Travel Destination Prediction Using Segmented Propensity Models
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Solution Overview
Problem
Current systems fail to accurately predict a cardholder's travel destination for targeted marketing campaigns, only determining when a cardholder may travel next, but not where, limiting the ability to promote campaigns effectively at the destination.
Innovation Solution
A travel destination prediction system using historical travel activity data and machine learning algorithms to identify a target user's propensity for travel to specific regions, enabling the prediction of both when and where the user will travel, allowing for personalized merchant offers and campaigns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing travel prediction models are used, then the timeframe when a cardholder may travel next can be determined, but the destination where the cardholder will travel cannot be accurately predicted
Solution Approach 1:
The prediction model is segmented into multiple independent prediction tasks: predicting whether the user will travel, predicting the continent destination, predicting the country destination, and predicting the city destination. Each segment is modeled separately using relevant features, and the results are combined to form the complete destination prediction. This segmentation allows the system to address the limitation of existing models by breaking down the complex destination prediction into manageable components.
Solution Approach 2:
The system transitions from a single-dimensional prediction (timeframe only) to a multi-dimensional prediction framework that includes temporal dimension (when to travel) and spatial dimensions (continent, country, city destinations). This dimensional expansion is achieved by creating separate prediction models for each level of geographic detail and integrating their outputs, thereby recovering the lost destination information that existing models fail to provide.
2Productivity
If targeted marketing campaigns are launched without accurate destination prediction, then communication can be sent to cardholders, but the campaigns cannot be effectively promoted at the specific destination
Solution Approach 1:
The system performs preliminary destination prediction and user segmentation before launching marketing campaigns. By predicting the continent, country, and city destinations in advance, the system enables merchants and issuers to prepare and promote destination-specific campaigns beforehand. This preliminary action ensures that marketing communications are tailored to the specific destination and sent at the optimal time, rather than using generic campaigns without destination context.
Solution Approach 2:
The marketing campaign system is enhanced with local quality by providing destination-specific predictions at multiple geographic levels (continent, country, city). This allows merchants to customize their marketing offerings according to the specific characteristics of each destination, such as local attractions, seasonal events, or popular activities. The segmented prediction results enable tailored marketing messages that resonate with users' specific travel plans rather than generic communications.
3Adaptability or versatility
If general travel predictions are made without destination specificity, then cardholders can be identified as travelers, but personalized offers and campaigns cannot be targeted to specific locations
Solution Approach 1:
The prediction system is segmented into hierarchical levels (continent, country, city) with each level providing progressively more specific location information. This segmentation enables campaign customization at appropriate granularities - global campaigns can use continent-level predictions, regional campaigns can use country-level predictions, and local promotions can leverage city-level predictions. The segmented architecture recovers the location-specific details that would be lost in general travel predictions.
Solution Approach 2:
The system dynamically adapts the level of prediction detail used for different campaign types and merchant needs. For some campaigns, only continent-level prediction may be sufficient, while others require country or city-level specificity. The system can dynamically select and combine prediction results at different hierarchical levels based on the specific campaign requirements, enabling flexible customization without being constrained to a single level of detail.
Data Source
AI summary
Embodiments include apparatuses, methods, and systems for segmenting a target region for a target user based on payment transactions within a payment processing system. The embodiments include identifying a target user from users based on payment transaction data, and determining target regions for the target user based on the payment transaction data of the target user and users. The target user may have a propensity for initiating transaction activity in the target regions within a predetermined time period. The embodiments include generating propensity scores for the target regions based on the payment transaction data, identifying a target region for the target user based on the propensity scores, and transmitting merchant offers to the target user based on the identified target region. The propensity scores may be ranked into a list of target region rankings, where the target region is a maximum score in the list of target region rankings.