How to analyze survey results: 6 simple methods to turn data into insights

You’ve launched your survey, watched the responses roll in, and now you’re staring at a dashboard full of data. The numbers are there, the comments are collected, but what does it all actually mean? If you’re feeling overwhelmed by the prospect of turning hundreds (or thousands) of individual responses into actionable insights, this article will help you get started.

This step-by-step guide will walk you through the essential methods for transforming your survey responses into clear, compelling insights. You’ll learn how to analyze survey data to spot meaningful patterns, avoid common analytical pitfalls, and most importantly, how to communicate your findings in a way that drives action across your team and organization.

What is survey data analysis?

Survey data analysis is the process of examining, cleaning, transforming, and interpreting the information collected through surveys to uncover meaningful patterns and actionable insights. It involves converting raw responses into digestible information that can guide decisions for you, your team and your busiess.

Effective survey analysis isn’t just about tallying up responses – it’s about understanding the context, identifying trends, and connecting different data points to paint a complete picture of what your audience is telling you.

The goal is to transform seemingly disconnected answers into coherent insights that inform your strategy, whether that’s developing new products, refining your marketing approach, or improving customer experience.

When done right, survey data analysis bridges the gap between what your audience says and what your business does. It turns feedback into foresight, helping you anticipate needs rather than just react to them.

Types of survey data

Your survey report will likely contain a mix of data types. Let’s explore what type of customer insights and feedback data you can get, from qualitative survey data to numerical data.

Quantitative data

If you can count it, it is quantitative data. Think age, spending amounts, how often someone buys something or how they would rate the quality of a product.

Qualitative data

Qualitative data is harder to interpret, but incredibly important to give meaning to the numbers. It’s words, meanings, descriptions and feelings.

Want to dive a little deeper? Watch this to find out more about the differences between quantitative and qualitative research.

Closed-ended questions

These questions provide respondents with a set list of answer options to choose from, such as multiple choice, rating scales, or yes/no questions. Closed-ended questions generate quantitative data that’s easy to analyze and compare across different groups. They’re particularly valuable when you need standardized responses that can be quickly processed and turned into charts or statistics. However, they can sometimes miss nuanced opinions that don’t fit neatly into predetermined categories.

Open-ended questions

Open-ended questions allow respondents to answer in their own words without restrictions. These generate qualitative data that provides rich context and unexpected insights you might not have considered. While they take more time to analyze, open-ended responses often reveal the “why” behind your quantitative findings and can uncover new opportunities or issues you hadn’t anticipated. The key is finding the right balance between structured and open questions in your survey design.

Ordinal data

This type of data has a clear order or ranking, like satisfaction scales (very dissatisfied to very satisfied) or frequency measures (never, rarely, sometimes, often, always). While you can see which responses rank higher or lower, the gaps between each level aren’t necessarily equal. For instance, the difference between “satisfied” and “very satisfied” might not be the same as between “neutral” and “satisfied.”

Nominal data

Nominal data consists of categories without any inherent order, such as gender, location, or product preferences. You can count how many people fall into each category and calculate percentages, but you can’t rank these categories as better or worse than others. This data is crucial for segmentation analysis and understanding the composition of your audience.

Key considerations before you start analyzing your survey results

Before you start rumaging around in your unstructured data, it’s it helps do run through these pre-checks to make sure you’re drawing meaningful conclusions.

“Did I get enough responses to my survey?”

More responses generally mean more reliable results, but it’s not that simple. Consider:

• Who didn’t respond? Non-response bias can skew your results if certain groups are less likely to participate.
• What’s my response rate? Always calculate and report this (responses divided by invitations sent). Even a seemingly low response rate (5-10%) can produce reliable estimates if your sample size is large enough.
• Do I have enough respondents per subgroup? If you plan to analyze differences between segments (like age groups or customer types), each segment needs sufficient representation.

“How confident should I be in these results?”

Before you proclaim that “62% of customers prefer our new design,” understand the statistical reliability of that number:

• What’s my confidence interval? This tells you the range within which the true value for the entire population likely falls. For example, with a 95% confidence level and a ±3% margin of error, that 62% might actually be between 59% and 65%.
• What’s my margin of error? This indicates how much your results might vary if you surveyed the entire population. The smaller your sample size, the larger your margin of error.

Always communicate these limitations when sharing your findings – it’s not just good practice, it’s ethical reporting.

“Are my responses representative of my target population?”

Your survey might have lots of responses, but if they’re all from one demographic when your customer base is diverse, you’ve got a problem:

• Compare respondent demographics to your overall population. If 70% of your customers are millennials but only 30% of survey respondents are, your results will likely be skewed.
• Consider weighting your data if certain groups are underrepresented. This adjusts the influence of each response to better reflect your actual population.

Follow-up questions to dig deeper

“Are these differences actually meaningful?”

Not every difference you spot in your data matters:

• Statistical significance tells you whether differences are likely real or just random noise. But remember: with large sample sizes, even tiny, practically meaningless differences can be statistically significant.
• Effect size measures how substantial a difference actually is. A statistically significant finding with a small effect size might not be worth acting on.
• Practical significance asks: “Is this difference big enough to care about?” A 2% improvement might be statistically significant but not worth a major investment to achieve.

“What’s the story behind the numbers?”

Quantitative data can tell you what’s happening, but qualitative responses often reveal why:

Look for patterns in the comments that might explain trends you see in your numerical data.

Don’t skip open-ended responses just because they take longer to analyze. They often contain your most valuable insights.

Steps to analyze your survey data

Now let’s get into the specific steps to take when you start your survey analysis.

1. Look at the results of your survey as a whole

Before you analyze your survey responses, familiarize yourself with all the overall survey data, lay out your expectations and learn what is all in there, before getting too specific.

Look at the results and see what stands out to you, at first glance. What were you expecting to see or most curious about? It’s okay to have assumptions: simply make them clear to yourself before the survey is launched, and then see if they are debunked or confirmed. 

You can also compare the results to similar surveys or studies to see if they’re in line with those findings. 

Once you’re familiar with all that data, it’s time to zoom in on what results are most telling. The next few tips will help you find the key insights in your survey data.

We found out interesting extra details, like board game enthusiasts are much more likely to back something on Kickstarter and buy from certain small independent stores.

Becky McKinlay, Head of Marketing at Big Potato Games

It’s all in the details of your data. Find out how Big Potato Games won big with their survey analysis

2. Dig into the segments and demographics

What if you don’t look at the survey as a whole, but filter survey responses based on specific segments and demographic factors? 

With Attest’s crosstabs and bespoke segments, for example, you can easily find interesting relationships between your most important groups and variables. You compare multiple sets of data within one chart to see if there are connections.

Play around with your survey data and see how specific it can get. For instance, women as a whole could be happy with your product, but when you zoom in on the younger generations, they might be driving down the average. That could be something to further focus on.

3. Compare responses to different questions to find deviations

It’s important to check for deviations before drawing conclusions, and possibly removing responses of people who don’t appear consistent in their answers. 

For instance, someone might score you highly on product quality, but further down the survey they give a different opinion, in an open-ended question. When comparing data, try to identify patterns – and don’t just focus on the most positive answer for you.

4. Find connections between specific data points with layered data

There are various ways data can be connected, and understanding these types of connections will help you in your survey data analysis.

For instance, causation and correlation are two different ways data points can be connected, and they might change your views on the strategy that’s needed. It could also be the case that there is a confounding variable at play. 

Here’s what that all means, if it’s been a while since you’ve opened a math textbook:

• Causation: when the value of one variable increases or decreases as a result of other variables changing, it is said there is causation
• Correlation: when one variable increases as the other increases the correlation is positive; when one decreases as the other increases it is negative.
• Confounding factor: A confounding variable is a third variable which influences both the independent and dependent variables.

5. Compare new data with you other data and insights

If you have any past data available, use it!

See how some things have changed and try to find explanations for them. Has customer satisfaction decreased drastically, but are you busier than ever? These could be related: for instance, you’re selling more, resulting in understaffing and longer waiting times. 

Comparing your new raw data to past industry insights can also help you gather fresh ideas for the future. Take Bloom & Wild, who uncovered that red roses for Valentine’s are a thing of the past:

We found that 79% of people would prefer to receive a thoughtful gift rather than something traditional, like red roses. 58% of people thought red roses were a cliché.

And they actually came bottom as the least favorite gift that people had received for Valentine’s Day. So, that gave us confidence that we had correctly sensed growing reluctance towards those sort of Valentine’s Day clichés.

Charlotte Langley, Brand & Communications Director at Bloom & Wild

Read more about how Bloom & Wild followed up their hunch with research, and saw big results.

6. Be critical, always

Data analysis requires you to be skeptical. Be aware of how ‘true’ the data really is.

It can help to look into whether you have statistically significant research insights. A statistical significance test compares two groups and tells you whether a particular insight comparison is a result of chance or whether there’s more of a causal link. 

We’ve built a feature in the Attest platform that tells you when you have insights that are statistically significant.

How to present your survey results

Visualizing your findings effectively

Choose the right chart type for your data story. Bar charts work well for comparing categories, line graphs show trends over time, and pie charts can illustrate proportions (though use them sparingly). Always include clear titles, labels, and legends. Consider using color strategically to highlight key findings, but ensure your visualizations remain accessible to colorblind viewers.

Tell a story with your data

Don’t just report numbers – connect them to business implications. Start with your most important finding, provide context about why it matters, and explain what it means for your organization. Use a logical flow that guides readers from discovery to actionable insights. Include relevant quotes from open-ended responses to bring the data to life and make it more relatable.

Summarize implications and next steps

Always conclude your analysis with clear recommendations. What should your team do differently based on these findings? Which insights require immediate action versus longer-term strategic planning? Prioritize your recommendations and include specific, measurable next steps that stakeholders can act upon. This transforms your analysis from interesting information into a roadmap for improvement.

Common mistakes in survey data analysis & how to avoid them

Survey analysis can go wrong in predictable and sometimes surprising ways. Here are the most common pitfalls and how to steer clear of them:

Misinterpreting small sample sizes

A small sample might give you directional insights, but don’t treat findings from 20 respondents as gospel truth for your entire customer base. Always report your sample size alongside your findings and acknowledge when your data is exploratory rather than conclusive. If you need to make decisions based on limited data, frame them as tests rather than permanent changes.

Overgeneralizing findings

Just because 80% of your survey respondents prefer feature A doesn’t mean 80% of all your customers do – especially if your survey only reached certain demographics or customer segments. Always consider who didn’t respond and whether your sample truly represents your broader audience. Be specific about the limitations of your findings when presenting results.

Ignoring qualitative data

Numbers tell you what’s happening, but comments tell you why. Skipping over open-ended responses because they’re harder to quantify is like reading a book’s statistics without understanding the plot. Those text responses often contain your most valuable insights and can explain puzzling trends in your quantitative data.

Over-relying on external benchmarks without context

Industry averages can provide useful context, but your business isn’t average. A Net Promoter Score that’s below industry standard might still represent improvement for your company, or it might reflect different customer expectations in your specific market. Use benchmarks as reference points, not absolute measures of success or failure.

Focusing only on headline numbers

Yes, 73% might have selected that feature, but did you notice that only 12% ranked it as their top priority? Context matters. Always look beyond the most prominent statistics to understand the full story your data is telling.

Ignoring segments and outliers

If you just look at averages, you’ll make an average marketing strategy that doesn’t activate your most valuable customers. Those averages look neat on paper, but they often mask crucial differences between customer segments. Your power users might have completely different needs than your casual browsers.

Confusing correlation with causation

Just because two metrics move together doesn’t mean one causes the other. That apparent relationship between customer age and product satisfaction might be explained by a third factor you haven’t considered. Always ask: “What else could explain this pattern?”

Approaching data with pre-existing biases

When you’re convinced your new marketing campaign is brilliant, you’ll naturally gravitate toward the survey data that confirms your belief. Beware of cherry-picking! Start your analysis by actively looking for data that challenges your assumptions.

The gist of our advice: don’t get carried away instantly. Use the filters and tools at your disposal. That way, you can look at results from different angles, which will help you turn your survey data from dry facts into a genuine competitive advantage.

How to improve survey data quality

The quality of your analysis is only as good as the data you’re analyzing. Here are practical strategies to ensure your survey data is as reliable and valuable as possible:

Survey design improvements

1. Keep it concise: Respondent fatigue leads to rushed, low-quality answers. Aim for surveys that take less than 10 minutes to complete.
2. Use simple, clear language: Avoid jargon, double-barreled questions, and complex phrasing that could confuse respondents.
3. Implement logic and skip patterns: Show respondents only the questions relevant to them based on their previous answers to improve completion rates and data quality.
4. Include attention checks: Incorporate questions that verify respondents are paying attention, such as “Select ‘somewhat disagree’ for this question.”
5. Balance your scale options: Provide an equal number of positive and negative options in rating scales to avoid bias.

During data collection

6. Pilot test your survey: Run your survey with a small test group first to identify and fix any issues before full launch.
7. Monitor responses in real-time: Check early responses for potential issues with question interpretation or technical problems.
8. Set quotas for representative sampling: Ensure your sample matches your target demographic profile, implementing quotas if necessary.
9. Offer appropriate incentives: Provide reasonable compensation that motivates thoughtful participation without attracting respondents who are only interested in rewards.

Data cleaning techniques

10. Remove speeders: Flag and review responses from participants who completed the survey significantly faster than the average time.
11. Address straight-lining: Identify and consider removing respondents who select the same answer option for all questions in a matrix.
12. Check for logical inconsistencies: Look for contradictory answers across related questions that indicate the respondent wasn’t answering truthfully.
13. Analyze open-text responses for quality: Flag responses containing gibberish, irrelevant content, or copied text.

Top tools for survey data analysis

Attest

Attest combines survey creation, data collection, and analysis in one intuitive platform. With built-in statistical significance testing, advanced filtering capabilities, and automated insights, Attest helps you move from raw responses to actionable insights quickly.

The platform’s demographic targeting ensures you reach the right audience, while features like crosstabs and segment analysis make it easy to uncover meaningful patterns in your data. Plus, you get expert research support throughout the process.

Other popular analysis tools

Excel and Google Sheets offer basic analysis capabilities and are familiar to most users, making them good starting points for simple surveys. SPSS provides advanced statistical analysis features for complex research projects, while Tableau excels at creating sophisticated data visualizations. R and Python offer the most flexibility for custom analysis but require programming knowledge.

How to choose the best tool for you

Consider your technical expertise, budget, and analysis needs. If you need an all-in-one solution with research support, platforms like Attest are ideal. For basic analysis of small datasets, spreadsheet tools may suffice. Complex statistical analysis might require specialized software like SPSS, while advanced visualization needs might call for tools like Tableau. The key is matching the tool’s capabilities to your specific requirements and skill level.

FAQs about survey data analysis