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Q: How Do I Measure online Display Advertising?

Online Display DoE, when expertly designed has the ability to deliver on the promised of incrementality at the vendor, campaign and audience level in a way that MMM cannot due to practical limits on data granularity and degrees of freedom.  For heavily biased tactics like retargeting, DoE incrementality results can be actively incorporated into MMM as Bayesian Priors to improve MMM models across the board.  For retargeting tactics, DoE offers the most unbiased measurement approach, as it randomly selects a subset of website visitors for exclusion from retargeting impressions, both in total and at the vendor level, in order to measure true incrementality of these tactics on a customer group that has already established interest and intent.  For prospecting tactics, DoE carefully selects a subset of prospects to serve as the control group, showing them a PSA advertisement usually for a charity of the marketers choice in order to determine the true incrementality of impressions.  Because this is designed at the group level, DoEs are not subject to all of the user level data challenges encountered by MTA requiring only that campaigns exhibit enough reach to establish statistical significance at the group level.  For most advertisers this statistical significance is achieved in a matter of weeks, and can be meaningfully updated afterwards on a weekly basis and be used to inform tactical campaign optimization at the weekly level.

MMM measures Online Display impact on aggregated business outcomes at the channel or sub-channel level, providing insights into channel contribution relative to other addressable and non-addressable media and informs strategic planning and forecasting at the annual or quarterly level.  MMM typically encounters challenges in Online Display with retargeting tactics as they are highly correlated to business outcomes and therefore must be handled with extreme care to prevent reads based on spurious correlation.  Due to these known challenge, retargeting contribution is oftentimes practically limited via Bayesian approaches rather than truly estimated via statistical techniques.  Where Bayesian priors are used they have typically been heavily informed by incrementality measurements.

MTA attributes Online Display impressions on conversion events at the user level.  These are typically connected via cookie ID and website level conversion activity.  For retargeting impressions served after a site visit data tracking at the user level is fairly accurate, however the inclusion of other user level impression data that happens prior to a site visit is subject to either data loss or the inability to track data the the user level (e.g. Facebook).  For these reasons, and due to the fact that multiple retargeting impressions occur in the vast majority of converting and non-converting sequences MTA is biased to highly overattribute the impacts and credit given to retargeting in converting sequences.  For online display impressions served prior to a site visit, e.g. prospecting tactics, user level data collection is subject to data loss at the user level which can be significant depending upon the channel and vendor partner.  So while well executed MTA can deliver meaningful insights at the user level where good impression level data tracking exists, in aggregate these tactics tend to be under attributed due to the data loss problem.