Targeted Audience Building Using Predictive Conversion Modeling
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Solution Overview
Problem
Online advertising campaigns struggle to effectively target audiences due to limitations in assessing campaign effectiveness, as click-through rates do not account for offline activities and conversion rates are delayed, making it difficult to optimize campaigns before offline purchases occur.
Innovation Solution
A system and method that utilize data from previous online advertising campaigns to identify similar campaigns, predict consumer conversion probabilities, and build targeted audiences for current campaigns based on predicted conversion probabilities, incorporating both online and offline data sources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If click-through rate is used to assess campaign effectiveness, then online advertising performance can be measured, but offline consumer behavior and transactions are not accounted for
Solution Approach 1:
The patent combines online click-through data with offline transaction data from point-of-sale systems into a unified measurement framework. This merging allows comprehensive assessment of advertising effectiveness by integrating both online engagement metrics and offline purchase outcomes, eliminating the information loss of using only click-through rates.
Solution Approach 2:
The patent introduces consumer identifiers (such as loyalty program IDs or device identifiers) as an intermediary that links online advertising impressions to offline transactions. This intermediary enables the connection between online ad exposure and offline purchase behavior, allowing accurate attribution of offline conversions to specific online advertising campaigns.
2Measurement precision
If conversion rate is used to assess campaign effectiveness, then offline transactions can be measured, but the response is delayed and difficult to learn from during the campaign
Solution Approach 1:
The patent performs preliminary actions by collecting and storing offline transaction data in real-time during the campaign, rather than waiting for campaign completion. This allows the system to have conversion data available immediately, enabling continuous learning and optimization throughout the campaign duration rather than after it ends.
Solution Approach 2:
The patent implements a feedback mechanism where offline conversion data is continuously fed back to the advertising system in real-time. This feedback loop enables dynamic optimization of targeting and bidding strategies during the campaign, allowing marketers to learn from actual conversion outcomes and adjust campaigns accordingly, rather than waiting for delayed post-campaign analysis.
3Measurement precision
If historical campaign data is utilized to predict consumer conversion, then targeted audience accuracy improves, but data processing complexity increases
Solution Approach 1:
The patent segments historical campaign data into distinct features such as consumer demographics, past behavior patterns, ad response history, and contextual information. This segmentation allows the use of machine learning models to process and analyze specific data segments independently, reducing overall processing complexity while maintaining high prediction accuracy through targeted feature analysis.
Data Source
AI summary
A system for building a targeted audience for a present online advertising campaign is disclosed. The system comprises a database for storing data related to each of the plurality of products with each product associated with a previous online advertising campaign and a processor in communication with the database and configured to execute computer-readable instructions causing the processor to utilize the data to identify at least one previous online advertising campaign as being similar to the present online advertising campaign, learn from the identified previous online advertising campaign(s) to predict a probability of conversion of each of a plurality of customers when exposed to an impression of the present online advertising campaign, and build the targeted audience for the present online advertising campaign based on the predicted probability of conversion. A method for building a targeted audience for a present online advertising campaign is also disclosed.