Free Ad Fatigue Simulator: Find the View Where Recall Peaks
Every extra ad view costs money. After a certain point, it also makes people remember your ad less.
Enter three numbers. The simulator plots memory and fatigue across a 16-view, four-week campaign and shows the exact view where you should stop.
Use it if you:
- ✓buy paid social, video or display and set frequency caps
- ✓need to explain to a client why 12 views a month beat 30
- ✓want a quick check before spending on a new campaign
Skip it if you:
- •need measured results for a live campaign. This is a planning model, so use your platform's own reports for real numbers
- •run one-off ads, such as a single-day sale, where frequency planning does not apply
Ad Fatigue Simulator
Adjust your 3 campaign settings on the left to see when your ad peaks and when to stop showing it.
People remember your ad best at View #8 (86% recall score). After View #8, boredom grows faster than memory — and by View #14, extra views do zero good.
What does the simulator tell you?
It shows the number of views at which an ad is remembered best, and the number at which more views start to do harm. Change attention time, frequency or creative count, and the curve moves straight away.
See it with real numbers
Two campaigns. Both get 2.0 seconds of attention per view. Only frequency and creative count differ.
| Metric / Parameter | Campaign A | Campaign B |
|---|---|---|
| Weekly frequency | 5 views | 3 views |
| Active creatives | 1 | 3 |
| Net recall peaks at | • View 3 (score 34) | • View 7 (score 77) |
| Net recall reaches zero at | • View 6 | • View 13 |
* These are model outputs, not results from a real campaign.
How much money does this affect?
Take an illustration. You reach 10 lakh people at ₹200 per 1,000 impressions. At 16 views each, that is 1.6 crore impressions and a ₹32 lakh budget.
If your peak is at view 7, the 9 views after it make up ₹18 lakh of that spend. In the simulation, those views add no net recall.
Attention
Threshold Factor 1
1. Attention sets how much each view is worth.
Media researcher Karen Nelson-Field found that an ad needs roughly 2.5 seconds of active attention to start building memory. Earlier research she cites found that 85% of digital placements fall short of that.
Newer work shows 1.5 seconds can be enough when a brand has strong, recognisable assets, such as a logo or colour people already know. If that describes your brand, test the 1.5 to 2.0 second range.
Spaced
Repetition Factor 2
2. Spacing sets how well views stick.
Memory research going back to Hermann Ebbinghaus shows that repeats spread over time are retained better than repeats bunched together. The simulator reduces the value of each view once you pass three views a week.
Gross Recall
Ceiling Factor 3
3. Repetition builds recall, with a ceiling.
A 2015 meta-analysis by Susanne Schmidt and Martin Eisend, published in the Journal of Advertising, found that in experiments consumer attitude peaked at about ten exposures. Recall kept rising until at least the eighth. The simulator's recall curve levels off in the same range.
Berlyne's
Two-Factor
Tedium Factor 4
4. Tedium cancels the gain.
Psychologist Daniel Berlyne described how repeated exposure first raises liking, then lowers it as people grow bored. The simulator assumes each creative holds up for about 2.2 views before fatigue begins. More creatives push that point back.
How does the model work?
The simulator combines four ideas from advertising and memory research. Each one controls a different part of the curve.
1. Attention sets how much each view is worth.
Media researcher Karen Nelson-Field found that an ad needs roughly 2.5 seconds of active attention to start building memory. Earlier research she cites found that 85% of digital placements fall short of that. Newer work shows 1.5 seconds can be enough when a brand has strong, recognisable assets, such as a logo or colour people already know. If that describes your brand, test the 1.5 to 2.0 second range.
2. Spacing sets how well views stick.
Memory research going back to Hermann Ebbinghaus shows that repeats spread over time are retained better than repeats bunched together. The simulator reduces the value of each view once you pass three views a week.
3. Repetition builds recall, with a ceiling.
A 2015 meta-analysis by Susanne Schmidt and Martin Eisend, published in the Journal of Advertising, found that in experiments consumer attitude peaked at about ten exposures. Recall kept rising until at least the eighth. The simulator's recall curve levels off in the same range.
4. Tedium cancels the gain.
Psychologist Daniel Berlyne described how repeated exposure first raises liking, then lowers it as people grow bored. The simulator assumes each creative holds up for about 2.2 views before fatigue begins. More creatives push that point back.
⚠ What the simulator cannot do
The curve shapes come from the research above. The exact constants, such as the 96% recall ceiling and the 2.2 views per creative, are Vakya Labs modelling choices. The papers do not publish them.
Real audiences differ by platform, product and creative quality. Treat the output as a starting point for a test, then check it against your platform's reporting.
How does it compare with other ways of setting frequency?
| Method | Best for | Limits |
|---|---|---|
| Gut feeling or "frequency of three" | Quick decisions | Ignores attention time and creative count |
| Platform reach and frequency planner | Forecasting reach and cost | Tells you who you reach, not how well they remember |
| Paid attention-measurement study | Measuring a real campaign | Needs budget and weeks of fieldwork |
| • This simulator | Planning caps and creative rotation before you spend | Model-based, so it needs checking against live data |
Use the simulator first, then confirm with live data.
How to use it: a four-step plan
Find your attention time
Use your platform's average view duration or an attention report. If you have none, start at 2.0 seconds for social feeds.
Enter your current frequency cap and creative count
Use what is live now.
Read the peak view
Set your total frequency cap at or just before it.
Test one change
Add a creative, or lower the weekly cap, and compare the curves. Then run the change as a real A/B test.