From Data to Decisions: Unveil your story with Suzy’s Data Explorer
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How can you unlock powerful data insights?Learn how Suzy’s in-platform tool, Data Explorer, works.
Running a survey is a great first step toward discovering valuable consumer insights in your research. But analyzing market research data is not often easy. What if you ran a survey and wanted to know how Gen Z and Millennials rated packaged designs, as well as how consumers in the Midwest and Northwest felt?
You could use traditional statistical software platforms, but it’s often a lengthy process to break down the data to get the analysis you need. You’d have to do the following steps:
Download raw data.
Manually create custom age ranges/regions in your spreadsheet.
Format for compatibility with statistical software and upload.
Reformat the data in the software and create crosstabs.
Download the dataset and reformat it.
Quality check and review.
That’s up to three hours of work before you can even begin to look at your data and make sense of it—time that could instead be spent on strategy and business solutions. And perhaps, as you’re uncovering those trends, you now have additional questions. You'd have to go back to your survey platform, find additional data, and download it, repeating some of the above steps and adding additional time.
While data is the backbone of the market research industry, data analysis is a pain point for many insights professionals. When you need to understand groups of consumers—whether that’s looking at specific actions or demographics from your survey—there’s a better way: in-platform data analysis.
In this article, let’s take a look at what data analysis is in market research, how it works, what it can help you uncover, and how you can speed it up for faster, more actionable insights by using in-platform data analysis.
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, like Q, SPSS, or Displayr, 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 key drivers analysis, brand tracking, and custom calculations.
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 out with actionable insights all in one place. With a one-stop shop, you can even consolidate your market research stack, saving 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 view breaks down question results by various groups, such as by age, ethnicity, or region. This allows researchers to better understand the differences in results between groups which can help to draw conclusions or spot trends in results.
Often, platforms have built-in crosstabs, like demographics based on gender or age. This data can be helpful, but you can go even deeper with advanced crosstabs that nest within each other.
There are a few key terms to be aware of to understand data when you look at a cross tab.
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.
Base Total: The number of unique respondents that answered a particular question.
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 nest banner groups and points, known as NETs. A nested banner group sits as a subset of another. For example, maybe you want to understand how many Black women snowboard or how many Hispanic men play hockey. Unnested banner groups will just look at the total number of consumers that snowboard or play hockey.
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
Monadic concepts while viewing results from the concept question block
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, allow you to go even further to confidence levels of 80% and 85%, allowing researchers to be more flexible in how they 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.
Create custom table views: Understanding shampoo preferences
With custom table views, you can create banners built from demographics, any target segments, panels, or quota targets, and even other survey questions (including ones outside your current mission!). You can look at nested or unnested banners, which can help you get super granular about consumer preferences and attitudes.
Let’s say you’ve recently run a survey on product concepts for a shampoo brand. With a custom table view, you could create a table that focuses on product feature concepts, like sudsing vs. non-sudsing, customer preferences, and even market trends. Then, you can cut that data again by using a question from a former survey about whether or not your target audience is into upcycled fragrances or not. That data can help you inform your product development strategy or even help you improve your current offerings.
Then, once you have your custom table view, you can apply your set-up to your entire mission—no need to break it down by each individual question.
Custom NETs: Understanding the nuances
Custom NET or nested cross tab is a great tool to use when you want to aggregate or combine two or more data points into one. This can be particularly useful when you're looking to simplify your data view by reducing the number of columns or rows in your table.
Let’s take a look at how an eco-conscious skincare brand harnessed the power of custom nets for a nuanced understanding of brand perception ahead of launching its eco-friendly moisturizer line. By segmenting survey data into targeted groups like "Eco-Conscious Parents" and "Sustainable Lifestyle Advocates," they uncovered key insights that shaped a highly effective marketing strategy.
This strategic, data-driven approach, enriched by deep analytics, led to a significant increase in brand perception among eco-conscious consumers and expanded the brand’s reach within new demographic segments.
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 of being bad actors, essentially cleaning your data. You can also use filters to look at certain demographics or segments.
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.
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.
Last words
Data analysis doesn’t have to be painful anymore. With Data Explorer, you can quickly survey and analyze your market research data all in one place, allowing you to make data-driven decisions faster than ever before. Want to see how it works? Book a demo with us today!
