Conversion Model for Predicting Lost Orders in Online Concierge Systems
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional online concierge systems fail to effectively account for factors influencing user access and order placement, such as availability of items and delivery restrictions, leading to missed opportunities for order fulfillment due to unmodified discrete time intervals and prices.
Innovation Solution
The online concierge system employs a conversion model trained on user access data to predict order receipt probabilities based on modifiable features like discrete time intervals and prices, allowing for adjustments to increase order generation by modifying these features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If discrete time intervals and prices are maintained as fixed parameters, then system operation is simple, but order placement is prevented due to unmodified fulfillment restrictions
Solution Approach 1:
The patent applies dynamics by transitioning from static discrete time intervals to dynamic continuous time adjustment. The system continuously monitors user access patterns and converts access data into modified time intervals, allowing fulfillment windows to adapt in real-time based on actual usage behavior rather than following fixed schedules.
Solution Approach 2:
The patent changes the parameter from discrete time intervals to continuous time parameters. By converting fixed time windows into continuously adjustable time parameters based on user access data, the system enables flexible modification of fulfillment timing while maintaining operational simplicity through automated parameter transformation.
2Device complexity
If conventional access tracking is used, then system complexity is low, but lost conversions cannot be identified due to lack of factor-specific analysis
Solution Approach 1:
The patent segments the access tracking process into distinct components: monitoring user access, identifying conversion events, determining factors influencing conversions, and calculating lost conversions. This segmentation allows comprehensive tracking while maintaining manageable system complexity through modular data processing.
Solution Approach 2:
The patent implements feedback mechanisms by continuously analyzing user access data and conversion outcomes to identify patterns and factors influencing order placement. This feedback loop enables the system to learn from actual usage behavior and improve its ability to predict and prevent lost conversions.
3Device complexity
If factors controlling order placement are not modified, then system management is simple, but user engagement decreases due to unadjusted fulfillment restrictions
Solution Approach 1:
The patent applies self-service by enabling the system to automatically adjust fulfillment parameters based on user access patterns without requiring manual intervention. The system self-regulates time intervals and fulfillment restrictions by processing user behavior data and generating appropriate modifications autonomously.
Solution Approach 2:
The patent implements preliminary action by pre-calculating and preparing time interval modifications based on predicted user access patterns. The system proactively adjusts fulfillment parameters before user interactions occur, optimizing engagement while maintaining manageable management complexity through automated pre-processing.
Data Source
AI summary
An online concierge system trains a machine learning conversion model that predicts a probability of receiving an order from a user when the user accesses the online concierge system. The conversion model predicts the probability of receiving the order based on a set of input features that include price and availability information. For each access to the online concierge system, the online concierge system applies the conversion model to a current price and availability and to an optimal price availability. The online concierge system generates a metric as the difference between the two predicted probabilities of receiving an order.