The Fogg Behavior Model says a behavior happens when motivation, ability and a prompt come together at the same moment, written as B=MAP.
If someone doesn't act, at least one of the three was missing at that moment.
For CRO, that turns "why didn't they convert?" into three narrower questions: did they want it enough, was it easy enough, and were they asked at the right time?
The model comes from BJ Fogg, who founded the Behavior Design Lab at Stanford.
Below we explain how it works, then check each part against real results from the LiftArchive test database.
The Fogg model: B=MAP in plain terms
Fogg first described the model in the 2009 paper "A Behavior Model for Persuasive Design", presented at the Persuasive 2009 conference.
The paper called the third element "triggers." Fogg switched the word to "prompt" in late 2017, which is why you'll see both terms used.
The model is usually drawn as a chart.
Motivation runs up the vertical axis and ability runs along the horizontal one.
A curved "action line" (the paper calls it the behavior activation threshold) sweeps from the top left down to the bottom right.
| Element | What it means | Subcomponents in the 2009 paper |
|---|---|---|
| Motivation | How much the person wants to do the behavior right now | Pleasure/pain, hope/fear, social acceptance/rejection |
| Ability | How easy the behavior is for this person at this moment | Time, money, physical effort, brain cycles, social deviance, non-routine |
| Prompt (trigger) | Something that says "do it now" | Spark, facilitator, signal |
If a person sits above the action line, a prompt gets the behavior.
If they sit below it, the same prompt does nothing.
The paper also says the chart is conceptual: "There are no units on this axis."
You can't measure where a visitor sits. The model is a way of thinking about a problem, not a calculator.
Motivation and ability trade off
The most useful idea for optimizers is that motivation and ability compensate for each other.
Fogg's own example: they had no wish to buy a new car, but would buy one if it cost $1. When motivation is very high, people push through hard tasks.
When it is low, the task has to be very easy.
The paper's advice follows from that. It says persuasive design "succeeds faster when we focus on making the behavior simpler instead of trying to pile on motivation," because people resist being motivated but like things that are simple.
Fogg also writes that simplicity "is a function of a person's scarcest resource at the moment a behavior is triggered."
On a mobile checkout, that scarce resource might be patience, a free hand, or a card that's in another room.
The current behaviormodel.org site renames the three motivators sensation, anticipation and belonging.
It also says there are three ways to raise ability: train people (the hard path), give them a tool, or scale back the behavior so it's easier.
Using the model to diagnose a page
The model earns its keep before a test is written.
The 2009 paper uses a newsletter form that converts one visitor in 200 and asks: are users lacking motivation, is the behavior too hard, or is the site not prompting well?
Each answer points to a different kind of test.
| Symptom | Likely missing element | Kind of test to run |
|---|---|---|
| Visitors start the form or checkout, then abandon on a particular field or step | Ability | Remove fields, cut steps, add express payment, autofill |
| Visitors read the page but rarely click the main CTA | Motivation | Value proposition, proof, guarantees, offer |
| Engaged visitors scroll past the CTA or leave with items in the cart | Prompt | Sticky CTA, cart reminders, better-timed popups or emails |
| Lots of prompts, few responses, annoyed users | Motivation or ability (the prompt is landing below the line) | Fix the offer or the task before adding more prompts |
Funnel data, session recordings and surveys tell you which row you're in.
The model doesn't. As the paper puts it, sometimes intuition answers the question and "other times, designers will need to do primary research."
Ability: what our tests show
Ability is the element with the most test evidence behind it, and most of that evidence points the way Fogg predicts.
Kareo Marketing removed unnecessary fields from the DoctorBase sign-up form and got 30% more new doctor sign-ups.
Qualicorp stopped asking for name and date of birth a second time on its quote form and saw +19.31% completed quote requests in a VWO test. Both cut time and brain cycles.
But "easier" doesn't always mean "fewer steps." HomEquity Bank turned a one-page reverse mortgage form into a seven-step form and saw +154% conversion rate.
More screens, but each one took less thought.
That fits Fogg's point that brain cycles count as much as time.
More on this on our cognitive load page.
Extra steps can also go wrong. MarketingExperiments reported that moving RegOnline's free trial from a two-step to a three-step sign-up, with different copy, cost 24.5% of completed leads.
Splitting a form helps when it lowers the effort of each step.
It hurts when it just adds work. Field-level tests are collected on the form fields page.
Shortcuts can backfire too. Blend Commerce tested a dynamic express checkout button under a travel brand's add-to-cart button: mobile conversion fell 14% and mobile revenue per visitor fell 21%.
A second "easy" path can add a decision, and decisions use brain cycles.
@CROguy_
MarketingExperimentsMotivation: what our tests show
Motivation changes in CRO usually mean lowering fear (guarantees, proof) or raising hope (benefits, offers).
Blend Commerce added a money-back guarantee badge below a health brand's add-to-cart button and got +30.33% desktop conversion rate.
Mobile underperformed, so only desktop got the change. Same idea, different result by device.
The trade-off between the two elements shows up in a MarketingSherpa test run with Act-On.
Letting visitors choose one of three free guides made no difference.
Offering the choice and also adding one more required field caused a significant drop in leads, compared with both the control and the choice-only version.
The extra pull of a choice wasn't enough to cover the extra effort.
Fogg warns that people "often resist attempts at motivation," and our database has its own warnings.
A more prominent money-back guarantee lost purchases for DRIP Agency, and pre-selecting subscriptions cost revenue for Dave Diederen (both are in the grid below).
Trust and offer messages are worth testing, but they aren't guaranteed to lift.
Prompts: timing is the whole point
The paper names three prompt types. A spark adds motivation for someone who isn't keen.
A facilitator makes the task look easy for someone motivated but stuck.
A signal is a plain reminder for someone who is already willing and able.
Using the wrong type is a common mistake: the paper says sparks and facilitators would be "either annoying or condescending" to someone who only needed a reminder.
Dave Diederen's sticky add-to-cart test shows why timing matters.
A sticky bar that scrolled shoppers back to the main buy box won +17.39% revenue per visitor and +11.67% conversion rate.
Dave's explanation: people who scroll down are often still deciding, so sending them back to the variant selectors works better than pushing an instant add to cart.
The prompt meets them where their motivation actually is.
The same kind of prompt can fall flat.
On a Shopify store tested by Clean Commit, a bottom sticky add-to-cart button gave +6.8% adds but -7.7% conversion rate over 7,948 mobile visitors, with nothing significant.
More prompts led to more clicks, not more buyers.
See all of them on the sticky add-to-cart page.
Signals work best on people who already showed intent.
DIME Beauty showed cart abandoners a "your cart is ready" reminder on the mobile homepage and reported +24.3% conversion rate in a test run with Visually (vendor case study, sample size not stated).
Those visitors had already crossed the line once. A reminder was all they needed.
@DaveDiederen
Visually
OptiMonk@DaveDiederen
Clean Commit
DRIP AgencyWhat research shows vs what A/B tests show
Most of the outside evidence for the model comes from public health surveys, not controlled experiments.
A 2022 paper by Sohail Agha in Vaccines applied it to four behaviors in low- and middle-income countries.
People rated high on both motivation and ability had much higher odds of the behavior: 27 times for COVID-19 vaccination among Nigerian health workers and 35 times for condom use among men in Pakistan.
Those results are associations from survey data, not proof that changing motivation or ability causes the behavior.
The paper also lists its weak points: some ability components weren't measured at all, and single-item scales for motivation and ability may add measurement error.
The model isn't stated in units, so it is hard to falsify in the way a precise theory would be.
A/B tests answer a different question. They show whether one specific change moved one metric on one site.
The tests above fit the model's predictions, but the model didn't produce those numbers, and the losers show it can't tell you in advance which change will win.
Treat B=MAP as a way to sort hypotheses, then let the test decide.
How to put B=MAP into a test plan
- Name one target behavior. "Submit the quote form," not "engage more." Fogg's site recommends mapping the chain of small behaviors that lead to the big one.
- Find the step where people stop. Use funnel reports and recordings to see whether the drop happens before or after people start the task.
- Label the gap. Pick motivation, ability or prompt, and write down the evidence for your pick.
- Try ability first. Both the paper and our test data suggest cutting effort is the more reliable lever. Then test motivation, then prompts.
- Match the prompt to the visitor. Signals for returning, high-intent visitors. Facilitators for people who are stuck. Sparks only when you also improve the offer.
- Segment the result. Ability depends on context, so check device and traffic source. The guarantee and express checkout tests above both split by device.
For related models of how people decide, browse the rest of our psychology hub.