When gathering and analyzing feedback, you can collect qualitative data from CSAT surveys, NPS surveys, in-app surveys, voice bots, heat maps, and session recordings. A low-code natural language processing (NLP) tool can make analyzing feedback easier.
Qualitative feedback offers detailed customer insights that can help you improve your product. Unlike quantitative data, it exposes the reasons behind specific behaviors.
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Understanding customer needs
When you collect customer feedback, qualitative insights can be challenging to identify. While quantitative data provides an overview of satisfaction levels, qualitative feedback focuses on understanding what drives customers to be satisfied (or dissatisfied).
Qualitative customer feedback is typically collected through open-ended questions and interviews. While this type of feedback is more subjective, it is essential to understand customer needs to improve your product or service.
For example, if you get feedback that your website is slow or has bugs, it’s crucial to know why. This will help you make the right changes to improve the experience for your customers. In addition, it’s essential to collect qualitative data consistently. This will allow you to analyze trends and make more informed decisions.
Another benefit of qualitative feedback is that it removes biases from your analysis. In contrast, when collecting quantitative feedback, it’s easy to skew results by wording or asking questions. Qualitative feedback lets you get a more honest and accurate picture of your customers’ thinking.
Qualitative customer feedback can be challenging to analyze, but there are a few ways to make it easier. One way is to use a tool that can automatically tag or categorize free-form customer feedback by theme. This can help you uncover the most common themes and identify patterns in your customer feedback.
Understanding customer motivations
Unlike quantitative feedback, qualitative data focuses on why a customer thinks a certain way. It can help businesses identify the areas where they need to improve and become more customer-centric.
Qualitative data can be collected through various methods, such as interviews, surveys, and questionnaires. The key to collecting qualitative data is asking the right questions. The questions should be open-ended and allow customers to describe their experiences in their own words. Avoid using multiple-choice answers that can be answered with a simple yes or no. This will give you more insightful responses and allow you to see your customers’ issues.
Qualitative feedback to uncover why customers leave your business can help you improve your product or service. For example, SaaS companies often use exit surveys to ask users why they cancel their subscriptions. This can reveal many reasons, including finding a competitor with better features or a lower price.
A natural language processing tool can help you analyze qualitative feedback at scale and quickly identify important themes. You can then use the results of this analysis to make informed decisions about your products and services. For example, if your customers complain about your customer support, you can focus on improving that department.
Understanding customer expectations
Customer expectations are critical to the Voice of the Customer (VoC). It’s essential to understand what your customers expect from your product or service so that you can design and deliver a superior experience. Customer expectation research can be done in several ways, including surveys, focus groups, and in-person interviews. In addition to determining customer expectations, VoC analysis can help you identify opportunities for improvement and develop a successful strategy.
Quantitative feedback can be measured using metrics, such as ratings or multiple-choice answers. It’s easier to analyze than qualitative data, which requires deeper explanations and open-ended responses.
Qualitative data provides more insights and can lead to more user-led growth. For example, it allows you to know why a user has given you a low NPS score or why they have left your site without making a purchase. It also enables you to uncover motivations and emotional responses hidden in quantitative feedback.
One of the best ways to collect qualitative feedback is to ask customers how they feel about your product or service, ideally while they’re still using it. You can use tools like session recordings or heatmaps to monitor their behavior, then trigger a feedback widget on specific pages to ask them how they feel about that page.
Understanding customer perceptions
Unlike quantitative feedback, which measures customer satisfaction, qualitative feedback is more descriptive and tells how customers perceive your products and services. It reveals the root causes of dissatisfaction or satisfaction, giving you actionable insights. Qualitative feedback also allows you to hear the voice of your customer, whereas quantitative data is more of an echo chamber and can sometimes be biased.
For example, if a customer leaves a positive review but complains about your customer service, you won’t get much help from their feedback. However, you can take their concerns seriously if they leave a negative review about your product or service and describe why they feel that way.
Luckily, many tools can help you analyze qualitative feedback automatically. These low-code Natural Language Processing (NLP) tools can easily mine your unstructured data and give you valuable insights quickly. For example, they can use sentiment analysis to automatically detect emotions in your feedback and perform topic analysis to discover common words or themes.
In addition, some NLP tools have additional features that allow you to tag and categorize free-form feedback and apply filters by feedback type, data source, or geographic location. This makes it easy to find a specific issue in your feedback and take action on it without having to read through it yourself.