How to analyze survey data to maximize company growth

Did you know that a study from McKinsey & Company discovered that organizations trained in data analysis had a 23 times higher likelihood of acquiring new clients? Survey data, whether it be employee or customer information, is essential to any business’s growth. Unfortunately, analyzing large sets of data can often be overwhelming, especially when it covers a wide range of topics. If the knowledge is too difficult to understand, what good is it? Regardless of experience level, everyone experiences this.


Large amounts of data don’t always translate to valuable insights. The most important thing is not how much information there is, but rather how that information is analyzed.

This article will provide you with an overview of survey data analysis and show how you can translate numbers into real-world results.

Types of survey data

To start, let’s first differentiate the different types of data. This is important for understanding the use cases of each type of data set and which ones you can use to meet your specific needs.

Quantitative and Qualitative Data

Quantitative data are numbers or ratings on a scale. These are tangible and concrete data sets that make it possible to create specific observations that can be statistically justified. ⁤⁤Professionals often utilize quantitative data fields such as yes/no questions, ratings on a scale of 1 to 5, and multiple-choice questions.

rating scale questions

Qualitative data, on the other hand, is more unique and descriptive. These results, in contrast to quantitative data, are not measurable in numerical terms. The most common way to gain qualitative data is through open-ended responses that allow the respondent to create their own answers.

Typically done through text fields, open-ended questions allow them to express their opinions without any limitations. A common open-ended question could be: “Is there anything else we could do to improve your experience?”

Finding themes or patterns in responses is crucial to finding areas of improvement and opportunities to grow your business.

Collect qualitative survey data using open-ended questions

Ordinal scales

Ordinal data provides categories in a logical order to determine how the respondent feels about a subject matter. Likert scale questions (e.g., strongly agree, agree, neutral, disagree, strongly disagree) are the most popular form of ordinal questions used to measure attitudes.

Ordinal Data

Nominal Data

Nominal data is gathered from questions that do not have a rating or order. An example could be: “What is your current occupation field”. They can be utilized to obtain certain insights when responses aren’t “better” or “higher” than others, nominal data questions are flexible and can be used in a large variety of surveys.

Nominal Data

Ratio Data

Ratio data is measured using numeric scales with a zero value, such as 0–10. These kinds of questions aid in categorizing respondents to identify patterns in data. Age, income, typical spending, etc. are a few examples of subjects.

Ratio survey data

Steps to analyze survey data

Gathering and analyzing survey data can often take more time than people initially believe.  It is a multi-step process that involves planning and proper execution to ensure data is reliable and related to your objectives. Let’s walk through the key steps to gathering survey data:

Step 1: Creating your survey

When creating a survey, you must first identify what want to achieve with the survey. Determining who your survey will target and what type of information you want to learn from that group will dictate the direction of your survey.  

Using the different types of questions discussed earlier, decide which type of questions you will ask to gather your data (e.g. open-ended questions like “What can management do to improve your experience?”).

Ensuring questions are in a logical order and relevant to your main objectives will improve the quality of responses. You may also pilot-test your survey on a small group first to identify issues with question wording or survey structure.

Step 2: Collect and clean data

Once you’ve finished creating your survey, choose a method to distribute it. Possible distribution channels include sending emails, phone calls, conducting in-person interviews, or posting the survey online.

Before your analysis, always make sure your data is cleaned. Data cleaning includes checking for incomplete responses, outliers, and inconsistencies. Doing this ensures that the information you analyze is reliable and useful for your objective.

You may also perform a preliminary analysis to go over basic trends and see broad patterns in the data.

Step 3: Interpret and analyze the data

Now that all the planning is done and the data is organized, you can begin making decisions based on the data!

  • Cross-tabulation is a great way to create insights from your data. This method is used to analyze the relationship between two or more variables (i.e. how employee compensation correlates to employee satisfaction).
  • This method of identifying data encourages identifying the root cause of issues and taking action to solve them. You may also sort respondents by demographics such as age and income to find trends among different targets.
  • Predictive analysis is another thing you can do with your survey data. How customers or employees feel about your company and whether they have positive associations with your brand is key to predicting growth within a company. A popular predictor is the NPS survey which managers use its score to assess whether their product will grow through word of mouth.
  • Comparing your findings with past data is a vital aspect of data analysis. Both internal (company past) and external (industry past) comparisons can be used to gain insights into trends and patterns in changes over time. Identifying your KPIs (Key Performance Indicators) will help you narrow down your data and find out what is truly relevant to your goals.

Step 4: Presenting your data

Turning your data into visuals is a key step in improving audience understanding and highlighting key findings from your information. Luckily, Formaloo makes it easy to turn your survey data into your choice of visuals. Whether it’s pie charts, bar graphs, or Kanban boards, Formaloo helps you to create powerful surveys and gather meaningful data!

Formaloo charts and tables

Boost your business growth with powerful survey analysis

By following these steps and leveraging a survey platform like Formaloo, you can transform raw survey data into actionable insights that propel your company’s growth. Formaloo’s powerful real-time reports empower you to uncover hidden patterns within your data, allowing you to tailor strategies to specific customer segments.

Remember, customer feedback is a goldmine of information waiting to be unearthed. With the right analysis approach and the power of Formaloo, you can unlock its potential and achieve sustainable growth.

Sign up for free and follow us on our YouTube channel for more useful tutorials.

Get productivity tips delivered straight to your inbox
Thank you! Your submission has been received!
Oops! Something went wrong while submitting the form.

Get started for free

Formaloo is free to use for teams of any size. We also offer paid plans with additional features and support.

How to analyze survey data to maximize company growth