Network Communication Customization for Provider Registration
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Solution Overview
Problem
In network systems providing geographic location-based services, candidate providers often fail to complete the registration process due to unclear or ineffective communications, leading to a lack of sufficient input data, which hampers the growth of a registered provider population.
Innovation Solution
The network system customizes communications and communication channels using a machine learning model to predict and personalize the preferences of candidate providers, ensuring that communications are delivered efficiently through the most effective channels, thereby facilitating the registration process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If the system uses third parties to provide communications, then the communication coverage is expanded, but the candidate providers perceive these communications as disjointed and not personalized
Solution Approach 1:
The patent introduces a communication customization module as an intermediary between the network system and third-party communication channels. This module personalizes communication content by selecting from multiple template options based on candidate provider characteristics, thereby maintaining personalization quality while utilizing third-party communication infrastructure for expanded coverage
Solution Approach 2:
The patent applies local quality by customizing different aspects of communication content according to specific candidate provider attributes. The system selects communication templates and content elements tailored to individual candidate characteristics, ensuring each communication is personalized rather than using a uniform approach for all candidates
2Area of stationary object
If the system delivers communications through multiple channels, then the reach to candidate providers increases, but the registration completion rate decreases due to unclear or ineffective communications
Solution Approach 1:
The patent implements dynamics by enabling the system to adapt communication content dynamically based on candidate provider responses and behaviors. The communication customization module adjusts template selection and content elements in real-time based on candidate characteristics and interaction history, making communications more effective across multiple channels while maintaining high completion rates
Solution Approach 2:
The patent incorporates feedback mechanisms where the system monitors candidate provider responses to communications and uses this information to refine future communication strategies. The communication customization module learns from candidate interactions and adjusts template selection and content personalization accordingly, improving registration completion rates across multiple communication channels
3Loss of information
If the network registration process includes multiple steps in a funnel, then the data collection completeness improves, but the candidate provider dropout rate increases due to process complexity
Solution Approach 1:
The patent applies segmentation by dividing the communication process into distinct stages that correspond to different steps in the registration funnel. The communication customization module selects and delivers appropriate communication templates for each registration stage, providing targeted information and guidance that reduces perceived complexity and maintains candidate engagement throughout the multi-step process
Solution Approach 2:
The patent implements preliminary action by providing candidate providers with relevant information and guidance before they encounter complex registration steps. The communication customization module delivers preparatory communications that explain upcoming requirements and guide candidates through the funnel, reducing dropout rates while maintaining data collection completeness
Data Source
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AI summary
A network system customizes communications and communication channels for delivery to candidate providers registering with the network system. Candidate providers progress through steps of a network registration process by providing input data at the various steps. The network system provides communications to request the input data and facilitate the candidate providers' progress. The network system can use a machine learning model to predict particular types of communications and communication channels (e.g., online messages, phone calls, physical mail, etc.) that are likely to be well-received by candidate providers. Thus, the network system is able to increase the expected number of candidate providers that successfully register to provide services to other users of the network system.