Multi-tenant Loyalty Platform Closed-loop Reward Attribution
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current loyalty program systems are unsophisticated and non-intuitive, particularly in multi-tenant environments, making it difficult for medical practices and other service providers to attribute the effectiveness of rewards on patient retention, referrals, and rapport, and to manage complex subscription arrangements and data privacy in a cross-tenant setting.
Innovation Solution
A multi-tenant loyalty program configuration platform that enables closed-loop reward redemption, allowing practices to create customizable loyalty programs with enhanced data structures and interfaces for flexible subscription management, data aggregation, and attribution analysis, while ensuring data privacy through protocols like PII, PHI, and PCI compliance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a multi-tenant loyalty program system is implemented to manage complex subscription arrangements, then the ability to manage subscriptions and rewards is improved, but the system complexity and difficulty of attribution analysis increase
Solution Approach 1:
The system segments the multi-tenant loyalty program into distinct modular components: tenant configuration modules, subscription management modules, reward tracking modules, and attribution analysis modules. Each module handles specific aspects independently, reducing overall system complexity while maintaining comprehensive functionality.
Solution Approach 2:
The patent introduces intermediary data structures and interfaces that mediate between different tenants and the central loyalty program system. These intermediaries standardize data exchange and interaction patterns, enabling complex multi-tenant management without proportionally increasing system complexity.
2Loss of information
If data aggregation is enabled for attribution analysis, then the effectiveness of rewards can be measured, but data privacy and security risks increase
Solution Approach 1:
The system implements local quality by allowing different data aggregation and sharing configurations for different tenants and data types. Sensitive data like PII and PHI can be protected with stricter access controls while less sensitive operational data can be aggregated more freely for attribution analysis, optimizing both measurement capability and privacy protection.
Solution Approach 2:
The patent introduces data anonymization and aggregation intermediaries that process raw data before it enters the attribution analysis system. These intermediaries strip personally identifiable information while preserving the analytical value needed for measuring reward effectiveness, thereby reducing data privacy risks.
3Reliability
If closed-loop reward redemption is implemented, then patient retention and loyalty increase, but the configuration and management complexity increase
Solution Approach 1:
The system performs preliminary action by providing template-based loyalty program configurations that come pre-configured with best practices for closed-loop reward redemption. Tenants can deploy these templates with minimal customization, achieving reliable patient retention programs without navigating complex configuration details from scratch.
Solution Approach 2:
The patent implements universal configuration interfaces and data structures that work across different tenant types and loyalty program variations. A single configuration framework handles diverse reward scenarios (points, discounts, referrals, retainers) uniformly, reducing configuration complexity while maintaining the ability to create customized retention programs.
Data Source
AI summary
In some embodiments, a multi-tenant loyalty program configuration platform is provided for selective configuration of loyalty programs in a multi-tenant environment. An example platform comprises a processor and a memory storing instructions which, when executed by the processor, configure the multi-tenant loyalty program configuration platform to cause presentation, in a user interface, of loyalty program options to a first tenant in the multi-tenant environment, the user interface allowing the first tenant and the multi-tenant loyalty program configuration platform to collaborate in a program configuration flow on selected program options made by the first tenant; receive, via the user interface, from the first tenant, first order data relating to a selected program configuration, specific to the first tenant, of a first loyalty program to be implemented using the first order data, the first order data including a first set of attributes relating to the first loyalty program; cause presentation, in the user interface, of loyalty program options to a second tenant in the multi-tenant environment, the user interface allowing the second tenant and the multi-tenant loyalty program configuration platform to collaborate in a program configuration flow on selected program options made by the second tenant; receive, via the user interface, from the second tenant, second order data, different from the first order data, relating to a selected program configuration, specific to the second tenant, of a second loyalty program to be implemented using the second order data, the second order data including a second set of attributes relating to the second loyalty program; store the first and second order data in a loyalty program data structure that includes loyalty program rules specific to each of the first and second tenants and the respective first and second loyalty programs; configure the first and second loyalty programs using data stored in the loyalty program data structure; and implement the configured first and second loyalty programs at the multi-tenant loyalty program configuration platform.