What is survey analytics?<\/strong><\/h2>Survey analytics is the process through which you can understand customer feedback from surveys to gain actionable insights<\/a> and find improvement opportunities.<\/p>When you successfully harness user survey insights from user feedback, you can build data-driven business strategies and continuously drive customer value. This can help drive increased customer satisfaction<\/a> and retention.<\/p>Types of survey data to analyze<\/strong><\/h2>Surveys help you collect multiple types of data when you ask the right set of specific questions<\/a>. Here are five major types of survey data:<\/p>Quantitative data<\/strong><\/h3>Quantitative data, called numerical data, makes it easy to analyze customer feedback<\/a> and spot trends and patterns. This enables you to get rich data-driven results that can be compared across time periods or user segments.<\/p>You can use Userpilot<\/a> to gather quantitative data from close-ended questions<\/a> like the one below. Based on the statistical data, you can easily analyze and measure data changes over time.<\/p>![\"Userpilot-onboarding-survey\"](\"https:\/\/blog-static.userpilot.com\/blog\/wp-content\/uploads\/2025\/11\/untitled-6_d5963a100eaee5e8783b9c40720d3c1b_800.png\")
<\/picture><\/figure>Qualitative data<\/strong><\/h3>Using qualitative data<\/a> is crucial because it includes important details that quantitative data can\u2019t provide.<\/p>But what exactly is qualitative data? It\u2019s non-numerical data, such as text, audio, etc., that you collect from users. But here\u2019s the thing\u2014this data is usually unstructured, and that makes it challenging to measure it compared to quantitative data.<\/p>
However, it\u2019s still possible to analyze this data to draw key insights<\/a> that you could miss out on if you solely rely on quantitative data.<\/p>You can collect qualitative feedback<\/a> from open-ended survey questions<\/a>. For instance, these could be follow-up questions in ranking\/score-based surveys like the Net Promoter Score (NPS) surveys, asking for detailed feedback.<\/p>On the other hand, you also have the option to create a single qualitative question in a survey, like the survey shown below.<\/p>![\"Qualitative-survey-Userpilot\"](\"https:\/\/blog-static.userpilot.com\/blog\/wp-content\/uploads\/2025\/11\/screenshot-2024-04-24-at-14329-am_0e0b7df3ede55a902600bcf6d7411f4f_800.jpg\")
<\/picture><\/figure>Categorical data<\/strong><\/h3>Categorical data is data that doesn\u2019t have any numerical value and can\u2019t be called qualitative data as well. However, you could use it to gather feedback from your users by providing them with specific options.<\/p>
For example, you can ask users to choose between different scenarios to identify their main use case<\/a>. You can even provide an option like \u201cOther\u201d and have them enter the relevant response themselves.<\/p>![\"Categorical-survey-Userpilot\"](\"https:\/\/blog-static.userpilot.com\/blog\/wp-content\/uploads\/2025\/11\/trial-end-survey_bd1bd76db4aa11cde414a93fbdb37222_800.png\")
<\/picture><\/figure>With the right categorical questions for surveys, you get discrete data that can be grouped into meaningful information, such as company sizes, user roles, most useful product features, etc.<\/p>
Ordinal data<\/strong><\/h3>This type of data is used primarily to group varied data into straightforward, ordered groups. Additionally, the options that are provided in these surveys are arranged hierarchically based on their levels of importance.<\/p>
You can give users the ability to rank the options in the survey relative to each other. It helps you figure out what users find more important among the options you\u2019ve given. For example, you can use ordinal scale questions to ask people to rate or rank three product features through a feedback survey<\/a>.<\/p>Scalar data<\/strong><\/h3>As the name suggests, this type of data is collected from surveys where you have a particular scale of measurement. For instance, you could have a question about the ease of use of your product and have users choose between a scale of 1 and 10.<\/p>
The scalar data can be used to capture customers\u2019 sentiments<\/a> to help you understand if any changes to your approach are needed.<\/p>The key here is that the scale you use must have a consistent distance between each point and should be measurable. For instance, in the rating question below, on a scale from very difficult (1 star) to very easy (5 stars), has an equal difference of 1 between the intervals.<\/p>![\"Scalar-data-survey-Userpilot\"](\"https:\/\/blog-static.userpilot.com\/blog\/wp-content\/uploads\/2025\/11\/in-app-survey_9f1edb00c6a8ab9764c25dabdabb26e4_800.png\")
<\/picture><\/figure>Step-by-step process on how to analyze survey results<\/h2>
If you\u2019re wondering how to analyze survey data<\/a>, use the following steps to get started:<\/p>1. Trigger surveys at different customer touchpoints<\/strong><\/h3>Before you start analyzing survey data points, you need to collect detailed and meaningful customer feedback. The key is to identify crucial touchpoints where it makes sense to launch in-app surveys<\/a>.<\/p>For instance, you can trigger a welcome survey<\/a> when a user logs into your product for the first time. Similarly, you can launch relevant surveys after a user completes an onboarding flow or makes their first purchase. You can easily identify these touchpoints using path analysis.<\/p>![\"Product-feedback-survey-Userpilot\"](\"https:\/\/blog-static.userpilot.com\/blog\/wp-content\/uploads\/2025\/11\/product-feedback-survey_078b54075f3696ec331d59421c1c91ec_800.jpg\")
<\/picture><\/figure>You should ask a variety of survey questions at each step to get the most in-depth feedback. Besides active surveys, which prompt users to submit feedback, you can also use passive feedback<\/a> options.<\/p>Passive feedback helps you collect feedback from users without disrupting their experience. For instance, you can add a \u201cGive Feedback\u201d option to the in-app help center<\/a>, like below:<\/p>![\"Passive-survey-Userpilot\"](\"https:\/\/blog-static.userpilot.com\/blog\/wp-content\/uploads\/2025\/11\/passive-feedback_ecc34a9b7eccf45fbadda2c96d2fe781_800.jpg\")
<\/picture><\/figure>2. Use a survey analytics dashboard to visualize data<\/strong><\/h3>The best way to analyze survey data is to use an analytics dashboard<\/a>. With one, visualizing and reporting survey results becomes straightforward.<\/p>The survey analytics dashboard gives insights into survey performance metrics, including dismiss rate, completion rate, time-to-completion, etc. What makes it even better is that you can visualize survey responses<\/a> in the form of charts and graphs.<\/p>The image below shows an NPS dashboard<\/a> that displays the NPS survey data, NPS average score, NPS score charts, etc.<\/p>![\"NPS-dashboard\"](\"https:\/\/blog-static.userpilot.com\/blog\/wp-content\/uploads\/2025\/11\/nps-overview_a127046141ec48ca5c27985938ca20a8_800.png\")
<\/picture><\/figure>If you notice a reduction in NPS scores among a particular user segment, you can create targeted re-engagement strategies<\/a> to improve their user experience.<\/p>3. Tag survey responses to identify trends<\/strong><\/h3>Tagging survey responses is very effective for gathering insights from qualitative data. Survey response tags let you categorize this data and identify customer pain points<\/a> from it.<\/p>The image below shows the responses collected from NPS response tags. You can add a qualitative follow-up question in NPS surveys<\/a> to further dive into the reasons behind a user\u2019s rating.<\/p>![\"NPS-responses\"](\"https:\/\/blog-static.userpilot.com\/blog\/wp-content\/uploads\/2025\/11\/nps-responses_f441a81d2eb3603f86dbcf49ecf92b02_800.png\")
<\/picture><\/figure>Tagging these responses helps you organize the feedback into categories like usability, customer support<\/a>, performance, pricing, and more. You can then look at the NPS scores of each category to understand how users feel about them.<\/p>When a category has a higher percentage of detractors than promoters, it could mean that there are serious issues that need to be resolved.<\/p>
4. Conduct a statistical analysis for an in-depth assessment<\/h3>
Statistical analysis is an umbrella term for various mathematical methods to quantify and interpret customer feedback data. These methods help you identify patterns and seasonal trends<\/a> in survey responses, enabling you to make data-driven decisions to improve your product and drive customer satisfaction.<\/p>The most commonly used statistical analytics methods include:<\/p>
- Regression analysis<\/strong> – It evaluates the relationship between a dependent variable and one or more independent variables.<\/li>
- T-test<\/strong> – It compares the means of two groups to understand how product development decisions impact user sentiment.<\/li>
- ANOVA (Analysis of Variance)<\/strong> – This compares the means of three or more data sets and can be used to analyze survey responses from different user segments.<\/li><\/ul>
5. Act on the insights from survey data analysis<\/h3>
Knowing how to analyze data from a survey isn\u2019t enough. You also have to derive actionable insights from the analysis and identify the necessary areas of improvement.<\/p>
Let\u2019s say most users rate the onboarding experience poorly and mention that it\u2019s too complex. You can use this information to cut down the number of steps in the onboarding flow<\/a> and walk users through the most essential features first.<\/p>
When you successfully harness user survey insights from user feedback, you can build data-driven business strategies and continuously drive customer value. This can help drive increased customer satisfaction<\/a> and retention.<\/p> Surveys help you collect multiple types of data when you ask the right set of specific questions<\/a>. Here are five major types of survey data:<\/p> Quantitative data, called numerical data, makes it easy to analyze customer feedback<\/a> and spot trends and patterns. This enables you to get rich data-driven results that can be compared across time periods or user segments.<\/p> You can use Userpilot<\/a> to gather quantitative data from close-ended questions<\/a> like the one below. Based on the statistical data, you can easily analyze and measure data changes over time.<\/p> Using qualitative data<\/a> is crucial because it includes important details that quantitative data can\u2019t provide.<\/p> But what exactly is qualitative data? It\u2019s non-numerical data, such as text, audio, etc., that you collect from users. But here\u2019s the thing\u2014this data is usually unstructured, and that makes it challenging to measure it compared to quantitative data.<\/p> However, it\u2019s still possible to analyze this data to draw key insights<\/a> that you could miss out on if you solely rely on quantitative data.<\/p> You can collect qualitative feedback<\/a> from open-ended survey questions<\/a>. For instance, these could be follow-up questions in ranking\/score-based surveys like the Net Promoter Score (NPS) surveys, asking for detailed feedback.<\/p> On the other hand, you also have the option to create a single qualitative question in a survey, like the survey shown below.<\/p> Categorical data is data that doesn\u2019t have any numerical value and can\u2019t be called qualitative data as well. However, you could use it to gather feedback from your users by providing them with specific options.<\/p> For example, you can ask users to choose between different scenarios to identify their main use case<\/a>. You can even provide an option like \u201cOther\u201d and have them enter the relevant response themselves.<\/p> With the right categorical questions for surveys, you get discrete data that can be grouped into meaningful information, such as company sizes, user roles, most useful product features, etc.<\/p> This type of data is used primarily to group varied data into straightforward, ordered groups. Additionally, the options that are provided in these surveys are arranged hierarchically based on their levels of importance.<\/p> You can give users the ability to rank the options in the survey relative to each other. It helps you figure out what users find more important among the options you\u2019ve given. For example, you can use ordinal scale questions to ask people to rate or rank three product features through a feedback survey<\/a>.<\/p> As the name suggests, this type of data is collected from surveys where you have a particular scale of measurement. For instance, you could have a question about the ease of use of your product and have users choose between a scale of 1 and 10.<\/p> The scalar data can be used to capture customers\u2019 sentiments<\/a> to help you understand if any changes to your approach are needed.<\/p> The key here is that the scale you use must have a consistent distance between each point and should be measurable. For instance, in the rating question below, on a scale from very difficult (1 star) to very easy (5 stars), has an equal difference of 1 between the intervals.<\/p> If you\u2019re wondering how to analyze survey data<\/a>, use the following steps to get started:<\/p> Before you start analyzing survey data points, you need to collect detailed and meaningful customer feedback. The key is to identify crucial touchpoints where it makes sense to launch in-app surveys<\/a>.<\/p> For instance, you can trigger a welcome survey<\/a> when a user logs into your product for the first time. Similarly, you can launch relevant surveys after a user completes an onboarding flow or makes their first purchase. You can easily identify these touchpoints using path analysis.<\/p> You should ask a variety of survey questions at each step to get the most in-depth feedback. Besides active surveys, which prompt users to submit feedback, you can also use passive feedback<\/a> options.<\/p> Passive feedback helps you collect feedback from users without disrupting their experience. For instance, you can add a \u201cGive Feedback\u201d option to the in-app help center<\/a>, like below:<\/p> The best way to analyze survey data is to use an analytics dashboard<\/a>. With one, visualizing and reporting survey results becomes straightforward.<\/p> The survey analytics dashboard gives insights into survey performance metrics, including dismiss rate, completion rate, time-to-completion, etc. What makes it even better is that you can visualize survey responses<\/a> in the form of charts and graphs.<\/p> The image below shows an NPS dashboard<\/a> that displays the NPS survey data, NPS average score, NPS score charts, etc.<\/p> If you notice a reduction in NPS scores among a particular user segment, you can create targeted re-engagement strategies<\/a> to improve their user experience.<\/p> Tagging survey responses is very effective for gathering insights from qualitative data. Survey response tags let you categorize this data and identify customer pain points<\/a> from it.<\/p> The image below shows the responses collected from NPS response tags. You can add a qualitative follow-up question in NPS surveys<\/a> to further dive into the reasons behind a user\u2019s rating.<\/p> Tagging these responses helps you organize the feedback into categories like usability, customer support<\/a>, performance, pricing, and more. You can then look at the NPS scores of each category to understand how users feel about them.<\/p> When a category has a higher percentage of detractors than promoters, it could mean that there are serious issues that need to be resolved.<\/p> Statistical analysis is an umbrella term for various mathematical methods to quantify and interpret customer feedback data. These methods help you identify patterns and seasonal trends<\/a> in survey responses, enabling you to make data-driven decisions to improve your product and drive customer satisfaction.<\/p> The most commonly used statistical analytics methods include:<\/p> Knowing how to analyze data from a survey isn\u2019t enough. You also have to derive actionable insights from the analysis and identify the necessary areas of improvement.<\/p> Let\u2019s say most users rate the onboarding experience poorly and mention that it\u2019s too complex. You can use this information to cut down the number of steps in the onboarding flow<\/a> and walk users through the most essential features first.<\/p>Types of survey data to analyze<\/strong><\/h2>
Quantitative data<\/strong><\/h3>
![\"Userpilot-onboarding-survey\"](\"https:\/\/blog-static.userpilot.com\/blog\/wp-content\/uploads\/2025\/11\/untitled-6_d5963a100eaee5e8783b9c40720d3c1b_800.png)
<\/picture><\/figure>Qualitative data<\/strong><\/h3>
![\"Qualitative-survey-Userpilot\"](\"https:\/\/blog-static.userpilot.com\/blog\/wp-content\/uploads\/2025\/11\/screenshot-2024-04-24-at-14329-am_0e0b7df3ede55a902600bcf6d7411f4f_800.jpg)
<\/picture><\/figure>Categorical data<\/strong><\/h3>
![\"Categorical-survey-Userpilot\"](\"https:\/\/blog-static.userpilot.com\/blog\/wp-content\/uploads\/2025\/11\/trial-end-survey_bd1bd76db4aa11cde414a93fbdb37222_800.png)
<\/picture><\/figure>Ordinal data<\/strong><\/h3>
Scalar data<\/strong><\/h3>
![\"Scalar-data-survey-Userpilot\"](\"https:\/\/blog-static.userpilot.com\/blog\/wp-content\/uploads\/2025\/11\/in-app-survey_9f1edb00c6a8ab9764c25dabdabb26e4_800.png)
<\/picture><\/figure>Step-by-step process on how to analyze survey results<\/h2>
1. Trigger surveys at different customer touchpoints<\/strong><\/h3>
![\"Product-feedback-survey-Userpilot\"](\"https:\/\/blog-static.userpilot.com\/blog\/wp-content\/uploads\/2025\/11\/product-feedback-survey_078b54075f3696ec331d59421c1c91ec_800.jpg)
<\/picture><\/figure>![\"Passive-survey-Userpilot\"](\"https:\/\/blog-static.userpilot.com\/blog\/wp-content\/uploads\/2025\/11\/passive-feedback_ecc34a9b7eccf45fbadda2c96d2fe781_800.jpg)
<\/picture><\/figure>2. Use a survey analytics dashboard to visualize data<\/strong><\/h3>
![\"NPS-dashboard\"](\"https:\/\/blog-static.userpilot.com\/blog\/wp-content\/uploads\/2025\/11\/nps-overview_a127046141ec48ca5c27985938ca20a8_800.png)
<\/picture><\/figure>3. Tag survey responses to identify trends<\/strong><\/h3>
![\"NPS-responses\"](\"https:\/\/blog-static.userpilot.com\/blog\/wp-content\/uploads\/2025\/11\/nps-responses_f441a81d2eb3603f86dbcf49ecf92b02_800.png)
<\/picture><\/figure>4. Conduct a statistical analysis for an in-depth assessment<\/h3>
5. Act on the insights from survey data analysis<\/h3>
![\"Step-by-step-walkthrough\"](\"https:\/\/blog-static.userpilot.com\/blog\/wp-content\/uploads\/2025\/11\/untitled-7_56b09ca2668941782e40b01d09bf12bd_800.jpg)
<\/picture><\/figure>","protected":false},"excerpt":{"rendered":"