Advertisement Distribution Optimization via Call Tracking Probability Density
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Solution Overview
Problem
Mail-based advertising services face challenges in optimizing the selection of print advertisements due to the lack of effective methods for determining the most optimal distribution based on geographical data from call tracking, which affects the effectiveness of advertising efforts.
Innovation Solution
The system generates a probability density function from call tracking data that includes geographical information, allowing for the optimization of advertisement distribution by determining the probability of receiving calls based on distance from the business entity, and uses this data to create an advertisement distribution plan that targets areas with higher lead generation potential.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If mail-based advertising services distribute a selection of different print advertisements in a single envelope or mailer, then the advertising coverage is broad, but the effectiveness of advertising is reduced due to inability to target specific geographical areas
Solution Approach 1:
The patent segments the geographical area into different zones based on call tracking data, creating distinct target regions for advertisement distribution. This segmentation allows the system to target specific areas with higher lead generation potential rather than distributing advertisements uniformly across all regions, thereby improving advertising effectiveness while managing complexity through systematic geographical division.
Solution Approach 2:
The system performs preliminary analysis of call tracking data to generate probability density functions and identify high-potential geographical areas before distributing advertisements. By pre-processing the data to determine optimal target zones, the system enables more effective advertisement placement without requiring complex real-time decision-making during distribution, thus improving effectiveness while controlling complexity.
2Productivity
If advertisement information is delivered to all geographical areas, then the coverage is maximized, but the cost increases and lead generation efficiency decreases
Solution Approach 1:
The patent applies local quality by tailoring advertisement distribution to specific geographical zones with different lead generation characteristics. Instead of uniform distribution, the system concentrates advertisement volume in areas with higher probability density functions (indicating higher lead potential), thereby improving lead generation efficiency while reducing overall advertisement distribution volume in less effective areas.
Solution Approach 2:
The system changes the distribution parameter from uniform coverage to probability-based targeted coverage. By using probability density functions derived from call tracking data, the system dynamically adjusts where advertisements are sent, concentrating resources in high-potential areas and reducing or eliminating distribution in low-potential areas, thus improving efficiency while reducing total distribution volume.
3Measurement precision
If call tracking data is collected and analyzed to determine geographical distribution patterns, then the advertisement targeting precision is improved, but the data processing complexity increases
Solution Approach 1:
The system employs self-service by automatically processing call tracking data through probabilistic models to generate probability density functions and identify target geographical areas. The automated data processing eliminates the need for manual analysis, achieving high geographical targeting precision while managing complexity through algorithmic self-processing rather than human intervention.
Solution Approach 2:
The system uses feedback from call tracking data to continuously refine probability density functions and improve geographical targeting precision. By analyzing actual call patterns and feeding this information back into the model, the system automatically adjusts targeting parameters, achieving high measurement precision while managing complexity through iterative automated refinement rather than manual recalibration.
Data Source
AI summary
In accordance with the teachings described herein, systems and methods are provided for optimizing distribution of advertisement information. In one example, call tracking data may be generated from a plurality of telephone calls made to a business entity, where the call tracking data includes geographical information to identify locations from which the plurality of telephone calls originated. A call distribution may be determined from the call tracking data, where the call distribution groups the call tracking data based at least in part on distances between the business entity and the locations from which the plurality of telephone calls originated. A probability density function may be generated from the call distribution, where the probability density function is for determining a probability that a telephone call will be received by the business entity in response to advertisement information delivered to a call location, and wherein the probability density function expresses the probability as a function of distance between the call location and the business entity. The probability density function may then be used in the generation of the advertisement distribution plan.