I get a lot of Excel questions in my work as a trainer, consultant, and content creator. After answering enough of them, certain patterns become hard to ignore. Some questions create momentum right away. Others never quite get off the ground, even when the underlying issue is simple. The dividing line is not skill or experience. It is whether the problem has been structured well enough to work on.
This post is about what makes an Excel question answerable, and how to bring more structure to the way you ask for help.
Start earlier than Excel: digital literacy
Before we talk about formulas, models, or Copilot, there is a quieter prerequisite: digital literacy.
By that I mean the ability to reliably capture and share what is happening on your screen. Screenshots. Files. Error messages. Links. A workbook someone else can open and inspect.
This layer is easy to underestimate because it feels basic. But when it is missing, everything above it collapses. Conversations slow down. Clarifying questions multiply. People start guessing instead of reasoning. I’ve written more about this here:
Excel help requires evidence. If you cannot isolate and document what you are doing, neither humans nor AI have anything solid to work with.
Know where you are in the process
There is an important difference between asking from curiosity and asking from friction.
Questions framed as pure possibility tend to stall. “How could I do X, Y, and Z?” or “What are the use cases for this?” sound reasonable, but they are almost entirely hypothetical. They ask someone else to supply the thinking, the experimentation, and the judgment all at once. There is nothing concrete to react to, so the conversation floats.
Questions that get traction usually come later. Something has been attempted. Something behaved differently than expected. Reality pushed back.
You do not need the right solution to ask a good question. You need an attempt.
Show the workbook, not a description of it
Excel is visual and structural. Most Excel problems are obvious once you can see the layout, the data types, the relationships, and the formulas interacting.
That is why a wall of text describing a workbook is almost always worse than a single screenshot. It forces the person helping you to reconstruct the problem mentally, and that reconstruction is usually wrong.
If at all possible, show the workbook.
This is easier than ever now. With generative AI, you can create synthetic datasets and simplified files that mirror your real structure without sharing sensitive data. I talk about that here:
The goal is not completeness but visibility.
“That didn’t work” is not enough
Another common failure mode is stopping too early.
Saying “I tried to do X and it didn’t work” does not move the conversation forward. What matters is what you expected to happen, what actually happened instead, and why you believe the result was wrong.
You must have an opinion, even if it turns out to be incorrect.
This is also where constraints matter. Excel problems are never purely technical. File size, refresh time, audience expectations, tooling restrictions, and downstream usage all shape what “good” looks like. If you do not explain your constraints, people will solve the wrong problem perfectly.
Reduce the problem until it can stand on its own
In programming communities, this idea is called a minimal, reproducible example (MRE). The same principle applies cleanly to Excel.
Strip the problem down until only the parts that matter remain. Fewer rows. Fewer columns. Fewer formulas. A new workbook if necessary.
You can read more about the idea of an MRE and how to build one here.
Translated into Excel terms, a good example is one that someone else can open, inspect, and break in the same way you did.
Research counts… and so does saying what you did
Good Excel questions often include a short record of what has already been ruled out.
- “I tried a PivotTable, but it doesn’t preserve row-level detail.”
- “I looked at XLOOKUP, but my keys aren’t unique.”
These sentences prevent helpers from starting in the wrong place narrows the solution space. It shows that you are engaging with the problem, not outsourcing it.
A practical framework: the Excel Help Stack
Before asking for help, make sure these layers are in place, from bottom to top:
- Digital literacy: You can capture, share, and link to what’s happening.
- An attempt: You have tried something concrete, even if it failed.
- Visibility: You can show the workbook, data, or a simplified example.
- Diagnosis: You can explain what you expected vs. what happened.
- Constraints: You can articulate what matters and what doesn’t.
- Reduction: You can isolate the problem into a minimal example.
If any layer is missing, help becomes guesswork.
A short checklist you can reuse
Before posting an Excel question, ask yourself:
- Can I share a screenshot or workbook?
- Have I tried something concrete?
- Did I show formulas instead of describing them?
- Did I explain expected vs actual behavior?
- Did I state my constraints?
- Did I reduce this to the smallest example that still breaks?
If you can check most of these boxes, your question is probably answerable.
For convenience, I’ve distilled this into a one-page downloadable cheat sheet, available below.
The quiet takeaway
Most frustration when seeking Excel help comes from a gap between how a problem exists in someone’s head and how it needs to be laid out for another person to actually engage with it.
Asking for help is its own technical skill. Not because it needs polish or perfect language, but because it requires translation. You are taking an intention, a hunch, or a mess of half-formed thoughts and turning it into something concrete enough for someone else to work with.
If you are part of my membership, this is the best place to use the framework. This is where individual, one-off Excel questions belong. When you are stuck on a workbook, a formula, a model, or a decision and you need another set of eyes, this framework gives us something real to work with and makes asynchronous help actually productive.
If you are reaching out as part of an organization and the goal is broader, like improving Excel and data literacy across a team, I run structured programs designed to change how people think and work with data, not just fix a single file:
Different problems need different containers. When the structure is right, the help works. And once something real is on the table, progress tends to follow.