Adaptive Subscription Model for Consumer Packaged Goods
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Solution Overview
Problem
Existing subscription systems for physical goods, especially consumables, face challenges in accurately determining user consumption patterns, leading to over-provision or under-provision, and impose a high cognitive load on users, causing them to cancel subscriptions due to inefficiencies in delivery frequency and quantity.
Innovation Solution
A computer-implemented adaptive subscription model that uses a consumption model trained on user data, including household properties and feedback, to predict consumption rates and adjust delivery quantities and frequencies, reducing errors and user intervention through a database-driven system with iterative model updates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If a fixed frequency and quantity subscription model is used for delivering consumable goods, then the subscription service is simple to implement, but the accuracy of matching user consumption patterns deteriorates leading to over-provision or under-provision
Solution Approach 1:
The patent implements dynamic subscription models where delivery frequency and quantity are not fixed but adapt over time based on user consumption patterns. The system transitions from static predetermined schedules to dynamic adjusted schedules, allowing the subscription parameters to change in response to actual usage data, thereby resolving the contradiction between simplicity and accuracy.
Solution Approach 2:
The system incorporates feedback loops where user consumption data is collected, analyzed, and used to adjust future delivery parameters. This feedback mechanism enables the system to learn from actual usage patterns and continuously improve delivery accuracy, moving away from fixed predetermined models while maintaining operational simplicity through automated adjustments.
2Adaptability or versatility
If users manually adjust delivery frequency and quantity, then the subscription can be customized to individual needs, but the cognitive load on users increases leading to subscription cancellation
Solution Approach 1:
The system enables self-service by automatically adjusting delivery parameters based on monitored consumption patterns without requiring user intervention. The subscription service autonomously adapts to user needs by detecting usage rates and modifying delivery schedules, thereby providing customization benefits while eliminating the cognitive burden of manual adjustment.
Solution Approach 2:
The system uses feedback from user consumption behavior to automatically adjust delivery parameters. By monitoring how quickly users consume goods and what patterns emerge, the system self-adjusts frequency and quantity, providing adaptability while removing the need for users to actively manage their subscriptions.
3Reliability
If delivery frequency and quantity are increased to prevent under-provision, then user needs are better met, but waste increases when users over-consume
Solution Approach 1:
The system dynamically adjusts delivery quantities and frequencies to match actual consumption rates, preventing both under-provision and over-provision. By making delivery parameters variable rather than fixed, the system can optimize the balance between reliability and waste reduction, delivering more when consumption is high and less when consumption is low.
Solution Approach 2:
The system changes delivery parameters (frequency, quantity, timing) based on observed consumption patterns. By adjusting these parameters dynamically rather than maintaining fixed values, the system optimizes the trade-off between ensuring adequate supply and minimizing waste from over-provision.
Data Source
AI summary
A database comprises user information records for users subscribing to one or more goods through a subscription service. In some aspects a user record may contain information about the user, one or more households of the user and the constituents of those households, and other information about the user, households, and/or constituents of household. User records are updated based on various signals corresponding to user feedback and amounts of provided goods and utilized in iterative training of consumption models by which consumption of different goods is determined based on household properties. The consumption model outputs a predicted consumption of goods for a household and amounts of goods are translated into one or more SKUs for fulfillment by the subscription service.