The prevalence of Internet or online advertising and marketing continues to grow at a fast pace. Today, an online user (e.g., prospect) in a given target audience can experience a high number of exposures (e.g., touchpoints) to a brand and products across multiple digital media channels (e.g., display, paid search, paid social, etc.) and across multiple devices (e.g., desktop computer, tablet, mobile phone, etc.) on the “journey” to conversion (e.g., buying a product, etc.) and/or to some other engagement state (e.g., brand introduction, brand awareness, etc.). Another online user in the same target audience might experience a different combination or permutation of touchpoints, yet might not convert. Large volumes of data characterizing the user interactivity with such high number of touchpoints is continuously collected in various forms such as in touchpoint attribute records, Internet cookies, log files, Internet pixel tags, mobile tracking, etc. The marketing manager of today desires to use this continuous stream of touchpoint data to learn exactly what touchpoints contributed the most to conversions (e.g., touchpoint attribution) in order to develop media spend scenarios and plans that allocate the marketing budget to those tactics that are deemed to be the most effective.
Certain “bottom-up” touchpoint response predictive modeling techniques can collect user level stimulus and response data (e.g., touchpoint attribute data, conversion data, etc.) to assign conversion credit to every touchpoint and touchpoint attribute (e.g., ad size, placement, publisher, creative, offer, etc.) experienced by every converting user and non-converting user across all channels. For example, such techniques can predict the contribution value of a given touchpoint for a given segment of users and/or for a given media channel. The marketing manager can use such predicted touchpoint contribution values to develop an intra-channel media spend plan. In some cases, certain sets of touchpoints can be presented to a single user on multiple devices. For example, a user might be exposed to touchpoint A on a desktop computer and then exposed to touchpoint B on a mobile phone. Unfortunately, legacy touchpoint response predictive models are limited at least in their ability to model such fragmented multiple- or cross-device effects in the touchpoint attribution.
Techniques are needed to address the problem of associating the fragmented cross-device touchpoints of each user when generating touchpoint attribution. None of the aforementioned legacy approaches achieve the capabilities of the herein-disclosed techniques for cross-device marketing touchpoint attribution. Therefore, there is a need for improvements.