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Category: Analytics

Discount or New Normal? Telling Fake Promotions from Real Price Moves

3 min readPublished: August 12, 2026

How does promotion detection work? How Senkrondata separates list price from special price to tell a genuine discount from a decorative anchor.

A competitor slapped a "40% off" tag on a product. Should you panic and cut prices too? The answer depends on whether that "discount" is really a promotion or has quietly become the new normal price — and confusing the two is one of the most expensive pricing mistakes there is.

The problem: a screen showing "119 instead of 199" doesn't mean the product ever actually sold at 199. That "struck-through" price is often a purely decorative anchor no one has paid in months.

The anchoring pattern: the list price is pushed up right before a discount starts, then "cut" from the same real price
The anchoring pattern: the list price is pushed up right before a discount starts, then "cut" from the same real price

Two prices, one tag

The foundation of correct promotion analysis is recording two numbers separately for every price observation:

  • List / regular price: the number shown as the product's "real" price.
  • Special / discounted price: what the customer actually pays (when there's no discount, the two are equal).

Flattening these into a single "price" field makes promotions invisible. Kept apart, you can measure the gap between the "actual selling price" and the "claimed discount." Add a promotion flag kept on each observation (was this price part of a campaign?) and the picture sharpens.

The patterns that give away a fake promotion

Reading price history (see the price history model) along the time axis, "real discount" and "decorative discount" leave different signatures:

  • A permanent discount = the real price. If a product has been continuously "on sale" for six months, that special price is the product's real price; the list price is just decoration. Compare against it and you'll think the competitor is pricier than they are.
  • The list price "jump." Pushing the list price up right before a discount starts, then "cutting" it — a classic anchoring trick to make the discount look deeper. History exposes this.
  • Discount rhythm. Some products go on sale on a predictable schedule (every month-end, every weekend). That isn't an "opportunity," it's the competitor's price architecture.
The permanent discount signature: a product on sale for 6 uninterrupted months means the special price is now the real price
The permanent discount signature: a product on sale for 6 uninterrupted months means the special price is now the real price

What the right signal changes

Being able to separate promotion from real price movement translates directly into better decisions:

  • You avoid overreacting. You don't stare at a competitor's decorative "40%" tag and burn margin on an unnecessary real discount.
  • You catch the real drop in time. If a competitor quietly (without calling it a campaign) lowered its price permanently, you see it as a strategic move, not "discount noise."
  • You benchmark your own campaign. Is your discount actually deeper than the competitor's, or are you anchoring too?

The principles that keep it trustworthy

  • Never confuse list price with paid price. Comparison must always be on the price the customer actually pays.
  • A promotion is judged with its history. A single observation says "there's a discount"; only the time series says whether that discount is real.
  • Permanence cancels the promotion. A discount that never ends isn't a discount.

The bottom line

"40% off" is a marketing sentence, not a fact. When you separate list price from special price and read it alongside price history, you can see which discount is a genuine opportunity and which is just a decorative anchor — and you respond to the competitor's real price, not their marketing language.

If you want to tell whether competitor discounts are real, talk to the Senkrondata team.

E

Emre

Price Intelligence & Data Engineering

Emre writes about the machinery behind competitor price data: product matching, normalization, collection at scale and the analytics layer on top.

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