Not legal advice: This article covers marketing practice and general legal awareness of consumer-protection rules, not a legal audit. If your business runs sales or discount promotions, confirm current disclosure requirements with a qualified lawyer.
Search "do discount codes actually work" and marketing blogs answer with the same confident, uncited handful of numbers — usually about how much better a unique code converts than a generic one. Search "are discounts bad for my brand" and a different set of blogs pushes back with an equally confident "discounting trains customers to wait," rarely citing anything past a vague "studies show." Both conversations are about the same string of letters sitting in a popup, a welcome email or a prize wheel, and neither side usually checks its own math.
We went and checked: the largest recent US survey we could find on how long people actually wait for a discount, the peer-reviewed marketing study most often cited on what deep discounts do to a customer's long-term value, the actual court record behind the coupon-extension scandal that just cost PayPal's Honey a chunk of its merchant roster, and Israel's own consumer-protection law on what a "was" price is legally allowed to mean. What we found: the appetite for a discount is real and well-measured; the case against acquiring customers with deep discounts is also real and considerably older than the current debate suggests; and the loudest claim in the whole subject — that a unique code converts some precise percentage better than a generic one — turns out to be the one number here that nobody can actually source.
Key takeaways
- CouponFollow's March 2026 survey (1,000 US adults, disclosed demographics) found 91% of Americans delay a purchase to wait for a discount, 61% will hold out a month or longer, and people say they need at least 26% off before the wait feels worth it — with 51% saying they've intentionally abandoned a cart hoping a discount email would follow.
- A peer-reviewed 2006 Journal of Marketing Research study of real newspaper-subscription and online-grocery purchase data found a 35% acquisition discount left customers with roughly half the long-term value of customers who didn't get one — the discount's depth, not its mere existence, is what tracked with the damage.
- A separate peer-reviewed study (Journal of Retailing, 2010) found "extreme cherry pickers" — shoppers who almost exclusively buy on deep discount — are a small slice of any store's customers (about 2%), and that a loss-leader strategy can still add to a retailer's bottom line overall despite the pure loss those shoppers generate on their own baskets.
- The specific claim that a unique or personalized code converts some named percentage better than a generic one circulates across dozens of marketing blogs — we couldn't trace it to a disclosed sample size, date range or named study anywhere; the vendors publishing "redemption rate benchmarks by channel" label their own ranges directional, not measured.
- What a public, sharable code is actually vulnerable to is well-documented: a December 2024 investigation exposed PayPal's Honey extension swapping a referring creator's affiliate tracking cookie for its own at checkout; a US court let the resulting class action proceed past a motion to dismiss in June 2026, and Honey's merchant roster has reportedly shrunk by roughly a fifth since.
- Israeli law (Consumer Protection Law §15) already requires disclosing a sale's pre-sale price and discount rate — but unlike the EU's Omnibus Directive (in force since 2022), it doesn't yet require that pre-sale price to reflect any real prior period; a 2022 proposal to add exactly that rule hasn't passed.
How many people actually wait for a discount before buying?
CouponFollow surveyed 1,000 US adults in March 2026 — a roughly even split by gender, skewed toward millennials (50%) and Gen Z (21%), with Gen X (22%) and baby boomers (7%) rounding out the sample — and the topline number is about as close to a consensus as consumer research gets: 91% said they delay a purchase specifically to wait for a discount. That's not a fringe habit; it's close to the entire sample. How long they'll wait matters more than the topline, though — 61% said they're willing to hold out a month or longer, and 8% said they'd wait indefinitely rather than pay full price, which is a meaningfully more expensive kind of patience for a retailer counting on that sale than "I'll wait a few days for a code."
Patience isn't spread evenly across categories, either: 79% said they specifically hold out for a deal on electronics and tech, versus 48% for clothing and fashion — a gap that tracks with how comparison-shopped and replaceable each category already is. And on average, respondents said a discount needs to clear at least 26% before the wait actually feels worth it, a number worth sitting with the next time a 10%-off popup goes live: for most of an audience, that offer isn't crossing the threshold that changes behavior — it's landing on people who were already going to buy.
The same survey gives the habit a specific, actionable shape for anyone running an online store: 51% said they've intentionally abandoned a cart hoping a discount code would land in their inbox afterward, 65% said they've created a brand-new account specifically to claim a first-time-customer offer, and 64% said they've abandoned an item after missing a sale window entirely — at an average forfeited value of $183. None of that is a reason to discount everything reflexively; it's a reason to make sure an abandoned-cart flow exists at all, since a real share of the carts it's built to catch were abandoned on purpose.
Do steep discounts actually hurt how valuable a customer becomes?
This part of the debate predates the current discount-fatigue conversation by nearly two decades, and it's backed by real purchase data rather than a survey of stated intentions. Michael Lewis published "Customer Acquisition Promotions and Customer Asset Value" in the Journal of Marketing Research in 2006, built on real customer-level purchase histories from two very different businesses — a newspaper subscription service and an online grocer. In both datasets, the depth of the discount used to acquire a customer in the first place tracked negatively with how often that customer went on to buy again, and with their estimated long-term value to the business.
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the estimated long-term value of a customer won with a deep, 35% acquisition discount, relative to a customer who paid full price for their first order — found in both a newspaper subscription business and an online grocer's real purchase data.
Source: Michael Lewis, "Customer Acquisition Promotions and Customer Asset Value," Journal of Marketing Research, Vol. 43 (May 2006), pp. 195–203
Is every deal-seeking customer actually a lost cause — and does depth matter more than the discount itself?
The Lewis finding isn't "never discount" — it's specifically about depth used at the acquisition moment. A modest welcome offer that gets a first order across the line is a different mechanism from a 35%-off code built to win someone away from full price entirely, and the data shows the relationship tracks with how deep the discount goes, not with whether one exists at all.
It's also not the full story on who a "deal-seeking" customer actually is. A separate peer-reviewed study, published in the Journal of Retailing in 2010 by Debabrata Talukdar, Dinesh Gauri and Dhruv Grewal, looked specifically at what it called "extreme cherry pickers" — shoppers who almost exclusively buy on deep promotion — using real transaction and survey data. That segment turned out to be small, about 2% of shoppers, and it mostly showed up at stores those same shoppers didn't consider their primary one. The paper's more useful finding for a retailer weighing whether to run deep promotions at all: a loss-leader strategy can still add to the bottom line overall, even accounting for the pure loss those extreme cherry pickers generate on the specific items they cherry-pick.
Read together, the two studies point at the same practical line: an occasional, modest discount that wins a first order is a normal cost of acquisition. A habitually deep discount used as the main acquisition lever is what shows up in real purchase data as a customer worth less over their lifetime — and the shoppers chasing only the deepest discounts are a small, mostly-not-your-regular-customer slice to begin with.
Do unique, single-use codes actually convert better than one generic code — or is that recycled marketing folklore?
This is the specific claim behind almost every "why you need dynamic coupon codes" post, and it's usually delivered as a precise-sounding percentage: a personalized code converts some exact amount better, lifts average order value by some specific range. We went looking for where those numbers actually come from — the disclosed methodology, the sample, the study — the same way we'd check any other statistic before repeating it.
What we found is closer to a familiar pattern from our last data check than to a real study: the same handful of figures rotate across dozens of marketing and e-commerce blogs, almost always uncited, sometimes attributed to a vague "industry data" and sometimes to no source at all. The platforms that do talk in specifics tend to be candid that they're not measuring anything: one commerce-marketing vendor's own guide to redemption-rate benchmarks lists ranges for email (1–5%), SMS (5–15%) and loyalty-program rewards (10% and up), and explicitly labels them "general industry benchmarks" and "a directional guide, not an absolute measure of success" — without naming a single dataset, sample size or study behind any of the ranges.
| What gets claimed about unique codes | What we found checking it |
|---|---|
| A personalized/single-use code converts a specific % better than a generic one, and lifts AOV by a specific range | The same figures rotate across marketing blogs with no disclosed study, sample size or date behind them — we couldn't trace the claim to an original source. |
| Redemption-rate benchmarks by channel (email, SMS, loyalty) prove personalization works | The vendor guide publishing those ranges labels them directional industry benchmarks, not measured results, and names no dataset behind any of them. |
| What's actually documented and verifiable | A code posted publicly has no built-in cap on how many times it's used and no way to trace a specific redemption back to a specific campaign or contact — a real, structural difference from a single-use code, independent of any conversion-lift percentage. |
None of this means personalization is fake — the structural case for a single-use code, below, holds up on its own without borrowing an unsourced percentage. What's manufactured is the specific lift number everyone quotes for it — the same pattern our QR code data check found in a completely different statistic.
What actually happens to a public coupon code — and why did PayPal's Honey extension end up in court over it?
A public, sharable code doesn't just risk unlimited redemptions — it can also be quietly intercepted by the browser extensions built specifically to "find" it. In December 2024, YouTuber MegaLag published an investigation into PayPal's Honey extension — installed in tens of millions of browsers, and marketed as a tool that automatically finds and applies coupon codes at checkout — showing Honey's "Apply Coupon" step doing something else at the same moment: replacing the affiliate tracking cookie of whichever creator had referred that shopper to the store with Honey's own, right before the sale completed. The practical effect: a creator who'd built an audience, recommended a product and earned an agreed commission on the resulting sale could watch that commission redirect to Honey instead — with no coupon code actually needed to trigger it.
The investigation became litigation. A group of content creators sued PayPal over the Honey extension, represented in part by the law firm Cohen Milstein, arguing the cookie-replacement behavior amounted to misappropriating commissions the creators had already earned. On June 22, 2026, a US court denied PayPal's motion to dismiss the consolidated class action, sending the case into discovery — meaning the claims survived their first real legal test instead of being thrown out. Industry reporting on the fallout describes three affiliate-network sanctions against Honey, one quiet reinstatement, and a merchant roster that's reportedly shrunk by roughly a fifth since the investigation went public.
The Honey case is about affiliate commissions, not shoppers being defrauded directly — but it's a concrete, court-tested example of what happens once a discount mechanism becomes public and automatable: something in the middle starts intercepting value nobody put there on purpose. A single-use code generated per contact, redeemable exactly once and never posted anywhere public, isn't exposed to that specific failure mode at all.
Generic public code vs. a unique single-use code — what's actually different?
Setting the unverified conversion-lift percentage aside, the structural differences between a public code and a single-use one are real and checkable on their own:
| A generic, public code | A unique, single-use code |
|---|---|
| Anyone who finds it can redeem it — no cap on total uses unless you build one separately | Redeemable exactly once, by design — a leaked code is worth one order, not unlimited orders |
| Every redemption looks identical — no way to trace a sale back to a specific contact or campaign | Every redemption is already tied to the contact and automation that generated it |
| Vulnerable to being harvested by coupon-aggregator sites and browser extensions built to surface exactly this kind of code | Never posted publicly in the first place — there's nothing generic for an extension to find |
| Free and simple to run for a short, low-stakes promotion | Needs a connected store and an automation (or a gamified mechanic like a prize wheel) to generate and track codes per contact |
oToK generates a unique, single-use coupon code per contact straight from an automation — or from a winning slice on a Spin to Win wheel — the moment a connected Shopify, WooCommerce or Wix store (available on Pro and up) makes that code redeemable, and the order it produces traceable back to the exact campaign that sent it.
Is a "was" price legally required to be real — and does anyone actually enforce that?
The 84%-of-shoppers-think-the-discount-is-fake problem isn't just cynicism — in most of the world, there's genuinely no law yet forcing a sale banner's "was" price to mean anything. A global consumer survey from Lightspeed Commerce, reported in Israeli business press ahead of Black Friday 2025, found 84% of consumers believe retailers inflate prices before a sale specifically to make the discount look bigger — a view held even more strongly, at 87%, among 18–24-year-olds. That skepticism didn't stop people from shopping: Israeli Black Friday spending still reached roughly ₪1.508 billion in 2025, up about 28% year over year — record spending and record distrust of the discount itself, happening at the same time.
Israeli law already regulates part of this. Section 15 of the Consumer Protection Law (חוק הגנת הצרכן, תשמ״א-1981) requires a merchant advertising a special sale to disclose which products are included, their price before the sale, and the discount rate or final price — that's existing, enforceable law, not a proposal. What the current law doesn't do is define what "the price before the sale" actually has to be: there's no rule today requiring it to reflect any real prior selling period, which is exactly the gap the 84% figure above is picking up on.
The EU closed an equivalent gap in 2022. Article 6a of the Omnibus Directive, in force since May 28, 2022, requires any advertised price reduction to be measured against the lowest price the trader actually charged in a defined prior period — 30 days is the reference window most member states adopted — specifically to stop a retailer from raising a price right before marking it down. Israel's own Consumer Protection Authority proposed a similar fix in 2022–2023: a reference price tied to the lowest price charged in the preceding three months, plus a cap on how much of a rolling six-month period can be spent "on sale." As of this writing, that proposal hasn't passed into law.
| Where | What the rule actually requires |
|---|---|
| EU — Omnibus Directive, Art. 6a (in force since May 28, 2022) | A discount must be measured against the lowest price charged in the preceding 30 days — a retailer can't raise the price first to inflate the markdown. |
| Israel — current law (Consumer Protection Law §15) | A sale must disclose the pre-sale price, the discount rate or final price, and the sale's terms — but the pre-sale price itself isn't required to reflect any specific prior period. |
| Israel — proposed amendment (Consumer Protection Authority, 2022–2023, not yet passed) | Would require the reference price to be the lowest price charged in the preceding three months, and cap sale time within a rolling six-month window. |
84%
of consumers surveyed globally by Lightspeed Commerce believe retailers inflate prices before a sale to make the markdown look bigger — 87% among 18–24-year-olds — in findings reported by Israeli business press ahead of Black Friday 2025.
Source: Lightspeed Commerce global consumer survey, cited by Israel Hayom (Nov 2025)
None of this requires waiting on the legislature to run an honest sale: disclosing a real pre-sale price and an accurate discount rate is already required under §15 today, regardless of whether the reference-price rule ever passes — and it's the one part of the trust problem a business fully controls before a shopper ever reaches the code field at checkout.
So how should a business actually run coupon-code marketing in 2026?
- 1Default to a unique, single-use code generated per contact from an automation rather than one code posted everywhere. Connect a Shopify, WooCommerce or Wix store (Pro and up) and any automation can hand out a one-time code the moment it fires, so a code that leaks is worth one order, not an unlimited number.
- 2If the discount is gamified — a Spin to Win wheel, say — cap the real cost with weighted odds per prize and an optional "first N winners" limit, so the promotion has a bounded ceiling even at high traffic, and the wheel does double duty as a list-growth mechanic rather than a pure margin cut.
- 3Keep the acquisition discount modest. Lewis's finding is specifically about depth: a 35%-off first-order code is a different mechanism from a 10–15% one, and the difference shows up later in repeat-purchase rate, not on the day of the first sale.
- 4Measure the campaign by revenue in Orders, not by redemption count alone. A coupon that drove a lot of first orders at a steep discount can still be a net loss once the Lewis effect plays out over a customer's actual lifetime, and Orders ties every code back to the campaign, automation or wheel that issued it.
- 5Route the code through whichever channel a contact actually engages with — an automation's channel router can try WhatsApp first and fall back to email (or the reverse) per contact, rather than betting a whole campaign on one channel's open rate.
- 6Keep the pre-sale price honest regardless of what the law requires today: disclose the real price before the sale and the real discount rate, the way §15 already requires. It's the cheapest available answer to the 84% of shoppers who already assume the "was" price is inflated before they even see the code.
None of this treats a discount code as either free growth or a trap to avoid. The disclosed data says both things are true at a modest depth at once: real, well-measured demand for a genuine deal, sitting on top of real evidence that the deepest version of that same mechanic quietly costs more than it looks like it earns.
Frequently asked questions
- Do unique, single-use discount codes actually convert better than one generic, shared code?
- Structurally, yes, in ways that are easy to verify on their own: a single-use code caps a leak at one redemption, ties every use back to a specific contact and campaign, and was never posted anywhere for a coupon-aggregator extension to find. The specific "converts X% better" figure that circulates in marketing blogs, though, doesn't trace back to a disclosed study or sample we could find — treat that particular number skeptically even while the structural case for single-use codes holds up on its own.
- Do deep discounts actually hurt a customer's long-term value?
- The depth used to win a first order does, per real purchase data: a 2006 Journal of Marketing Research study of a newspaper subscription business and an online grocer found a 35% acquisition discount left customers with roughly half the long-term value of customers who didn't get one. A modest, occasional discount is a different mechanism from a habitually deep one — the finding is about depth, not about whether a discount exists at all.
- What was the PayPal Honey coupon scandal, and does it affect a business running its own discount codes?
- A December 2024 investigation found Honey's browser extension replacing a referring creator's affiliate tracking cookie with its own at checkout, redirecting commissions. A class action followed, and a US court let it proceed past a motion to dismiss in June 2026. It doesn't directly touch a business's own single-use codes, but it's a real, court-tested example of what a public, automatable coupon mechanism becomes exposed to once it exists — the specific risk a private, per-contact code doesn't share.
- Is a retailer legally required to prove its "was" price is real before a sale in Israel?
- Partly. Section 15 of Israel's Consumer Protection Law already requires disclosing the pre-sale price and the discount rate or final price for any advertised special sale — that's current, enforceable law. What it doesn't yet require is that the pre-sale price reflect any specific real prior period, unlike the EU's Omnibus Directive (in force since 2022). A 2022–2023 proposal to add that reference-price rule to Israeli law hasn't passed as of this writing. This isn't legal advice — check with a qualified lawyer for compliance specifics.
