Customer Feedback Analysis: How To Analyze Reviews?

According to statistics, 88% of C-suite executives consider feedback data a critical decision-making tool. However, collecting customer feedback makes no sense without its subsequent analysis. By delving deeper into the first-hand insights you receive and intelligently interpreting them, you can make data-driven decisions.

In this article, we will explain why and how to analyze customer reviews. We will also provide you with ideas, techniques, and helpful tools for automated customer feedback analysis. So, keep reading to get the most from clients' reviews for your business.

Types of Customer Feedback for Analysis 

Customer feedback is divided into two main types: quantitative and qualitative data. 

Quantitative data consists of numerical values or percentages, answering questions like ‘how many’ or ‘how often.’ It is convenient to create graphs and charts to visualize the statistics obtained. The types include:

• Customer satisfaction scores (CSAT)
• Ratings
• NPS (Net Promoter Score)
• Closed-ended answers

These surveys do not require respondents to spend a lot of time, which is why they are usually launched via popups. By the way, soon you'll have the option to collect NPS with Claspo. Make sure to seize this opportunity to survey your customers. According to a study, 75% of consumers are willing to recommend a company that provides excellent customer service to their friends and family. 

Qualitative data, on the other hand, is richer and more descriptive, involving a deeper level of customer survey. It can come to a researcher in various formats, such as:

• Open-ended answers
• Posts on social media
• Customer service requests (chat or audio call recordings)

This type of data addresses questions such as ‘why’ and ‘what caused it.’ Its analysis helps identify the motives behind the target audience's behavior, understand their values, and more.

Using our pop-ups, you can create an extended survey with multiple closed-ended answers and a field where the user may leave an extended answer.

Try this template

The window with such an extended survey is available as a drop-down list with closed responses. This saves space and allows you to add more options. With Claspo, you can effortlessly begin collecting customer feedback and promptly initiate the data analysis process.

Customer Feedback Analysis Steps

After collecting customer feedback, it's time to analyze it. The process involves several sequential stages. Let's take a closer look at them.

1. Organization and Categorization

Customer feedback comes from different channels. So, first of all, you need to centralize the data. Use specialized platforms to collect feedback from all channels and analyze them in one place. Integrate your CRM with the chosen platform to connect specific reviews with the customers who provided them.

Sorting feedback initially

Imagine that the feedback you receive is a messy attic, considering the customer reviews come from different channels. You need to sort through it. Without initial categorization, analyzing a huge number of reviews can be overwhelming. Focus on what matters. Not all feedback is equal. Separate the insightful information, such as:

• Discover hidden needs by unearthing customer pain points you weren't aware of.
• Pinpoint improvement areas by identifying what's working and what needs fixing.

For example, if you see customer responses to an error that appears during checkout due to an inability to select the correct shipping address, it is insightful feedback that requires urgent response and fixing.

Avoid non-insightful feedback that confirms the obvious. Don't waste resources chasing after comments that lack depth, detail, or useful information, such as 'good job!' or 'this is terrible.'

Instead, focus on actionable insights from customer feedback to make data-driven decisions that improve your business.

Category Assignment Principles 

By grouping similar comments, you can quickly understand the overall sentiment and identify areas for improvement.

Group similar comments by topic. For example, feedback about product features can be categorized as ‘usability’ or ‘functionality.’ This initial organization helps identify recurring themes and areas that need attention. Another variant of categorization is by stages of the sales funnel, from advertising and the first visit to the site to transactions, problem-solving, repeat visits, and so on.

Based on the amount of feedback in each category, you can decide which issue is the most urgent and which is a secondary problem to solve. This allows you to manage your time better and prioritize tasks for the entire team. You can link categories to different departments of the company — sales, customer support, marketing, logistics, and so on.

The Purpose of Adding Subcategories

You should also create subcategories to structure a more detailed database and better understand your customers' problems. Like tags in automated customer support systems, subcategories are usually created situationally. They can refer to:

• a specific product,
• a company employee,
• a territorial division,
• the reason for the request, and so on.

One response can refer to several subcategories simultaneously, allowing you to understand its essence better and move faster to practical insight.

2. Assembling a Puzzle of Numbers and Words

Quantitative analysis delves into the numerical side of things, as numbers add weight to your findings. This may include NPS feedback or any surveys with closed-ended answers. We can also calculate metrics such as the CSAT to measure satisfaction levels. 

Platforms for collecting and analyzing consumer feedback typically provide dashboards with tools to summarize quantitative data. These tools often include graphs and charts that marketers can use to compare survey results or responses to specific questions across different segments.

By aggregating and analyzing large volumes of feedback data, quantitative analysis can reveal trends and patterns in consumer behavior, preferences, and opinions. For example, you can analyze NPS scores to identify patterns or fluctuations over time. Look for trends such as improving or declining satisfaction levels and try to understand the underlying reasons.

Numbers paint a picture, but words tell the story. That's why we need qualitative analysis, where we dig into the ‘why’ behind the themes. We analyze specific comments and interviews to understand the root causes of customer pain points and the emotions driving their feedback. For instance, analyzing the comments about the ‘limited variety of workouts’ can reveal users' desires for more targeted workouts or a wider range of fitness activities.

Many systems use a 'word cloud' to highlight such topics. Once you see the word most used by consumers, you can add it as a tag and search for it across all reviews. Then, the system will run the analysis to get a summary of the content. Or read them manually if the text is small.

Importance of Sentiment Analysis

Sentiment analysis delves deeper than words, analyzing user interactions to uncover hidden feelings and opinions.

This approach is powered by:

• Machine Learning (ML): ML algorithms are trained on massive amounts of text data, allowing them to recognize patterns and identify emotional tones.
• Natural Language Processing (NLP): NLP helps bridge the gap between human language and machine understanding. It breaks down text into components, enabling the AI to grasp the context and sentiment behind the words.

Artificial Intelligence is the conductor, using ML and NLP to deliver insightful sentiment analysis. Sentiment analysis goes beyond a simple thumbs-up or thumbs-down. Here's how it dives deeper:

• Fine-grained analysis method goes beyond basic positive/negative categorization. It analyzes a spectrum of emotions, including mixed ones.
• Aspect-based analysis technique helps to understand algorithms when the client's feedback is not expressed clearly in a positive or negative way. It analyzes the details of a case to determine whether it is a problem or praise. For example, the technique can help recognize feedback such as 'The smartphone reboots without any notifications or requests.'    
• Emotion detection helps recognize emotions in complex feedback, even when the system is dealing with non-obvious judgments. For example, standard word processing would not work for a sentence like ‘This dress looks insane on me, even though it almost broke my husband's bank account!’. Without context, algorithms would classify such a review as negative. However, emotion detection, powered by ML-based classifiers, can interpret such nuances and provide a more accurate picture of customer sentiment.
• Intent analysis helps determine a customer's goal. Is the customer interested in purchasing a product or just browsing? This information is crucial for optimizing marketing strategies and creating targeted customer experiences.

Businesses can gain a complete understanding of their customers' feelings and motivations by using various types of sentiment analysis.

3. Visualizing and Making Conclusions

We've analyzed the data collected, identified themes, and painted a clear picture of customer sentiment. Now, it's time to turn those insights into action. Data visualization tools such as charts, graphs, and infographics transform complex data sets into easily digestible formats. Your final report should be a story that turns customer insights into actionable recommendations. Here's what your report should include:

• Summary — provide a brief overview of the key findings and recommendations.
• Customer segmentation — highlight repeated topics in specific customer segments.
• Practical recommendations — suggest steps to address customer concerns and improve the product or service for each theme.

A well-written report bridges the gap between customer insights and business impact, enabling stakeholders to understand customer needs and make data-driven decisions about improvements.

4. Turning Customer Insights into Action

The journey doesn't end at analysis – it's about transforming insights into action. Here's how to maximize the impact of your customer feedback loop.

• Engage in the conversation. Respond to all feedback across social media, forums, and your website. This shows customers you value their voice. A whopping 81% of consumers write local business reviews occasionally, and a staggering 41% prioritize businesses that respond. The message is clear: timely and relevant replies to reviews are crucial for attracting customers.
• Run triggered email campaigns. This stage is not possible for comments on social media, but it is necessary for targeted feedback collection. For example, you've run a post-purchase survey using a Claspo popup and have the respondents' contact details. You need to send them a triggered email. For this purpose, you need to set up an integration between Claspo and CRM, ESP, or CDP to automatically transfer the data about filling out the feedback form. This allows you to segment customers based on their score and send targeted trigger emails. Thank promoters for their positive feedback and directly address the concerns of detractors.
• Fuel innovation and improvement. Insights can guide product development, service enhancements, and content marketing strategies. For example, using NLP, Stitch Fix analyzes feedback to create customer-specific product descriptions.
• Measure and adapt. Track the impact of changes driven by feedback. Set relevant metrics and KPIs to measure product adoption, customer satisfaction, and other key areas. This ongoing measurement allows you to continuously refine your approach and ensure your feedback loop drives real results.

By implementing these steps, you can transform customer feedback from passive data into a powerful tool for growth.

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How To Analyze Customer Reviews: 7 Tools To Try

Analyzing customer feedback is a difficult task. For a long time, it was mostly tedious manual work that took a lot of time and effort. Fortunately, machine learning and artificial intelligence technologies now make processing hundreds and thousands of requests possible within a minute. 

Some companies combine both manual and AI-driven methods for fast and accurate analysis. Automated tools can help you complete many tasks simply by uploading your data. Let's look at the best tools available for customer feedback analysis.

1. Brandwatch

Brandwatch can analyze the emotional tone of reviews, showing whether customers are raving fans or hiding disappointment. It uses a combination of machine learning, rule-based systems, and knowledge bases to categorize online mentions as positive, negative, or neutral. 

The algorithms not only focus on keywords but also take into account the context of what is written and even detect sarcasm. Brandwatch turns sentiment data into actionable insights. You can track sentiment over time, identify emerging trends, and set up alerts for spikes in negative sentiment.

Possibility of free use. The platform offers no free options, and you need to book a demo to get information about paid plans.

2. Lexalytics 

Lexalytics Semantria is a text analytics platform that offers sentiment analysis alongside other features like entity extraction and topic categorization. The platform uses machine learning to analyze text and assign sentiment scores, going beyond basic keyword matching to capture the overall tone and nuance of the language.

Lexalytics also offers a service called Spotlight that analyzes unstructured textual feedback collected in social networks, reviews, or surveys. The system identifies respondents' main themes and sentiments. Dashboards can be created and exported to PowerPoint, etc.

Possibility of free use. Access to the platform is not free. Prices are shown in the demo. There is a demonstration of the system's analytical capabilities. 

Unfortunately, you can't test your own comment; you can only see how the pre-loaded text is processed. But at least you can get an idea of what to expect when analyzing feedback. 

3. Dovetail

Dovetail focuses on customer review analysis and user research. It can be a centralized place to store and collect research data, consolidating interview notes, videos, images, and more. The platform provides features for tagging and categorizing research data, making identifying patterns and trends in customer insights easier. 

It is enough to add the collected reviews, for example, in Google Docs, to the platform to get a customer feedback analysis. The system will extract the text from the document and analyze its content. Next, click the Summary button to see a summary of the research. The system will identify problem areas and highlight the positive aspects most frequently mentioned in the comments. 

It also helps researchers combine customer feedback into compelling narratives to effectively communicate research findings to stakeholders. For this purpose, the system has a library of report templates.

Possibility of free use. These are the basic tools available in the free plan. Paid plans expand the options for summarizing results, using different chart types, adding multiple projects, etc. However, even free access will give you plenty of material to think about and build hypotheses on improving the customer experience.

4. Ipsos Synthesio

Ipsos Synthesio is an AI-powered consumer intelligence platform focused on social listening and market research. Synthesio collects data from a wide range of online sources, including: 

• social media
• forums
• news sites
• reviews

It then analyzes this data to provide insights into brand mentions, audience demographics, and sentiment. It uses AI for tasks such as topic modeling, which helps identify emerging trends and automatically categorize conversations. 

The platform provides customizable dashboards and reports that allow you to track key metrics over time, compare sentiment across different segments, and identify areas for improvement.

Possibility of free use. There is no free use. Information about paid plans can be found by ordering a demo.

5. MonkeyLearn

MonkeyLearn is a cloud-based platform that provides text customer feedback analysis. It allows users to build and train machine learning models for tasks such as sentiment analysis without coding. The system has classifiers that sort feedback into specific categories, such as sentiment, topic, intent, priority, and more. 

There is also an extractor tool that extracts valuable information from text, including keywords, features, names, dates, prices, and more. The platform allows you to build graphs and reports and provides tools for cleaning, labeling, and visualizing text data. It also provides NPS and CSAT analysis. 

Possibility of free use. MonkeyLearn offers a free online tool for sentiment analysis. It can help you get a general understanding of the tone of the reviews. 

However, it works more accurately if you upload one review at a time, so whether it makes sense is a big question.

6. SurveyMonkey

SurveyMonkey is a widely recognized online survey creation platform that also provides tools for feedback analysis and reporting. It automatically generates charts and graphs to visually represent your data. 

You can analyze how different groups of respondents answered your questions and create cross-tabulations to compare responses by demographics, location, etc. This helps you segment your audience and identify trends within specific groups.

Possibility of free use. You can create free survey forms limited to 10 questions and collect only up to 25 responses per survey. Therefore, the analysis will be limited to this number of respondents.

7. Thematic

Thematic uses AI to automatically detect repeating themes in customer text feedback. This data can come from various sources, such as surveys, reviews, emails, or conversations on social media. The platform can analyze unstructured text data. It doesn't just count keywords but also analyzes sentiment and context in reviews. Thematic transforms the identified topics into actionable reports with visualization in the form of dashboards.

Possibility of free use. The service provides a free trial version. You need to order a demo for this purpose.

These platforms automate the entire analysis process, saving you significant time, resources, and effort compared to manual methods. But they work most effectively with large amounts of data and when you need to track the dynamics of changes in customer sentiment, etc. If you have dozens, not hundreds, of customer reviews, you can use Chat GPT to analyze them.

Using Chat GPT for Customer Feedback Analysis

ChatGPT is helpful for quickly categorizing reviews into negative and positive ones. Additionally, the AI will promptly identify key themes that were most common among reviews from different people. With its help, you can see the overall picture, measure the prevailing mood among customers, and highlight issues deserving attention. The algorithm for working with ChatGPT can be as follows.

Step 1: Provide context and feedback. In this initial step, copy all reviews and instruct the AI with the command ‘Remember these reviews on a company’. Upload the copied feedback to the system. If you have a large amount of text, you'll need to break it up and upload it in parts. Even at this stage, artificial intelligence will begin to provide its conclusions, but this is not what we need.

Step 2: Formulate conditions for analyzing feedback. Here is an example of a prompt we sent to ChatGPT:

‘Please analyze customer feedback and do the next:

• Sort them into positive and negative. 
• Highlight recurring problems in them. Write these topics and indicate how many times each problem occurred in the feedback.
• Highlight the recurring positives in the reviews. List these themes and indicate how many times each of them occurred in the reviews.’

After that, Chat GPT will generate a customer review analysis that you can already work with. First, the system will categorize the feedback into negative and positive and give you a short summary with a list of topics and the number of times they were mentioned.

Then, the AI will go into more detail about the problems customers have encountered and the popular positive aspects of client interactions with the company.

Step 3: Generate recommendations. If you are short on time and need to quickly formulate conclusions based on the feedback analysis, you can once again turn to Chat GPT for help. We gave it a simple request: ‘Give me a report based on the analysis’. The AI summarized its findings and generated recommendations on how to improve the situation. 

Overall, much of its advice is obvious, but perhaps some of it will provide valuable insights.

Why is Customer Feedback Analysis Important?

A company's success depends on the effectiveness of its interaction with customers. 80% of consumers believe that a company's customer experience is as important to them as its products and services. The cornerstone of this is a deep understanding of the target audience, their pain points, their aspirations, the specifics of using the company's products, and so on. Collect and analyze customer feedback to learn about all of the above. Customer review analysis provides the following advantages.

1. Identifying The Strengths And Weaknesses of Your Business

A customer's perspective differs from that of a company employee, manager, or investor. While a product may seem convenient and profitable from a business standpoint, it can fail to meet customer needs and expectations. Listening to customers' opinions is the only way to determine its true market potential and identify the best areas for improvement.

This applies not only to product policy but to every aspect of the company's interaction with the target audience — advertising, commercial offers, transaction processes, support, and subsequent communications. Feedback can refer not only to products but also to the brand itself — its image, reputation, and tone of voice.

2. Getting to Know Your Customers Better

No matter how effective your forecasting models are, you can't know everything about your customers. This is especially true when it comes to the social-psychological characteristics of your target audience. By analyzing feedback in detail, you may learn:

• how consumers see the ideal product to fulfill their needs,
• what features are critical to them,
• what prevents repeat purchases,
• what they expect from your brand.

All of this allows you to customize your marketing strategy more accurately, choosing communication methods and messages. Detailed feedback analysis will help you ‘find the key to their hearts,’ increasing conversion rates.

3. Improving Your Tactics And Strategy

Analyze customer feedback to identify patterns and trends. With this information, you can solve current problems and prevent future ones. Sometimes, the data obtained by the marketing department allows you to optimize other aspects of the company's development strategy, such as human resources, technology, infrastructure, etc. 

Research feedback to understand customer challenges and knowledge gaps. Craft targeted content that addresses these needs and positions your brand as a thought leader.

Conclusion

Analyzing customer feedback allows you to gain a deeper understanding of your client's needs, wants, and frustrations. As a result, you'll make data-driven decisions that enhance your products, services, and overall customer experience.

Utilize the findings from the analysis to prioritize improvements. Focus on addressing the most common customer pain points and areas for enhancement. Develop targeted solutions by tailoring your products, services, and marketing strategies to better meet the specific needs of various customer segments.

Finally, regularly monitor the impact of your changes and continue to gather feedback and analyze customer reviews to ensure you're on the right track. This strategy will enable you to foster customer loyalty and drive sustainable business growth. 

FAQ

What is Customer Feedback Analysis?

Customer feedback analysis is extracting valuable insights from the feedback received. Thanks to it, you can interpret first-hand data to see the actual situation and avoid guesswork and assumptions when making decisions. 

Analyze customer feedback to understand whether clients’ actual opinions match your hypotheses and make your brand more customer-centric.

Where to Find Valuable Customer Feedback to Analyze?

Start by embedding survey pop-ups on your website or app, strategically placed to capture feedback at critical moments, such as post-purchase or before they exit. Monitor social media for brand mentions, reviews, and discussions to understand how customers perceive your brand online. Target specific customer segments with email surveys. Treat every customer support interaction as a feedback opportunity by analyzing chat logs, emails, call recordings, and more.

How Often Should I Analyze Customer Feedback?

Consistent analysis is crucial. While the frequency may vary based on your feedback volume, strive to analyze customer feedback at least every quarter. This ensures you stay on top of trends and promptly address any emerging issues.