budget calculator

Want to maximize your chances of finding winning ads and creative assets with high confidence? Use this calculator to see how much you should spend.

The more actions (clicks, purchase, leads, etc.) attributed to your ads, the cheaper it will be to gain high confidence on your results. To estimate budget, we need to understand how many actions we need to collect for a given number of impressions.

To estimate your action rate, look back at your historical ad performance and divide the number of total results for your desired action by the total number of impressions it took to obtain those results.

Action rate = Total actions/Total impressions

If the product or service being advertised is expensive, it may take more impressions to reach high confidence for an action like purchase. In these scenarios, it may be necessary to target a metric like clicks or engagement to gain high confidence.

To estimate your action rate, look back at your historical ad performance and divide the number of total results for your desired action by the total number of impressions it took to obtain those results.

Action rate = Total actions/Total impressions

If the product or service being advertised is expensive, it may take more impressions to reach high confidence for an action like purchase. In these scenarios, it may be necessary to target a metric like clicks or engagement to gain high confidence.

Sometimes, meaningful differences in results exist but are small, and difficult to detect. Detectability lets you decide how small of differences to find. The lower the detectability, the more budget you will need to reach high confidence and the more likely you are to find a meaningful difference between your variants.

You could spend thousands of dollars and still not reach high confidence on anything being tested. Why? Because there’s nothing to be confident in. Statistical significance is the likelihood that the difference in conversion rates between a given variation and the baseline is not due to random chance.

If there is no meaningful difference between what is being tested, then we most likely need to change what we are testing. It’s entirely possible that the changes between the test variants just don’t matter.

You could spend thousands of dollars and still not reach high confidence on anything being tested. Why? Because there’s nothing to be confident in. Statistical significance is the likelihood that the difference in conversion rates between a given variation and the baseline is not due to random chance.

If there is no meaningful difference between what is being tested, then we most likely need to change what we are testing. It’s entirely possible that the changes between the test variants just don’t matter.

Imagine we want to predict how often a pro basketball player is going to make their free throws.

If they take 10 shots and make all of them, their conversion rate is 100%. But is it reasonable for us to predict that they will continue to make 100% of their shots going forward? Probably not.

Luckily, there is a metric that helps us make predictions given what we already know.

Confidence is another way to describe probability. For example, a 95% confidence level means you are confident that 95 out of 100 times the estimate will fall between the upper and lower values specified by the confidence interval. The confidence interval is simply a range between two numbers that we feel we can accurately make a prediction within.

Based on what we know of our pro basketball player, we can predict that they will make anywhere between 1 and 9 of the next 10 shots. Not very helpful, right? Well, we need to collect more data. The more data we collect, the more confidence we gain and the smaller (and more accurate!) our confidence interval becomes.

Let’s say after collecting over 1,000 observations of our basketball player, we can now predict with high confidence that they will make anywhere between 7.4 and 8.1 of the next 10 shots.

The more we observe the better we become at predicting the future. And here, the same goes for your ad creative.

If they take 10 shots and make all of them, their conversion rate is 100%. But is it reasonable for us to predict that they will continue to make 100% of their shots going forward? Probably not.

Luckily, there is a metric that helps us make predictions given what we already know.

Confidence is another way to describe probability. For example, a 95% confidence level means you are confident that 95 out of 100 times the estimate will fall between the upper and lower values specified by the confidence interval. The confidence interval is simply a range between two numbers that we feel we can accurately make a prediction within.

Based on what we know of our pro basketball player, we can predict that they will make anywhere between 1 and 9 of the next 10 shots. Not very helpful, right? Well, we need to collect more data. The more data we collect, the more confidence we gain and the smaller (and more accurate!) our confidence interval becomes.

Let’s say after collecting over 1,000 observations of our basketball player, we can now predict with high confidence that they will make anywhere between 7.4 and 8.1 of the next 10 shots.

The more we observe the better we become at predicting the future. And here, the same goes for your ad creative.

This calculator will work for either A/B or Multivariate testing structure.

If you’re currently only A/B testing, enter the total number of ad variants you plan to test.

If you’re multivariate testing (bravo, BTW), just enter the number of variants in your largest variant group.

Ex. You are testing 3 headlines, 4 background images, and 2 layouts. There are 24 total variants from 3 separate groups. Since the background variant group is the largest, you would input 4 into MAX VARIANTS.

If you’re currently only A/B testing, enter the total number of ad variants you plan to test.

If you’re multivariate testing (bravo, BTW), just enter the number of variants in your largest variant group.

Ex. You are testing 3 headlines, 4 background images, and 2 layouts. There are 24 total variants from 3 separate groups. Since the background variant group is the largest, you would input 4 into MAX VARIANTS.

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