With 2018 set to become The Year of AI, it is only natural that this also be viewed in the context of DMP evolution — and there is certainly plenty of good reason to be bullish on the prospects of applying machine learning and deep learning onto DMP data sets, in particular when equating DMP 1:1 user profiles with marketer SCV profiles.
Concretely, we see AI as playing a transformative role across both how audiences are created and managed in DMPs, as well as how marketers will co-ordinate their marketing activities across channels; always seeking to hit that optimally personalized next best action scenario driving conversions and CLV. For the former, we see a shift from Boolean rules based audience definition (discreet inclusion/exclusion) to machine predicted audience definition (probabilistic), wherein there are several tiers of AI application from simplistic Lookalike modeling to fusing entire data sets to fully automated audience discovery using deep learning algorithms. The foremost benefit of all three AI tiers of audience discovery for marketers is that they all reduce the need for marketers themselves to manually define which signals matter most for which activity amidst an influx of high velocity and high variance data.
The second realm of AI infusion happening in the DMP will transform how next best action scenarios are defined across marketing automation, as well which consumer engagement channels these personalized actions can be delivered in. First, we see the manually defined customer journey paths being gradually replaced by AI’s probabilistic decisioning. This is catalyzed by the inclusion of a rich new set of real-time customer engagement data harnessed by the DMP across programmatic advertising, social, paid search, and onsite personalization engagement, and fused with more stable and persistent data from marketing systems of record (yet also far less abundant and diverse). Equally, the DMP will enable automated triggering of more customer engagement channels, most notably paid search, social, and programmatic — all across devices and with frequency controls.
So to conclude, whilst a hype in itself is hardly ever a justification for investing one’s time or other resources, we at Cxense are seriously bullish on the outlook of cleverly designed, functionally oriented, and customer problem focused application of machine learning and deep learning technologies — jointly referred to as AI — within the context of our DMP. The future for AI certainly has all the right things going for it, and the timing also feels to be right. There certainly is no putting the Genie back into the bottle on AI as we move into 2018.
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