Understanding cross-tabulation analysis of survey data

The vast volume of data and the need for fast analysis created several useful analytics methods that help researchers see results quickly. One of them is cross-tabulation analysis.

Cross-tabulation analysis is a highly useful analytical tool mostly applied in the market research industry. Marketers and researchers leverage it to analyze categorical data.

By using cross-tabulation analysis for survey data, you can see the relationship between the answers of several groups of respondents. This can provide valuable insight into your audience's feedback and behavior to help streamline your sales and marketing strategy.

Let's take a closer look at what cross-tabulation analysis is all about and how to use it for leveraging survey data.

Cross-tabulation is a data analysis method that helps compare the results of one or more variables with the result of another. Also known as cross-tab or crosstab, cross-tabulation analysis is one of the most useful approaches to analyzing survey responses or any other type of categorical data.

This method involves creating data tables, known as contingency tables, that represent the results of respondent groups and subgroups. By looking at these tables, you can examine important relationships that may not be obvious when you use other ways to analyze survey responses.

An example of cross-tabulation is a simple survey with two questions:

1. How old are you?

2. What social media platform do you use the most?

If you put the results in a table, they would look like this:

• 12 – 20 — TikTok (60%), Instagram (30%), Facebook (10%)

• 20 – 35 — TikTok (30%), Instagram (40%), Facebook (40%)

• 35 – 55 — TikTok (10%), Instagram (20%), Facebook (70%)

When you analyze 1,000 survey responses, the correlation between age and social media platform use may not be obvious. However, the relationship becomes visibly clearer when you use a cross-tabulation table. You can now use this information to create effective touchpoints with your audience.

Applied to survey answers, cross-tabulation allows marketers to understand prospective data better. This helps them identify market trends, find patterns and discover opportunities for new product developments without spending hours sifting through survey responses.

You may want to consider using cross-tabulation when analyzing categorical data. This is the data that can be divided into mutually exclusive categories. For example, the target audience aged 20–35 and the target audience aged 35–55 are mutually exclusive categories.

Crosstab is also a great way to gain additional information from the survey. For example, when you arrange a net promoter score (NPS) survey, you get limited data. 

Now, add the age, income level, location, or any other demographic data into the mix. You can learn additional information. For example, a younger audience gives you a higher score than older customers. Or perhaps, the local audience isn't as satisfied with your services as your customers overseas are.

Cross-tabulation allows you to identify patterns and trends you may not have seen before. It's a great way to examine categorical variables since you can easily identify relationships between several data sets that may have seemed unrelated initially.

While marketers and researchers benefit from cross-tabulation tremendously, they are hardly the only ones. Other areas you can apply crosstab include:

HR departments

Like evaluating customer and user behavior, you can use crosstab to measure employee satisfaction, identify issues in the workplace, evaluate employee engagement, and much more. This approach to data analysis can help you identify issues in places you haven't looked before.

Customer support

Crosstab can help the customer support team evaluate their work and measure customer satisfaction. Cross-tabulation can help compare customer support quality over a certain period or in relation to different customer groups.

Education

Educational institutions can take advantage of the crosstab method to examine student data. From student satisfaction to scheduling efficiency, cross-tabulation can provide valuable insight into the school's operations and course structure.

Product research

When a company develops a new product, it can use cross-tabulation to evaluate user responses and make changes during the development and implementation stages.

Overall, marketers, researchers, product developers, and HR managers use cross-tabulation analytics to understand their audiences, streamline satisfaction, and gain insight into future operations.

Cross-tabulation can be a highly useful analytics tool with various applications. The main benefits of using this approach include:

Simplicity

The key benefit of using cross-tabulation is the simplicity of the approach. By creating cross-tabulation tables, you can see the relationship between variables at a glance.

When you have only a few categories, visualization in the form of tables can provide immediate insight. This can be highly useful for making quick educated decisions.

If you are comparing multiple variables, the analytics may be more complex. However, for the majority of marketing needs, two variables are sufficient.

Precision

Marketers and researchers usually have to deal with raw data. When you first start handling this data, it can seem confusing and unstructured. It's easy to make mistakes and gain the wrong insight.

Cross-tabulation categorizes data and creates an easy-to-understand picture quickly. This minimizes the number of wrong results and adds precision to the analytics process.

New insights

One of the most important advantages of using the cross-tabulation method is seeing something that wasn't obvious before.

You may miss something important when you handle raw data and apply the traditional analytical method. By comparing categories in a table form, crosstab allows you to notice relationships and correlations you could have missed with other analytics.

Various applications

Cross-tabulation analytics is highly versatile. When it comes to leveraging surveys, you can use crosstab to analyze various question types as long as it's possible to group answers into distinct categories.

Accordingly, you can use it with multiple choice, Likert scale, and close-ended (yes/no, true/false, agree/disagree) questions. Since employee, student, and customer surveys can have different answer types, the crosstab's ability to analyze them is handy.

Overall, crosstabulation is an excellent way to get actionable and quick insights into something that may not have been obvious from other analytical methods.

Disadvantages

While crosstab comes with many benefits, it also has a few downsides. The disadvantage of using the crosstab method is the lack of precision. You only get a general understanding of your data according to your research needs.

Additionally, it can't be applied to all data types. The results are only possible with categorical data.

You can apply cross-tabulation analytics to a wide variety of surveys, including:

Employee engagement surveys

Since employee engagement is integral to business productivity, many companies arrange regular surveys to measure it.

With crosstabulation in mind, you can design survey questions that can help you gain insights into employee behavior. Once you receive answers, you can group them by department, location, gender, age, and salary, for example.

With crosstabulation, it can be easier to identify employee engagement problems and streamline productivity.

Customer satisfaction surveys

By evaluating customer satisfaction levels, companies can gain the necessary information about marketing strategies, product development, customer relations, and much more.

Crosstabulation can evaluate customer replies based on different audience segments and demographic information. It can also help marketers understand more about their target audience and make educated decisions about new strategies.

Learning experience surveys

Educational institutions arrange regular surveys to evaluate student experience and gain insight into the learning structure and tools. For example, crosstab can evaluate the results for students who study full-time, part-time, and remotely. Measuring student satisfaction according to the different majors, classes, or branches is also possible.

Cross-tabulation is a highly useful approach to survey data analysis. It allows you to gain valuable insights into categorical data without implementing complex and time-consuming methods. This approach applies to various survey types across different industries and niches.

By using cross-tabulation, you gain a powerful analytics tool that can provide fast insights for educated decision-making.

To gain valuable insights, data used in cross-tabulation needs to be categorical. The data you analyze has to be divided into different categories, which are mutually exclusive. For example, if you are studying answers from a group of people, you can group their answers by age range.

Cross-tabulation usually looks at two variables. However, it's possible to use multiple variables for more complex analysis.

Tabulation represents data in a table (rows and columns). Cross-tabulation involves identifying the relationship between variables represented in the table.