Real-Time Digital Marketing Forecast Model Updates
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Solution Overview
Problem
Existing digital marketing forecasting models are often updated using historical data that is outdated, leading to inaccurate predictions and errors in decision-making due to resource constraints, such as data availability and computational overhead, making real-time or near real-time updates impractical.
Innovation Solution
The solution involves encoding forecast values as arguments in a URL associated with an ad impression won in an online advertising auction, allowing for immediate tracking and updating of predictive models using stochastic gradient descent optimization, eliminating the need to access large data warehouses and enabling real-time updates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If forecast models are updated using historical data from data warehouses, then model accuracy can be maintained, but real-time updates become impractical due to computational overhead and data availability constraints
Solution Approach 1:
The forecast value is computed and encoded into the URL before the ad impression is served, so that when the ad is won, the forecast value is already available for immediate model updating without requiring subsequent data retrieval or processing delays
Solution Approach 2:
The forecast value extraction is performed at the time of URL encoding rather than requiring later extraction from large data warehouses, thereby removing the computational burden of accessing historical data when real-time updates are needed
2Measurement precision
If real-time model updates are implemented using current data, then prediction accuracy improves, but computational resources and data processing complexity increase significantly
Solution Approach 1:
The forecast value is pre-computed and encoded into the URL at the time of ad impression request, before any auction occurs. This preliminary action eliminates the need for complex real-time data processing and model updating operations during the auction decision-making process
Solution Approach 2:
The forecast value is copied into the URL as a parameter, creating a self-contained data structure that carries the necessary information for model updating without requiring access to external data warehouses or complex processing systems
Data Source
AI summary
Techniques are disclosed for automatically creating or updating predictive models, including digital marketing forecast models. A predictive model is updated in real-time or near real-time using a stochastic gradient descent optimization method based on one or more predictive values associated with an advertising impression that is won in an online advertising auction. Each predictive value, which is obtained from the predictive model, is encoded as an argument in a uniform resource locator (URL) corresponding to the ad impression being auctioned. If and when the ad impression is won, the predictive value(s) and other information can be tracked and immediately available for updating the model using information encoded in the URL.