3 Forecasting Mistakes That Kill Margin

Hey founder,

Forecasting isn’t just about guessing how many units you’ll sell. It’s about aligning your inventory, cash, marketing plan, and ops capacity to prevent chaos. And yet—even the most successful DTC brands still fall into the same traps every year.

Let me walk you through one we saw up close.

A beauty brand we worked with forecasted 3x demand for BFCM based on their ad budget.

Seemed reasonable, until they went viral. 

Sales spiked 6x.

By the second week of November, they were out of their best seller.

They air-freighted a small restock at 3x cost. But the delay meant a $78K loss in projected revenue, not including the fulfillment headaches.

Here’s why this happened and how you can avoid it:

Top 3 Forecasting Traps (And How to Fix Them)

1. Relying Too Heavily on Last Year’s Sales Data

What happened last Q4 isn’t always a good indicator. Paid strategy, influencer velocity, and even TikTok trends can flip the forecast upside down.

How to fix it:

• Use rolling 3–6 month sales averages as your baseline.

• Layer in upcoming changes (new channels, new SKUs, marketing campaigns).

• Don’t assume flat lift—model 3 scenarios: conservative, expected, and aggressive.

2. Forecasting at Category Level Instead of SKU Level

You might say: “We’ll need 10,000 skincare units.”
But if 7,000 of those are serums and you only ordered 3,000—you just stocked out and missed a hero moment.

How to fix it:

• Break forecasts by SKU.

• Use ABC segmentation:
  

  • A SKUs = high velocity → tight planning + frequent checks

  • B SKUs = moderate → 10–15% buffer

  • C SKUs = slow → pull back and protect cash

3. Not Simulating the Actual Fulfillment Timeline

It’s not just “how many will sell.” It’s also how fast you can reorder, package, and ship.

How to fix it:

• Run a mock demand spike for your top 3 SKUs.

• Ask: if we sell out in 7 days, how fast can we replenish?

• Include supplier lead time, production slot availability, freight duration, 3PL intake time.

Real Numbers, Real Results

We worked with a self-care brand that had:

• Forecast Deviation: +32% (they over-ordered)

• Inventory Turnover: 2.2x/year

• Dead stock value: $90,000

After a 45-day audit and adjustment:

• Forecast Deviation dropped to <10%

• Inventory Turned: 4.1x/year

• $140K in working capital unlocked

 Next Steps for You

• Pull your sales data by SKU (not category).

• Build 3 forecast scenarios with your team this week.

• Align with your supplier: What’s your max reorder capacity within 30 days?

You won’t ever predict perfectly. But with the right systems, you’ll protect your margins when demand hits and scale without panic.

Let’s make better forecasting strategies together,
Lara

P.S. If you’re more of a visual learner, check out our YouTube channel where I share weekly behind-the-scenes stories and breakdowns from real factories, warehouses, and DTC brands.