Dynamic Number Insertion Pool Optimization via Demographic Matching
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Solution Overview
Problem
Current dynamic number insertion (DNI) systems for call attribution in internet-based advertising do not consider the physical characteristics of assigned phone numbers or demographic information of the target audience, leading to suboptimal consumer engagement and sales.
Innovation Solution
Optimizing the DNI number pool by analyzing historic utilization data, determining consumer parameters with positive correlations, and modifying number selection rules to align with specific demographics, thereby selecting more suitable phone numbers for each channel.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If DNI number assignment is based only on channel correspondence, then the system is simple to operate, but the call conversion rate is suboptimal
Solution Approach 1:
The system changes the parameters considered in DNI number assignment from only channel correspondence to include demographic characteristics (age, gender, location, income) and number characteristics (toll-free, area code, suffix patterns). This multi-parameter approach enables the system to select numbers that resonate with specific demographic groups, thereby improving call conversion rates while maintaining operational simplicity through automated analysis.
Solution Approach 2:
The system implements feedback mechanisms by analyzing historic call data, consumer demographics, and channel performance to continuously optimize number assignments. Call conversion rates and demographic responses feed back into the system to refine future number selections, creating a closed-loop optimization process that improves productivity without increasing operational complexity.
2Device complexity
If DNI number assignment ignores demographic information, then the system complexity is low, but consumer engagement is suboptimal
Solution Approach 1:
The system segments the consumer base into distinct demographic groups (age cohorts, gender, geographic regions, income levels) and assigns different phone number characteristics to each segment. This segmentation enables targeted number assignment that resonates with specific groups, improving consumer engagement while managing complexity through structured demographic categorization and rule-based assignment protocols.
3Speed
If random number selection is used from the pool, then the assignment process is fast, but the call attribution effectiveness is reduced
Solution Approach 1:
The system performs preliminary analysis of demographic data, channel characteristics, and historic call patterns before number assignment. By pre-processing and storing demographic profiles and number performance metrics, the system can quickly retrieve and match appropriate numbers without real-time computation delays, maintaining assignment speed while improving attribution effectiveness through informed selection.
Data Source
AI summary
Optimizing a number pool for dynamic number insertion (DNI) used for call attribution is achieved by considering physical characteristics of the DNI number assigned to a specific channel versus characteristics of consumers within that channel—such as by determining a plurality of distinguishable consumer parameters having a business-positive correlation to a subset of numbers from among the pool of numbers—in order to make more optimized DNI number assignments.