Individual Purchase Hazard Prediction Analytics
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Solution Overview
Problem
Current methodologies for tracking consumer patterns are limited to group behavior patterns, failing to accurately predict individual user needs in real-time, as they aggregate data from multiple customers, making it difficult to determine when an individual user will make a purchase or perform a specific action.
Innovation Solution
A system and method that calculates the time gap and average duration of consecutive purchase events to determine a purchase hazard probability, allowing for individual-level predictions and timely notifications to users based on their specific purchasing habits and location.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If group behavior patterns are used to track consumer patterns, then statistical confidence and modeling can be applied to predict future acts, but the specificity to predict individual user behavior in real-time is lost
Solution Approach 1:
The patent segments the data analysis from group-level to individual-level by creating separate tracking mechanisms for each user's purchase events. Instead of aggregating data across all customers, the system maintains individual event histories and calculates personalized hazard probabilities, thereby achieving both statistical reliability and individual specificity.
Solution Approach 2:
The patent applies local quality by customizing the prediction model to each individual user's specific behavior patterns. Each user receives personalized hazard probability calculations based on their unique purchase history, rather than using a uniform group-based model, thus achieving high measurement precision for individual behavior while maintaining prediction reliability.
2Adaptability or versatility
If data is aggregated from multiple customers to understand common behaviors, then statistical methods can be applied, but the ability to predict what individual customers want and when is compromised
Solution Approach 1:
The system segments the aggregated data into individual user-specific event histories, allowing the analysis to maintain both the breadth of behavioral prediction capability and the precision of individual purchase timing. Each user's unique purchase pattern is preserved and analyzed separately.
Solution Approach 2:
The patent changes the parameter of analysis from group-level statistical aggregates to individual-level event-specific parameters. By calculating hazard probabilities based on individual time gaps between purchases rather than group averages, the system achieves precise individual purchase timing prediction while maintaining adaptable behavioral prediction.
3Measurement precision
If individual purchase events are tracked separately, then personalized predictions can be made, but the complexity of processing and analyzing the data increases
Solution Approach 1:
The system implements self-service by automatically calculating hazard probabilities and generating personalized predictions without requiring complex manual analysis. The automated algorithm processes individual event data and produces actionable insights, reducing the operational complexity burden while maintaining high measurement precision.
Solution Approach 2:
The patent simplifies the data processing complexity by transforming complex individual behavior patterns into a single key parameter: the hazard probability. This parameter encapsulates all the nuanced individual purchase timing information in a form that is easy to process and act upon, reducing computational complexity while preserving prediction accuracy.
Data Source
AI summary
Disclosed herein are systems and methods of individual level learning that include receiving purchase event data from a merchant device that indicates that a purchase event occurred by a user on a user device, and transmitting the purchase event data to an analytics server. The methods may also include processing the purchase event data. The processing may include calculating a time gap for each of two sequential purchase events in a list of purchase events, and calculating an average duration of consecutive events by averaging all of the purchase events in the list of purchase events. The method may determine a purchase hazard probability that a purchase event will occur on a given day, when the average duration of consecutive events is larger than a standard deviation of the event occurring. When the purchase hazard probability is above a threshold, the system may push a message to the user device.