For the vast majority of beauty brands selling through retailers like Sephora, Ulta, Target, Boots, or national department stores, demand is inferred from sell-through data. Daily sales, promotional uplift, and store performance reports are treated as reliable indicators of what the market wants.
However, this can be misleading as these numbers only show what could be sold, not what should have been sold.
When a SKU is out of stock in a store, demand does not queue up waiting to be recorded later, it’s simply lost. Shoppers substitute, delay, or abandon the purchase entirely. In that moment, every potential sale that could have been made or recorded simply disappears and doesn’t get logged as a data point.
- No lost sales get logged.
- No signal is fed back into planning.
- No record exists that demand ever occurred.
For brands selling through retail distribution, this creates a structural issue: you are planning future inventory based on demand figures that are understated.
Why Out-of-Stocks Distort Forecasting Long After Inventory Returns
Out-of-stocks (OOS) are typically treated as temporary planning issues, something that gets resolved once the inventory is replenished. However, in reality, the impact of OOS can be felt long after stock returns to store shelves.
When a SKU goes out of stock:
- Sales velocity drops immediately, not because demand has weakened, but because there’s nothing on the shelves to be sold.
- Forecasting models then project forward using artificially low sales data as the true demand signal.
- Future orders are placed based on understated market demand, perpetuating the original stock shortfall
When stock finally gets replenished, it often sells through again at pace. But it’s already too late, the next forecasting, production and replenishment cycle is already underway.
In many wholesale and retail supply chains, the time between identifying an issue, adjusting forecasts, placing orders, and receiving inventory can easily exceed 60–90 days. During that period, the business continues to project demand using incomplete demand data.
This creates a recurring loop:
- In store consumer demand exceeds supply on shelves
- Forecast data is understated versus true market demand
- Supply chain is blindsided, and usually responds too slowly to backfill
- In store stockouts repeat
- True market demand remains invisible and sales are missed
Over time, brands don’t just miss individual sales, they systematically under-plan for products that the market is repeatedly telling them it wants. Along with lost sales, there’s also a diminishing reputation for being a reliable retail partner.
Comfort in False Availability Metrics
Your brand might report “98% availability” across your retail network. On the surface, this suggests inventory is largely under control and puts smiles on executives’ faces.
But if you peel back the surface layer, you might find that your availability metrics are only as meaningful as the assumptions behind them. Let me explain…
Generally speaking availability is commonly measured in one of a few ways: the percentage of SKUs technically in stock, or inventory as a percentage of sales demand available across stores. Both can look healthy while masking significant commercial risk.
For example, a brand may have strong availability across slower-moving SKUs while repeatedly running out of a small number of high-velocity, high-margin products. From a reporting perspective, availability remains high. From a revenue perspective, demand is being missed where it matters most.
More importantly, these metrics rarely consider when and where demand occurs.
A product that is available for most of the week but out of stock during peak shopping periods, promotional windows, or high-traffic days is often treated as “available” in aggregated reporting. Yet from the customer’s point of view, it was unavailable at the exact moment they wanted to buy.
This is where inadequate availability reporting stops being reassuring and starts becoming misleading.
The real risk is not just that missed sales go unnoticed, but that teams make confident planning and allocation decisions based on incomplete signals. Stockouts are deprioritised, forecast corrections are delayed, and the same gaps repeat cycle after cycle.
Brands that move beyond this trap, shift the question they ask. Instead of “Was the SKU technically in stock?”, they ask “Was the full market demand actually served?”
Linking availability to demand, at store and SKU level, is what turns availability from a static percentage into a commercial insight. It’s also where the scale of revenue leakage becomes visible, and where the case for a more sophisticated analytical approach becomes unavoidable.
The Hidden Commercial Impact of Out-of-Stocks
Because lost demand is never explicitly recorded as a data point, its commercial impact tends to surface indirectly rather than as a clear metric.
Brands often experience:
- Promotions that underperform expectations
- Store or door-level performance that appears inconsistent
- Categories that plateau despite strong consumer interest
First-hand perspectives from beauty operators working directly with Ulta and Sephora provide additional context on how these availability distortions surface in practice, as outlined in this article How Beauty Brands Identify Lost Revenue Caused by Out of Stocks.
At a global level, industry research has consistently shown that out-of-stocks can cost retailers and brands several percentage points of revenue annually, with estimates often ranging between 2–4% of sales, depending on category and substitutability. In beauty where brand loyalty, shade matching, and routine repurchase are common, the cost can be materially higher when customers are unable to find the exact product they want and substitute or replacement products can’t fill the void.
Even modest improvements matter.
For a brand doing $1m in wholesale revenue, even a small increase in effective availability can represent tens of thousands of dollars in recovered sales without increasing demand or marketing spend.
One example of this shift in practice is outlined in our Bubble Skincare case study, where retail data consolidation improved visibility into constrained demand.
Reconstructing True Demand in a Retail Environment
Brands that address revenue leakage don’t start by consolidating more spreadsheet reports or chasing incremental reporting improvements.
They start by reframing the question.
Instead of asking, “What did we sell?”, they ask:
“What would we have sold if inventory had been consistently available?”
Answering that requires:
- Analysing sales patterns before, during, and after stockouts
- Identifying unconstrained demand at store and SKU level, not just in aggregate
- Separating genuine shifts in consumer behaviour from availability-driven distortions
When this insight is fed back into planning, it allows the brands to:
- Correct demand signals earlier
- Allocate inventory more effectively across doors
- Reduce repeated stockouts in high-impact SKUs
- Improve conversations with retail partners using evidence rather than assumptions
This is where Accelerated Analytics is typically used, as a method of harmonizing fragmented retail data across partners like Sephora, Ulta, and Target, and restoring visibility into demand that traditional reporting structures fail to capture.
A practical overview of how this consolidation works across retail partners can be seen in the beauty brand retail analytics overview.
Ask Yourself
As you reflect on your own business, it’s worth considering:
- How many sales were lost when a product was out of stock and lost demand was never recorded?
- Which SKUs consistently sell out shortly after replenishment?
- How long does it take for planning and supply chain adjustments to take effect once an issue is identified?
- What would a 1.0% improvement in true availability mean in revenue terms for your brand?
Without visibility into true market demand, these questions are difficult to answer with confidence. With a reliable analytics tool, they become accessible, measurable, and more important actionable.
See How Accelerated Analytics Reveals Hidden Demand Signals
Retail sales reports tell you what has already happened.
But the most valuable insight often comes from understanding the demand signals behind those outcomes.
When retail datasets from multiple partners are analysed together, patterns begin to emerge that traditional reporting structures often hide. These can include recurring stock-out patterns, regional demand differences, and products whose performance is constrained by availability rather than lack of demand.
Accelerated Analytics helps brands reconstruct this broader demand picture by bringing retail datasets together into a unified analytical environment.
This allows commercial teams to:
- Identify hidden demand signals across retailers
- Detect emerging product demand earlier
- Understand how availability affects sales performance
- Uncover revenue opportunities masked by fragmented reporting
If you would like to see how this works in practice, we can walk through how the platform identifies demand patterns across retail datasets.
This approach is already used by brands operating across retailers such as Sephora, Ulta, and Target to better understand demand behaviour beyond traditional retail reporting.
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