Predicting Enrollment Rates for Online Ad Allocation
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Solution Overview
Problem
Organizations face challenges in determining the likelihood of enrollment and imputed revenue for inquiries generated from online advertisements, as historical enrollment data is not always available in real-time, making it difficult to allocate advertisements effectively across different media channels.
Innovation Solution
A system that uses a decision tree model to predict enrollment rates based on historical enrollment data, assigning inquiries to nodes or segments depending on available data, and calculates imputed revenue by combining predicted enrollment rates with actual revenue and quality levels, allowing for adjustments in advertisement allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If historical enrollment data is used to predict enrollment rates, then prediction accuracy is improved, but real-time availability deteriorates
Solution Approach 1:
The system pre-calculates and stores predicted enrollment rates and imputed revenue values for different inquiry characteristics using historical enrollment data before actual allocation decisions are needed. This preliminary computation enables real-time allocation adjustments without waiting for historical data processing, resolving the contradiction between prediction accuracy and real-time availability.
2Productivity
If advertisement allocation is adjusted based on predicted enrollment rates, then revenue optimization is improved, but system complexity increases
Solution Approach 1:
The system segments inquiries into distinct groups based on characteristics such as media channel, program type, and demographic factors. Each segment is assigned predicted enrollment rates and imputed revenue values independently. This segmentation simplifies the allocation decision-making process by breaking down complex predictions into manageable segments, reducing system complexity while maintaining revenue optimization capabilities.
Solution Approach 2:
The system uses imputed revenue as a transformed parameter that combines enrollment probability, revenue per enrollment, and other factors into a single allocation metric. By changing the parameter from raw enrollment data to imputed revenue, the system simplifies the allocation optimization process while maintaining the ability to maximize actual revenue.
3Measurement precision
If imputed revenue is calculated for all inquiries, then allocation precision is improved, but computational burden increases
Solution Approach 1:
The system applies different levels of prediction precision to different inquiry segments based on their characteristics and the availability of historical data. For segments with sufficient historical data, detailed predicted enrollment rates are calculated. For segments with limited data, the system uses aggregated or default values. This local quality approach maintains allocation precision for critical segments while reducing computational burden for others.
Data Source
AI summary
In one embodiment, a system for predicting imputed revenue of inquiries includes: one or more computing devices comprising computer hardware and configured to: obtain data relating to a plurality of inquiries. Each of the plurality of inquiries is (a) indicative of a request for information about one or more programs providing a service, and (b) received from a particular channel of a plurality of online channels for presenting the one or more programs. The plurality of inquiries are represented using one or more data structures in one or more data sources. The computing devices are further configured to determine a model for predicting enrollment rates of the plurality of inquiries based at least in part on historical enrollment data of at least one program of the one or more programs, the model specifying a predicted enrollment rate value for one or more characteristics associated with the plurality of inquiries. The computing devices are further configured to: using the model, determine a predicted enrollment rate for respective inquiry of the plurality of inquiries; determine an imputed revenue of the respective inquiry based at least in part on the predicted enrollment rate for the respective inquiry, the imputed revenue indicative of a potential revenue that can be generated from the respective inquiry; and adjust an allocation of presentation of the one or more programs among the plurality of online channels, the adjusting based at least in part on the determined imputed revenue of the respective inquiry.