What is the sales history?

What does it take to make an accurate prediction?

The right question isn't "how many years," it's "how much trust"

How many years of historical data are needed to make accurate forecasts? This is the question asked every time a forecasting tool is deployed, and it almost always elicits a cautious expert response: “It depends.”

The answer is true, but it hides a more useful question—one that determines whether a two-year sales history is better than a six-month sales history: Does this sales history reflect actual demand, or just what the store has managed to sell?

A long history is no guarantee if some of that data is silently misleading. Conversely, a short but clean history may be sufficient for an accurate forecast based on a stable benchmark.

The bottom line: Depth matters. Reliability matters even more.

Booper has already published a comprehensive article on the mechanics of calculating a sales forecast— Sales Forecasting with AI: Method and KPIs —which addresses historical data and granularity in a single sentence, as part of a broader methodology. This guide revisits that specific point and goes further: the necessary depth depending on the category, data gaps that skew historical data without indicating it, changes in context that invalidate part of the past, and cases where historical data is simply no longer sufficient.

The required depth depends primarily on the category

A yogurt sold year-round and a swimsuit sold for six weeks don't have the same need for historical data—the issue is considered category by category, not store by store.

Maintaining the appropriate depth by category helps avoid two common mistakes: underweighting a seasonal product based on a 12-month history, or overweighting a stable product with a five-year history that offers no additional insight beyond the last 12 months.

The Censored Request: What an "Out of Stock" Message Hides in Your Order History

This is the most subtle and systematic bias of all—because it doesn’t show up in the numbers. An empty shelf on a Tuesday afternoon doesn’t register as a lost sale.

The point-of-sale system records “0 sales” even though actual demand continued to exist. This unrecorded sale is not the absence of a signal: it is a false signal, indistinguishable from a genuine decline in demand if no one corrects it.

Researchers call this phenomenon “censored demand ”: the observed sales history does not represent actual demand; rather, it represents actual demand capped by what was available on the shelves. A forecasting model trained on this uncorrected history learns a consistent falsehood—it systematically underestimates demand for SKUs that have been out of stock, and therefore recommends restocking levels that are still insufficient.

The bias perpetuates itself: fewer units ordered, more stockouts, and an even more skewed history the next time around.

The practical consequence is not merely theoretical. A sales history that has never been corrected for its discrepancies artificially caps all the forecasts based on it—until someone asks why an item that “never sells well” is systematically allocated too little inventory to prove otherwise.

History is reliable only if the context has not changed around it

A stockout is not the only event that renders part of the transaction history unusable without any notification. Four context changes—which are very common but rarely documented in point-of-sale systems—have the same effect:

• A change in the product lineup. A product that was discontinued and then reintroduced, modified packaging, a new line replacing the old one under a similar name: the system continues to track a single, continuous sales series even though there are actually two different products.
• A point-of-sale promotion or product highlight. A spike in sales resulting from an end-cap display or an in-store promotion is not a normal level of demand. If the historical data does not distinguish between weeks with and without promotions, the model will learn a sales pattern that will not be replicated without the same marketing effort.
• A change in shelf placement. When a product is moved from a shelf at eye level to a less visible location, sales volume declines without any disruption or price change to account for the drop in the data.
• An unrecorded price change. A sales history that does not track past price changes—increases, decreases, promotional offers—is just as unreliable as one that ignores stockouts. A sale at the regular price and a sale at 30% off do not reflect the same level of demand; a model that treats them as equivalent learns a false signal.

These changes don’t just happen out of the blue: they’re deliberate decisions. Along with the purchasing team, the pricing team is one of the main sources of this uncharted context—every price increase, decrease, or promotional launch deserves to be dated and documented with the same rigor as a stockout, precisely because it obscures the historical record in the same way.

This is not a rare anomaly. A landmark study of inventory data—covering 37 U.S. retailers and nearly 370,000 tracked SKUs—measured the extent to which on-the-floor data diverges from the physical reality of the shelf as soon as something moves on the floor without being tracked in the system.

The lesson isn't to throw everything out. It's to be able to pinpoint changes in context—such as a simple log of sales transactions, planogram changes, and known stockouts—so that the forecasting model (or the person overseeing it) knows which weeks in the historical data are comparable to one another, and which are not.

When History Is No Longer Enough

There are two situations in which the history is structurally inadequate, regardless of its length or how well-organized it is.

Launching a Product with No Track Record

By definition, a new product has no past sales to analyze.

The forecast should therefore be based on comparable products that have already been sold—a comparable product line, a similar price point, and similar seasonality—rather than waiting several months to collect data before producing an initial usable figure.

This exercise is different from reading historical data: it relies on assessing similarity, not on calculating a time series.

The Structural Disruption of the Market

A lasting change in purchasing behavior—a new consumer habit, the arrival of a competitor that changes the local landscape, or an economic shock that redefines the purchasing power of a trade area—makes some of the historical data misleading rather than useful.

The data are accurate, complete, and properly dated. They simply describe a market that no longer exists in this form.

Continuing to train a model using data from the past three years is like trying to predict the future using a map of a territory whose borders have changed.

In both cases, the best approach is the same: reduce the weight of past data in the forecast, and make this adjustment explicitly rather than allowing a model to continue weighting a past that no longer serves as a reference.

Ensuring the reliability of historical data before using it for forecasting

A reliable history is just a starting point

A historical record that has been properly adjusted for disruptions and changes in context provides a solid basis for forecasting—but it says nothing about what will happen next week if an event outside the store changes the situation: weather, a floating holiday, current events, or a competitor’s actions. This is a topic in its own right, covered in another article in this series devoted to exogenous events.

Key takeaway: The quality of the history remains the foundation.

An organization that invests in a sophisticated forecasting module without first addressing its stockouts or documenting its assortment changes is building an accurate forecast on a flawed foundation—the opposite of the right order of priorities.

A pricing and sales data analysis—such as the one Booper offers through its pricing analytics service—almost always begins with this question: How much can we trust historical data before building anything on it?

Sales campaigns themselves—including promotions and marketing activities —should be tracked with the same rigor as a price change, precisely because they are one of the most common causes of a history that is misleading without saying so. This is also the point made in the key article of this series on the overall governance of sales forecasting: the quality of input data is never a purely technical issue; it is a matter of organization and collective discipline.

Want to know if your sales history is ready for a reliable forecast?

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Frequently Asked Questions

Also in this series

• Sales Forecasting: Methods, AI, and Best Practices (Feature Article)
• External Events: How to Incorporate Them Into a Sales Forecast

Sources

• arXiv, FreshRetailNet-50K: A Stockout-Annotated Censored Demand Dataset, 2025 — arxiv.org
• Nicole DeHoratius, Ananth Raman, “Inventory Record Inaccuracy: An Empirical Analysis,” *Management Science*, INFORMS, 2008 — pubsonline.informs.org
• Booper, AI-Powered Sales Forecasting: Methodology and KPIs (Supplementary Article, Technical Details) — booper.fr
• Booper, internal product data (`context/socle_booper.md` §3) — GENIUS Predict, GENIUS Monitoring.