Quantitative and qualitative customer feedback are both valuable sources of business data, and yet most qualitative data goes unused.
Quantitative data is easier to measure because it’s represented in the form of numerical data. But qualitative data is descriptive – often containing ideas and opinions – and can’t be quantified in the same way as numerical data. First, it needs to be analyzed and structured using advanced data analysis techniques, which we’ll explore later on in this post.
Both quantitative and qualitative data are valuable sources of customer feedback: quantitative data provides a birds-eye view of your business, while qualitative data digs into customer comments and their personal feelings, helping you to truly understand your customers’ needs.
In this post, we’ll help you understand the main differences between the two types of customer feedback, the benefits of each one, and why you can no longer ignore your qualitative customer feedback.
Quantitative feedback is customer data that provides numerical results. It helps you quantify aspects of your business, like customer service performance, product success, campaign success, and much more.
Insights are clear-cut, presented as numbers or counts, and can easily be transformed into charts and graphs for even easier analysis.
Quantitative feedback is the most popular method for businesses to measure performance because the rules and tools for quantitative analysis are well established, but there are many other benefits of quantitative feedback that make it a valuable business asset.
While quantitative feedback is a great starting point to gauge what’s going on in your business, it doesn’t explain the whole story. That’s why it’s important to back up your quantitative analysis results with qualitative feedback analysis.
Qualitative feedback is non-numerical information that measures opinions and views from an individual perspective.
Qualitative feedback is used by businesses to gain a deep understanding of customer issues or motivations. It helps them discover the ‘why’ behind quantitative results.
For example, why has a customer left a low customer satisfaction score? Qualitative data goes beyond mere statistics, to provide detailed insights that can lead to product, service, and overall business improvements. It’s found in open-ended survey responses, emails, social media conversations, and other unstructured data types – from a company’s internal systems’ data or all over the internet.
While qualitative feedback is harder to analyze than quantitative data, you’ll gain detailed customer insights that will help you make key business decisions. And with low-code tools like MonkeyLearn making it easier than ever to analyze qualitative feedback, now is the time to stop ignoring it and start making use of it.
Qualitative feedback helps businesses become more customer-centric. By continuously listening to the voice of the customer and understanding where you need to improve, you’ll begin to see exponential growth.
However, you’ll need to make sure that your qualitative results are accurate, which depends on the skills and integrity of the person carrying out the qualitative analysis, as well as the performance of the tools you use.
It’s likely that you already have a repeatable process and tools in place for analyzing your quantitative feedback. But how can you extract insights from your qualitative feedback?
MonkeyLearn provides a suite of ready-to-use text analysis tools that help you mine huge amounts of qualitative feedback in next to no time. You can use sentiment analysis to automatically detect emotions in your data, or topic analysis to discover which aspects of your business customers mention most often. Other capabilities, like keyword extraction, are useful to highlight common words or themes within your data or summarize whole texts.
This all-in-one text analysis and data visualization solution provides a template for all types of qualitative data, whether social media, reviews, surveys, or email, and automatically runs your data through each analysis technique (topic, sentiment, and keywords).
Finally, results are displayed in a striking dashboard, so you can easily spot trends and patterns in the same way you would with quantitative data.
Sign up to MonkeyLearn to start analyzing your qualitative data. And connect your data via MonkeyLearn’s robust API or using one of the many available integrations. You can also collect your data in a CSV or Excel file and upload directly to MonkeyLearn’s text analysis models.
Each type of feedback serves a different purpose. Quantitative feedback is a great option if you need quick results that provide an overview of your business or a particular aspect. Qualitative feedback, on the other hand, delivers more detailed insights that lead to improved customer experiences and better decision-making.
Qualitative and quantitative data are supportive of each other, so it’s best to perform both a quantitative and qualitative analysis.
Now, with easy-to-use text analysis tools, there’s no excuse for not analyzing your qualitative feedback. Sign up to MonkeyLearn to try out our tools for yourself, or schedule a demo and we’ll walk you through how to analyze your qualitative data.
January 20th, 2021