Brand protection ROI: honest math for SMB brands

Every vendor in this category, us included, has an incentive to make the ROI story big. The difference we can offer is that our methodology ships inside the product where customers audit it monthly, not inside a sales deck — so it has to survive contact with reality. Here is that methodology, the payback math at $199/mo, and why we read the category’s bigger claims skeptically.

Why do vendor ROI numbers feel inflated?

Because most of them are upper bounds presented as expectations. The standard machine has three dials, and Red Points’ published ROI framework — credit to them for showing the working — illustrates all three. Their model takes removed listings, multiplies by average retail price, then by a “lost sale rate”: 5% conservative, 12% weighted, or a flat 15% “market benchmark,” which they use as the default baseline. Their worked example: remove 1,000 listings at a $50 average price and report $2,500, $6,000, or $7,500 recovered depending on the dial.

The structural problems with budgeting on that arithmetic:

• The dials multiply. Listing count × assumed price × assumed rate compounds three estimates into one confident-looking dollar figure. Nudge each 30% vendor-ward and the output more than doubles.
• Listings are not sales. Plenty of removed listings were zombie pages with no inventory or velocity. Valuing every removal at retail-price-times-rate credits the program with sales that never existed.
• “Economic value” is not revenue. Headline figures like “$X billion in counterfeits removed” typically mean listing volume × retail price — the sticker value of fake goods, not money any brand recouped.
• Published averages carry selection effects. Red Points’ Revenue Recovery Program advertises an average of $120,000 recovered within 3–6 months — plausible for the litigation-worthy cases that enter such a program, which are precisely the biggest ones. Your median counterfeit incident is not that case.

None of this is an accusation of bad faith — publishing the framework at all beats the category norm. The narrower point: when the assumptions are the vendor’s, treat the output as the ceiling, not the plan.

How do we estimate recovered revenue instead?

Brand Protector’s dashboard shows one impact line — “N listings removed · ~$X estimated recovered revenue” — computed with deliberately boring rules:

• A flat, low value per successful removal.~$300 when the removed listing was on Amazon, eBay, or Walmart; ~$150 on other surfaces. No retail-price multiplication, no lost-sale rate, no velocity model — we don’t know a given counterfeit’s sales velocity and decline to pretend otherwise.
• Only removals count.Detections, filings, and pending takedowns contribute $0 until the listing actually comes down — and a nightly verifier re-checks succeeded takedowns, so a listing that reappears doesn’t keep its credit.
• Exclusions stay excluded. AI shopping-tool corrections are a real win and count $0, because no revenue event can honestly be attributed to them. Domain takedowns get the standard rate, not a premium, unless you override it.
• Estimates are labeled as estimates.The tilde and the word “estimated” appear everywhere the number does, and tenants who know their own economics can override the per-channel rate.

Why set the defaults low? Because the number’s job is to be believed. An estimate you trust at $300 beats one you suspect at $3,000 — and if the conservative math still clears your subscription cost, no optimistic assumption was ever needed.

What does the payback math look like at $199/mo?

Read the table in both directions. Down: one successful removal a month clears the bill, and brands with a real counterfeit problem typically see more. Up: quiet months with zero removals are real, and the model credits them $0 even though deterrence is worth something. We also leave whole categories unpriced rather than guess: hours your team didn’t spend sweeping marketplaces, MAP compliance recovering margin, a lookalike domain caught before it phished anyone. Modeled the way enterprise calculators model lost sales, those would make the number look better and mean less.

What should you actually measure?

• Removals that stick. Takedowns succeeded minus reappearances — the only metric that maps to shelf reality.
• Time-to-takedown. Detection to filing to removal. Counterfeits do damage per day, so halving this beats most coverage expansions.
• Filing precision. Rejected or contested notices cost platform credibility and carry § 512(f) bad-faith exposure — why every Brand Protector filing passes a triple-validation gate first. A program that files less and lands more is winning.
• Hours not spent. Whatever your pre-tool weekly sweep cost, count it — the one ROI input you can measure exactly.

And the metric to retire: “economic value protected.” If a number can’t change a decision — renew, expand, cancel — it is decoration. The cost side of the equation is covered in What brand protection software actually costs; the vendor-fit side in our comparison pages.

Frequently asked questions

The cheapest way to test all of this is empirical: run the 7-day trial, let the first scans land (about 30 minutes in), and do the arithmetic on your own detections before day 8 — when the $199 starts, or doesn’t. Pricing stays public at /pricing.