Sell-In vs. Sell-Through
Quick Answer: Sell-In records when your brand ships product to a retailer. Sell-Through records when that retailer sells the product to a consumer. Both are supported in Moselle — and understanding the difference is key to accurate forecasting.
Understanding the difference between Sell-In and Sell-Through data is essential for accurate demand forecasting. Both data types are commonly provided by major retail partners like Sephora and Ulta Beauty — and Moselle is built to process both.
Overview
When brands sell through retail partners, there are two distinct points at which a "sale" is recorded:
Sell-In — when the brand ships product to the retailer
Sell-Through — when the retailer sells that product to the end consumer
These two numbers are often very different, and understanding the gap between them is one of the most powerful signals available in demand planning.
Sell-In Explained
Sell-In (sometimes called "ship-in" or wholesale shipment data) captures the movement of inventory from the brand or supplier into the retailer's distribution network.
What does Sell-In represent?
Sell-In reflects purchase orders (POs) placed by the retailer. A unit is counted when it ships from the brand to the retailer — not when it reaches a consumer.
Who owns Sell-In data?
The brand owns Sell-In data. It comes from the brand's own order management or ERP system, though retailers like Sephora and Ulta may also provide a version of this data through their vendor portals (e.g., Sephora's Brand Portal or Ulta's Partner Hub).
What does a typical Sell-In file contain?
PO Number
Purchase order identifier
Ship Date / Receipt Date
When product was shipped or received
SKU / UPC
Product identifier
Units Ordered
Quantity the retailer requested
Units Shipped
Quantity actually fulfilled
Wholesale Price
Cost per unit at the brand-to-retailer level
Destination
Store number or distribution center (DC)
Sell-Through Explained
Sell-Through (also called POS data, retail sales data, or consumer offtake) captures the movement of inventory from the retailer's shelf to the end consumer.
What does Sell-Through represent?
Sell-Through reflects actual point-of-sale (POS) transactions. A unit is counted when a consumer purchases it — whether in-store or online.
Who owns Sell-Through data?
The retailer owns Sell-Through data. Sephora, Ulta, and other retail partners share this data with brands through their vendor portals on a weekly basis. It is one of the most valuable data assets a brand can receive.
What does a typical Sell-Through file contain?
Week Ending Date
The week the sales occurred
SKU / UPC / Retailer Item #
Product identifier (may differ from brand's internal SKU)
Store Number or "Dotcom"
Location of the sale (store ID or e-commerce flag)
Units Sold
Consumer units sold in that period
Retail Sales ($)
Revenue at the consumer price point
On-Hand Inventory
Units remaining in stock (included by some retailers)
Weeks of Supply
Estimated coverage based on current sell rate (included by some retailers)
Key Differences
Measures
Brand shipments to retailer
Consumer purchases at retail
Data Owner
Brand
Retailer
Timing
Can precede actual demand by weeks
Reflects real-time consumer demand
Format
PO/shipment records
Weekly POS reports by store
Common Source
ERP, order management system, vendor portal
Sephora Brand Portal, Ulta Partner Hub
Best Used For
Supply and production planning
Demand forecasting and replenishment
Why It Matters for Forecasting
Sell-In data alone can be a misleading demand signal. A retailer may place a large initial PO ahead of a product launch or promotional event — making sell-in numbers look strong — while actual consumer sell-through remains slow. If a brand plans production based on Sell-In without monitoring Sell-Through, the result is often excess inventory at retail, markdowns, or returns.
Sell-Through data is the ground truth of consumer demand. It reflects what shoppers are actually buying, week over week, at each store. Forecasting from Sell-Through leads to:
More accurate replenishment recommendations
Earlier visibility into slow-moving SKUs
Better alignment between production and real market demand
Fewer surprise returns or markdown situations
Sell-Through Rate — calculated as Sell-Through Units ÷ Sell-In Units — is a key retail health metric. A healthy sell-through rate (typically 80%+ depending on category) means inventory is moving efficiently. A low rate signals a buildup that may require promotional support or production adjustments.
How Moselle Processes These Files
Moselle supports ingestion of both Sell-In and Sell-Through files from major retail partners including Sephora and Ulta Beauty. Files are typically delivered in Excel (.xlsx) or CSV format.
Frequently Asked Questions
What if I only have Sell-In data?
Moselle can still generate forecasts using Sell-In data. However, forecasts will be more accurate once Sell-Through (POS) data is available. We recommend requesting POS access from your retail partners as early as possible in the onboarding process.
What if I only have Sell-Through data?
Sell-Through data is the preferred input for demand forecasting and is fully supported on its own. Mo will use it as the primary signal to generate forecasts and replenishment recommendations.
My retailer uses different SKU codes than my internal system. Will Moselle handle that?
Yes. Moselle supports SKU mapping between retailer item numbers and your internal SKUs/UPCs. This is configured during onboarding. Reach out to your Customer Success Manager if you need to update or add mappings.
How often should I upload Sell-Through data?
Most retail partners (Sephora, Ulta) provide POS data on a weekly basis. We recommend uploading on a weekly cadence to keep your forecasts current and ensure Mo has the most recent demand signal available.
What is a healthy sell-through rate?
This varies by category, channel, and product lifecycle stage. As a general benchmark, a sell-through rate of 80% or above is considered healthy in most beauty and personal care categories. Your Customer Success Manager can help you interpret your sell-through data in context.
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