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Survey analysis: tools and best practices for analysing your data

11 min read
Data can do beautiful things, but turning your survey results into clear, compelling analysis isn’t always a straightforward task. We’ve collected our tips for survey analysis along with a beginner’s guide to survey data and analysis tools.

What is survey data analysis?

Survey data analysis is the process of turning the raw material of your survey data into insights and answers you can use to improve things for your business. It’s an essential part of doing survey-based research. There are a huge number of data analysis methods available, from simple cross-tabulation, where data is arranged into rows and columns that make it easier to understand, to statistical methods for survey data analysis which tell you things you could never work out on your own.

Types of survey data

Different kinds of survey questions yield data in different forms. Here’s a quick guide to a few of them.

Categorical (nominal) data

This kind of data exists in categories which have no hierarchical relationship to each other. No item is more or less, better or worse, than the others. Examples would be primary colours (red v. blue), genders (male v female) or brand names (Chrysler v Mitsubishi).

Multiple choice questions often produce this kind of data (though not always).

Ordinal data

Unlike categorical data, ordinal data has an intrinsic rank that relates to quantity or quality, such as degrees or preference, or how strongly someone agrees or disagrees with a statement.

Likert scales and ranking scales often serve up this kind of data.

Scalar data

Like ordinal data, scalar data deals with quantity and quality on a relative basis, with some items ranking above others. What makes it different is that it uses an established scale, such as age (expressed as a number), test scores (out of 100), or time (in days, hours, minutes etc.)

You might get this kind of data from a drop-down or sliding scale question format, among others.

Natural language data

Answers written in someone’s own words are also a form of survey data. This type of response is usually given in open field (text box) question formats.

The type of data you receive affects the kind of survey results analysis you’ll be doing, so it’s very important to consider carefully when you’re writing your survey questions and designing survey flows.

Benchmarking your survey data

One of the most powerful aspects of survey data analysis is that you can use it not just to uncover insights from your results, but to build those insights over time.

Using consistent types of data and methods of analysis means you can use your initial results as a benchmark for future research. Maintaining your question and data types and your data analysis methods means you achieve a like-for-like measurement of results over time. And if you collect data consistently enough to see patterns and processes emerging, you can use these to make predictions about future events and outcomes.

Another benefit of data analysis over time is that you can compare your results with other people’s, provided you are using the same measurements and metrics. A classic example is NPS (Net Promoter Score), which has become a standard measurement of customer experience that companies typically track over time.

Presenting your data

Most data isn’t very friendly to the human eye or brain in its raw form. Survey data analysis helps you to turn your data into something that’s accessible, intuitive, and even interesting to a wide range of people.

You can present data in a visual form, such as a chart or graph, or express discoveries in plain language, for example in phrases like “customers in the UK consistently preferred potato crisps to tortilla chips”

Another approach is to express data using the power of storytelling, using a beginning-middle-end or situation-crisis-resolution structure to talk about how trends have emerged or challenges have been overcome.

As well as presenting your data, always be sure to share the insights it has led to. Insights come when you apply knowledge and ideas to the data in the survey, which means they’re often more striking and easier to grasp than the data by itself. Insights may take the form of a recommended action, or examining how two different data points are connected.

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Tools for survey analysis

If you’re planning to run an ongoing data insights program (and we recommend that you do), it’s important to have tools on hand that make it easy and efficient to perform your research and extract valuable insights from the results. It’s even better if those tools help you to share your findings with the right people, at the right time, in a format that works for them. Here are a few attributes to look for in a survey analysis tool.

  • Works on any platform
    Don’t restrict your team to a single location where software is located on a few terminals. Instead, choose a cloud-based platform that’s optimised for mobile, desktop, tablet and more.
  • Integrates with your existing setup
    Stand-alone analysis tools create additional work you shouldn’t have to do. Why export, convert, paste and print out when you could use a software tool that plugs straight into your existing systems via API?
  • Easy to use (for non-experts)
    Look for software that demands minimal training or expertise, and you’ll save time and effort while maximising the number of people who can pitch in on your experience management program. User-friendly drag-and-drop interfaces, straightforward menus, and automated data analysis are all worth looking out for.
  • Incorporates statistical analysis
    Choose a system that gives you the tools to not just process and present your data, but refine using statistical tools that generate deep insights and future predictions with just a few clicks.
  • Comes with first-class support
    The best survey data tool is one that scales with you and adapts to your goals and growth. A large part of that is having an expert team on call to answer questions, propose bespoke solutions, and help you get the most out of the service you’ve paid for.

Survey analysis tips

We’ve run more than a few survey research programs in our time, and we have some tips to share that you may not find in the average survey data analysis guide. Here are some innovative ways to help make sure your survey analysis hits the mark, grabs attention, and provokes change.

Write the headlines

The #1 way to make your research hit the mark is to start with the end in mind. Before you even write your survey questions, make sample headlines of what the survey will discover. Sample headlines are the main data takeaways from your research. Some sample headlines might be:

  • The #1 concern that guests have with staying at our hotel is X
  • X% of visitors to our showroom want to be approached by a salesperson within the first 10 minutes
  • Diners are X% more likely to choose our new lunch menu than our old one

You may even want to sketch out mock charts that show how the data will look in your results. If you “write” the results first, those results become a guide to help you design questions that ensure you get the data you want.

Gut Data Gut

We live in a data-driven society. Marketing is a data-driven business function. But don’t be afraid to overlap qualitative research findings onto your quantitative data. Don’t be hesitant to apply what you know in your gut with what you know from the data.

This is called “Gut Data Gut”. Check your gut, check your data, and check your gut. If you have personal experience with the research topic, use it! If you have qualitative research that supports the data, use it!

Your survey is one star in a constellation of information that combines to tell a story. Use every atom of information at your disposal. Just be sure to let your audience know when you are showing them findings from statistically significant research and when it comes from a different source.

Write a mock press release to encourage taking action

One of the biggest challenges of research is acting on it. This is sometimes called the “Knowing / Doing Gap” where an organisation has a difficult time implementing truths they know.

One way you can ignite change with your research is to write a press release dated six months into the future that proudly announces all the changes as a result of your research. Maybe it touts the three new features that were added to your product. Perhaps it introduces your new approach to technical support. Maybe it outlines the improvements to your website.

After six months, gather your team and read the press release together to see how well you executed change based on the research.

Focus your research findings

Everyone consumes information differently. Some people want to fly over your findings at 30,000 feet and others want to slog through the weeds in their wellington boots. You should package your research for these different research consumer types.

Package your survey analysis findings in 5 ways:

  • A 1-page executive summary with key insights
  • A 1-page stat sheet that ticks off the top supporting stats
  • A shareable slide deck with data visuals that can be understood as a stand-alone or by being presented in person
  • Live dashboards with all the survey data that allow team members to filter the data and dig in as deeply as they want on a DIY basis
  • The Mock Press Release (mentioned above)

How to analyse survey data

Reporting on survey analysis results will prove the value of your work. Learn more about statistical analysis types or jump into an analysis type below to see our favourite tools of the trade:

eBook: 5 Practices that Improve the Business Impact of Research