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Competitor Price Change Detection Lag Calculator

Reviewed for 2026, updated September 8, 2026.

Estimates how long a competitor's price move goes unnoticed at your current check frequency, how many product-days per month you are exposed, and how much revenue is at risk during that lag, for teams deciding how often to check competitor prices.

Your numbers

Results update as you type.

Your estimate

Average time to notice a change...
Exposed product-days per month...
Monthly revenue at risk now...
Monthly revenue at risk at the faster frequency...

Estimates only. Assumptions are listed below, and you can change every input.

If you check a competitor's price once a week, a cut they make on Tuesday morning sits undetected until the following Monday. Multiply that lag by every undercut across every product you watch and you get a running count of product-days where you are quietly the expensive option and nobody knows. The cost of that lag is invisible in any report, which is why check frequency is usually set by habit rather than by numbers.

This calculator assumes competitor changes happen at random moments between your checks, so the average time to notice one is half the check interval. It adds your reaction time, multiplies by the number of undercuts you see in a month, and converts the resulting exposed product-days into revenue at risk using the daily revenue of a typical affected product and your own estimate of how much of it you lose while undercut. It then reruns the same math at a faster check frequency you pick, so you can see what closing the gap would be worth. The revenue loss share is your estimate, not a prediction.

How to use this tool

  1. Enter how many days pass between checks of the same competitor price today, and how long it takes you to act once you see a change.
  2. Enter how many undercuts you typically spot in a month, the daily revenue of a typical product that gets undercut, and your honest estimate of the share of that revenue you lose while you sit above the competitor.
  3. Pick a faster check frequency to compare against and read the difference in monthly revenue at risk. Weigh that against the cost of checking more often, whether by hand or with software.

What the math assumes

  • Competitor price changes are assumed to land at random moments between your checks, so the average detection lag is half the check interval; a change made right after a check waits nearly the full interval.
  • Every undercut is treated as affecting one product with the daily revenue you enter; if several competitors undercut the same product in the same week, the tool counts each as separate exposure.
  • The revenue loss share is your own estimate and is applied flat for every exposed day; the tool does not model how shoppers respond to a price gap of a given size.
  • Reaction time is added to every undercut regardless of check frequency, so faster checks reduce the lag but not the time it takes you to act.
  • Only revenue is estimated; margin, the cost of responding, and cuts you would deliberately choose not to match are not included.

Frequently asked questions

Why is the average lag half the check interval and not the full interval?

Because a change can happen at any point between two checks. One made right after a check waits almost the full interval; one made right before waits almost nothing. Averaged over many changes, the wait is about half the interval.

How do I estimate the revenue loss share?

Look at a past undercut where you responded late and compare that product's sales during the gap with a normal week. If you have no data, start conservative and note that the answer scales in a straight line with whatever you enter, so you can see the range quickly.

Does this calculator prove that daily monitoring pays for itself?

No. It shows the revenue exposed at your current lag versus a faster one. Whether that justifies more checks depends on what those checks cost you and on margin, which this tool does not model.

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