Behavioral economics examples in marketing include preselected defaults, price anchors, loss-framed offers, scarcity messages and social proof: each uses a predictable shortcut in how people decide.
The research shows these shortcuts exist, but A/B tests show the lift is never guaranteed.
In our database, a preselected subscription raised one store's subscription rate by 50% and cut another store's revenue per visitor by 9.17%.
This page walks through the main ideas, with real tests for each one, and then shows how to turn behavioral science into tests you can run.
For the full list of biases, see our psychology of conversion hub.
Behavioral economics in marketing: examples from real tests
Behavioral economics studies how real people make economic choices, as opposed to the fully rational buyer of classical theory.
Richard Thaler won the 2017 Nobel prize in economics for this work.
The Nobel committee credited Thaler with the theory of mental accounting, research on fairness and self-control, and coining the term "nudging."
The field rests on earlier psychology. Daniel Kahneman and Amos Tversky described anchoring in a 1974 Science paper and loss aversion in prospect theory (1979).
Marketers borrowed these ideas because they map neatly onto pages: what's selected, what price appears first, how an offer is worded.
Here is how each idea has played out in A/B tests from our database.
1. Defaults: the option you preselect
Research: Johnson and Goldstein (2003) showed that European countries with opt-out organ donation had far higher consent rates than opt-in countries.
Madrian and Shea found that 401(k) participation was significantly higher under automatic enrollment, and many new hires kept the default contribution rate.
A meta-analysis of 58 studies found an average effect of d = 0.68 for defaults, with wide variation (Jachimowicz et al., 2019).
A/B tests: defaults are among the most reliable levers we see, but they cut both ways.
- Blend Commerce preselected the subscription option on Jackson's product page and saw +50% subscription rate, +23% add-to-cart rate and +10% AOV.
- SplitBase set Dr. Squatch's new quantity selector to 2 bars by default: up to +54% revenue per user for soap buyers, across several iterations.
- Dave Diederen introduced subscriptions with the option pre-checked and saw -9.17% revenue per visitor and -2.86% conversion rate upfront, while subscriptions rose to +1,295% of orders. Dave's read: the default asked for more commitment, so the first order suffered while the long-term value may not have.
- Convertibles tested a shipping protection toggle off by default against the old two-button widget: conversion went from 1.53% to 1.90% (+23.7%), with AOV down 6%.
The lesson: a default changes what people pick, so measure the metric the default actually moves.
More on this in our default effect guide.
2. Anchoring: the first number people see
Research: in Tversky and Kahneman's study, people who saw a wheel stop at 65 gave higher estimates of the share of African nations in the UN than people who saw 10. The first number pulled later judgments toward it.
A/B tests: subscription pricing gives the clearest examples.
- Preu AI added an annual plan priced at 60% of the monthly price next to the monthly-only plan: +80% proceeds per user and +22% trial conversion.
- Mojo showed its annual plan as a per-month price billed annually instead of a yearly total: new revenue per paywall impression rose +45% in Brazil, +26% in Mexico and +8% in the USA.
The Mojo result varied more than fivefold by country, which is a good reminder that a single anchoring win doesn't transfer automatically. See anchoring in pricing for more tests.
3. Loss aversion and framing: how the offer is worded
Research: prospect theory says losses weigh more than equal gains.
A 2024 meta-analysis of 607 estimates put the average loss aversion coefficient at 1.955 (Brown, Imai, Vieider and Camerer), lower than the 2.25 often quoted.
Framing research also finds smaller effects in persuasion than in lab gambles: a meta-analysis of 93 studies found no meaningful advantage for loss-framed messages about disease prevention (O'Keefe and Jensen, 2007).
A/B tests: framing works when it changes what the buyer thinks they get.
An Optimizely retail customer presented a free trial as a built-in part of the plan instead of an add-on and saw +37% enrollment.
Our loss aversion and framing effect pages cover more tests.
4. Scarcity: limited stock and limited time
Research: scarcity makes things seem more valuable. In the classic cookie-jar study, cookies from a nearly empty jar were rated as more desirable than the same cookies from a full one (Worchel, Lee and Adewole, 1975).
Lab findings like this say nothing about how big the effect will be on a product page.
A/B tests: results depend on whether the claim is believable and where it appears.
- Convertibles replaced a "High demand" banner with a stock meter on a golf brand's product page: +26.4% revenue per visitor (368 vs 297 orders). The meter ran on a timer, not live inventory, which raises the ethical question covered in our dark patterns page.
- Blend Commerce added limited-time copy, with no discount, for Stone Creek Coffee's seasonal coffees: +16% conversion rate.
- Clean Commit added a "high demand" message to a cart and got +2.7% conversion rate over 141,998 visitors, which was not statistically significant.
- Conversion Fanatics placed an urgency message only above the order button and the control converted 32% better.
Oliver Kenyon's team found that scarcity worked when it matched a real constraint: authentic scarcity messaging for a brand built on limited stock forecast +$46,900 per month. See scarcity and urgency.
5. Social proof: what other people do
Research: in hotel studies, a message saying most guests reused towels raised reuse from 35.1% to 44.1%, but a German replication found the norm message did no better than a standard one.
A/B tests: Conversio added popularity messages to Hunter Boots product pages and saw +9% conversion rate in the UK and +6% in the US.
Yet Dave Diederen removed a social proof slider from the top of an advertorial and got +2.96% revenue per visitor.
Dave's explanation: cold traffic needed a reason to keep reading before it was ready to trust.
More in social proof and the bandwagon effect.
@DaveDiederen
Blend Commerce
Convertibles@DaveDiederen
Conversion FanaticsBehavioral science in marketing: what the evidence supports
Behavioral science marketing is the broader practice of using findings from psychology and behavioral economics to design pages, offers and messages.
The honest summary is that the effects are real but usually smaller than the famous studies suggest.
Lab effects shrink at scale
A 2022 PNAS meta-analysis of more than 200 studies reported an average nudge effect of d = 0.45.
A reanalysis in the same journal argued that after correcting for publication bias, no evidence for an overall effect remained, though the evidence was inconclusive, rather than negative, for structural nudges such as defaults.
The most useful comparison for marketers comes from DellaVigna and Linos, who studied 126 trials run by two US government nudge units, covering 23 million people.
Nudges in academic journals averaged an 8.7 percentage point effect.
Nudges run at scale averaged 1.4 percentage points, about an 8.0% increase.
Selective publication explained about 70% of the gap.
| Source of evidence | Average nudge effect | What it means for you |
|---|---|---|
| Academic journal studies | 8.7 percentage points (+33.4%) | Inflated by publication bias and small samples |
| Nudge unit trials at scale | 1.4 percentage points (+8.0%) | Closer to what a real rollout delivers |
| Published A/B test wins, including ours | Varies widely | Also a biased sample: losing tests get posted less often |
How to turn a behavioral idea into a test
- Start from a customer problem, not a bias. "Buyers don't see that the annual plan is cheaper" is testable. "Use anchoring" is not.
- Pick the metric the idea moves. A subscription default moves subscription share and lifetime value, not just first-order conversion, as Dave Diederen's pre-checked test showed.
- Keep claims true. Fake stock counts and countdowns that reset can win a test and still mislead buyers. A short-term lift built on a false claim costs trust later.
- Size the test for a small effect. If at-scale nudges average 8% relative lifts, you need enough traffic to detect that. Check statistical significance before calling a winner.
- Expect losses. Several tests above lost or came back flat. Browse all test results by element to see both sides before you copy an idea.
Behavioral economics is best used as a source of hypotheses.
The research tells you where to look; your own A/B test tells you whether it works for your customers.
For a wider list of effects, see our cognitive biases guide.