With businesses increasingly relying on data-driven customer insights to support and inform decision making, the ability to understand how best to collect, analyze, and extract insights from survey data is a workplace skill more valued than ever before.  

In this guide, we’ll be walking you through everything you need to know about how to analyze survey data, from selecting the right survey method through to data analysis techniques and much more.

Here’s what we’ll be covering: 

• Choosing the right survey method 
• Data cleaning
• Data analysis techniques
• Visualizing your data
• Drawing insights and making informed decisions 

Ready to learn more about how to analyze data from a survey? Then let’s get started. 

Choosing the right survey method

Choosing the right survey method is crucial to gaining the trust of your participants and gathering the data you need. However, with numerous different types of surveys out there, knowing the right one to choose requires some careful consideration. As you’ll see, each survey method has its own advantages and disadvantages depending on:

• The question you are trying to answer / your objective for running the survey 

• The goals of the business
• The kind of data you want to collect
• Your target audience
• Your research expertise
• Your budget
• Your timeline
• Your technological capabilities
• Your human resources 

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Let’s take a look at some of the most common survey methods available and the advantages of each. 

Online surveys 

Most of us have filled out an online survey at some time or another. This survey format is typically a questionnaire that individuals complete in their internet browser or via an app. The structure of an online survey is like that of a traditional form and includes multiple-choice, closed, and open questions. Due to the varied nature of the questions, an online survey is able to collect both quantitative and qualitative data. 

Pros of online surveys: 

• Simple to put together
• Can be created at speed
• Support a wide range of question formats
• Responses can be collected instantaneously 
• Can have a wide reach
• Easily accessible 
• Data can be automatically extracted and analyzed

Paper surveys 

Paper surveys are questionnaires printed on paper that are filled in by recipients by hand. They might be distributed individually, left in a visible collection spot such as at a doctor’s reception, or sent by post to their intended audience. Similarly to an online survey, paper surveys can be made up of multiple-choice, closed, and open questions for the collection of both quantitative and qualitative data. 

Pros of paper surveys: 

• Can reach audiences which don’t have internet access
• Can reach audiences which are uncomfortable using the internet
• Can be easier to read for visually-impaired people (due to larger print) 

Telephone surveys

A telephone survey consists of an interviewer asking the survey respondent a series of questions over the phone with responses either being recorded or typed into a computer system by the interviewer. 

Pros of telephones surveys: 

• Easy to add a personal touch to the process 
• There are opportunities for follow-up questions
• Can reach large numbers of people (due to prevalence of mobile phones) 

In-person surveys 

An in-person survey provides a more personal approach to surveys, with interviewers asking survey respondents questions in a one-to-one setting and recording their answers as they go. 

Pros of in-person surveys: 

• Responses can be explored and clarified with follow-up questions 
• Offers respondents a more personal touch 
• Makes the survey more accessible to those with low literacy skills or visual impairment 
• Are an opportunity to gather extensive insights that inform qualitative data 

Data cleaning

Once you’ve chosen a survey type that suits your organization’s needs, conducted your survey, and received the desired number of responses, you’ll need to check that the data you’ve gathered is both accurate and error free. To do this, you’ll need to perform data cleaning. Also referred to as data cleansing, data cleaning is the process of identifying and correcting any false, duplicate, incomplete, corrupt, or inaccurate records from your database. 

Here’s a quick runthrough of how you can clean your data to ensure that the conclusions you draw from the final analysis are based on accurate, reliable, and relevant datasets. 

1. Remove duplicate or irrelevant datasets and observations 
2. Ensure consistent labeling throughout 
3. Determine the validity of outliers and remove any which are not valid
4. Find a solution for missing data (ie, input missing values based on other observations)
5. Perform a quality assurance (QA) check 

3. How do you analyze survey data? Data analysis techniques

With your data cleaned, you’re ready to start analyzing survey data. There are a few different data analysis techniques that you can use to do this, including descriptive statistics, correlation analysis, factor analysis, and regression analysis. Let’s dive into what we mean by each of these terms so you can learn more about the very best ways to analyze survey data.

Descriptive statistics 

A descriptive statistic is a commonly-used technique to analyze survey data. It is a summary statistic that describes a dataset or the key features of it using numbers. Descriptive statistics (plural) is the term used to describe the process of analyzing those statistics. This type of analysis helps to describe, demonstrate, or summarize different data points in a way that helps reveal patterns in the data.

Benefits: It can help to summarize and visualize your data.

Correlation analysis 

Correlation analysis is another popular method used by data analysts that looks at potential relationships between two variables or datasets. Once a relationship is uncovered, the data analysis investigates how strong that relationship may be.  

Benefits: It can help you identify relationships between variables.

Factor analysis 

Factor analysis is a methodology used to analyze survey data which helps analysts reduce a large number of variables into a smaller number of factors. It does this by taking maximum common variance from the variables and placing them into a common score. 

Benefits: It can help you to identify underlying factors that could potentially influence a group’s opinions or preferences. 

Regression analysis 

Regression analysis is a set of analytical techniques which are used to estimate the relationships between dependent and independent variables. This kind of analysis can assess the strength of the relationship between the variables as well help to predict the future relationship between them. 

Benefits: It can help identify the variables that most influence a certain outcome.

4. Visualizing your data

Visualizing your data is an important part of data analysis. Not only does it help you to understand your audience better, it also helps you communicate your findings to those in authority when you want to initiate improvements or changes. These tools can also help you identify patterns and trends that may not at first glance be obvious from a numerical analysis. Here are some of the most common data visualization tools for you to consider: 

Pie charts  

You’re probably familiar with a pie chart: it’s a circular graphic divided into sections that are used to illustrate different percentages or numbers. Pie charts are particularly useful for displaying relative proportions of different classes of data, summarizing large datasets visually, and, for the audience, digesting complex sets of information easily. 

Bar charts 

A bar chart (sometimes called a bar graph) is a chart that displays categorical data using bars with their heights or lengths proportional to the data values that they are visualizing. Bar charts are great for displaying a large data set visually, displaying relative numbers or proportions of various different categories, summarizing data visually, and presenting trends.  

Line graphs 

A line graph (sometimes known as a line chart or a line plot) is when information is displayed by plotting a series of data points and connecting them using a single, straight line. Line graphs are ideal for demonstrating changes over time, comparing data sets, presenting data variables and trends clearly, informing forecasts, and displaying dependent variables against one independent variable. 

5. Drawing insights and making informed decisions

When studying the data you’ve collected, you’ll want to look for patterns and trends which will tell you about your users’ behavior, preferences, and opinions, as well as any significant changes in audience responses that have taken place since the last time you conducted the survey. You’ll also want to keep an eye out for correlations in the collected data which could help inform future situations, and make a note of any anomalies in your data. When these pop up, ask yourself why these might have occurred. 

Once you’ve analyzed your data and identified trends, patterns, and anomalies, you’ll find you have a deeper understanding of your audience and how they feel about your product or service. From here you can use these insights to identify areas for improvement, prioritize decisions, and inform solution generation. They’ll also help to ensure that future marketing campaigns, business strategies, and product development are more aligned with user needs, preferences, and goals. 

Conclusion

We hope you’ve enjoyed our guide on how to analyze survey data and gain a deeper understanding of your audience. If you follow the steps we’ve outlined here covering the different techniques and ways to analyze survey data, you’ll find you’re able to collect meaningful quantitative and qualitative insights to better inform and guide your business strategy, customer relationships, marketing, and product development. Getting buy-in from key stakeholders will also be considerably easier with the support of tangible insights and accurate forecasting extracted from your dataset.

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