You could be asking all the right questions, but if you don’t know how to listen to the answers, there’s no point.
One of the most important steps of running a survey? Analyzing the results. Interpreting what consumers are trying to tell you is what will inform your marketing strategies, messaging and new products or services, possibly for years to come—so it’s crucial that you get it right.
Often, analyzing data from your surveys is an underestimated skill. Some survey tools churn out jazzy graphs on flashy dashboards, but without the right filters and knowledge, and some healthy critical thinking, you could be looking at a different (read: incorrect) version of the truth.
To find accurate and actionable data, you need to look deeper, find connections and eliminate assumptions and deviations.
In this article, we’ll help you master the art of analyzing survey results. Well-informed business decisions start here!
Here are our top tips:
Look at the results of your survey as a whole
Take a look at the demographics of those who responded
Compare responses to different questions to find deviations
Find connections between specific data points with layered data
Compare new data with past data
Be critical, always
Why conduct an in-depth survey analysis?
When conducting a survey to gather insights, you’re putting a lot of trust in your customers or consumers in general. Sure, you’ll have to make sure you’re asking the right questions, but ultimately it’s them who will give the answers and therefore shape your strategy for marketing, product, sales etc.
Surveys are used for a wide range of purposes. You could be developing a new product line, and turn to your customers for input on what features they value. Or you’re trying to expand into a new market and want to know about people’s shopping habits.
Crystal clear questions and a logical survey structure will help you avoid gathering wrong information, or getting confused, vague answers from respondents. But on top of that, you need to learn how to accurately interpret the data you receive.
Getting it wrong could have disastrous effects on your business. You could launch the wrong product, publish an offensive ad or target completely the wrong potential customers, all because you were trying to read between the lines, unsuccessfully.
Take extra caution when you start to analyze survey data. Statistical analysis isn’t a guessing game.
Let’s look at how you can confidently draw conclusions when analyzing survey data.
Types of survey data
Let’s start by categorizing the types of survey responses and data you’ll be looking at. Knowing what you’re dealing with will help you connect the dots, uncover patterns and extract actionable insights.
Questions that cover demographic aspects help you learn more about who is answering your survey, and how there are differences between certain demographic groups. Certain education levels, ages, or even locations could have significantly different answers than the average, which is definitely something you should know.
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 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 below to find out more about the differences between quantitative and qualitative research.
How to analyze survey results
Time to learn how to analyze your survey results after you’ve sent out that beautiful survey and the data starts rolling in. If survey analysis can leave you feeling like you’re in The Matrix, read on…
What should you consider? What’s ‘statistical significance’ and ‘cross tabulation’ all about? How should you think about different types of data collected from different types of surveys?
All fantastic questions, which we hope you’ll have good answers to by the end of this guide.
Before you get started…
Ask the right survey questions, find the right survey respondents, and choose the right tool.
We can’t ignore the importance of sending your survey the right way to the right people, because even the best analytical thinker can’t get any relevant customer insights if the research was sent to the wrong people and written in the wrong way.
Take the time to craft a survey that really gets to the heart of the research you’re running. While you’ll of course want your research process to be as efficient as possible, it really helps to get your colleagues’ views on your research questions. Don’t be afraid to tweak them to make sure your survey analysis will uncover the most valuable insights.
Pro tip: This ^ is what Attest’s Customer Research Team does day-in-day-out. They’re here to help brands create consumer and customer surveys that get to the heart of your research requirements.
For research findings you can trust, it’s also important to think about your sample size: how many respondents you’re sending your research to, and making sure they are representative of your target group. Make sure your sample size is big enough—which is easy to do with Attest, thanks to our pool of 125 million high-quality respondents across 58 countries.
What sample size do you need?
Working out what sample size you need for your research? We’ve built a sample size calculator so you can get your ideal number of survey respondents.
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 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
What if you don’t look at the survey as a whole, but filter survey responses based on specific demographic factors, or other variables?
With cross tabulation, you can find interesting relationships between variables. You compare two 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
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.
Top mistakes to avoid
It’s crucial you proceed with caution, so we’ll also lay out some pitfalls that a lot of people trip over in survey analysis.
Comparing apples with oranges: when you do start comparing data, make sure both variables are relevant to each other. For instance, when looking at past data, make sure you match the month, to correct for seasonal influences.
Relying too much on ‘averages’: if you just look at averages, you’ll make an average marketing strategy that doesn’t activate your customers. Look at variables where you can really make progress.
Not following up: running a survey just once is a common mistake. Follow up a few months to a year later to see if the decisions you’ve made have impacted the results.
Not being specific about percentages: it’s easy to just quote a percentage, or even a change in percentage, but without any context, you could easily misinterpret it. Make sure to explain what lies underneath.
Draw meaningful conclusions from your survey data
When you want to start drawing conclusions, there are two important things to do: categorizing results, and visualizing them.
Categorizing will help you make clear distinctions between what is related and relevant, and what isn’t.
Visualization will help you better understand your survey data, but it also helps you communicate your survey data analysis in an effective way to stakeholders, marketing managers and anyone else who needs access to the insights.
When drawing up your marketing, product or sales strategy, these categories and visuals will help your strategic thinkers to test whether their ideas are in line with what the survey data is telling them.
Basically, for every decision they make, they have something to go back to and ask themselves: does this make sense based on the survey data analysis?
Presenting survey data and results
Time to organize your data and get it ready for your presentation. Here’s how you do that, in a way that will help everyone make the most out of your survey data analysis, without having to comb through a pile of data.
Choose which data to share (and which to keep to yourself!)
Make sure your survey report is complete, but also concise: leave out any data that didn’t turn out to be relevant. It’s important to keep it as a backup so people can refer back to it in case of confusion, but try to avoid stating the obvious.
Choose the right graph format
Bar, line or pie? If you want to help your colleagues interpret the results accurately, make sure the right format is picked. Here’s a little cheat sheet:
Bar graphs: works for quantitative data, but can also show qualitative research data, for instance by counting sentiments.
Line graphs: how quantified data evolves over time by tracking the ups and downs of the data.
Pie charts: show the breakup of all the data into categories.
Venn diagrams: overlapping circles show the logical relationships between two or more sets of items.
Creating an internal report
Simply sending out a spreadsheet with all the survey data won’t be the most time efficient way to deal with the data.
Take your time to craft a report that tells a story, to help people who need to make decisions based on the data understand the context of the survey.
Think about it this way: if someone gets hired, but wasn’t around when the survey was conducted, you’ll still want them to understand how the decisions were ultimately made. So, invest in creating a sleek report that covers the whole survey journey and most importantly: its results.
Make data more readable using infographics
Reading numbers can be exhausting and confusing. People have a short attention span as it is, and many people are better visual thinkers.
So if you want to make your report even better, shape your data into infographics that are easy to share and communicate.
This will help your creative colleagues’ brains and the results can then also be shared throughout the company, in a way which is engaging and understandable.
Turning data into stories is certainly possible. For instance, you can take specific target demographics and make them more real by creating a detailed persona with a name and face to it.
Let them do the talking: write the survey responses out as if it were an interview, to help everyone better understand what is going through the customer’s mind.
Create a marketing strategy driven by data analysis
Knowing how to interpret and present survey results enables you to create a marketing strategy that is driven by accurate data directly from consumers. It doesn’t get any better than that.
If you need a tool that helps you create and analyze surveys in a powerful way, it’s about time you meet Attest.
When data starts coming in, you have everything you need in your dashboard to filter and analyze it.
And you don’t have to do it alone. If you want to make sure that you analyze survey data exactly right, find out how we can help you.
Consumer insights with expert support
Intuitive, easy-to-use tech combined with human research expertise at every step—that’s what you get with Attest. Start gathering quality insights today!
Most survey tools come with reporting features and a dashboard that presents all the data, but it’s you who has to play with filters to find significant connections in the survey results. You can then create graphs that help you identify trends and track data.
How can I analyze survey results?
It all starts before creating a survey: what is it you want to measure? Set a goal for your survey and build it based on that. You can analyze your survey results easily in your dashboard, playing around with filters to find connections.
What is the best way to analyze my survey results?
With a lot of critical thinking, being wary of assumptions and keeping statistical significance in mind. For accurate survey data analysis, make sure you remove any data that’s wrong and incomplete before you start drawing conclusions. Plus, if possible, test the accuracy of the data with past or other relevant survey responses.
What are some important survey analysis best practices?
It all starts with formulating clear and concise research questions, and going from there. Select the right respondents and a tool that helps you analyze the results quickly and accurately.
How do I analyze open-ended responses?
Mixing and matching qualitative feedback with demographic data and numbers is tricky. Make sure you can lead open-ended responses back to specific groups of people and see how their answers match to other questions.
Customer Research Manager
Nikos joined Attest in 2019, with a strong background in psychology and market research. As part of Customer Research Team, Nikos focuses on helping brands uncover insights to achieve their objectives and open new opportunities for growth.