Domain Renewal Prediction Using Historical Attribute Segmentation
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Solution Overview
Problem
Current domain name registration systems face revenue loss due to offering unnecessary discounts to registrants who would renew their domain names without incentives, as well as failing to persuade registrants with weak domains to renew by not targeting specific domains effectively.
Innovation Solution
A computer-implemented method defines domain attribute sets and performs historical analysis to predict the probability of domain name deletions, allowing for targeted marketing campaigns to be tailored to specific domains, thereby incentivizing renewal without unnecessary discounts to registrants who would have renewed anyway.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If discounts are offered to every registrant with an expiring domain name, then more domain names are renewed, but revenue is lost by offering unnecessary discounts to registrants who would have renewed anyway
Solution Approach 1:
The system segments registrants into different groups based on their renewal likelihood. By analyzing historical data and domain characteristics, it identifies high-value registrants who would renew without discounts and low-value registrants who need incentives. This segmentation allows the registrar to apply discounts only to the appropriate segment, avoiding revenue loss from unnecessary discounts while maintaining high renewal rates among those who would have renewed anyway.
Solution Approach 2:
The system changes the parameter of discount application from a universal approach (offering discounts to all) to a targeted approach (offering discounts only to specific registrants based on predicted renewal likelihood). By adjusting this parameter, the system optimizes the balance between renewal rates and revenue preservation, offering discounts only where they are most likely to be effective.
2Loss of energy
If no discounts are offered, then revenue is maintained, but registrants with weak domains that would renew with discounts are lost
Solution Approach 1:
The system segments registrants based on their predicted renewal likelihood and identifies those who are most responsive to discount offers. By targeting discounts at this specific segment rather than applying them universally, the system minimizes revenue loss while maximizing the impact on renewal rates for registrants who are actually influenced by discounts.
Solution Approach 2:
The system uses historical renewal data and domain characteristics as feedback to predict which registrants are most likely to respond to discount offers. This feedback mechanism allows the system to continuously refine its targeting strategy, improving revenue preservation while maintaining or increasing overall renewal rates through more effective discount allocation.
3Adaptability or versatility
If a registrar develops a custom discount targeting system, then targeted marketing can be implemented, but development costs and system complexity increase
Solution Approach 1:
The system introduces an intermediary component that acts as a decision-making layer between the registrar and the discount offering process. This intermediary analyzes registrant data, predicts renewal likelihood, and determines optimal discount strategies, thereby enabling targeted marketing without requiring the registrar to develop complex custom systems. The intermediary handles the complexity internally while presenting a simplified interface to the registrar.
Solution Approach 2:
The system implements self-service capabilities by automatically analyzing registrant data and making discount recommendations without requiring manual intervention or complex configuration by the registrar. The system serves itself by continuously learning from historical data and automatically adapting its targeting strategy, reducing the complexity burden on the registrar while maintaining high adaptability.
Data Source
AI summary
A system, method, and computer-readable medium, is described that provides a probability of deletion (or renewal rate) prediction for a domain name based on a historical model of expired and renewed domain names. Domain name attribute sets are defined using domain attribute/value combinations. These sets are used to classify past expired and renewed domain names into each of the applicable sets where the domain attribute and values match the expired or renewed domain names. The percentage of renewed domain names in a set is used to predict the likelihood that a user will renew a domain name set to expire in a defined window and that matches the attribute/value combinations that make up the domain attribute set. This predicted percentage is used to target domains and deliver marketing offers to the domain contacts.