Multi-tenant loyalty platform segmentation
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Solution Overview
Problem
Current user interfaces and data structures for managing loyalty programs are not optimized for cross-vertical use and complex subscription arrangements between providers and customers, lacking flexibility and configurability, especially when dealing with bundled offerings from multiple tenants.
Innovation Solution
A multi-tenant loyalty platform with a rules engine that allows for customizable loyalty programs, enabling tenants to configure unique programs based on specific criteria and attributes, and integrates with a subscription engine for managing subscriptions and rewards in a networked environment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a multi-tenant loyalty platform with customizable programs is implemented, then adaptability and versatility are improved, but device complexity increases
Solution Approach 1:
The loyalty platform is segmented into multiple independent tenants, each capable of configuring their own loyalty programs with custom rules, rewards, and criteria. This segmentation allows each tenant to have tailored programs while sharing the underlying platform infrastructure, resolving the contradiction between adaptability and complexity by distributing configurability across independent units.
Solution Approach 2:
The platform implements a universal multi-tenant architecture that serves multiple verticals and industries through a common infrastructure. The system provides universal loyalty program management capabilities that can be customized for different tenants, achieving high adaptability without proportionally increasing overall system complexity through shared core components.
2Productivity
If real-time reward processing is implemented, then productivity is improved, but use of energy increases
Solution Approach 1:
The platform pre-calculates and prepares reward eligibility criteria, customer profiles, and program rules in advance before transactions occur. By performing preliminary actions such as pre-validating customer enrollment status and pre-determining reward structures, the system reduces real-time computational requirements while maintaining high processing speed.
Solution Approach 2:
The system optimizes computational resources by applying different processing intensities to different operations. High-computation tasks like program configuration and rule validation are performed locally when changes occur, while routine transaction processing uses pre-computed data with minimal computational overhead, balancing productivity and energy consumption.
Data Source
AI summary
The present disclosure provides a multi-tenant loyalty platform 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 platform to: cause presentation of loyalty program options to a first tenant in a multi-tenant environment; receive, from the first tenant, first order data relating to a selected program configuration; cause presentation of loyalty program options to a second tenant; receive, from the second tenant, second order data different from the first order data; 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; and configure the first and second loyalty programs using data stored in the loyalty program data structure.


