Why Your Forecast Is Failing You (and What to Do Instead)
A smarter approach to monthly performance planning for e-commerce leaders
In many growth teams, forecasting is treated like a quick math problem:
“We need $X more this month. There are Y days left. Divide and conquer.”
It feels logical. Clean. Simple.
But the truth? It’s dangerously misleading.
Especially when you're operating across multiple markets, with complex user behaviors, shifting promo calendars, and inconsistent trends.
Recently, I led a forecasting challenge that reminded me why linear forecasting often creates more confusion than clarity, and how building a scenario-based model can realign the team, sharpen execution, and protect profitability.
What’s Wrong With the Classic “Revenue ÷ Days” Approach?
At first glance, this method looks solid. It gives you a number, fast.
But here’s what it doesn’t do:
It treats every day the same. Weekends, pay weeks, low-traffic days, all are weighted equally. That’s not how real demand works.
It ignores historical behavior. Early-month performance might have been low due to a campaign delay or high due to a one-off spike. You’re averaging noise.
It offers no accountability. A flat daily target doesn’t tell your what each market team need to deliver.
It erases context. There’s no view into AOV, conversion rate, seasonality, or margin. Just a single number.
It’s binary. You’re either “on pace” or “off pace,” with no shades of gray or options to shift.
A forecast should guide decisions.
Not set false expectations.
The Better Alternative: Scenario-Based Forecasting
Instead of betting everything on one number, we built a model that simulates multiple realistic outcomes.
This is what we created:
ScenarioOutcomeStrategic UseBaseline (90%)Conservative outcomeNo major changes; steady run-rateModerate Uplift (95%)Controlled growthRequires light adjustments to reachClose-the-Gap (100%)Hits monthly targetRequires stronger executionAggressive (105%)Builds a bufferAdds 15% weekday lift above plan
What’s different here?
Historical results are locked. Once a day has passed, its actuals are frozen. No moving goalposts.
Each market is modeled separately. No more vague “one-size-fits-all” targets.
We apply weighted behavior. For example, weekends carry a discount, pay weeks get a lift, low-volume days are dampened.
Every lever is exposed. AOV, order volume, weekday trends, and behavioral assumptions are all visible, and adjustable.
Forecasting ≠ Fortune Telling
Why perfect accuracy is the wrong goal, and what to focus on instead
Let’s be real: demand forecasting has never been harder.
And it’s not because we got worse at performance marketing, it’s because the environment got more complex.
We’re operating in a world full of volatility:
Inflation, currency shifts, and economic pressure are reshaping consumer behavior by the week.
Geopolitical uncertainty causes demand to spike or freeze overnight, sometimes with no warning.
Consumer attention is scattered. Platforms shift, preferences evolve, and algorithms keep changing the rules.
In this context, we need to stop treating forecasting like it’s a prediction game.
Just like in investing, the goal of forecasting isn’t to be perfectly right, it’s to make better decisions under uncertainty.
A good forecast should:
Ground you in historical performance
Offer a range of possible outcomes
Help you adapt before you miss the target
That’s why I don’t aim to hit 100% accuracy.
I aim to build models that help us respond intelligently.
That’s what matters.
What Scenario Forecasting Enables
If you’re still relying on static targets and end-of-month reporting, here’s what this new approach gives you:
✅ Clear visibility of revenue, AOV, and order targets per day, per country
✅ Flexible assumptions that adapt with market signals
✅ A team that knows what success looks like, not just in theory, but in numbers
✅ A buffer plan when volatility hits
✅ A structured way to communicate with leadership
It moves the conversation from:
“We’re off-track.”
To:
“Here’s what we can still control.”
How to Build Your Own
If you’re leading performance, here’s how to start:
Lock historical actuals – Don’t let your model rewrite the past.
Use weighted behavior – Some days (like weekends) underperform. Others (like pay weeks) spike. Model accordingly.
Build at least three scenarios – Conservative, target, aggressive. Show the trade-offs.
Expose your levers – Revenue is an output. Surface inputs like order volume, AOV, and campaign timing.
Make assumptions visible – Whether on a dashboard or Google Sheet, use a clean assumptions tab to test different inputs.
Final Thought
Forecasting is not about proving you’re right.
It’s about creating clarity for decision-making, especially when things get uncertain.
You don’t need a complex system. But you do need one that’s responsive, realistic, and aligned with how your business actually performs.
If you’re in growth, acquisition, or performance marketing, it’s time to stop looking at targets as fixed points, and start using forecasts as dynamic tools for alignment, pace, and accountability.
Because in fast-moving environments, the teams that win aren’t the ones who guessed right.
They’re the ones who planned for what could happen, and moved early, fast, and with purpose.