A 6-step guide to successful VoC analysis

Nowadays, any customer with a smartphone or laptop can share their thoughts and feelings about your business with a global audience in seconds. 

But feedback piles up fast—and the valuable insights hidden in those data mountains aren’t going to just surface themselves. That’s what voice-of-the-customer analysis is for.

It helps you categorize and analyze quantitative and qualitative VoC data to draw insights that can be shared and acted upon to improve your customer experience. 

In this chapter of our comprehensive VoC guide, we explain, step by step, how to analyze voice-of-customer data and turn that analysis into action.

Keep reading to find out how to

• Categorize VoC feedback using AI-powered sentiment, topic, and keyword analysis

• Calculate the success of your customer experience using quantitative analysis

• Understand the ‘why’ behind customer feedback with experience analytics

• Share and act on insights—and repeat and improve 

1. Define your goals

Before you even start collecting VoC data, you need to decide what you want to gain from it.

The best way to do this is to formulate questions (about how your customers are behaving and feeling) that urgently need answering. Questions like:

• Are our customers happy with our new product line’s pricing?

• Why did churn rates for our mobile app go up last quarter?

• Do customers like using our product’s new flagship feature?

• What’s stopping customers from subscribing to our service?

Remember: you don’t need to answer every question at once. 

By starting with one question, you can come to grips with how the analytics process works—and incorporate those learnings into your next project.

2. Collect and categorize your voice-of-the-customer data

Now that you know what you want to investigate, you need to collect the feedback relevant to that goal. 

There are various methods for gathering quantitative (numerical) and qualitative (non-numerical) voice of the customer data. Chapter 2 of our guide details those methods and explains how Contentsquare helps collect VoC data.

Having collected your data, you next need to categorize it so you can easily

• Focus on the feedback that’s most relevant to your defined goal 

• Prioritize feedback according to its urgency

• Compare feedback given at different stages of your user journeys and by different customer segments

Having a team of analysts manually categorize each incoming piece of feedback is labor-, time-, and cost-intensive—and vulnerable to human error.

Instead, use an AI-powered voice of customer analytics solution to categorize feedback for you. Automation speeds things up, ensures every piece of feedback is categorized, and makes the insights you derive from data analysis more trustworthy.

A good voice of customer analytics platform categorizes textual feedback for you by tagging it according to its sentiment, topics covered, and keywords used. 

• Sentiment analysis uses natural language processing (NLP) and other AI processes to analyze textual feedback for its emotional content (positive, negative, or neutral), making it quick and easy for you to find, act on, and share both negative and positive feedback

• Topic and keyword analysis works similarly to sentiment analysis, interpreting textual feedback to identify topics covered and keywords used

This lets you filter feedback to view only what’s relevant to your project and prioritize fixes and optimizations by identifying the most frequently occurring topics and keywords.

💡 How Contentsquare helps 

Our platform’s Voice of Customer product lets you switch on automatic tagging to assign incoming feedback with the sentiment, topic, and keyword tags of your choice. 

Once feedback is tagged, you can effortlessly search through and filter it according to your needs. You can also choose to be alerted when negative feedback spikes or when particular topics come up in feedback.

Set up auto topic tagging in Contentsquare for no-effort categorization of your customer feedback. Topic tags include ‘Pricing’, ‘Bug’, ‘Feature request’, ‘UX’, and ‘Spam’

Our platform also provides you with AI-generated summaries of your survey responses. These will tell you the overall sentiments and pick out key recurring positive and negative trends in feedback.

AI-generated summary reports highlight the general sentiment of responses, pick out both positive and critical quotes, and even suggest next steps you can take based on customer feedback

3. Conduct quantitative voice-of-customer analysis 

Quantitative VoC data is numeric data (it can be counted, measured, or otherwise given a numeric value). One way to acquire it is by running quantitative surveys across your customer touchpoints that ask your customers to rate their experience with your product, service, or experience (on a scale of 1-5, for example).

It gives you a quick overview of customer satisfaction and points you in the direction of areas of your user experience that need improvement.

Knowing, for example, that most surveyed customers are giving your checkout experience a low score is an invaluable signal for your teams to investigate what they can do to improve it. 

The three most popular types of quantitative feedback to track with surveys are:

1. Net Promoter Score® (NPS®) 

2. Customer Satisfaction (CSAT) score

3. Customer Effort Score (CES)

A voice of the customer analytics platform should not only calculate feedback scores for you but also enable you to segment your results by (for example) the score given, the demographic respondents fit into, and where respondents are in your journey.

Segmentation also helps you share survey results and insights with people across your business. This is critical for getting buy-in for optimization efforts.

💡 How Contentsquare helps 

Contentsquare’s Voice of Customer product lets you set up all kinds of quantitative surveys—including NPS® surveys—in seconds. 

And once the results are in, our platform has powerful tools that let you analyze those numbers, so you can understand what’s driving them.

Run quantitative surveys such as NPS® to get a general overview of customer satisfaction

Create audience segments for specific scores and compare segments using Journey Analysis (which visualizes user journeys through your site, app, or product) and Zone-Based Heatmaps (which visualizes user interaction with page and screen elements) to discover what’s driving user sentiment trends.

Where there are low scores, use Journey Analysis and Session Replay (which reconstructs individual user sessions so you can replay them) to understand the context leading up to a user’s dissatisfaction. 

Use Session Replay to investigate the events leading up to a dissatisfied customer giving your business a low score

4. Dig deeper with behavioral analysis

Qualitative voice-of-the-customer data is non-numeric data that covers what customers say about your business and what they do when interacting with it. It’s vital to collect and analyze this data because it tells you why customers feel good, bad, or indifferent toward your user experience. 

We’ve already covered how to analyze what customers are saying about your business through sentiment, topic, and keyword categorization of textual feedback. 

But to truly understand your customers (and your experience), you need to supplement VoC data with insights from behavioral analytics tools, like session replays and heatmaps, which reveal how people interact with your website, app, and products. 

Behavioral analytics data gives you insight into the behavior and feelings of customers who don’t provide you with numeric or textual feedback (the vast majority), and helps you contextualize and understand the feedback you do get. 

What’s more, while behavioral analytics helps you understand VoC feedback, VoC feedback also helps uncover the causes behind confusing behavioral trends revealed through analytics.

It’s a two-way street. That’s why an ideal VoC analytics platform will also offer behavioral analytics, so your teams can seamlessly link feedback to behavior, and better understand both.

💡 How Contentsquare helps 

Contentsquare’s experience intelligence platform seamlessly integrates voice of the customer with behavioral analytics capabilities, including

• Experience Analytics auto captures the (anonymized) behavior of your website and app users over a single session. This includes their on-page and on-screen behavior (including scrolls, clicks, swipes, and hovers) and overall journey path.

• Product Analytics (PA) lets you track the behavior of your product users over multiple sessions, helping you understand how particular features and touchpoints impact engagement and retention

• Experience Monitoring enables you to track user frustration, alerting you when customers exhibit symptoms of frustration (such as ‘rage clicking’), technical errors occur, and your site or app’s performance dips 

By combining these capabilities with our platform’s VoC capability, you effortlessly bridge the gap between user feedback and user behavior. 

This means you can

• Use behavioral analytics to investigate feedback: have you ever received a piece of feedback you didn’t understand? Jump into a session replay and see what happened to the respondent before they left the feedback

• Use VoC to understand confusing behavior you’ve tracked with behavioral analytics: let’s say you spot an issue disrupting the user journey on one of your pages in a session replay but you can’t figure out what’s going on. By using VoC to set up a survey on that page, you can potentially get customers to clarify what’s going on

Use Session Replay to figure out what vague textual feedback is referring to

5. Share insights and act on them

Voice-of-the-customer data can be the evidence you need to make any number of customer-delighting, revenue-boosting optimizations to your customer experience.

But for this evidence to be compelling, you have to be able to share it with other teams and decision-makers in a format they can easily understand and interact with—whether you present it, forward it, or give them access to the same analytics platform you’re using so they can dig into the data themselves and discover insights to share with you. 

💡 How Contentsquare helps 

Use our Voice of Customer product to export your survey results data (filtered by the factors you choose) as a .csv, .xlsx, or Google Sheets document—and forward it by email or any available integration (ex: Slack, Microsoft Teams, Zapier).

Supposing you’ve enabled topic and sentiment tags, the data will contain a sentiment and tags breakdown that displays the distribution of topics and sentiment in your answer pool, both currently and over time. Plus, anyone in your business with access to the platform can also view those AI-generated in-depth reports we mentioned earlier. 

Survey results feature a breakdown of topic tags and sentiment, showing their distribution over time

6. Learn and repeat

Listening and reacting to the voice of the customer is an ongoing process, not a one-off event.

Your customer experience should constantly evolve to meet customer expectations, and you need to continuously collect and analyze customer feedback to ensure you’re hitting the (moving) mark.

Therefore, you should be looking to learn from every survey and test you run, understanding what worked and what didn’t, so you can get better at listening to what your customers want.

💡 How Contentsquare helps 

VoC analysis can only ever be as effective as the data you’ve collected allows it to be.

With Contentsquare, you ensure that every survey you run yields better VoC data than the last, thanks to automatic survey analysis. 

Our VoC performance interface showcases essential data points related to your survey, including impressions and completion rates. It also highlights which sections of your survey received the most attention and caused the most drop-offs.

Our platform's performance interface allows you to see at a glance where your surveys performed strongly and where they could be improved

Let your VoC data speak to you 

Make the right steps and use the right tools, and voice-of-the-customer analysis will help you move your customer experiences (and KPIs) firmly toward ‘amazing’.

We’ve covered the steps you need to take to analyze your VoC feedback in this chapter—but what about the tools you need? We’ve got you covered: jump to Chapter 5 of our guide to discover which VoC tools help you turn raw data into strategic gold.

FAQ about VoC analysis