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How to analyze customer feedback to get better insights

Last updated

16 April 2023

Reviewed by

Miroslav Damyanov

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Gaining customer feedback is a critical way of improving your business and staying on the leading edge. Customer feedback helps you to understand whether you’re delivering on promises, whether customers are satisfied with your product, and where you could better your offering. 

While continual customer feedback is essential, without a practical and logical way of digesting those findings, it’s unlikely that feedback will lead to actionable changes. 

That’s where analysis can help. Customer feedback analysis helps you turn raw customer feedback into a reliable source of knowledge covering paint points, satisfaction levels, usability, loyalty, and more. 

What is customer feedback analysis?

A customer feedback analysis is a process by which to turn customer feedback into actionable insights. Completing a best practice analysis means you’re more likely to deeply understand what your customers want and need from your company. It can also help you attract new customers while retaining your current ones.

A customer feedback analysis typically operates in five main steps:  

  1. Collating customer feedback into a readable and understandable report 

  2. Analyzing the feedback to deeply understand the messages from your customers

  3. Paying attention to common themes or patterns across the feedback

  4. Deciphering which common issues are the most critical to be solved 

  5. Ensuring that you address those issues

Why analyzing customer feedback is critical

Paying attention to what your customers have to say is one of the most important ways to guarantee long-term business success. Customer feedback is a critical way to understand where issues and clunky aspects arise so that you get the chance to do better for your customers. 

While receiving negative feedback can be tricky, in the words of Bill Gates, “[y]our most unhappy customers are your greatest source of learning.” 

When customers do provide feedback, it’s essential to react to it and see how you can improve your offering. Doing so can have many positive effects, including: 

  • Business growth. Businesses that react to customer feedback are more likely to stay competitive and continue growing.

  • Customer experience. Analyzing feedback can streamline areas of friction across products, improving the overall customer experience.

  • Improved products. As you gather and respond to feedback and make improvements, your offering will likely become better over time.

  • Boosted NPS. The Net Promoter Score (NPS) is a common way of calculating customer loyalty. Participants are more likely to be satisfied if you’re continually improving your products for their benefit. 

The challenge of analyzing customer feedback

While it’s a critical aspect of improving a business, analyzing customer feedback isn’t necessarily straightforward. 

Feedback comes from a range of different sources—surveys, social media comments, call center conversations, and more. The broad range of sources means that feedback comes in various forms—written comments, conversational feedback, and scores through surveys. Categorizing, understanding, and acting upon that diverse feedback can be challenging. 

The quality of feedback can impact the analysis too. Some people may use complex language when giving feedback. Others may have poor literacy making their point harder to decipher, leaving room for ambiguity or misunderstandings. 

To uncover the most relevant and helpful insights, discernment is necessary. 

The difference between insightful and non-insightful data

That discernment means recognizing the difference between data that’s insightful––and therefore helpful to the business––and data that’s non-insightful––and therefore useless. 

Non-insightful data is feedback that doesn’t tell you anything new or is irrelevant to your business––such as feedback written by internet trolls or feedback that tells you about an issue that’s already being fixed. What’s more, feedback from random people who are not part of your target audience might be completely useless.

Insightful data tells you something new or can add weight to a proposal to optimize or release a new feature. 

If many customers, for example, are mentioning that the payment aspect of your website is challenging to use, that’s a great case for a more seamless payment feature. As each customer gives this feedback, it proves there’s a real need to prioritize this improvement. If not, your customers could soon drop off. 

Insightful data can lead a company to:

  • Take critical actions, including bug fixes, optimization, new features, and even new product releases 

  • Make changes to the business strategy 

  • Validate (or invalidate) new ideas and plans 

How to discern insightful and useless data

Discerning whether data is useful or not can be tricky. These are some questions to ask when sorting data:

  • Is this feedback authentic?

  • Do we fully understand what the customer wants to tell us? Should we follow up and ask more questions (instead of making assumptions)? 

  • Does this feedback tell us about a new issue?

  • Does this feedback add weight to a proposed fix or improvement?

  • Is this feedback providing us with something new?

  • Do we know the motivations and reasons behind particular behavior?

Paying attention to these questions as you sort feedback will help ensure you rely on the most insightful data. 

Keep in mind that feedback will sometimes be something you may not want to hear. But it’s essential to pay attention to negative comments, as they can help drive business improvements. 

The process: getting actionable insights from customer feedback 

Listening to feedback is one thing, but turning it into actionable insights for your business is another. That’s why we recommend these best practice steps to effectively gather, analyze, and act upon comments from customers.

1. Gather customer feedback

Customer feedback can come from a variety of sources.

The most common sources of feedback include:

Customer surveys

Surveys are one of the most common ways to collect customer responses. That’s because they’re a quick, cost-effective way to gather large amounts of data. 

Net Promoter Score (NPS)

The NPS is a helpful way of discovering customer satisfaction across the business, not just in one area. The NPS asks participants to rank the likelihood that they would recommend your product or service to someone they know (a friend or colleague). In sum, the NPS provides macro-level insights about the current satisfaction of your customers with your business. To get an actionable insight about your score, you need to include an open-ended follow-up question asking why your customer gave a particular score. 

NPS calculator

Your Net Promoter Score is calculated by subtracting the percentage of Detractors from the percentage of Promoters.

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NPS score


Detractors
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1
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Passives
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Promoters
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NPS score

Customer Effort Score Surveys (CES)

The CES is a benchmark that helps you measure the perceived effort customers must put in to achieve a result, whether that’s resolving an issue, fulfilling a task, or getting the information they need. 

It’s calculated by asking customers whether a particular interaction was easy. This can lead you to focus on particular processes and collect more evidence about emerging friction points, enabling you to act on the flaws and ultimately streamline processes to be simpler and more satisfying for the customer. 

Customer Satisfaction Surveys (CSAT)

The CSAT survey is a key method of measuring customer satisfaction. The survey asks customers how satisfied they are with a certain product, service, or interaction. This means it focuses on one experience and is a helpful way to quickly see whether an isolated process is working well for the users. 

Customer reviews

When your customers are talking about your business, it’s a crucial time to be listening. Reviews are critical when it comes to business. A whopping 98% of those surveyed read online reviews for local businesses. That means reviews can be the difference between a customer deciding to use your business or not. 

Social media comments

Comments made on social media are also important, especially given that they appear publicly, so in a way, they act like reviews too. 

Call center feedback

Call center notes can be very important to understand how people feel when they contact your customer service center. Conversations between customers may reveal some interesting insights. 

Chat conversations

Similar to call center feedback, feedback received in live chat can be very useful for seeing trends and themes emerge.  

Customer feedback interviews

These allow you to speak directly to your customers, either over the phone or in person, to discover what their pain points are, how your competitors compare, and what their overall experience with your brand is. 

2. Categorizing customer feedback

A categorization system is important to digest this large amount of information from multiple sources. Rather than lumping all feedback together, use helpful categories to break it down and turn the feedback into actions. 

It can be helpful to place feedback into two main categories: 

  • Type of feedback. First, classify the feedback into types. This means deciding whether it relates to a bug, a feature request, general feedback, a usability issue, or user education.

  • Feedback theme. Then decide which area of the business the feedback relates to. It could relate to payments, onboarding, the app, sales, marketing, the website, user profiles, or more. This is achieved through conducting a thematic analysis.

This categorization helps to group feedback into manageable sections. 

3. Code the feedback

Once feedback is categorized, it’s the best practice to turn feedback into a code that defines what the user is requesting or speaking about. 

The code defines exactly what the issue is to make the feedback simple to understand. Say, for example, a customer said they would like to edit their Google documents within your platform. The resulting code could be the “ability to integrate with Google Docs.” Similar feedback that comes through can then fall under this code.

Each piece of feedback should be given a code to ensure it’s ultimately actionable. 

4. Analyze feedback codes

Coding feedback can be a complex task. It’s often necessary to go through the codes multiple times to ensure you’ve covered all feedback notes. 

A piece of feedback may initially receive just one code. But after going through it once more, you may realize that you need to break the feedback down into two or more codes to cover all requests and comments. 

This way, you’ll have a more thorough approach to coding. 

5. Score feedback codes

Once you complete the coding process, it’s then useful to see which codes are most popular. Group similar codes together to build an overall picture of which pieces of feedback ought to be actioned more quickly. 

Give a score to each piece of feedback. This will help you begin to see patterns and understand what feedback is most common and should therefore be categorized. 

6. Provide a summary

Once you’ve collated the feedback and completed the coding process, summarize the analysis into a shareable and digestible document. 

You should share this across teams and stakeholders to ensure feedback is listened to, understood, and actionable throughout the business. 

Utilize tools for customer feedback analysis

Collating feedback manually can be a painstaking process––particularly when working with large data sets from multiple sources. Manual processing can also increase the chances of errors. 

Making use of tools for customer feedback that are specifically designed for analysis is something many businesses lean into. Tools can fasten the process, increase accuracy, keep all your data and insights in one place, and ensure that feedback is actioned effectively across the business.

Dovetail, for example, allows you to get from data to insights fast. With Dovetail, you can store all feedback in one place while uncovering insights across all kinds of customer touch-points—whether it’s from user interviews, product feedback, or surveys. From there, it’s simple to see patterns, collate insights, and share feedback across your business for fast action. 

Feedback analysis template

To help you start better analyzing customer data, we’re sharing this customer feedback survey analysis template.

Analyze your data to identify common themes and patterns within the responses, which can provide useful information for making informed decisions. This example demonstrates how you can import feedback, tag raw data to capture useful observations, and then transform your findings into actionable insights.

Analyze feedback for better insights

Customer feedback is a key process to better understand your customers—their needs, wants, and pain points. 

Deeply understanding your customers and listening to what they have to say helps you deliver products that are easy to use, satisfying, and ultimately solve the problems they were designed for. 

Customer feedback analysis provides a best practice way to gather feedback, categorize it, and turn it into useful actions, all for the benefit of your business––and, importantly, your customers.

Learn more about customer analysis software

FAQs

What is a customer feedback strategy?

A customer feedback strategy is the process of gathering responses and insights from your customers and then using those to drive positive change across the business. 

Which analytics is best for analyzing customer feedback?

There’s no one right way to gain or analyze customer feedback. However, score-based feedback tools such as the NPS and CSAT can be simpler to analyze than other types of open feedback. 

When analyzing feedback, consider your business objectives to decide on the right approach for your organization. 

What are the limitations of AI in customer feedback analysis?

AI can be very powerful for customer analysis, speeding up the process of gaining insights while increasing accuracy. However, there are limitations. Where there’s little or missing data, the results can be less reliable. The better the data collected, the more relevant and helpful the AI feedback analysis will be.

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