Call Tracking System Using Sampling Algorithm
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Solution Overview
Problem
Existing call tracking systems face challenges in accurately attributing phone calls to advertising sources due to the high cost and operational issues associated with using multiple phone numbers, which can lead to increased spam calls and distorted data, especially when a large number of advertisers and sources are involved.
Innovation Solution
A call tracking system that employs automated filtering functions such as CAPTCHA, blacklists, and interactive voice response (IVR) to filter out unwanted calls, and a pooling algorithm to manage a pool of phone numbers, allowing for dynamic adjustments to achieve desired accuracy levels, while using sampling algorithms and signal processing to estimate call distributions across publication channels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a separate phone number is designated for each advertiser and source combination, then call tracking accuracy is improved, but system cost and operational complexity increase significantly
Solution Approach 1:
The patent combines multiple phone numbers into a single pooled resource that is dynamically shared across multiple advertisers and sources. The system merges the functions of what would traditionally require separate dedicated numbers, using a single phone number that can be dynamically associated with different advertisers through a pooling algorithm and time-based allocation strategy.
Solution Approach 2:
The system implements dynamic phone number allocation where a single phone number's association with advertisers changes over time based on display timing and call attribution rules. The phone number pool dynamically assigns numbers to different advertisers at different times, replacing static one-to-one mappings with flexible many-to-many relationships managed through temporal rules.
2Adaptability or versatility
If more phone numbers are utilized by the call tracking system, then call tracking coverage is improved, but the likelihood of spam calls and misdials increases
Solution Approach 1:
The patent converts the harmful effect of spam calls and misdials into a beneficial filtering opportunity. By pooling phone numbers and implementing automated filtering functions, the system uses the presence of unwanted calls as a signal to improve attribution accuracy - filtering out spam before it distorts the data, thereby turning a potential source of error into a quality control mechanism.
Solution Approach 2:
The system introduces an intermediary filtering layer between the phone number pool and call attribution. Automated filtering functions act as mediators that inspect incoming calls, identify spam or misdials, and prevent them from reaching the attribution system, thereby protecting the integrity of call tracking data without requiring separate numbers for each advertiser.
3Loss of energy
If phone numbers are recycled to reduce costs, then system cost is reduced, but potential customer confusion increases
Solution Approach 1:
The system performs preliminary actions by establishing time-based rules and display tracking before phone number recycling occurs. By monitoring when phone numbers are displayed to customers and setting appropriate recycling timeframes, the system ensures that numbers are not recycled during active customer interactions, preventing confusion while enabling cost-effective reuse after a safe interval.
Solution Approach 2:
The system implements feedback mechanisms that monitor call patterns and customer interactions to dynamically adjust phone number recycling timing. By continuously observing system performance and customer behavior, the system can optimize recycling intervals to balance cost reduction with minimal customer confusion, extending or shortening retention periods based on actual usage patterns.
Data Source
AI summary
A call tracking system and method that estimates a number of calls associated with a publication channel. The system uses a sampling algorithm to perform the estimation. The sampling algorithm selects a group of advertisements to serve as a sample group, and determines a distribution of calls for each of the publication channels associated with the sample group. The distribution of calls from the publication channels is assumed to be the same for all advertisements, and the data from the sample group is used to model the performance across all advertisements. The selected advertisements may be associated with one or more advertisers. Pooled or dynamic signal processing methods may also be used to estimate a number of calls associated with a publication channel. The number of calls associated with a publication channel may be utilized to determine the efficiency of, or calculate the revenue share associated with, an advertising campaign.