7 Voice of Customer Examples and Samples

Ever wondered what your customers really think, beyond the polite survey responses and emoji-filled chats? Welcome to the juicy world of Voice of Customer (VoC), where businesses dig through reviews, feedback forms, and even the occasional ranty tweet to uncover golden insights. In this post, we’re diving into real-life examples of Voice of Customer (VoC) and case studies of brands that turned customer whispers (and shouts) into smarter strategies, better products, and fewer “please hold” moments. 

7 Examples of Voice of Customer (VoC)

Let’s explore seven main VoC examples and how companies use them in real-world situations. 

1. CSAT and Sentiment Analysis Using AI

Customer Satisfaction Score (CSAT) is a crucial metric that reflects how satisfied customers are with a specific interaction, product, or service, typically measured by asking, “How satisfied were you with your experience?” It can be calculated either through manual post-interaction surveys or automatically using AI that analyzes the full context of each customer conversation.

• Sentiment analysis, on the other hand, uses natural language processing (NLP) to interpret the emotional tone behind customer conversations, helping identify whether a user feels frustrated, neutral, or delighted. 

Tools like Crescendo.ai leverage AI to automatically calculate CSAT and sentiment for 100% of customer conversations, across chat, voice, and email, without relying solely on manual surveys. 

The AI analyzes 

• tone, 
• speed of resolution, 
• use of language, 
• escalation points, and 
• how the conversation ended 

to determine what caused a high or low CSAT and sentiment. 

It then provides actionable feedback by identifying patterns like frequent handoffs, long wait times, or issue types that trigger negative experiences, empowering CX leaders to optimize support processes, coach agents, and ultimately drive higher customer satisfaction.

Example:

Here’s a screenshot from Crescendo.ai demonstrating how it analyzed a specific customer conversation and generated a sentiment analysis summary as part of its VoC insights.

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This screenshot showcases the VoC dashboard in Crescendo.ai, featuring AI-powered analysis of a customer conversation, including sentiment analysis, CSAT score, key performance metrics, a full conversation transcript, and more.

2. Manual Customer Surveys

Voice of Customer (VoC) is often captured through manual surveys, which fall into two main categories:

1. Feedback Surveys After Customer Support:
These surveys are triggered right after a customer interacts with the support team. Customers may receive a follow-up via phone call, chat prompt, or email asking how the conversation went. The goal is to evaluate the performance of support agents, identify issues that led to high or low satisfaction, and uncover trends that affect the customer service experience. The data is used by CX leaders for agent training, performance reviews, and process improvements.

2. Feedback Surveys for Product or Service Quality:
These surveys focus on understanding how customers feel about the product, service, or delivery experience itself. They help business leaders gain insight into customer satisfaction beyond support interactions, highlighting issues related to product performance, usability, delivery delays, or overall value. The feedback is critical for refining offerings and aligning product or service improvements with customer expectations.

The Limitation:
Despite their usefulness, these surveys often suffer from low response rates—typically only 5% to 30%—and mainly attract feedback from customers at the extremes (either very happy or extremely dissatisfied), leaving a large gap in understanding the broader customer experience.

Example of manual surveys for VoC

Here’s a real-world Voice of Customer example from HappyOrNot, a Finnish company known for its iconic smiley-button terminals placed in airports, stadiums, stores, and hospitals. These terminals allow customers to provide instant feedback, “very happy” to “very unhappy”, with a single button press after a service interaction.

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An image showing HappyOrNot’s survey screen to analyze VoC. Image source: HappyOrNot

Case Study 1: XXL Retailer

XXL implemented HappyOrNot terminals across its 90+ stores to collect immediate, anonymous customer feedback after checkout. By analyzing daily trends, the retailer could pinpoint fluctuations in satisfaction across different locations and timeframes. For instance, when feedback dipped during peak hours, management discovered that long wait times at checkout were the cause. In response, XXL adjusted staffing and optimized checkout flows, resulting in improved customer experiences and higher satisfaction scores across the chain.

Read the full XXL case study

Case Study 2: Knox Community Hospital

Knox Community Hospital in Ohio used HappyOrNot to capture real-time patient feedback in departments like imaging and outpatient services. By reviewing daily and weekly sentiment trends, the hospital identified specific concerns, such as scheduling delays or unclear instructions, and made process changes to address them. Staff engagement also improved as positive feedback boosted morale and created a culture of continuous improvement. The hospital credits HappyOrNot with helping them align care quality more closely with patient expectations.

Read the full Knox Community Hospital case study

These examples show how post-interaction feedback surveys, even simple, one-click responses, can offer powerful insights when collected consistently. With real-time data, both retailers and healthcare providers can act swiftly, measure improvements, and align their services with what truly matters to customers.

3. Call Recordings and Chat Analysis (Manual)

Many companies manually analyze call recordings and chat transcripts to understand the Voice of Customer (VoC) and uncover patterns in customer behavior, expectations, and pain points. 

CX managers or quality assurance teams listen to selected calls or review chat logs to assess how agents handled different scenarios, such as 

• complaint resolution, 
• tone of voice, 
• product knowledge, or 
• empathy. 

These insights are then used to identify recurring issues, evaluate agent performance, and detect gaps in training or support processes. 

Businesses often use this data for coaching sessions, performance reviews, and to develop strategic initiatives, such as revising knowledge base content, optimizing escalation paths, or updating policies, to enhance the overall customer experience. 

While effective, this manual process is time-consuming and often covers only a small percentage of total interactions.

Real-World Example of VoC via Call Recordings 

HomeServe UK: Turning Voice of Customer Into Actionable Strategy with Verint

HomeServe, a major home assistance provider in the UK, used Verint Speech Analytics to unlock deeper VoC insights from over 2 million customer calls annually. Key highlights:

• 100% Call Analysis: Implemented Verint Speech Analytics to analyze every recorded call—not just a sample.
  
  
• 54 Speech Categories: Created a custom taxonomy to track sentiment, frustration, compliance, knowledge gaps, and other VoC signals.
  
  
• Root Cause Discovery:
  
  
  • Found 20% of calls were unnecessary repeat contacts due to document confusion or misrouted inquiries.
    
    
  • Identified long hold times as a major pain point.
    
    
• Actionable Results:
  
  
  • Reduced average hold time by 20 seconds per call.
    
    
  • Decreased total call volume by 10% by fixing upstream issues.
    
    
• CX Improvements:
  
  
  • Used findings to coach agents more effectively.
    
    
  • Informed digital and process improvements across the business.
    

Result: A data-driven VoC program that improved efficiency, reduced effort, and elevated the customer experience.

Read the full HomeServe case study

Typical Manual Analysis Workflow

1. Sample and Review
   QA or CX teams randomly select recorded calls and chat transcripts—focusing on critical paths like escalations or support handoffs.
   
   
2. In-Depth Evaluation
   Reviewers look for key indicators like resolution speed, tone, empathy, knowledge gaps, compliance issues, and root customer pain points.
   
   
3. Structured Scoring & Feedback
   Agents are scored against criteria such as call resolution, dosed tone, and adherence to process. These evaluations inform targeted agent coaching, performance reviews, and training needs.
   
   
4. Strategic Insights
   Aggregated findings reveal recurring issues—e.g. scripting problems, slow processes, or product gaps—allowing leaders to reprioritize training programs, update knowledge bases, or redesign workflows.
   
   
5. Performance Tracking
   Progress is monitored via monthly or quarterly audits of agent transcripts to confirm ongoing improvement and establish benchmarks.
   
   

Tools & Software That Empower This VoC Analysis

Several platforms streamline this manual VoC process by offering transcription, scoring, and feedback features:

1. Crescendo.ai – Automatically captures all call recordings and chat transcripts, giving CX leaders the option to manually review any interaction. It also uses AI to analyze 100% of conversations, providing CSAT scores, sentiment analysis, and smart recommendations for both individual interactions and broader ticket categories.
2. Verint Speech Analytics – Automates transcription of 100% of calls, sentiment tagging, and QA evaluation. HomeServe UK reported $5M+ in savings using this toolkit
3. Calabrio ONE – Combines workforce management, interaction analytics, and quality management to score and review interactions at scale
4. NICE CXone Quality Management – Offers AI-enhanced facets like automated scoring, evaluation templates, and real-time alerts for coaching opportunities 
5. Invoca – Uses AI to analyze phone conversations, detect calls with churn risk, and feed insights back to agent training programs 

4.Smart Categorization

Categorizing customer support tickets by topic and type is a crucial part of effective Voice of Customer (VoC) analysis. It gives businesses a structured view of recurring issues and helps them prioritize what matters most to customers.

Key Benefits of Ticket Categorization:

• Group tickets by query type (e.g., billing, product issue, order status).
  
  
• Calculates average CSAT, sentiment scores, and handoff rates per category.
  
  
• Highlights which types of queries that cause repeated dissatisfaction or frustration, even after support intervention.
  
  

Categorization Specific VoC Example:

Imagine a red t-shirt with SKU TSH-RED-M.
VoC categorization shows this SKU receives frequent complaints about sizing—customers report it runs small, causing disappointment. This insight helps product and merchandising teams identify and fix the issue proactively, improving satisfaction and reducing returns.

Time-Based Filtering:

Another powerful feature is timeline filtering:

• Understand seasonal trends (e.g. coupon or shipping-related queries during holidays).
  
  
• Prepare your support team for predictable spikes in specific categories.
  
  

By combining categorization with timeline analysis, businesses can fix root issues, optimize staffing, and enhance the overall customer experience.

Here is a Voice of Customer example with categorization from Crescendo.ai. 

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An image from Crescendo.ai’s VoC tool showing smart categorization of support tickets

5. User Interviews

User interviews are one of the most effective ways companies gather deep, qualitative VoC insights. Unlike surveys or automated tools, these conversations uncover the why behind customer behavior and sentiment.

How Companies Use User Interviews to Understand VoC:

• Understand Customer Emotions
  Hear directly from customers about what frustrates, delights, or confuses them.
  
  
• Validate Product Ideas
  Teams at companies like Airbnb, Slack, and HubSpot use interviews during product development to test assumptions and refine features.
  
  
• Identify UX and Service Gaps
  Interviews often reveal usability issues, broken flows, or service gaps that data alone can’t capture.
  
  
• Support Strategic Decisions
  Insights are used to improve onboarding, update messaging, build knowledge bases, and train support teams more effectively.
  
  

Why They’re Valuable:

• Provide rich context and real stories behind customer feedback.
  
  
• Help teams build empathy and create more human-centered experiences.
  
  
• Complement data from CSAT, NPS, and sentiment analysis.
  
  

When used consistently, user interviews help companies make smarter, customer-focused decisions that boost satisfaction, retention, and trust.

User Interview Platforms

1. User Interviews

• What it does: Recruits participants and schedules interviews for product research.
  
  
• Features: Participant panel access, screener surveys, scheduling, incentive handling.
  
  
• Use case: Ideal for UX, product, and VoC teams needing fast access to targeted users.
  
  

2. dscout

• What it does: Remote qualitative research platform with video, diary, and survey tools.
  
  
• Features: Video interviews, diary studies, sentiment tagging, longitudinal studies.
  
  
• Use case: Captures in-context behavior and customer sentiment over time.
  
  

3. Lookback

• What it does: Live user testing and remote interviews.
  
  
• Features: Real-time and recorded sessions, note-taking, team collaboration.
  
  
• Use case: Great for watching users interact with a product while discussing their experience.
  
  

4. UserTesting

• What it does: Provides on-demand feedback from real users via recorded video tests and interviews.
  
  
• Features: Test scripting, automatic transcriptions, segmentation, analytics.
  
  
• Use case: Fast VoC feedback on new features, websites, or marketing content.
  
  

Voice of Customer Example with Intuit’s Case Study

Intuit, the company behind QuickBooks, needed fast and reliable user feedback to refine new AI-powered features. Traditional qualitative research methods were too slow and resource-intensive to keep up with their agile development cycles. They needed a solution that could scale quickly, provide deep insights, and integrate smoothly into product workflows.

Solution
To meet this need, Intuit turned to User Interviews to rapidly recruit small-business owners for research. They combined this with Outset’s AI-moderated interview platform, which enabled intelligent, dynamic conversations that could adapt in real-time based on user responses. Within just two days, the team conducted three studies involving 36 participants—an impressive turnaround that delivered high-quality insights with minimal manual effort.

Key Insights & Actions

• Detected the “fat-finger phenomenon”: users accidentally entered incorrect invoice amounts due to a lack of oversight.
  
  
• Uncovered unexpected usability issues and workflow improvements

Efficiency Gains

• AI moderation handled transcription, dynamic probing, and initial analysis
  
  
• Reduced manual workloads and enabled faster iteration cycles
  

Outcomes

• Delivered actionable findings rapidly to product and CX teams
  
  
• Prompted the formation of a dedicated engineering team to fix invoicing errors
  
  
• Empowered data-driven decision-making and improved customer experience

Reference for the above case study: Intuit VoC example

6. Social Listening

Social listening is a powerful Voice of Customer (VoC) strategy that allows companies to track, analyze, and respond to customer conversations happening across social media platforms, forums, blogs, and review sites. Instead of waiting for direct feedback through surveys or support channels, businesses monitor mentions, hashtags, comments, and sentiment in real time to understand what customers are saying about their brand, products, competitors, and industry trends.

Social listening also reveals competitor insights, customer expectations, and unmet needs, which can be used to refine product development, customer service strategies, and brand positioning. By integrating these insights with internal VoC data (e.g., CSAT or chat logs), companies gain a more holistic understanding of customer sentiment, leading to more responsive, data-driven decisions that improve customer experience and loyalty.

Top Social Listening Tools

1. Brandwatch

• Overview: Enterprise-grade social listening and consumer intelligence platform.
  
  
• Key Features: Real-time monitoring, sentiment analysis, image recognition, topic clustering.
  
  
• Use Case: Ideal for brands needing deep insights into online sentiment, trends, and competitor benchmarking.
  

2. Sprout Social

• Overview: A comprehensive social media management and listening tool.
  
  
• Key Features: Monitor brand mentions, hashtags, sentiment scoring, and competitor analysis.
  
  
• Use Case: Best for mid-to-large businesses managing engagement and extracting VoC insights across channels.
  

3. Mention

• Overview: Tracks brand mentions across social media, blogs, forums, and news sites.
  
  
• Key Features: Keyword tracking, sentiment filtering, influencer identification.
  
  
• Use Case: Great for startups and growing brands looking for real-time alerts and basic VoC monitoring.
  

4. Hootsuite Insights (powered by Brandwatch)

• Overview: Combines Hootsuite’s social management with Brandwatch’s powerful analytics.
  
  
• Key Features: Real-time sentiment analysis, trend spotting, customizable dashboards.
  
  
• Use Case: Perfect for teams already using Hootsuite to manage posts and now want to integrate listening.
  

5. Talkwalker

• Overview: AI-powered consumer intelligence platform for global brands.
  
  
• Key Features: Multilingual listening, visual analytics, and competitive benchmarking.
  
  
• Use Case: Suitable for enterprises that want to combine social listening with broader market research.
  

6. Awario

• Overview: Affordable tool for tracking brand mentions across the web and social platforms.
  
  
• Key Features: Boolean search, sentiment analysis, lead generation.
  
  
• Use Case: Ideal for small businesses and agencies on a budget.

Ryanair’s Social Listening Example for VoC

How They Used It
Ryanair closely monitored social media mentions and sentiment using Brand24’s social listening platform. They tracked comments about pricing, service experience, and brand reputation in real time, both the praise and the criticism.

Actions Taken

• Engaged directly: When a customer tweeted frustration over a €12 seat fee, Ryanair responded with humorous, sarcastic tweets to ease tension and show personality.
  
  
• Turned criticism into opportunity: Their witty reply went viral, generating over 2,000 mentions and reaching nearly 17 million people.

Impact on VoC Strategy

• Enhanced visibility: Real-time tracking enabled Ryanair to spot and respond to sentiment spikes quickly.
  
  
• Tone alignment: They reinforced their brand image by engaging customers in a voice that felt authentic and relatable.
  
  
• Increased brand equity: A mix of humor and responsiveness not only mitigated negative sentiment but also amplified their share of voice online—8% of all Ryanair mentions were tied to these engaging exchanges

Reference for the above case study: Ryanair VoC example

7.Online Reviews

Companies analyze online reviews from platforms like Google, Amazon, Yelp, and Trustpilot to gain valuable Voice of Customer (VoC) insights. By reviewing customer feedback, ratings, and recurring themes, businesses can identify common pain points, popular features, and unmet expectations. These insights inform product improvements, service enhancements, and marketing messaging. 

For example, consistent complaints about delivery delays or usability issues can trigger operational changes. Online reviews offer an unfiltered, real-world perspective that helps companies align offerings more closely with customer needs and expectations.

Example of Voice of Customer through online reviews

Company: iHeartRaves (fashion and festival apparel retailer)
Platforms Monitored: Amazon, Google, Yelp, Trustpilot, and social media

The Challenge

As a fast-growing online fashion brand, iHeartRaves needed to better understand customer perception, beyond just sales metrics, by analyzing what shoppers were saying about their festival wear across review platforms.

Approach

• Comprehensive review monitoring: Gathered customer feedback from Google, Yelp, Trustpilot, and Amazon.
  
  
• Sentiment analysis and pattern detection: Identified recurring themes such as “quality issues,” “size mismatches,” and “delivery delays.”
  
  
• Actionable segmentation: Tagged reviews by topic to highlight urgent concerns, e.g., sizing complaints on Amazon vs. shipping delays on Google.
  
  

Results & Strategic Changes

1. Product refinement: Noticed consistent feedback about size inconsistencies. In response, they revised their size chart and added detailed “fits like” guidance.
   
   
2. Customer support priority: Reviews citing shipping problems triggered a fast escalation path in their support team, leading to faster resolution times.
   
   
3. Marketing updates: Positive reviews, where customers praised colorful prints and festival-ready styles, were leveraged in social media ads and on-site testimonials, enhancing brand credibility.
   
   
4. Reputation boost: As CMSWire reports, iHeartRaves' review strategy “helped the brand gauge improvement areas” and strengthened digital presence, directly impacting conversion rates

Reference for above case study: Example of iHeartRaves’s VoC anaysis

Crescendo.ai: The Best VoC Tool for Customer Support Tickets

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So, if you’re tired of playing detective with scattered support tickets, guessing what your customers really meant, or relying on post-chat surveys no one fills out, Crescendo.ai is your backstage pass to clarity. It listens to every conversation, pulls out insights like a mind reader, and hands you a playbook to actually improve your support. No extra forms, no customer nagging, just real-time AI-backed VoC magic baked right into your existing support flow. Go ahead, give your CX team the superpower they deserve.

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