subjective. We can ask things around “Was an agent’s response appropriate?” and “Did it cover all of the customer’s concerns?” – giving far greater insight into what’s happening on a particular interaction.

Beyond this, it’s now also possible to ask for the unknowns, so rather than having to ask which particular trend this fits into, you can provide the AI model with the trends that you know about and ask it, “Was this interaction about something different?” If so, “What was it?” and “How many times am I seeing that?”

This gives leaders the ability to understand exactly what’s going on in their organization – in ways they couldn’t have done before.

Contributed by: Richard Manthorpe, Product Director, Content Guru

Use Insights to Inform Agent Coaching, Product Improvements, and Automation Opportunities

To truly upgrade how they listen, teams must analyse the full universe of customer interactions across voice, chat, and digital channels.

AI-powered conversation intelligence allows organizations to process 100% of interactions, automatically detect themes, sentiment shifts, emerging issues, and operational friction points.

Instead of waiting for a customer to fill out a survey, leaders gain a continuous stream of feedback directly from real conversations.

The key is closing the loop… Insights should not remain in dashboards! They must inform agent coaching, product improvements, and automation opportunities. When feedback from every interaction feeds directly into operational decisions, contact centres move from reactive listening to proactive experience management.

Contributed by: Ashish Nagar, CEO, Level AI

Treat Feedback as Evidence – Not Just a Score

Too often, feedback is collected after the fact and turned into reports, rather than being used to improve what happens day to day.

The shift is simple: treat feedback as evidence. Not just a score, but a way to understand whether customers are getting clear, consistent service – and where things are going wrong!

That means looking beyond surveys, combining complaints, call insight, sentiment, and frontline observations to identify patterns and fix root causes.

Contributed by: Ben Scales, Head of Sales, Elephants Don’t Forget

Uncover “Unknown Unknowns” and Emerging Trends – Without Manual Tagging

Organizations must move beyond basic surveys and legacy keyword-based analytics toward modern AI and LLM-based conversational platforms.

These platforms ingest 100% of omnichannel data – including calls, chats, surveys, social media, and review sites – to autonomously detect ultra-specific pain points and root causes.

For example, uncovering “unknown unknowns” and emerging trends without manual tagging. Rather, a conversational AI agent allows anyone in the organization to ask plain-language questions and receive definitive, financially quantified answers instantly.

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

Ensure Low Scores or Negative Sentiment Trigger Fast Follow-Ups

All customer conversations should be treated as feedback sources. Apply AI conversational analytics – sentiment analysis and speech analytics – to call transcriptions and interaction data to identify recurring friction points, satisfaction shifts, and service gaps at scale.

The critical final step is closing the loop: ensure low scores or negative sentiment trigger fast follow-ups and inform operational changes. These insights deliver more immediate value when they are embedded into CRM systems and other existing tools.

This gives agents a unified view of customer sentiment, comments, and interaction outcomes across voice and digital channels, enabling them to respond with full context.

Contributed by: Rodney Hassard, Head of Product, Applications, Vonage

Embed Feedback Directly Into Daily Workflows

The greatest value of customer feedback comes from how leadership acts on it. Too often, insights remain isolated in reports rather than influencing day-to-day operations.

To improve how feedback is applied, contact centres must embed feedback directly into daily workflows. This includes using insights to guide agent coaching, refine processes, and reinforce behaviours that drive positive outcomes.

Emerging tools also enable teams to interact with their data more dynamically, ask questions, and receive actionable recommendations in real time. This helps bridge the gap between insight and execution, allowing faster and more precise responses to customer needs.

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

Treat Feedback as Continuous

Contact centres need to treat customer feedback as a continuous, end-to-end signal rather than a single post-contact event. This means capturing insight across all channels, including voice, chat, email, digital messaging, and, where possible, self-service drop-outs.

Critically, feedback should also cover digital experiences such as website journeys, giving visibility into why customers needed to make contact in the first place, and what happens afterwards.

AI and machine learning tools should help analyse every interaction at scale, identifying themes, sentiment, intent, and emerging issues in near real time rather than relying on small survey samples that often occur too late to resolve problems.

Customer feedback alone can paint an incomplete picture, so it is important to triangulate it with agent evaluations and broader journey analytics.

Combining these perspectives creates a more accurate view of performance and root causes, enabling faster, evidence-based improvements boosting customer experience and operational effectiveness.

Contributed by: Lewis Gallagher, Senior Solutions Consultant, Netcall

Have You Recently Upgraded How You Listen to Customer Feedback?

Click here to join our Readers Panel to share your experiences and feature in future Call Centre Helper articles.

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

• Where Self-Service Scheduling Tools Have the Biggest Impact
• Don’t Let Tech Adoption Be an Afterthought
• Where Are Contact Centres REALLY Seeing AI Success?

Author: Megan Jones
Reviewed by: Xander Freeman