How to analyze survey data in 6 easy steps

Surveys make it easy to get to know your customers and users, but even a short, simple questionnaire can leave you with a headache-inducing amount of user data to sort through. 

So, how do you make sense of it all? Read this guide chapter to get a step-by-step framework that takes you through the entire process of analyzing your survey data, from setting goals and using the different survey data analysis methods to presenting your findings to your team and taking action.

1. Establish your analysis goals

It can feel daunting not knowing how to analyze your data after you create a survey and send it out into the world. But let's look at the big picture: the key to successful data analysis is planning.

To get meaningful insights from your survey data, first determine what goals you want to accomplish with your survey. 

Ask yourself:

• What’s the main problem I need to solve or question I need to answer?

• Which customer segments can give me this insight?

• When and where should I ask them about it?

• What type of survey would best achieve this?

Deciding the key research questions you want to answer with your survey data helps you determine which data points to analyze, and in which order.

2. Organize your results 

Once your survey responses are in, it’s time to organize the raw data into categories that group responses by type or theme, even if individual answers are worded differently. In theory, you could go through every answer to identify response categories one by one, but we recommended using a text analyzer to identify the most common words in your responses.

If your survey asks multiple questions, you might find some respondents skip questions or leave fields blank. This isn't always a problem, but if you’re trying to compare how respondents differ in their answers to multiple questions, incomplete responses may skew your results. In this situation, it’s better to remove incomplete responses.

Use Contentsquare to filter survey data and conduct sentiment analysis

Use Contentsquare’s AI text analyzer to categorize your data and survey responses through keyword analysis and automated tagging. It uses algorithms and natural language processing (NLP) to reveal overarching themes and user sentiments, such as positive, neutral, or negative survey answers, and how they change over time.

Sentiment analysis helps you evaluate your customers’ vocabulary and put their behavior into context to predict their future actions, like what drives them to make a purchase.

Contentsquare’s Voice of Customer product lets you track user sentiment over time

3. Analyze your quantitative survey data

Asking closed-ended questions in your survey gives you quantitative data (or numerical values) that you can analyze with graphs, charts, and comparison tables. Respondents have to answer in a specific way, like giving a yes or no answer, completing a 1–5 rating scale, or choosing from predetermined responses, making your results consistent and easy to evaluate.

Quantitative data is therefore relatively straightforward to analyze. These 3 analysis methods help you get valuable insights from your quantitative survey data:

Make simple comparisons to identify customer preferences

If you ask a multiple-choice question, the answers let you identify and compare specific customer preferences—for example, when you're testing a new product feature, service, or design.

To analyze a comparative data set, add the total number of responses for each multiple-choice option. Then, create a comparison chart to organize the number of responses or percentages for each answer, like this

A data comparison chart

Use cross-tabulation charts and graphs to compare results from different audience segments

If you include survey questions that let you categorize respondents by demographic, you can see how different audience segments answer the same question—for example, how answers vary by age group or industry.

To analyze these responses, use a cross-tabulation chart to compare answers from each segment:

A cross-tabulation chart that breaks responses down by subgroup

Analyze rating scale data using mode, mean, and bar charts

Asking your customers a rating scale question that measures how strongly customers feel about specific topics, product features, or services you provide is a great way to understand and improve customer satisfaction and the customer experience.

There are three easy ways to analyze data collected with rating scales:

1. Calculate the mode, which represents the most common answer that appears in a set of data and gives you a quick snapshot of which rating on the scale respondents chose most often. The mode is simply the value or response that appears the most.

2. Calculate the mean, which is generally the ‘average’. Calculate it by adding up all the scores and then dividing the total by the number of responses to give you a figure that represents the typical response, which is helpful if you want to compare how customers’ responses to the same question change over time.

3. Create a bar chart showing response rates. This gives you a quick snapshot of which rating on the scale respondents chose most often.

For example, you can use means to calculate your Net Promoter Score® (NPS®) survey results, which measure customer loyalty and satisfaction.

However, there's no need to calculate your NPS® manually: Contentsquare’s VoC Surveys tool automatically calculates your Net Promoter Score® for you and gives you a visual breakdown in the survey response tab.

Contentsquare records how your NPS® changes over time

Use Contentsquare to continually collect VoC  data  

Contentsquare’s Voice of Customer tools let you collect quick qualitative data insights with short, descriptive scale questions that capture text or 🙂/ 😐/ 🙁 responses. 

For example, you can lace an unobtrusive feedback button on your site and leverage real-time data to 

• Understand why users get frustrated

• Identify and optimize underperforming pages

• Fine-tune product releases

• Fix problems proactively

• Improve the user experience

A Contentsquare survey that asks users to rate their experience on a descriptive emoji scale

4. Analyze your qualitative survey data

Open-ended questions are great for getting authentic feedback because they give people a chance to describe what they’re experiencing in their own voice. Analyzing qualitative survey questions is an excellent opportunity to empathize with your audience, gather essential insights, and make the right decisions.

But you may be wondering: how do you efficiently analyze more than 100 replies? Or even 1,000?

Here are 3 easy ways to analyze insights from any amount of qualitative survey data:

Create visual representations of survey data

Qualitative data from surveys often involve hundreds or thousands of unique answers—we feel your pain. One great way to process this information and make quick decisions is data visualization. 

For example, you could generate a word cloud from terms that frequently crop up in responses or an infographic that summarizes the demographics and behaviors of respondents (like user personas). While visualizations won’t necessarily provide a definitive answer to a question, they’re a great jumping-off point for discussion.

Read individual responses to uncover hidden insights to shape your product and messaging

Qualitative data analysis isn’t always about spotting trends: it’s also about uncovering the motivations, objections, and desires your audience won’t (or can't) tell you about directly.

Sarah Doody, Author of UX Notebook, used qualitative data to shape the messaging of her user experience (UX) training courses:

“A question I like to ask is this: ‘Before taking this course, the biggest challenges I faced were __________,’ or ‘The thing that was holding me back was ___________.’”

When asking questions like these, you’re likely to get many different answers. It can seem overwhelming, but taking the time to read open-ended responses gives you a deeper understanding of what your audience really needs from your product, thus leading to more meaningful insights.

Turn qualitative insights into quantitative data

If you have enough qualitative survey data, sort your responses into categories and use them to create graphs, tables, and charts. 

Analyze open-ended responses like quantitative data in these 5 steps:

1. Add your responses to a spreadsheet

2. Look for ways to categorize individual responses

3. Assign each response to a category

4. Organize your survey data by categories

5. Represent your data visually to reveal the prevalence of certain categories

How to use qualitative data to reinforce quantitative data

Qualitative data is often most helpful when used to support and explain quantitative data. Together, quantitative and qualitative survey data help you build a complete picture of what’s happening—and of what customers need from your product.

Where quantitative data analysis often reveals audience trends and preferences, qualitative data analysis reveals the why behind them.

Let’s apply this to a software as a service (SaaS) company that wants to determine why free trial users aren't becoming paid users:

Let’s say by asking a closed-ended question, you identified that 70% of trial users found the product useful, which indicates the problem probably isn't with the product itself.

If you also asked an open-ended question like “What’s stopping you from signing up for our paid plan?”, you could sift through the answers to discover that trial users also objected to the pricing.

Use Contentsquare’s AI technology for effortless data analysis

Analyzing qualitative data just got easier: to save you time manually sifting through answers to open-ended questions, Contentsquare’s AI assistant analyzes your qualitative survey responses for you and summarizes them in a concise report that includes key findings, quotes, and actionable next steps to take.

Contentsquare’s AI-powered Voice of Customer product generates a survey report for you

5. Draw meaningful conclusions

Once you’ve organized and analyzed your quantitative and qualitative survey data, it’s time to draw conclusions that lead to an action or solution by identifying trends, calculating the statistical significance of your data, and benchmarking it against previous results.

Here are 3 ways to gain actionable insights from your data analysis:

Look for trends in your data

Start by looking at survey data that ties most closely to the analysis goals you set in Step 1.

Let’s return to the SaaS example: to understand why more users aren’t signing up for a paid plan after completing a free trial, you might ask free trial users to rate how useful they find the product.

Now imagine the survey data says 70% of trial users found the product useful. Your conclusion might be that your product isn’t the reason trial users aren’t continuing. 

Looking for trends in this way allows you to hone in on the problem through a process of elimination.

Check that your findings are statistically significant

Drawing meaningful conclusions from survey data can be tricky. Data often suffers from ‘noise’ because people sometimes make mistakes when entering their answers.

If you only have a handful of responses, that ‘noise’ carries a margin of error that will affect survey results even more. The less data you have, the less likely it is that your findings have statistical significance.

Use a sample size calculator to check that your data pool is big enough to trust the validity of the insights you’re finding, and make sure you’re not jumping to conclusions in your data by assuming correlation means causation.

Pro tip: to avoid confusing correlation with causation in your data analysis, consider every factor that could influence trends. Also, use behavior analytics tools to investigate and understand what's happening on your website to avoid acting on assumptions and find out the real reason behind issues like a drop in conversions or customer satisfaction.

Compare your data against previous benchmarks

Whenever possible, try to get a frame of reference for interpreting your data. Looking at historical data helps you make sense of the trends you identify.

Let’s go back to the example of a SaaS company trying to understand why free trial users aren’t signing up for a paid plan:

Your company might compare its results against benchmarks from a similar survey the previous year.

Now let’s imagine that trial users find the product more useful this year than last year, but paid sign-ups still haven't increased. This could indicate that you need to focus on optimizing other factors—like customer experience (CX) or pricing pages—instead of further developing your product.

Use Contentsquare’s tools to enhance your survey insights 

For more in-depth insights, supplement your survey results with data from Contentsquare’s following experience analytics tools and methods:

• Conduct user interviews with your own community or effortlessly recruit participants from our extensive pool and refine your selection based on demography or screening questions. This tool lets you automate scheduling, and record and transcribe your interviews for maximum value.

• Use the Heatmaps tool to see where users click and scroll the most on your page.

• Conduct journey analysis to contextualize your survey data and know where (and how) to improve the customer experience

• Watch session replays of individual users navigating your product or site to find out the reasons for their negative feedback—for example, a broken link causing customers to u-turn from your landing page

Contentsquare’s customizable dashboards provide a visual overview of your insights from key sessions—like u-turns and rage clicks—alongside behavior data.

💡 Pro tip: check out our chapter on survey software and tools to learn more about which platforms to use to create and analyze your surveys.

How HARTING used Contentsquare to increase downloads by 38%

HARTING Technology Group—a global industrial technology supplier—knew that its product description pages (PDPs) were at the heart of its ecommerce customer experience, with PDPs accounting for about 27% of the company’s website’s page views, and 33% of site visitors starting their journey via a product page.

This being the case, HARTING paired Contentsquare’s Journeys and Heatmaps tools with customer feedback to understand why users were having a negative experience and dropping off. 

To address the issue, the team redesigned their PDPs, making specific optimizations based on insights from VoC and experience analytics tools, like reducing information overload, clarifying page structure, and making products more searchable. 

After implementing the changes, HARTING used Contentsquare to analyze the performance of their newly designed pages. They discovered that the number of downloads from PDPs increased by 38%.

6. Present your findings to your team and determine next steps

After gathering actionable insights from your survey (and experience analytics), it’s time to share the findings with your team.

If you’re sharing your insights in a meeting, remember that it can be challenging for people to digest raw numbers quickly. It’s best to present data concisely with charts, graphs, or infographics in these situations.

A simple bar graph generated with Contentsquare

However, if you’re creating a more detailed report your colleagues can read in their own time, consider including more in-depth figure breakdowns.

Once you’ve shared everything with the team, start strategizing how you want to address the findings you uncovered. Leverage key insights to take actions such as improving the customer experience, increasing retention, or promoting brand loyalty.

Use Contentsquare integrations to share analysis insights with your team

Easily bring the voice of your customers to your team and keep stakeholders in the loop using Contentsquare’s integrations with Slack and Microsoft Teams. These integrations automatically alert your team of incoming survey data and changes in digital experience metrics so you can swiftly take action.

Contentsquare integrates with messaging apps like Slack to share user insights with your team and take action sooner

Start analyzing your survey data today 

Analyzing survey data helps you understand customer behavior and track your company's performance, but working with large amounts of data can quickly become overwhelming.

Keep things simple, and remember to

• Design your surveys with clear, simple goals from the beginning

• Use quantitative data to spot initial trends, then use qualitative data to look for in-depth explanations

• Ensure your conclusions are valid by using benchmarks, checking that your sample size is big enough, and considering correlation vs. causation

And don’t forget that survey data isn’t the only type of data to look at: dig deeper and better understand user behavior by comparing survey results with insights from user interviews, session replays, and heatmaps.

FAQs about analyzing survey data