ML Marketing Attribution via Wave Decomposition
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Solution Overview
Problem
Marketing strategies lack clear, quantifiable methods to attribute success to specific advertisements, making it difficult for marketers to make informed decisions due to the nebulous nature of results and the complexity of handling large datasets.
Innovation Solution
A machine learning-based system that records precise timestamps for broadcast events and correlates them with key performance indicators (KPIs) to generate effectiveness ratings, using wave decomposition and hierarchical Bayes models to attribute success even in cases of overlapping events, and optimizing marketing strategies through combinatorial tradeoff optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional arbitrary or convoluted systems are used to attribute marketing success to advertisements, then marketers can make decisions without complex data analysis, but the attribution accuracy and quantifiability of results deteriorate
Solution Approach 1:
The patent replaces traditional mechanical/manual marketing attribution systems with a machine learning-based automated system. The system uses algorithms to analyze media records, KPI data, and broadcast event information, automatically attributing marketing success to specific advertisements without manual intervention. This substitution enables precise quantification of marketing effectiveness while handling complex data relationships that would be intractable for human analysts.
Solution Approach 2:
The patent introduces machine learning models as intermediaries between raw marketing data and actionable insights. These models serve as mediators that process large datasets including media records, product/service KPI data, and broadcast event information, transforming them into attributed effectiveness ratings. The intermediary system handles the complexity of data integration and analysis, providing clear attribution results without requiring marketers to directly manage the computational complexity.
2Loss of information
If marketers manually analyze large amounts of data including media records, product/service KPI data, and offers, then they can potentially identify patterns, but the time required and productivity deteriorate
Solution Approach 1:
The patent implements a self-service system where the machine learning infrastructure automatically performs data analysis, pattern recognition, and attribution without requiring manual marketer intervention. The system ingests media records, KPI data, and broadcast event information, then autonomously processes this data through trained models to generate effectiveness ratings and recommendations. This self-service approach ensures complete data analysis while dramatically improving productivity by eliminating manual analysis steps.
Solution Approach 2:
The patent employs preliminary action by pre-training machine learning models on historical marketing data before actual attribution tasks. The system performs upfront work in model training and validation, creating ready-to-use attribution algorithms that can quickly process new data. This preliminary preparation enables the system to rapidly analyze large datasets and provide timely marketing decisions without requiring manual analysis at the time of decision-making.
3Measurement precision
If precise timestamps and wave decomposition methods are used to attribute events in overlapping broadcast events, then attribution accuracy improves, but the computational complexity and processing time worsen
Solution Approach 1:
The patent extracts and isolates individual broadcast events from overlapping media records by identifying precise timestamps and event boundaries. The system separates overlapping events into distinct analyzable units, allowing for precise attribution of each event's effectiveness. By extracting individual event characteristics and isolating their impacts on KPIs, the system achieves high attribution precision while managing computational complexity through focused analysis of discrete events rather than continuous overlapping data.
Data Source
AI summary
Introduced herein are methods and systems for determining machine learning marketing strategy. For example, a computer-implemented method according to the disclosed technology includes steps of identifying one or more business metrics to be driven by a marketing plan; generating one or more response functions of the business metrics by performing a machine learning process on a marketing dataset; optimizing a spending subject of the marking plan subject to constraints to generate a marketing strategy based on multiple decision variables; and presenting the marketing strategy to an advertiser.