How to analyze survey data

The survey results are in: Customer feedback gives you valuable information about how to improve your business. The next step is to analyze that survey data. You don’t need an advanced math degree to understand the results. Instead, a few simple tools can uncover the most helpful information.

Identify the response rate

It’s important to gather information about how well your customers responded to the survey. How many people had the chance to participate in the survey? Of those people, how many completed the survey?

This percentage of people varies depending on how and when you offered the survey. For example, you can expect an exit survey to have a lower response rate compared to questions designed for a highly engaged visitor.

Also, your response rate might vary based on the types of questions used. Open-ended questions require more effort, which means that you can typically expect higher response rates from closed-ended questions.

Analyze four types of survey questions

Now it’s time to look at the information gathered through the survey questions. Customize this analysis based on the type of question. Most survey questions fit into one of these four categories:

  • Categorical data. When the customer chooses an answer from a list of responses, this is known as categorical data. For example, “What is your favorite product feature?” might have responses such as ease of use, size, and affordability. Categorical data is simple to evaluate because the analysis involves counting and dividing the information to identify the most popular responses.
  • Ordinal data. If the responses fall into a logical order, then you are gathering ordinal data. An example is a survey question like “How often do you visit our restaurant?” with potential answers including rarely, once a year, once a quarter, once a month, and weekly. This information will help you see how often your customer is interacting with your product or service.
  • Ratio data. Any question that asks for precise information falls in the category of ratio data. For example, you might ask about the customer’s budget, with an open-ended input field. Ratio data can be helpful in looking at measures of variance or calculating averages.
  • Interval data. Using interval questions can be valuable in segmenting your customers so that each group gets relevant questions. For example, you might ask a question about their preferred budget, with potential answers listing predetermined dollar amounts: <$20, $21 – $100, $101 – $200, and $201+. When the intervals are sized equally, you can calculate data averages to summarize the information.

It’s usually best to look at the numbers before you review long-form answers. Crunching the numbers first means that you have a foundation to use when reading through the open-ended responses.

Cross-tabulation comparisons

If you want to gain the most insight from these survey results, include cross-tabulation in your analysis. This process gives the data context so you can see the factors that might affect specific outcomes. Additionally, it can be helpful to distinguish how different groups of people respond.

With cross-tabulation, you can see differing satisfaction levels between age groups or other categories. For example, if you determine that 56 percent of the participants were highly satisfied with the product, and 44 percent were dissatisfied, then consider mapping another variable to identify factors influencing their experience. You might find that the satisfaction levels change with age, budget, or any other category relevant to your target demographic.

Tools for survey analysis

Depending on the number of participants and the frequency of survey responses, it can be helpful to use a survey tool to analyze the responses. Manual analysis is feasible if you have fewer than 100 responses. But it can turn into a time-intensive project when you need to evaluate hundreds or thousands of survey responses.

When you consider the time requirements for manual analysis, it’s important to note that this method could actually cost more money overall. Plus, manual methods increase the likelihood of inaccurate analysis. Excel is sometimes used as a DIY analysis tool, but this only works if you know how to use the program’s more technical features.If you’re wondering how to analyze survey data effectively, you need to start at the beginning: How are you gathering the information? Efficient survey analysis tools include software programs with built-in features for consolidating data and comparing results. The JotForm online survey maker provides a simple solution to share an online survey and collect responses instantly.

This article is originally published on Jun 19, 2020, and updated on Jun 23, 2020
Data collection analyst. Seeing life in 1's and 0's. Can't resist to a good cup of coffee.

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