Customers often want to know whether a campaign is "worth it" to measure. As such, we provide the following guidelines that outline where we have seen success in the past.

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Feasibility Does Not Guarantee Significance

Meeting these guidelines does not guarantee positive significant results. It is impossible to guarantee statistical significance, because whether or not a result is statistically significant depends on the magnitude of the result and the number of observations.

For example, if a particular campaign ran with a black screen as the creative, it would not be reasonable to expect there to be any lift, and thus would be impossible to guarantee signifiance even if the campaign had 1 billion impressions.

On the flip side, these are not hard thresholds, so if you do not meet these guidelines, it's still possible to observe significant results

Impression Guidelines

Feasibility guidelines vary by product type and region. Below are the impressions guidelines for each product and region:

If the estimated impressions are under below these threshold, it is still possible to observe meaningful results, so always feel free to reach out to our support team.

Conversion Guidelines

In addition to needing a certain number of impressions, we also want to ensure that the conversion environments are well-trafficked as well. For example, a customer wants to measure impact on a website that has 1 unique visitor per day, it is unlikely we will be able to observe a statistically significant lift, because the volume is so low.

Additional Considerations

Campaign Duration

We recommended a minimum campaign duration of at least 1 month. If the campaign impression count is below the recommended threshold, a length of longer than 1 month is recommended. Longer campaign durations are recommended (longer than one month), even if the campaign achieves impression recommendations.

Markets and Geographical Diversity

Using multiple markets will yield the highest number of unique devices. For campaigns in one or two markets, we are more likely to observe statistically significant results if the campaign units are distributed well across the markets, rather than concentrated in one area.

Explanation

OOH attribution is based on sampling the population and running a statistical hypothesis test. As such, to obtain meaningful insights we need to have enough observations.

While we can measure and report on any sized campaign, with very small campaigns it is unlikely that we will be able to statistically prove that the campaign had an effect on the population (that is, on all users, including those we did not observe). For example, if we only observe 100 users in each of the control and exposed group, we might observe a 15% lift in conversions, but because the sample is so small we cannot say it is statistically significant.

The statistical significance of the results we report typically depend on three things:

• the sample size (number of users in the exposed and control groups)
• the number of successful conversions (or: the conversion rate in conjunction with the sample size)
• the size of the effect (i.e. the magnitude of the net lift)

This is a complicated balancing act that makes it difficult to establish thresholds that we are certain will lead to significant results if met. For example, it's possible to observe a significant result with a small number of users if the lift is very large; but it is also possible to not observe a significant lift with enormous sample sizes. This is expected -- if there is truly no effect of the media on conversion, then we shouldn't be detecting significant differences between the exposed and control groups even if we observed the entire population.