In this guide, we'll teach you the ins and outs of feedback analysis so you can turn surveys, reviews and support conversations into actionable customer insight.

What's in the guide:

1. What is customer feedback analysis?
2. Why is it important?
3. What analysis methods are there?
4. How to manually analyze feedback (step-by-step)
5. Feedback analysis template
6. Examples analyses
7. Smart tools to automate your feedback analysis

What is customer feedback analysis?

So, someone’s asked you to do a customer feedback analysis?

Here’s what they probably mean:

Feedback analysis is the process of breaking customer feedback down into something that’s easy to understand and insightful.

The way you break the data down depends on the goal you’re trying to achieve.

For example, let’s say you’ve got together as a team and want to kick off a new customer experience project. But, oh no! You don’t know which improvement areas to prioritize.

You think, “shouldn’t we be listening to our customers? Let’s fix the issue that impacts the most amount of customers.”

Customer support chat logs are a great place to find out why customers are frustrated, so you turn there to gather feedback.

A feedback analysis in this context would look like this:

1. Gather customer support conversations in one place.
2. Read each one and identify why the customer is frustrated.
3. Look for patterns and themes.
4. Quantify the biggest issue—e.g. the most frequent reason customers complain.
5. Prioritize this issue to be fixed next.
   

Of course, this is just a shortened illustration of a customer support ticket feedback analysis. We've actually written a full guide for support conversation analysis which you can use to dive deeper.

The 5 step example we just gave is just one lens through which you can analyze customer feedback.

There are a number of different ways to do it. For example, you could instead choose to prioritize issues affecting high paying customers or issues that make customers most angry—even if there’s only a handful of cases like that.

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Why is it important to analyse customer feedback?

Listening to your customers is probably the most important element of long-term success for a business.

You’ve probably heard companies like Amazon, Apple and Google called ‘the most customer-centric companies in the world’.

The business world holds attributes their success to how well they listen to people and build services that they want to buy.

But, the purpose of listening to customer feedback goes beyond identifying which products to build.

Customer feedback plays a critical role in customer retention and loyalty. Your customer’s pain or friction points when interacting with your brand are areas you must work to improve to keep customers for the long term.

Why does continuous improvement need to happen? Because as a consumer it has never been easier to switch to a new provider.

These days, if you want your customers to stay, they have to be HAPPY. Consumers know it, so brands need to catch up and ingest a customer-first philosophy if they’re going to become the next Amazon, Google or Apple.

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Customer feedback analysis methods

Customer feedback is generally qualitative, written in surveys, reviews, customer service conversations and customer complaints.

To be confident in your analysis you’ll need to turn your qualitative feedback into something quantitative (e.g. ‘payment issue’ was mentioned 600 times).

To quantify the qualitative, companies typically use these methods:

Which analytics method is best used for analyzing customer feedback?

There are a number of different techniques out there for feedback analysis.

We use these three advanced methods within our AI platform at SentiSum.

1. Sentiment Analysis

Sentiment analysis is the process of detecting positive or negative sentiment in text.

Since customers express their thoughts and feelings more openly than ever before, sentiment analysis is becoming an essential tool to monitor and understand that sentiment.

2. Keyword or Aspect Analysis

A keyword or aspect analysis identifies specific 'things' in the text. For example, if a customer mentions the word 'discount' it will label or categorize the feedback as being about discounts.

A keyword analysis is very dependent on the language used by the customer, making it prone to error and inaccuracies.

3. Topic Analysis

Topic analysis, or classification, is a form of AI-powered analytics that reads and analyses like a human does, but considerably faster.

A topic analysis doesn't simply see a keyword, and label the piece of feedback. It takes into account the context of that word and the meaning of the piece of text it sits within. Correct categorisation is not dependent on any specific words used, making the results much more accurate.

For example, a topic analysis tool can identify that a customer is complaining about 'discount code not working' even when they say something like 'the offer didn't apply at checkout'.

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How to analyze customer feedback manually

Before we get stuck into the step-by-step process of feedback analytics, it’s important to note that it’s time-consuming and subjective.

This method will work best with a small number of feedback pieces, a lot of time, and just one person doing the analysis—any more may change the results because everyone interprets feedback differently.

For larger volumes of feedback, it’s simple, affordable and provides significantly better results to automate the analytics process. Explore our product—which analyses surveys, reviews and all customer support channels in real-time—and book a demo with us here.

For those who endeavour to go on, let's get stuck in.

For this manual analysis, we’re going to do a high-level ‘topic analysis’ and a general ‘sentiment analysis’ (both defined above).

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Step 1: Choose your feedback channels

There's a ton of different feedback channels out there. Here's the main ones and their nuances:

Support conversations

Support conversations are the most useful data source for customer feedback. Support ticket logs (from emails, calls and live chats) contain unbiased, qualitative feedback that's an unbeatable source of customer insight.

We've written extensively about why support is the most valuable source of insight, here's one such example on support ticket insight.

Customer reviews

Reviews are a core driver of sales as most customers look there before making a purchase. This makes understanding the reasons behind negative reviews important for business growth.

P.s. British Airways currently uses our sentiment analytics tool to analyse all their customer reviews.

NPS & CSAT survey results

NPS and CSAT are the most common ways to collect customer feedback. Understanding the drivers behind positive and negative sentiment at scale is a powerful use case for AI-powered sentiment analytics.

However, NPS surveys are now considered a biased form of feedback, so you'll need to work hard to ensure your results are actionable.

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Step 2: Collate your feedback in one place

Export your feedback and collect all the raw data in one place (e.g. an Excel sheet).

With it, include any extra information that might help later on like...where did the feedback come from? Who is the customer? What CRM data can we pull on them?

You’ll want to collect the same data on each piece of feedback to have a complete dataset and a meaningful result ultimately.

Step 3: Categorize your feedback

Once your customer feedback data set is in one place, you need to think about how you’re going to categorise the data.

You’ll need two spreadsheets. One for the feedback you’ve already collated, and another to store the categories, themes and sentiments with which you’ll code the feedback.

For our feedback dataset—CSAT survey results—we decided to categorize the feedback into four tags: ‘delivery issues’, ‘payment issues’, ‘refund issues’ and ‘packaging issues’.

We also added which department will find the feedback most relevant, and will apply a sentiment score using a basic 1-5 scale.

You can expand these to as much as you like, the more detailed you can go the more complex your analysis will be but also the more actionable the results will be.

Here’s an example of our basic taxonomy applied to one survey result:

Step 4: Doing a root cause analysis of customer feedback

A root cause analysis requires you to go deep with your analysis, getting to the heart of the issue at scale.

Unlike in our example above, which uses a flat tagging structure, you’ll want to use a hierarchical tagging taxonomy.

A hierarchical taxonomy allows you to categorize feedback at multiple levels (e.g. Payment issue → Paypal isn’t working) so you can draw patterns even at a granular level.

If you’re looking to do a really granular analysis, you should create a clear, codified taxonomy with which to categorise your feedback.

We interviewed a number of experts to write a guide to building a tagging taxonomy here.

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Step 5: Feedback analysis report: How to present the results to drive actions

How you report your feedback analysis is almost as important as the analysis itself.

Your feedback analysis report needs to be not only understood by others but acted upon. The customer feedback you present should strike a chord with the audience emotionally, while filling them with confidence that they can take this feedback seriously.

Read our eBook here to see how industry experts like yourself are making it easy to understand the value of CX data.

Let’s also take a look at some examples of how you can report your feedback analysis results.

These are taken from the SentiSum platform:

1. Time-Series: Biggest changes over time
   

If you’ve done an aspect-based sentiment analysis like you can with the SentiSum analytics platform, you can easily present the topics along with how customers feel about those topics.

If you’re working on a new project to improve a particular feature, a report on changing sentiment can help you track performance.

Furthermore, this report can help your team identify dramatic swings in sentiment that could do with a root cause analysis.

2. Sentiment overtime for a topic
   

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This graph shows how customer sentiment has changed over time for a topic, in this case ‘shopping experience’.

It’s helpful at a high level to understand how customer sentiment is improving for a topic and a visual graph shows clear progress to your team.

Here’s how we present it. Lots of visuals, time series, patterns, ability to deep-dive. Those are some ideas, you can attempt within google sheets.

3. Show topic quantity
   

In the example below, taken from the Sentisum deep-dive view, one of our customers is able to report to their team which topics are driving customer support contact and in what volume.

You can use help statistics like % of Contact Per Order (CPO) to give context to your colleagues. In this example, customers contacted support about ‘recipe feedback’ nearly 10,000 times, which contributes 0.87% of their overall CPO.

You might want to include a core statistic you’re tracking, or a more simple one like ‘what % of overall customer feedback is about this topic?’.

It’s useful to know that in your survey ‘payments’ were mentioned 100 times, but if you started with 10,000 surveys then 100 mentions of this topic makes up just 1% of the issues mentioned.

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Feedback analysis template

We built a very simple feedback analysis template to get you started.

Download it for free below 👇

When leveraging the template created for this article, you should start by reaching out to colleagues across your company.

Every team has different interests and needs from feedback, and with their help you can create a tagging taxonomy (tab 2 of the template) that's genuinely useful to your company.

If other departments are involved early, and their goals are taken into account, they’re also much more likely to listen to and take action on your final analysis findings. It's a win-win.

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Feedback Analysis Examples

We're lucky enough to be able to use our tool in-house to analyse unlimited volumes of customer feedback.

To show what our tool can do, we recently ran our topic and sentiment analysis tool across 100,000s of Trustpilot reviews for companies in two sectors: banking & delivery.

Here's what we did:

"We leveraged Text Analysis to understand this large volume of data, identifying topics and corresponding sentiment automatically.

As part of this text analysis, each review was automatically tagged with one or more topics and corresponding sentiment. For each sector, we discovered which topics customer were talking about most, and

In one of our analyses, we compared the public reviews for two banking technology companies, Revolut vs. Transferwise.

Looking at sentiment over the last two years, both Transferwise and Revolut customers show high satisfaction with Security (>80%).

This analysis is high-level, focusing on only four categories that mattered to customers.

In our topic analysis of food delivery companies, we also took a granular approach to our feedback analytics.

As you can see, our sentiment analytics software discovered which 8 topics which were most mentioned in the customer reviews. 'Taste' was a critical driver of customer feedback—it was mentioned in more than 30% of customer reviews.

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Customer Feedback Analysis Tools

There is a wealth of feedback analysis tools to choose from, which can make it difficult to choose.

We suggest booking a number of product demos to look for a tool that can help you with your problem—you’ll get a good insight into the company and how they work through their sales process. A company you want on your side will invest time in teaching you about the market and solutions available.

When choosing a tool, we suggest asking yourself these three questions:

1. What channels of feedback do I want analysed? Some tools only do NPS, others only do reviews, hardly any include support conversations. A powerful tool does all of your channels, giving you the flexibility to include or exclude any feedback channels you want.
2. How granular do you want the insights to be? If the tool uncovers really granular insights from your customer feedback, it makes your life a whole lot easier. Some automation tools do surface level analytics, which means you have to still do a lot of manual analysis to truly understand the feedback. On the other hand, the latest developments in machine learning and sentiment analytics offer a highly detailed automated analysis.
3. What do I want to do with the analysis next? Sometimes you just need a quick analysis which you can present back to your team in a Powerpoint. Other times, you may want cross-functional teams and individuals to be able to self-serve customer insights. For the latter, you’ll want to invest in a feedback analysis tool that is easy-to-use by anyone and that includes unlimited log in’s in the package.

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5 best-in-class feedback analysis tools

SentiSum

SentiSum uses AI technology to completely automate your feedback analytics at a root cause level. We specialise in customer support conversations, but have solutions across NPS, surveys and almost all feedback channels. Learn more here.

Qualtrics

Qualtrics is an SAP owned enterprise software that enables organisations to collect feedback at every stage of the customer journey. They enable enterprises to uncover trends and drive customer loyalty across multiple channels.

Chorus AI

Chorus captures and analyses all customer calls, meetings, and emails to identify top performers and uncover insights that could be used as testimonials.

IBM Watson

IBM offers many APIs for sentiment analysis based on NLP. The Watson Tone Analyzer, for example, which focuses on support tickets and satisfaction surveys and monitors agent sentiment – whether they’re polite and eager to help, and if they truly solved the customer’s issue.

Rosette

Rosette has an API that uses AI to analyse natural language. They've branched out from social media to analyse entire documents, for example, the sentiment expressed by customers when they mention a specific product, company, or person.