3 Excel Forecasting Mistakes That Will Wreck Your Credibility (And How to Avoid Them)

The Truth About Excel Forecasting Mistakes Nobody Tells You

Here's a secret I learned the hard way after years in finance: you can hand the same data set to 15 different analysts and get 15 different forecasts. And they'll all have one thing in common—they're all going to be wrong.

That's just the nature of the beast.

But here's what took me years to figure out: accuracy isn't actually the most important part of building a forecast. Precision is.

What does that mean? It means that while you might not nail every single number, if you have a precise and sound approach—and everyone's aligned with your assumptions—you're going to be in good shape. You'll have a defensible position. You'll be able to explain variances. You'll look like you know what you're doing.

So let's talk about the biggest forecasting mistakes I've made (and seen made) throughout my career—and more importantly, how to avoid them.

Mistake #1: Ignoring Seasonality

This seems like it should be so simple, yet here we are.

Not all businesses are created equal. A software company behaves differently than a manufacturing company or a consumer goods company. You've got to recognize when your business has seasonal peaks and troughs.

Examples of seasonality:

• Agriculture: Harvest season drives everything
• Accounting firms: Tax season is a massive spike
• Retail: The holidays are make-or-break

If your business has seasonal patterns, you can't just smooth out your growth across the year and call it a day. That's why this is mistake number one—it can seriously mislead your planning, especially cash flow.

And rumor has it, cash is kind of important.

How to Handle Seasonality in Excel

First, let me be clear about what not to do: don't just use Excel's FORECAST function and call it a day. If you're building a legitimate forecast, you should not use an out-of-the-box function and then present it to someone saying "well, this is what Excel told me."

Use your brain. Understand your business. Understand the drivers.

I feel similarly about AI and statistical models—there comes a point where you need to ask: where does the human stop and the machine take over? Where does the accountability lie?

Here's my approach instead:

Step 1: Calculate your seasonal factors

For each month, calculate what percentage of annual revenue typically occurs in that month:

= Revenue for Month / SUMIFS(All Revenue, Year, This Year)

This tells you: "On average, 8.6% of our annual revenue comes in January."

Step 2: Use historical averages

Take the average of your seasonal factors across multiple years:

= AVERAGE(Jan 2021 Factor, Jan 2022 Factor, Jan 2023 Factor)

The more years you include, the more robust your sample.

Step 3: Project forward

Multiply your projected annual revenue by each month's seasonal factor to get your monthly forecast.

This approach is dead simple, but I'll put it up against any fancy statistical model. Here's the thing: the more fancy you get with the spices, the more likely you're going to be wrong in inexplicable ways.

Focus on the powerful few drivers that have the most impact. Keep it simple.

Mistake #2: Baking in Anomalies

This one ties in nicely with seasonality.

Let's say a celebrity randomly tweets about your product and sales skyrocket for a month. Should you bake that into your forecast for next year?

Hard no.

I've actually been asked: "If these random events happen from time to time, shouldn't we try to plan for them?"

Riddle me this: Would you rather walk into a meeting having to explain a missed forecast because something crazy happened that you couldn't have predicted? Or would you rather explain why your forecast showed revenue doubling in one month because you expected something crazy to happen... and it just didn't?

I know which camp I'm in.

How to Smooth Anomalies

Rule #1: Don't touch actuals. Actuals are actuals. Leave them alone.

Rule #2: Adjust your seasonal factors. Instead of using the actual percentage for the anomaly month, replace it with an average of surrounding months:

= AVERAGE(Previous Months' Factors)

This smooths out the spike so it doesn't corrupt your projections.

Rule #3: Document everything. Add a note to any cell you've adjusted:

"Smoothed anomaly from prior year—celebrity tweet drove

one-time spike in June 2023"

Trust me, you (or whoever inherits this model) will forget what you did in two months. Document it.

When that extraordinary event happens again? Use it as a variance explanation, not a forecast assumption.

Mistake #3: Building with a Short-Term Mindset

You don't want a one-and-done disposable forecast that you throw away at the end of each quarter.

Your model should be something you can update, refine, and improve over time as you gain a better understanding of the business and its drivers. If you're just slapping together numbers for a quick forecast and not thinking about how to:

• Feed actuals into the model
• Use it on a rolling basis
• Modify growth drivers
• Build out new scenarios

...you're wasting time and energy.

Building for Longevity

Make actuals easy to load. Don't transform numbers, paste values, and do all sorts of manual gymnastics. Build a repeatable process.

Use easy-to-access controls. Put your assumptions in one place where anyone can find and update them.

Document your functions. When someone else opens this model (or you open it in six months), it should be obvious what's happening.

Build in flexibility. Scenarios, sensitivity toggles, the ability to extend the timeline—all of this should be baked in from the start.

Bonus: Two CFO Pro Tips

If you've made it this far, here's the good stuff.

Bonus #1: Don't Overshoot the Impact of Initiatives

Every budget season, everyone comes in with their pet project trying to justify it by saying it's going to drive massive growth. Maybe it's a marketing campaign. Maybe it's a new product launch.

Here's the reality: initiatives rarely roll out exactly as planned. They're often late or don't perform as expected.

If you bake in too many initiatives—each one carrying aggressive targets—you're setting yourself up for a painful year of constantly explaining why you missed forecast.

And the truth will be hard to pinpoint: "I don't know, we shot for the stars and landed somewhere out in the Atlantic. Send help."

Avoid the hockey stick forecast where everything magically improves in Q4. It won't.

Be the voice of reason. Plan for one, maybe two or three initiatives that the whole business can rally behind. Don't shoot the moon with any one of them.

Bonus #2: Anticipate Questions and Build Scenarios

Always be prepared for the tough questions:

• What if this doesn't go to plan?
• What happens if we only hit 80% of our sales target?
• What if that product launch doesn't happen on time?

This is where scenario planning comes in. You need to show:

• Best case: We fire on all cylinders, all initiatives hit
• Worst case: We miss on everything and flounder
• Most likely: We hit on a couple initiatives and improve organically

Come prepared with these scenarios and you'll look thoughtful, prepared, and highly capable when those tough questions come your way.

The Bottom Line

Here's your checklist:

• Don't ignore seasonality — If your business has it, model it
• Don't bake in anomalies — Smooth them out, document them
• Build with longevity in mind — Your model should evolve with the business
• Be careful with initiatives — Don't compound aggressive assumptions
• Come prepared with scenarios — Best case, worst case, most likely

Remember: precision beats accuracy. You're not trying to predict the future perfectly. You're trying to build a defensible, explainable forecast that helps the business make better decisions.

That's what separates good analysts from great ones.

Common Questions About Excel Forecasting

Q: Should I use Excel's built-in FORECAST function?

No. Not for anything you're presenting to stakeholders. Those functions don't understand your business drivers. Build your forecast manually using seasonal factors and assumptions you can defend.

Q: How many years of history do I need for seasonal factors?

I recommend at least 3 years. More is better, but watch out for years with major business changes (acquisitions, pivots, etc.) that might skew your averages.

Q: What if my business doesn't have obvious seasonality?

Then you probably don't need seasonal adjustments. But you still need to account for anomalies and build with longevity in mind. Most of the mistakes I covered still apply.