Merchandising leaders are under more pressure than ever to deliver accurate forecasts. Seasons are shorter, demand is more volatile, and customers are less forgiving when brands miss the mark. Yet despite all the forecasting tools, dashboards, and analytics available, planning accuracy is slipping—not improving.
And here’s the uncomfortable truth:
Data isn’t the problem. Data integrity is.
When product data is incomplete, outdated, inconsistent, or scattered across dozens of offline files, even the most sophisticated forecasting models can’t produce reliable outputs. Merch teams end up buying too much of the wrong product, too little of the right product, and making costly mid-season adjustments that erode margin.
If you can’t see an accurate picture of the line, you can’t forecast it.
The Root Causes of Bad Forecasting Inputs
Most forecasting issues have nothing to do with the planner’s skill or the accuracy of the planning system itself. They originate upstream—in the merchandising workflow—long before a single number gets entered into the demand plan.
Here’s what typically goes wrong:
- Static spreadsheets that fall out of date immediately
Line boards captured in spreadsheets are outdated the moment they leave the merch team’s hands. Visuals change. Attributes change. Strategy changes. But the spreadsheet doesn’t. Forecasting teams are left working off partial or stale information, creating blind spots and inaccurate projections. - Missing attributes that distort the picture
Key product attributes—like colorways, silhouettes, channel strategies, sustainability flags, pricing tiers, or region-specific versions—often don’t make it into early line documents. Without complete attribution, forecasting teams can’t model demand correctly. - Incomplete category-level insights
The line is viewed product by product instead of by strategic category. Missing or inconsistent rollups skew how much volume a category can truly support, leading to over- or under-investment. When planners don’t have a unified view of category intent, forecasting becomes guesswork.
These issues aren’t caused by a lack of effort—they’re symptoms of a fragmented system where product data lives in too many places and updates are too manual to keep up with the pace of the line.
The Downstream Impact Isn’t Just Operational—It’s Financial
When your forecasting inputs are wrong, everything downstream gets more expensive, less efficient, and harder to fix.
Excess inventory
Bad inputs overinflate buys, leaving teams with too many units, too many SKUs, and too much capital tied up in product that won’t sell at full price.
Missed demand
Incomplete or inaccurate data causes planners to underestimate the products customers actually want. The result: stockouts, backorders, and lost revenue for a product that should have been a hit.
Costly markdowns
Inventory that doesn’t move on time ends up marked down—cutting into margin and diluting the power of the assortment.
Every forecasting error compounds throughout the season. And because these problems originate early, they often aren’t discovered until it’s too late to course-correct.
What “Good Data” Looks Like for Merchandising
Forecasting accuracy improves dramatically when planners and merchants have access to the right data—not just more data.
Here’s what strong forecasting inputs actually look like:
- Fully attributed line plans
Every product includes complete, accurate attributes that reflect how the customer shops and how the brand analyzes demand. From color and fabric to price tiers and strategy tags, planners get a comprehensive and trustworthy dataset. - Real-time product updates
When a colorway is dropped, a silhouette changes, a region’s assortment shifts, or a new SKU is added, the forecasting inputs update instantly. Planners no longer work off outdated snapshots—they work off the live line. - Visual data paired with financial data
Images, tech flats, and product visuals integrated alongside rollups and metrics reflect how merchants actually evaluate the line. When planners can see what they’re forecasting—not just numbers—they forecast more accurately.
Good data isn’t complicated. It’s connected, complete, and continuously updated.
The Business Outcomes of Getting It Right
Brands that fix the integrity of their product data see immediate forecasting improvements.
More accurate buys
Teams invest in the right products at the right depth, reducing inventory risk and improving sell-through.
Stronger demand planning alignment
Merchandising and planning operate from a shared understanding of the line, reducing friction and accelerating alignment.
Improved adoption accuracy
Executives and regional leaders make clearer early decisions because they’re reviewing complete, up-to-date product data—not partial snapshots.
Strong forecasting starts with strong data.
Modern Platforms Strengthen Forecasting Inputs
Today’s merchandising teams can’t rely on spreadsheets and PDFs to fuel multimillion-dollar forecasting decisions. A modern platform, like VibeIQ, that centralizes product data, automates updates, and connects visuals with attributes give planning teams the clean, real-time inputs they need to drive accuracy season after season.
When your teams can see the full picture, your forecasts finally reflect reality. Get in touch to learn how VibeIQ can help your teams make better, more informed decisions earlier in your calendar.
