The answer to the billion-dollar question of attribution in the absence of cookies lies in the deep strides AI-based deep learning made over the past few years.
Deep learning has evolved and revolutionized text and image-based analysis and recommendation systems. Advanced research and the new strides made in deep learning can now help solve the most complicated problem of omnichannel attribution.
Unified causal learning powered by deep learning successfully solves the problem of omnichannel attribution comprehensively without the requirement of cookie-based data or individual-based data.
What is Causal learning and How is it solving the attribution problem
Causal learning tries to distill the interconnected effect and individual effect of each marketing stimulus at a very granular level. As the system learns the patterns and impact at a very granular level, attribution is granular, robust, and very accurate.
Causal learning also eliminates the multiple biases which used to get built in the system during the Cookie-based attribution model.
Why is it the best solution for the omnichannel attribution problem?
The modern-day consumer journey is a non-linear journey influenced by multiple channels during the entire purchase process. Each of the different marketing inputs – paid activities and content marketing activities has an interconnected effect and a cumulative effect on the final decision process.
Attribution is comprehensive when the measurement system can understand the individual impact as well as interconnected effects.
None of the existing systems, including the multi-touch attribution system, can solve this measurement problem.
Why will Causal learning always win?
Causal learning will always win compared to any other approach as it is
Comprehensive: Causal learning can help understand all marketing stimulus attribution – paid, non-paid, measured, and non-measured. E.g. the impact of content marketing activities, brand strength, channel partner/affiliate used can be identified using Causal learning.
Interconnected effect: Causal learning can understand the interconnected impact of one channel over the other which is very critical for the attribution of non-linear consumer journeys
Agile: The continuous learning models can help the user make real-time decision making and optimize the plans and channels in a dynamic manner
Faster: The time to value for AI-based modeling is swift and the time to value can be as low as days and not weeks.
Granular: As the learning is a bottom-up approach, the level of insights we can get will be very granular – e.g. region level, channel level, and campaign level.
What-if Scenarios: Causal learning can help the user build multiple what-if scenarios and evaluate the numerous hypothesis before the execution.
No individual data: The most significant advantage of Causal learning is that it doesn’t require personal level data/ PII data so that it is future proof and privacy compliant.
Prescriptive Planning: Causal learning can deliver a very actionable and granular level of forecasting and optimization possibilities which will help improve the campaigns’ ROI by 20%.
Our experience :
We at Data Poem have been working on this problem for the last two years and the robust results of attribution powered by causal learning on real-time client data sets give us confidence that the future of MTA attribution is deep learning-based causal learning.
We strongly believe that this new paradigm will change the course of attribution. It is faster, smarter, better, and agile. Welcome to the future.
