Bayesian Marketing Attribution Model for Channel Synergy
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Solution Overview
Problem
Current marketing attribution methods fail to accurately determine the influence of various marketing channels on customer purchase decisions, particularly due to the difficulty in accounting for timing and interaction effects between different marketing events, leading to inefficient allocation of marketing efforts and resources.
Innovation Solution
A Bayesian statistical model is employed to compute channel-specific and interaction terms, incorporating decay parameters and interaction strength parameters, to determine the probability of a target outcome based on marketing events, enabling more precise attribution of marketing influence across channels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional marketing attribution methods are used, then the analysis is simple and quick, but the measurement precision of channel influence is low
Solution Approach 1:
The patent transforms the attribution model from simple credit assignment to a probabilistic framework with multiple parameters including decay rates (λ), interaction strength (γ), and baseline probability (μ). These parameter changes enable precise measurement of channel influence while accounting for timing and synergistic effects between channels.
Solution Approach 2:
The patent introduces interaction terms as intermediary components that mediate between individual channel effects and overall conversion probability. These interaction terms capture synergistic effects between marketing channels, allowing the model to account for how channels work together rather than in isolation.
2Productivity
If marketing efforts are misdirected to inefficient channels, then resource allocation is simple, but the loss of time and sales increases
Solution Approach 1:
The patent implements a feedback mechanism where the probabilistic attribution model continuously learns from observed conversions and non-conversions. This feedback updates the understanding of channel effectiveness, allowing marketers to reallocate resources dynamically based on actual performance data rather than assumptions.
Solution Approach 2:
The patent introduces dynamic decay parameters that reflect how the influence of marketing channels changes over time. This dynamic approach allows the model to capture the temporal nature of marketing effects, enabling more accurate prediction of which channels will be most effective at different stages of the customer journey.
3Reliability
If interaction effects between marketing channels are not accounted for, then the model is easier to interpret, but the reliability of attribution is reduced
Solution Approach 1:
The patent segments the attribution model into distinct components: baseline conversion probability, individual channel effects with decay, and interaction effects between channels. This segmentation allows the complex model to be broken down into interpretable parts while maintaining the ability to capture sophisticated marketing dynamics.
Data Source
AI summary
Systems and methods are described for a causal marketing attribution process that includes the receiving of a plurality of marketing events associated with a customer and computing a sum of a plurality of channel-specific terms corresponding to the plurality of marketing events, wherein each of the plurality of channel-specific terms comprises a channel-specific base parameter and a channel-specific decay parameter. Additionally, the causal marketing attribution process computes a sum of a plurality of interaction terms, wherein each interaction term comprises a product of a pair of channel-specific terms, and determines a probability of a target outcome for the customer based on the sum of the plurality of channel-specific terms and the sum of the plurality of interaction terms.