Track recall improvement using samples, spend, and reach. View rates, lift, significance, and incremental gains. Turn campaign survey data into confident optimization decisions fast.
This example shows how the calculator interprets a typical campaign survey setup and turns it into lift, significance, and efficiency metrics.
| Metric | Example Value |
|---|---|
| Exposed Sample Size | 1,200 |
| Exposed Recalled Count | 420 |
| Control Sample Size | 1,200 |
| Control Recalled Count | 330 |
| Campaign Spend | $18,000 |
| Impressions Served | 250,000 |
| Exposed Recall Rate | 35.00% |
| Control Recall Rate | 27.50% |
| Absolute Lift | 7.50 percentage points |
| Relative Lift | 27.27% |
| Projected Incremental Recalls | 18,750 |
| Cost per Incremental Recall | $0.96 |
Exposed Recall Rate = Exposed Recalled ÷ Exposed Sample
Control Recall Rate = Control Recalled ÷ Control Sample
Absolute Lift = Exposed Recall Rate − Control Recall Rate
Absolute Lift Percentage Points = Absolute Lift × 100
Relative Lift = (Absolute Lift ÷ Control Recall Rate) × 100
Pooled Rate = (Exposed Recalled + Control Recalled) ÷ (Exposed Sample + Control Sample)
Standard Error = √[Pooled Rate × (1 − Pooled Rate) × (1/Exposed Sample + 1/Control Sample)]
Z-Score = (Exposed Recall Rate − Control Recall Rate) ÷ Standard Error
P-Value uses a two-tailed normal test.
Lift CI = Absolute Lift ± (Critical Z × Unpooled Standard Error)
This range shows the plausible interval for the true lift.
Incremental Recalled in Sample = Absolute Lift × Exposed Sample
Projected Incremental Recalls = Absolute Lift × Impressions
Cost per Incremental Recall = Campaign Spend ÷ Projected Incremental Recalls
Ad recall lift measures the difference in ad memory between an exposed group and a control group. It shows whether the campaign improved audience memory beyond the baseline level.
A control group helps isolate the campaign effect. Without it, you cannot tell whether recall came from the ad itself or from existing awareness, seasonality, or outside media exposure.
It means the observed lift is unlikely to be caused by random sample variation alone. A lower p-value gives stronger evidence that the campaign truly increased recall.
Yes. A negative lift means the exposed group recalled the ad less often than the control group. That can signal weak creative, targeting issues, noisy data, or insufficient sample quality.
Absolute lift still works normally. Relative lift becomes undefined because you cannot divide by zero. In that case, use the absolute lift and significance values for interpretation.
Those inputs add an efficiency lens. They help estimate projected incremental recalls and cost per incremental recall, which makes campaign memory outcomes easier to compare across flights.
No. Equal groups are convenient, but the calculator supports different sample sizes. Larger, balanced samples usually improve stability and make significance testing more reliable.
No. This tool is excellent for quick measurement and planning, but full studies may include audience weighting, reach adjustments, segmentation, survey design controls, and broader brand outcome analysis.
Important Note: All the Calculators listed in this site are for educational purpose only and we do not guarentee the accuracy of results. Please do consult with other sources as well.