In this guide, we're going to show you how to effectively examine customer feedback, whether it originates from reviews, surveys, or customer support conversations.
Additionally, we'll explore some advanced feedback analysis techniques, especially useful for those handling large amounts of customer feedback.
Let's get started.
What Is Customer Feedback Analysis?
Customer feedback analysis involves examining and interpreting the responses and opinions your customers provide about your products or services.
The approach to analyzing feedback varies based on its origin and quantity. For instance, manually analyzing +100,000 monthly support tickets can be an overwhelming task.
As we will discuss further, support tickets are just one avenue for garnering customer feedback. You may find that analyzing different feedback channels yields varied insights, influenced by the timing and method of collection. Another example includes feature requests received through chatbots, which can offer a distinct perspective on customer needs and preferences.
Different Methods for Gathering Customer Feedback
Many businesses gather customer feedback through various data sources such as surveys, reviews, and logs of customer support interactions.
Each of these feedback collection methods offers unique benefits and should be selected according to your specific objectives.
The three most trusted and frequently used channels for feedback are Customer Satisfaction (CSAT) surveys, Net Promoter Scores (NPS), and analysis of customer support conversations.
Let's briefly examine each:
- Customer Satisfaction (CSAT) Surveys: These are generally employed to gauge immediate customer experiences. They're distributed to customers following specific interactions, like dealing with customer service. Analyzing CSAT surveys provides insights into customer preferences or grievances related to that specific encounter.
- Net Promoter Surveys (NPS): NPS can be tailored to be either transactional or relational. A relational NPS survey assesses customer loyalty towards your company over time, beyond just single transactions. NPS gauges the likelihood of customers recommending your brand, serving as an indicator of loyalty and overall brand health. Analyzing NPS feedback sheds light on the factors influencing these aspects.
- Customer Support Conversations: This feedback is automatically captured by customer service software. We advise our clients to prioritize analysis of support conversation logs as they tend to be the most unbiased source. Surveys and reviews often attract extreme sentiments, either very positive or negative, skewing the results. Support tickets, however, provide more passive and diverse feedback.
In conclusion, the type of feedback you collect and how you analyze it should align with your overall goals.
Why Is Analyzing Customer Feedback Important?
- Enhanced Customer Experience: Gaining insights into customer emotions through feedback analysis enables companies to adapt their offerings and interactions to more effectively meet client expectations and requirements.
- Gaining a Competitive Edge: Sentiment analysis tools offer valuable insights into customer perceptions of both your brand and your competitors, revealing opportunities to differentiate and excel.
- Risk Reduction: Prompt identification and response to negative sentiments can mitigate potential harm to your brand's reputation.
- Informed Business Choices: Quantitative data from sentiment analysis tools aid in shaping informed decisions, influencing everything from product creation to marketing approaches.
- Refining Products and Services: Understanding customer emotions helps pinpoint areas for enhancement and innovation in your offerings.
- Efficient Scaling: Leveraging such platforms automates the process of interpreting customer feedback, streamlining the expansion of customer service and product development initiatives.
Customer Feedback Analysis Methods - which one is the best for you?
Customer feedback is typically qualitative in nature, often comprising written responses found in surveys, reviews, chats, and complaints.
To analyze customer feedback efficiently, it's crucial to convert this qualitative text into quantitative data.
Instead of making decisions based on subjects brought up in individual survey responses, turning these issues into quantifiable data offers a more dependable basis for thorough analysis and informed decision-making.
Best Strategies for Analyzing Customer Feedback
When dealing with a manageable number of customer reviews, say around 50-150, a practical approach might be to manually categorize them (for example, noting that 10% discuss a certain topic, while 30% cover another).
For such volumes, a manual review is adequate to interpret feedback and identify areas for improvement — and we'll guide you through this process shortly.
However, for most businesses grappling with thousands or even hundreds of thousands of qualitative survey responses, reviews, and customer support interactions, manual analysis isn't a viable option on a regular basis.
Sampling a small portion is an alternative, but for building trust and credibility, especially when aiming to gain internal support for addressing and rectifying poor experiences, a more comprehensive approach is beneficial.
Fortunately, as of 2024, we have access to practical and efficient technologies like AI (Artificial Intelligence) to facilitate insightful customer feedback analysis.
In our platform, we employ machine learning-based topic analysis. This AI system processes surveys, reviews, and support interactions, interpreting nuances similarly to a human. It then quantifies topics that influence positive and negative sentiments, reasons for customer contact, and evolving trends, aiding in a deeper understanding of customer feedback.
If you’re looking at regular analysis of substantial customer feedback volumes, consider scheduling a meeting with us to explore our feedback analysis platform.
For those with a smaller amount of customer feedback, we’re about to show you a step-by-step method to manually analyze your customer feedback for topic and sentiment insights.
Analyze Customer Feedback Manually
Before delving into the detailed process of feedback analytics, it's crucial to acknowledge that this method is both time-intensive and subjective.
This approach is most effective when dealing with a limited amount of feedback, provided there's ample time and only one analyst involved. Having multiple people analyze the feedback could lead to varying interpretations due to individual perceptions.
In our manual analysis, we will focus on a broad 'topic analysis' and a basic 'sentiment analysis' of your customer feedback.
Our objectives are to:
- Identify and quantify the main topics mentioned by your customers.
- Gauge the impact of these topics on your customers (whether it's positive, negative, or neutral).
Now, let's dive into the process.
Select your feedback channel
Understanding your objectives for conducting feedback analysis is crucial. Once you're clear on your goals, selecting the right feedback channel to analyze becomes straightforward.
For instance, if you're a restaurant owner looking to comprehend the reasons behind negative Google reviews, your task is quite direct. Simply export all your Google reviews and begin your analysis.
On the other hand, if your aim is to enhance the overall customer experience and pinpoint key areas for development in 2024, the task is more complex.
In such cases, we suggest opting for the most recent and voluminous set of feedback data. If you receive a significant amount of customer support queries each month, that's a good starting point. Conversely, if you conduct regular surveys but lack a customer support system, then analyze the survey data from the past 6 to 12 months.
For the purpose of this example in feedback analysis, we will focus on CSAT surveys.
Collect customer feedback in one place
Gather all your feedback data and consolidate it into a single location, such as an Excel spreadsheet.
Include additional details that could be useful for later analysis. For instance, consider noting the source of the feedback, the customer's identity, and their monthly spending with your business.
Ensuring that each piece of feedback is accompanied by the same set of data will help you create a comprehensive dataset, which is crucial for obtaining significant and meaningful results from your analysis.
Categorize Customer Feedback
After consolidating your customer feedback data in one location, the next step is to plan your approach for categorizing this data.
Prepare two separate spreadsheets for this task. The first should contain the collated feedback, while the second will be used to record various categories, themes, and sentiments that you will use to classify the feedback.
For the categorization process, we suggest utilizing our free sentiment analysis tool and CSAT calculator. These resources will streamline your workflow and also minimize the potential for errors that can occur with manual sentiment analysis.
Perform Root Cause Analysis of your Customer Feedback
Conducting a root cause analysis demands an in-depth approach, aiming to uncover the core issues comprehensively.
Instead of the basic tagging structure, a hierarchical tagging taxonomy should be employed.
This hierarchical system enables categorization of feedback on several levels (for instance, "Payment Issue → PayPal Problem"), allowing for the identification of patterns and trends even in the finer details.
If your goal is to perform a detailed analysis, it's essential to develop a well-defined, codified taxonomy to systematically categorize your feedback.
To facilitate your work and analysis, we also offer a complimentary solution. For a comprehensive overview and efficient root cause analysis, our free Voice of Customer (VoC) tool can provide you with a competitive edge during this phase. If you're seeking a deeper, more accurate analysis and wish to automate the entire customer feedback process, our customer feedback and VoC tool is available for a one-month free trial. This period allows you to conduct thorough research and gain an understanding of how this process can be streamlined into a few simple clicks, saving time and resources without any financial investment.
Feedback Analysis Report
The manner in which you present your feedback analysis is nearly as crucial as the analysis itself.
It's essential that your feedback analysis report is clear and convincing to others, ensuring that they find it compelling enough to act upon.
Effective feedback reporting should resonate emotionally with your audience and instill a sense of confidence, as both elements are vital for driving change.
We suggest incorporating at least two fundamental types of reports in your analysis. The first is a Time Frame Report, which is a time series analysis depicting how sentiments around specific topics have evolved over a period. This is feasible only with regular feedback analysis and ongoing monitoring of trends.
The second essential report is the Topic Driver Report. This analysis highlights the frequency of each topic, serving as a gauge for the impact of various issues. For instance, if your objective is to decrease the number of customers needing support, this report can identify the main topics prompting them to seek assistance. Such insights can provide your team with clear direction on which areas to focus on to meet this objective.
Choose The Best Customer Feedback Analysis Tool For You
Choosing from the wide array of available feedback analysis tools can be a challenging task.
We strongly suggest arranging several product demonstrations to find a tool that addresses your specific needs. These demos often provide valuable insights into the company's customer service approach, just through their sales process.
When evaluating an analytics tool, consider the following key questions:
- Range of Feedback Channels: What types of feedback does the tool analyze? While some tools are limited to NPS or reviews, others might include support conversations. Opt for a tool that encompasses a broad range of channels. This not only future-proofs your investment but also ensures comprehensive insights from various feedback sources.
- Depth and Precision of Insights: How detailed and accurate are the insights provided by the tool? Tools that delve into the nuances of customer feedback facilitate more effective root cause analysis. Be wary of tools that only offer surface-level analysis, as they might still require considerable manual effort for in-depth understanding.
- Application of Analysis Outcomes: What are your plans for the analysis results? Your needs might range from a straightforward analysis for a team presentation to a more complex requirement where cross-functional teams access customer insights independently. If the latter is your goal, choose a tool that is user-friendly and includes unlimited user access in its package.
These considerations will guide you in finding a feedback analysis tool that not only meets your current needs but is also adaptable for future requirements.
ClientZen has rapidly become a favored choice among Voice of Customer, Customer Experience and Support Specialists, and Product Managers. Its popularity isn't just because it significantly lightens your workload (as demonstrated in our case study), but also because of its user-friendly and intuitive design, making it exceptionally straightforward to learn and use.
If you're looking to produce precise customer feedback reports efficiently and quickly, consider starting with our 30-day free trial, or book a call with us to discover more about how we can simplify your analysis process.
Customer feedback made easy
Customer feedback tagged automatically
Real-time customer sentiment scores
Pain-points evolution over time
