Running a survey is a great first step toward discovering valuable consumer insights in your research. But let’s face it, the next part—analyzing that data—can often be a headache of a process involving downloading, formatting, and manual analysis of your data.

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Fortunately, there’s a better way: In-platform data analysis with Suzy’s Data Explorer. Data Explorer is a powerhouse tool that lets you pull together research and insights in one spot—fast. This way, you can quickly get to the heart of the data and make the smart, impactful decisions that drive your business forward.

In this article, we’ll explore what market research data analysis entails, how it operates, the insights it can reveal, and how leveraging in-platform data analysis with Data Explorer can streamline the process, delivering quicker, more actionable insights.

What is data analysis in market research?

In market research, data analysis is the process of inspecting, cleaning, manipulating, and modeling data to discover useful information, informing conclusions, and supporting decision-making.

Data analysis is often conducted using dedicated data analysis software or it can be done with a tool that does both data collection and data analysis, like Suzy. There are many ways to do data analysis, including complex data cuts with defined subpopulations, derived questions, custom calculations like means and stat testing, data filtering, data weighting, and more.

The power of in-platform data analysis

With data analysis built right into your consumer research platform, you can survey consumers, analyze your market research data, and come up with actionable insights all in one place. Now, Data Explorer makes this process so easy that anyone on your team can gather statistically significant insights. With a one-stop shop like this, you can consolidate your market research stack, saving both time and money. Let’s break down some more use cases for in-platform data analysis.

Table views and crosstabs

Crosstabs, short for cross tabulation, are a form of data analysis that looks at questions or survey results by groups. This allows you to identify meaningful differences in how various groups answered your questions, helping you draw conclusions and spot trends in results.

There are a few key terms to be aware of to understand data when you look at a crosstab.

• Banner: What you’re cutting your data with. In the example below, we’re cutting our data by both Gender and Ethnicity to break down winter sports participation.
• Banner Group: Each individual subgroup that makes up our banner. Here, Gender is a banner group, as is Ethnicity.
• Banner points: The subgroups of the larger banner group. In this case, Female and Male.
• Table Cell: These are individual data points within your cross tab.

Your data is your playground; you can mix and match to get the valuable insights you need. For example, you can hide banner points that aren’t statistically significant. Or, you can aggregate banner points together in a NET. A nested banner group sits as a subset of another. For example, maybe you nest two attributes into a banner group: one group being gender and another being a question about winter sports. By nesting, you can understand how many Black women snowboard or how many Hispanic men play hockey. If these groups were not nested (ie: were separate banner groups), they wouldn't be combined to create the data cuts you need.

Want to understand how you stack up with your competitors? Filtering your data in Data Explorer can help you run competitive analysis and create a plan of attack. For example, you could filter your data by consumers who have purchased a competitor’s product in the past year. Then, you can identify trends and opportunities in the market and inform your product and marketing teams of what might work best going forward.

Data cuts with custom groupings

Data Explorer empowers you to create and analyze your data with precise groups to uncover valuable insights, whether it’s based on simple data cuts or complex, multi-layered banner groups.

Derived questions allow you the flexibility to define banner groups based on criteria from any survey on your dashboard, combine criteria from multiple surveys, and leverage Suzy’s proprietary panel for comprehensive respondent access. With the ability to access respondents across different surveys, derived questions allow you to combine multiple data points to create new metrics, enabling you to generate sophisticated, multi-layered criteria for more precise data cuts.

What is statistical testing?

Statistical testing helps market researchers understand if data trends are statistically significant. Basically, it adds a layer of rigor to research and shows whether or not the difference between two data points is meaningful. It’s critical for actionable insights.

On Suzy’s Data Explorer, you can stat test:

• Scale questions
• Rating and ranking questions
• Monadic concepts
• Monadic multiple-choice concept questions
• Monadic rating concept questions

Stat testing can be used in various ways, such as comparing the performance of different concepts, understanding the impact of a change in your product or service, or identifying significant differences in responses across demographic groups.

Confidence level

Often, researchers look at a confidence level (or how confident you can be that the data is statistically significant) of either 90% or 95%. But some platforms, like Suzy’s Data Explorer, allows you to explore lower confidence levels, like 80% and 85%. This means you can be more flexible in how you evaluate trends to allow for more directional differences.

Data Explorer allows two confidence levels to be used at once. Uppercase letters represent a higher confidence level and lowercase letters represent a lower confidence level.

What can you learn with in-platform data analysis?

Let’s look at a few examples of market research data analysis with an in-platform tool like Data Explorer.

Filters: Quick competitive analysis

Filtering your data can ensure you’re only seeing the most actionable data. For example, you can filter out incompletes so you only get the full picture. Or filter out respondents that you suspect by completes, segments, or other cohorts. You can also use filters to rebase your data to look at specific demographics or segments.

Retargeting for deeper insights

Perhaps the most valuable feature of doing data analysis all in one consumer research platform like Suzy is that you can quickly iterate on your research. If questions arise during your data analysis, you can retarget the consumers you surveyed for further learning with qualitative or quantitative research. Then, you can apply your custom table views to your new research, or even cut your data by answers to your new questions.

Streamline Your Data Analysis with Data Explorer

Data analysis doesn’t have to be painful anymore. With Data Explorer, you can swiftly survey and analyze your market research data all in one place, enabling you to make data-driven decisions faster than ever before.

Plus, more is coming soon! Keep an eye out for more enhancements to Data Explorer.

Want to see how it works? Book a demo with us today and transform how you interact with data!