Data Layering For Content Marketers: How To Turn One Survey Into Dozens Of Insights
At the beginning of 2024, I set out to do something unprecedented for me. At Nectar, we decided to survey 1,000 employees every month so we could pull together 12,000 survey responses for our 2025 State Of Company Culture Report. The goal was to find a mix of questions. We wanted questions that we could repeat to see trends over the year, and we also pulled together some one-off questions to explore more timely topics.
The first quarter went well, but I quickly realized that I was spending too much time buried in spreadsheets, brainstorming question ideas, and setting up surveys. On top of that, I was still managing the Nectar blog, working with our freelancer Rebecca, and trying to keep everything running as normal.
I needed someone (or something) to help me with analysis, so I could stop Googling spreadsheet formulas, and move on the data I was gathering quicker. Up until then, I had only dabbled with ChatGPT, never fully committing to the myriad of ways it could help me as a busy content marketer. That all changed when I realized how I could work with it to describe the analysis I wanted to run, and save myself dozens of hours squinting at percentages and crunching numbers.
If you’ve ever run a survey, you know that data isn’t cheap. It’s a significant investment, especially for small businesses. If you want to invest heavily in data, the worst thing you can do is stop at the surface-level insights. Yes, you could share that 30% of survey respondents report feeling burnt out, but what if you could share data based on age, gender, or even whether an employee has experienced a layoff at work? All of this is possible with data layering.
In this post, I’ll show you:
What data layering actually is (and why it matters for content marketers)
How to spot layering opportunities before you even send your survey
My exact process for layering with ChatGPT
How to practice what you’ve learned with free datasets
And much more!
What Is Data Layering?
First, let’s quickly address the concept of data layering to ensure we're all on the same page. Data layering is the process of analyzing the relationship between two or more variables. It’s also known as cross-tabbing or segmenting data.
Why Should Content Marketers Care About This?
Data layering is an essential skill for content marketers because it’s often where the true story is. Everyone has a generic stat about burnout, but they are less likely to have a stat about how burnout impacts other areas of the employee experience. Without data layering, you aren’t getting to the meat of what your data shares with you.
Layering Starts At Survey Design
You can’t add layers after a survey has already been sent. Even if you pause a survey to add layers, some of your data will be missing. You want to start the study with all of the layers you want to add.
I personally like using Pollfish for my data collection because it includes some demographic information like age range, gender, ethnicity, etc. It helps me take care of the basics so that I can spend my money on other questions.
Often, when I am brainstorming questions, I’ll mark which ideas would make good layering questions. Marking layering questions is especially useful when I am running through the survey with my manager or another collaborator. When you give them insight into the possible headlines or insights you can gain from the data, the questions you want to ask start to click.
If you aren’t sure where to start with layering questions, this chart can help you brainstorm the types of questions you can add:
| Layer Type | Description | Example Survey Question | Possible Insights |
|---|---|---|---|
| Demographics | Attributes about the respondent that can reveal differences in experiences or opinions across groups. | “What is your age range?” / “What is your gender?” | Burnout rates by age group or gender identity. |
| Role & Function | Job title, department, or team size for segmentation. | “Which department do you work in?” | Comparing recognition satisfaction between marketing and operations teams. |
| Tenure/Experience | How long the respondent has been with the company or in the industry. | “How long have you worked at your current organization?” | Engagement trends by tenure (e.g., newer employees vs. veterans). |
| Company/Org Characteristics | Industry, company size, revenue range. | “What is the approximate size of your organization?” | Turnover intention differences between small and large companies. |
| Behavioral Data | Frequency or type of behaviors related to the topic. | “How often do you receive feedback from your manager?” | Correlation between feedback frequency and job satisfaction. |
| Attitudinal Data | Agreement or sentiment toward a statement. | “I feel recognized for my work.” (Likert scale) | Recognition satisfaction by engagement level. |
| Situational Context | Specific experiences that may influence responses. | “Has your company had layoffs in the last year?” | Comparing burnout rates between layoff impacted vs. non impacted employees. |
| Outcome Measures | Metrics you want to explain or predict with other variables. | “Overall, how satisfied are you with your job?” | Identifying the top drivers of job satisfaction. |
Tip: Don’t Fill Your Survey With Only Demographic Layering Questions
Some questions, like “How often do you work extra hours?” are valuable on their own and as layers. For example, you can see overall results, break them down by demographics like industry, or compare them to burnout levels. Demographics such as age, gender, or company size are only helpful in relation to other questions, so keep them concise. They can eat up survey space fast, so focus on the ones most relevant to your analysis.
How To Layer Data: 5 Common Methods
What does the data layering process actually look like? There are many ways to layer data, from simply using survey platform filters to hiring or contracting a data analyst. Each process comes with its own pros and cons, and how you decide to layer data depends on the project and the number of layers required.
| Method | How It Works | Pros | Cons | Best For |
|---|---|---|---|---|
| Survey Platform Filters (ex, Pollfish or SurveyMonkey) | Use the platform’s segmentation tools to compare subgroups. | Quick, visual, no extra tools needed. | Limited combinations, tedious for many layers. | Small datasets, single demographic splits. |
| Spreadsheet Analysis (Excel, Google Sheets) | Cross tab, pivot tables, and formulas on exported raw data. | Flexible, customizable, and can work offline. | Requires spreadsheet skills, can be error prone. | Complex but controlled multi variable layering. |
| AI Assisted Analysis (ChatGPT, Claude, etc.) | Upload or paste data and prompt AI to calculate layered stats. | Conversational, quick iterations, minimal technical skills. | Must validate outputs, risk of timeouts. | Rapid exploration, model building, repeatable slices. |
| Data Analysis Software (Tableau, Power BI, SPSS) | Import data into specialized tools for advanced visuals and stats. | Handles large datasets and high quality visuals. | Steep learning curve, licensing costs. | Ongoing analysis programs or high stakes reports. |
| Hiring a Data Analyst | Bring in a specialist to run complex or custom analyses. | High accuracy, deep insight, custom modeling. | Expensive, slower turnaround. | Major research projects or when precision is critical. |
Deep Dive: ChatGPT For Data Layering
Now that we’ve covered the myriad of ways that you can layer data, I want to talk specifically about how I use ChatGPT to perform data analysis quickly and efficiently. Before ChatGPT, it was just me, Google Sheets formulas, and a pink calculator. I struggled to get all the analysis done, and I definitely couldn’t document much about what I was doing. ChatGPT is a practical resource for content marketers who need to do data work.
My Layering Workflow In ChatGPT
Layering in ChatGPT is very simple because it’s like having a conversation with someone quick with data analysis. It can hallucinate, but you can easily course correct and get things back on track. This four-step process will help you become a data layering pro with ChatGPT.
Step 1: Prep Your Data Set
If you use a tool like Pollfish or SurveyMonkey, you should be able to export your data into a spreadsheet. ChatGPT works well with Excel files or CSVs.
To make the analysis smoother, take a few minutes to clean and format your file:
Give each column a clear, descriptive title.
Make sure responses within each column are consistent for quantitative questions (e.g., always “Yes” instead of a mix of “Y,” “yes,” and “Yes”).
If you have multi-select questions, split answers into separate columns with descriptive headers.
Mark or note missing data clearly (“Not sure,” blank, etc.) to avoid misinterpreting totals.
This light cleanup makes it easier for ChatGPT to interpret your dataset accurately and quickly.
Step 2: Upload Your Data Set And Describe Your Goals
Once you have a ChatGPT-ready dataset, it’s time to upload your data and start prompting. ChatGPT can make assumptions about column names, but if you have multiple columns on a topic (like burnout or social media usage), use the exact column wording in your prompt.
| ChatGPT Ready Prompt | Filled In Example |
|---|---|
| Can you tell me how {Variable 1} impacts {Variable 2} in this data set? | Can you tell me how industry impacts burnout levels in this data set? |
| Can you give me the most interesting insights from this data set? | Can you give me the most interesting insights from this survey on manager empathy? |
| Please layer {Variable 1} with {Variable 2} and show me the results. | Please layer age with job satisfaction and show me the results. |
| Are there any noticeable differences in {Variable 1} across {Variable 2}? | Are there any noticeable differences in engagement across company size? |
| Summarize how {Variable 1} and {Variable 2} interact in this data. | Summarize how gender and layoff experience interact in this data. |
Not sure where to start? That’s okay. Sometimes the best insights come from just poking around. Try prompts like these to see what jumps out:
What are three unexpected insights in this dataset that I might miss at first glance?
Can you highlight surprising differences between groups in this data?
Which variables seem most strongly related in this dataset?
Can you point out any patterns or anomalies that stand out here?
If you were turning this dataset into a story for a blog, what angles would you suggest?
Step 3: Let ChatGPT Run The Analysis
Next is the fun part: watch ChatGPT work its magic and share insights that were buried in your dataset.
During this phase, it’s worth keeping an eye on whether ChatGPT is overthinking your ask. If analysis feels slow, click the “analyzing” drop-down to see what it’s doing. That log can look dense, but the key question to ask yourself is, “Did my analysis really need all these steps?” Even without knowing every statistical model, you’ll notice when it’s doing more than you asked.
If that happens, don’t start over. Just clarify your output. For example, say “give me a simple table” or “summarize in 3 sentences.”
My other advice during this step is to save the work that ChatGPT does regularly. If it gives you a spreadsheet, download it right away. If it gives you a table, copy it into your own doc. Files and outputs aren’t stored permanently. You can lose access if ChatGPT times out, overwrites a version, or if too much time passes before you download. Saving as you go ensures you don’t lose work you’ll want later.
Step 4: Validate The Analysis
After you’ve got a workable analysis, it’s smart to pause and validate the results. You don’t have to run through every method. Pick the one that feels right for your skill level and comfort:
| Validation Method | How It Works | When to Do This |
|---|---|---|
| Logic check | Compare results against what you already know. Example: if burnout is 30 percent overall, one group should not show 0 percent while another shows 90 percent. | Best for beginners or when you just need a quick pulse check. |
| Spot check one variable | Manually calculate a simple variable in Sheets or Excel and compare to ChatGPT’s output. | Great for more confident users who want extra reassurance without redoing the full analysis. |
| Ask ChatGPT to explain its work | Use a prompt like “How did you get this result?” to see the reasoning and reconcile differences. | Useful if you do not want to calculate manually but want to double check the logic. |
When your numbers don’t match ChatGPT’s, the goal isn’t just to force agreement. It’s about reconciling the two results. Sometimes ChatGPT makes a mistake in its math or interpretation, but other times, your own spot check might have overlooked a filter, grouping, or calculation step.
That’s why asking ChatGPT to explain its reasoning is powerful. For example: “When I ran this myself, I got X. You got Y. Can you walk me through your steps so I can see where we might differ?”
Often, ChatGPT will break down its process in a way that shows you whether the discrepancy is due to its overthinking, a skipped detail on your end, or just a different but valid approach to the same data.
Real-World Data Layering In Action
Data layering is cool in theory, but I wanted to share a few published examples of data layering that I’ve done during my time at Nectar. We’ve published several stories and eBooks that relied heavily on layering data to tell a story.
Gender Recognition Gap
One of my favorite stories we worked on was our Gender Recognition Gap article. For this analysis, we looked at recognition frequency across different groups: executives, managers, and peers. Then we layered in gender. The differences were striking:
Executives: 36% of women said they never receive recognition from leadership, compared to 19.4% of men.
Managers: More than half of men (53.4%) reported daily or weekly recognition from their managers, while only 40.5% of women said the same.
Peers: 36.8% of men reported daily peer recognition, compared to 25.6% of women.
These gaps made the story much more powerful than if we had simply reported recognition frequency overall. The gender lens turned a basic data set into a meaningful narrative about workplace equity. Our writer, Rebecca, built on this by interviewing subject matter experts and adding quotes from our CEO. Together, these perspectives deepened the story and explored what companies can do to ensure women receive equal appreciation at work.
Replaceable Manager Study
One of my most powerful data stories was our Replaceable Manager article. We didn’t just report whether employees would replace their manager. We layered that insight against outcomes like burnout, job security, and turnover intent to tell a more impactful story.
Here’s what really stood out:
Burnout: 72.3% of employees who would replace their manager reported feeling burnout, compared to only 30.9% who said they wouldn’t.
Job Security: Nearly half (45.4%) of employees with replaceable managers said they were worried about their job security, versus 16.4% of employees satisfied with their manager.
Turnover Intent: 57.2% of employees with replaceable managers said they plan to look for another job in the next three months, while just 22.2% of those without replaceable managers planned to leave.
By layering these variables together, a simple question became an urgent narrative about how leadership (or lack thereof) ripples into well-being, psychological safety, and retention risk.
2025 State Of Company Culture Report
The last example of data layering I want to share is our 2025 State of Company Culture Report. This 40-page report consolidated the 12 surveys we ran in 2024, representing 12,000 employee responses on topics like recognition, engagement, and retention.
We used layering to move beyond surface-level statistics. For example, we didn’t just report how many employees were job hunting. We connected that outcome to factors like layoffs, job security, burnout, and the Sunday Scaries, showing which stressors were most strongly tied to turnover intent.
Another compelling layer compared recognition expectations to reality. In one survey of 1,000 employees, we asked how often they wanted recognition, and in our larger 12,000-response dataset, we tracked how frequently they actually received it. The gap between expectation and reality revealed a clear opportunity for organizations to improve recognition practices.
Practice Exercise: Try Data Layering Yourself
The best way to understand data layering is to practice it. One of my favorite free resources for this is the Maven Analytics Data Playground.
The Data Playground is a library of clean, downloadable datasets across topics like HR, retail, finance, entertainment, and more. Each dataset comes in an easy-to-use CSV format, so you can jump right into filtering and layering insights without wasting time cleaning messy data. These datasets are also labeled with the number of records and fields so that you can find the correct data for your skill level.
Here’s how you can use it for practice:
Pick a dataset: Maven Analytics has compiled over 60 datasets, so there is something interesting for everyone to dig into.
Ask a broad question: Each practice set by Maven Analytics has some recommended analysis questions to get you started.
Layer your data: Break those results down further by another variable.
Tell the story: Once you’ve uncovered an insight, frame it as if you were writing a blog post: what’s surprising, what’s useful, and what should a reader take away?
By practicing on datasets like these, you’ll develop the ability to spot interesting intersections in data and shape them into stories that resonate with your audience.
Common Data Layering Mistakes To Avoid
If you are new to data layering, it’s important to think about the mistakes that could be made. Segmenting your data opens up a whole new world of possibilities, but it is also error-prone if you don’t do it correctly. As we wrap up our data layering journey, I wanted to walk through a few AI data layering mistakes that marketers often make. There’s no need for you to learn these mistakes the hard way:
| Mistake | Why It’s a Problem | How to Avoid It |
|---|---|---|
| Layering for the sake of it | Wastes time and produces irrelevant insights that do not serve your story. | Define your research goal upfront and only create layers that could produce meaningful, audience relevant findings. |
| Not validating AI outputs | ChatGPT and other tools can miscalculate or hallucinate numbers. | Always check totals, spot check math, and compare to expected ranges. |
| Ignoring subgroup size | Small groups (less than 50 respondents) may not be reliable and can mislead. | Add a response count column and note when numbers are based on small samples. |
| Overanalyzing or data overload | Endless digging can lead to fatigue and losing sight of the key narrative. | Set limits on how many layers you will explore and take breaks to reset focus. |
| Skipping context | Numbers without context can be misinterpreted or undervalued. | Pair stats with interpretation, examples, or implications for your audience. |
Conclusion: AI Opens Up A Whole New World Of Data Exploration
AI democratizes something that has historically been very expensive. Software like Tableau and SPSS aren’t in the budget for most startups, and they have a steep learning curve. ChatGPT is free to start, with advanced options available at a low cost. AI isn’t without its faults or frustrations. Switching to ChatGPT for analysis isn’t a perfect solution, but it can be a great starting point.
No matter how you decide to approach data layering, let’s wrap up this article with some best practices to keep in mind. Remember, layers are where the actual story is. Don’t be afraid to dig a little deeper to find the best data.
Think About Survey Layering At The Design Stage
Remember, you can’t add layers once a survey is sent. Think about how you might want to slice the data early, so you can have all the layers you need for analysis.
Spot Check Or Work With ChatGPT To Validate Data
ChatGPT can hallucinate. Use your logic, tools like Google Sheets/Excel, or communicate with ChatGPT to ensure the statistics are correct.
Stop At The Strongest Stories
Data analysis with ChatGPT can be exciting because it opens up new possibilities, but you can’t get bogged down in analysis. It’s easy to analyze too much. Use your best judgment or taste to determine when you’ve pulled the strongest data out of your spreadsheet. Remember, you can always go back to a survey if you have a new angle later.
Practice, Practice, Practice
Data layering is a skill you can get better at. If you don’t want to use a survey from work, use tools like the Maven Analytics Data Playground. There are plenty of interesting data sets to download and analyze.
