SEO Forecasting Framework for Media Effectiveness
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Solution Overview
Problem
Current Media Mix Modeling (MMM) approaches fail to effectively measure the impact of media investments on SEO-driven demand, as they do not account for recent marketing activities, cannot forecast future recommendations, and lack insight into the time required for media spends to influence SEO demand and revenue.
Innovation Solution
A data-driven forecasting framework using an ensemble of time series and machine learning models is created to learn relationships between media investments and SEO-driven consumer demand, identifying top media drivers and their lagged effects to determine the time to maximum value of media spends on SEO demand.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If Media Mix Modeling (MMM) is used to measure media impact on demand, then historical media spend analysis is provided, but the impact on SEO-driven demand cannot be measured and time to value cannot be estimated
Solution Approach 1:
The patent segments the demand measurement into paid channel demand and SEO-driven organic demand, allowing separate analysis of each channel's contribution. This segmentation enables the system to specifically measure media impact on SEO demand rather than treating all demand uniformly, thereby resolving the inability to measure SEO-specific media effects.
Solution Approach 2:
The system performs preliminary actions by training forecasting models on historical data before actual measurement occurs. The models are pre-trained to recognize relationships between media spend and SEO demand, enabling the system to estimate time to value and measure impact before new marketing campaigns are executed.
2Reliability
If MMM analyzes only historical quarters, then past media performance is evaluated, but recent marketing activities are not accounted for in recommendations
Solution Approach 1:
The patent implements dynamic forecasting models that continuously adapt to new data rather than relying on static historical analysis. The system updates its predictions based on recent marketing activities and changing conditions, allowing it to account for the most current campaigns while maintaining the reliability of historical performance evaluation.
Solution Approach 2:
The system incorporates feedback loops where actual campaign results are fed back into the forecasting models to improve future predictions. This continuous feedback mechanism ensures that recent marketing activities are quickly incorporated into recommendations, reducing the time lag between campaign execution and strategic adjustment.
3Ease of operation
If traditional forecasting models are used, then simple predictions are made, but they cannot identify top media drivers or forecast future recommendations
Solution Approach 1:
The patent introduces an intermediary layer of machine learning models that sit between raw historical data and final forecasts. These models act as mediators that extract patterns and identify top media drivers from complex data, then translate them into actionable future recommendations while maintaining operational simplicity for end users.
Solution Approach 2:
The system changes the parameters of the forecasting approach by moving from traditional statistical methods to machine learning-based parameter estimation. This allows the system to capture non-linear relationships and interactions between media channels, enabling identification of top drivers and generation of forward-looking recommendations while keeping the interface simple.
Data Source
AI summary
One example method includes using an ensemble including machine learning (ML) forecasting models, determining respective relationships between past media spends and changes in search engine optimization (SEO) driven consumer demand for a product or service, ranking the relationships according to a criterion, based on the ranking, generating a forecast that comprises recommended future media spends, and effects expected to be achieved by those future media spends, and implementing the recommended future media spends.