API Server for Real-Time Personalized Offer Delivery
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Solution Overview
Problem
Current systems face challenges in delivering personalized offers to a large customer base in real-time due to scaling issues, outdated customer data, and increased time for data transfer and analysis, leading to reduced offer effectiveness.
Innovation Solution
A computer-implemented method and system using an API server to detect client device access events, retrieve personalized offers from a database server based on user-specific criteria, and dynamically populate communications with up-to-date content, enabling real-time delivery of personalized content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If personalized offers are generated for each customer using customer data, then offer personalization is improved, but the time required for data analysis and offer delivery increases, reducing offer effectiveness
Solution Approach 1:
The system pre-generates personalized offers for each customer based on their customer data and stores them in a database before the customer actually accesses the communication. This preliminary generation eliminates the time delay that would occur if offers were generated in real-time at the moment of communication access, thus resolving the contradiction between personalization quality and delivery speed.
2Quantity of substance
If personalized offers are generated for an entire customer base of millions of customers, then offer personalization coverage is improved, but scaling challenges make the process impracticable
Solution Approach 1:
The system segments the large customer base into individual customer records, each with their own pre-generated personalized offers stored in the database. This segmentation allows the system to handle millions of customers by processing and storing offers in manageable individual units rather than attempting to generate all offers simultaneously as a single monolithic process, thus making the scaling practicable.
Solution Approach 2:
The system performs preliminary generation of personalized offers for the entire customer base in advance, storing them in the database before they are needed. This batch processing approach allows the system to handle large quantities of personalized offers without the complexity of real-time generation for each customer, resolving the scaling challenge.
3Loss of time
If customer data is analyzed and offers are generated in advance, then data processing time is reduced, but the customer data becomes outdated, reducing offer relevance
Solution Approach 1:
The system dynamically determines whether to use pre-generated offers or generate new offers in real-time based on the currency and relevance of the customer data. When customer data is updated or becomes outdated, the system can regenerate offers to ensure they remain relevant, thus resolving the contradiction between processing speed and data currency.
Data Source
AI summary
Techniques for real-time delivery of personalized content are provided. An example method includes detecting that a delivery server invoked an API in response to the delivery server detecting that a client device is accessing a communication provided to the client device by the delivery server, the communication formatted in accordance with a template; receiving a request for offers, the request including a user identifier associated with a user of the client device and offer query criteria based on the template; building the offers by: retrieving candidate offers specific to the user from a database server using an API request; and, based on the offer query criteria, identifying the offers, from the candidate offers, to deliver to the delivery server; and transmitting the offers to the delivery server to cause the delivery server to populate the communication with content corresponding to the offers.