While Rockerbox aims to deterministically track as many marketing touchpoints as possible, walled garden and user opt-outs for tracking can cause loss in visibility from deterministic tracking alone.
Rockerbox solves for this through a synthetic modeling process that inserts probabilistic view-based touchpoints on a user's path to conversions. Read on for more detail!
Synthetic Modeling Methodology
The process of identifying users most likely to have arrived on-site after interacting with an ad from a specific placements is outined below.
- Platform Data: Synthetic data begins with an understanding of campaign performance reported by the platform from their hourly 1 day post-view conversion data. This allows Rockerbox to attribute aggregate view-based touchpoints to users most likely to have viewed a campaign with a specific targeting type (ex prospecting vs retargeting).
- First Party Data : your first-party data informs which users were most likely to have had a synthetic touchpoint on their path to conversion based on user properties. User eligibility for having a synthetic touchpoint on their path is determined using:
- Site Visit Data: determines when a customer arrives on-site and if they've visited previously
- Device Graph Analysis: identifies the other marketing activity that may rule out the platform as an eligible touchpoint using a device graph to expand the set of anonymous identifiers associated with a single user.
- Multi-Touch Attribution (MTA) Data: helps establish eligibility criteria for each user by understanding their interactions with other marketing channels
- UTM and Querystring Parameters: further refines eligibility by disqualifying visits from other attributable, non-navigational channels. For example, a user may be eligible for a synthetic touchpoint if they arrives on-site from a brand search click, but disqualified if their site visit is tied to a click from a channel like Snap.
- Data Synthesis: this process involves synthesizing platform reported data and first party data to further assess which campaigns drove specific visits. In addition to the above, the synthesis also involves user ranking, through which we select the highest eligible user for each campaign until all platform-reported campaign conversions are filled in based on hourly data.
Output of Synthetic Modeling
The modeling process outlined above ultimately creates a marketing touchpoint on a user's path to conversion. This synthetic touchpoint is then treated like any other marketing touchpoint, meaning
1. Where to see deterministic vs synthetic touchpoints: conversions will be reported in all first-party reporting down to the ad level as an aggregate of views + clicks. Deterministic vs synthetic touchpoints (and view vs click based touchpoints) will not be reported on separately.
2. How credit differs for Synthetic views vs clicks: it doesn't! The amount of credit given to a synthetic touchpoint vs a click will not differ. Rather, it's the other touchpoints on a user's path that determine how much credit is given to the touchpoint.
Synthetic modeling can only be applied to 1 conversion event in Rockerbox. This is most often the primary conversion segment you optimize against (ex Purchase).
The corresponding platform conversion event must also be identified during setup. For example, you may apply Facebook Synthetic Modeling to your Purchase conversion segment in Rockerbox, using your Purchase - CAPI Facebook event as an input.