User Journey Encoder for Accurate Next-Event Prediction
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Solution Overview
Problem
Existing methods for predicting online user activity and influencing consumer behavior are limited by relying on self-reported, third-party, or imputed information, leading to inaccurate insights and suboptimal marketing strategies.
Innovation Solution
A machine learning model utilizing encoders to encode user journey data into composite vectors, predicting next steps with improved accuracy and suggesting tailored content to guide users toward desired outcomes, such as conversions, by analyzing time-stamped interactions like impressions, emails, clicks, and website visits.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If self-reported, third-party, or imputed information is used to predict online user activity, then data collection is simple, but prediction accuracy deteriorates
Solution Approach 1:
The patent introduces an encoder as an intermediary component that transforms raw user journey data into composite vectors. This encoder acts as a mediator between the data collection process and the prediction model, enabling accurate predictions without requiring complex manual data gathering. The encoder processes diverse data types (impressions, clicks, emails, website visits) and converts them into a unified representation that the machine learning model can effectively utilize.
2Measurement precision
If traditional methods are used to analyze user journeys, then computational resources are heavily utilized, but prediction accuracy remains limited
Solution Approach 1:
The patent extracts only the essential features from user journey data by using encoders to generate composite vectors. Instead of processing all raw data through complex computational processes, the system extracts key patterns and representations that are sufficient for accurate prediction. This extraction approach maintains high prediction accuracy while significantly reducing the computational burden on processing resources.
3Loss of information
If detailed user journey data is processed without encoding, then comprehensive analysis is possible, but computational complexity increases
Solution Approach 1:
The patent merges multiple types of user journey data (impressions, clicks, emails, website visits) into unified composite vectors through encoders. This combining process preserves the essential information from each data type while representing them in a consolidated format that reduces computational complexity. The encoders integrate diverse data sources into a coherent representation that maintains information completeness without requiring separate processing of each data type.
Data Source
AI summary
The subject technology identifies a series of journey event types in an online user journey, the event types including an impression event, an email event, a click event, and a website visit, and assigns an encoder to each event type. Using an assigned encoder, the technology encodes each event type to generate an encoded vector for each event type. The encoded vector is representative of at least a portion of the online user journey relating to that event type. The technology generates an encoded vector for each event type to create a set of encoded vectors, the set of encoded vectors including one or more of an impression event encoded vector, an email event encoded vector, a click event encoded vector, and a website visit encoded vector. The technology aggregates the set of encoded vectors to generate an output of the online user journey encoder, the output including a composite encoded user journey vector for modeling, transmits the output of the online user journey encoder to a user journey training model for training of the model and, using a trained model, generates an occurrence probability for at least one further event in the online user journey.