Customer Journey Prediction With Real-Time UI Intervention
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Solution Overview
Problem
Conventional techniques for tracking customer journeys on electronic platforms are too slow to adapt to rapidly changing user behaviors, leading to inefficient navigation and troubleshooting by customers.
Innovation Solution
A computer system that dynamically adjusts user interfaces in near real-time using behavior pattern recognition, predicting next user activities and providing recommendations to guide users towards positive outcomes based on statistical analysis of aggregated user data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If conventional tracking techniques are used to monitor customer journeys, then customer behavior data can be collected for analysis, but the system response is too slow to adapt to rapidly changing user behaviors
Solution Approach 1:
The system performs preliminary actions by pre-processing and storing user behavior data in real-time as it occurs, organizing it into structured formats that enable immediate analysis. This preliminary data preparation allows the system to quickly respond to behavior changes without waiting for batch processing, thus reducing response time while maintaining adaptability.
Solution Approach 2:
The system implements continuous feedback loops where user behavior data is constantly monitored, analyzed, and used to dynamically adjust the user interface. This real-time feedback mechanism enables the system to adapt quickly to changing user behaviors by immediately implementing adjustments based on analyzed patterns, resolving the contradiction between fast response and high adaptability.
2Ease of operation
If real-time behavior analysis is implemented to improve user experience, then user satisfaction increases, but system complexity increases
Solution Approach 1:
The system segments the complex task of real-time behavior analysis into distinct modular components: data collection modules, processing modules, analysis modules, and interface adjustment modules. Each module handles a specific aspect of the workflow, making the overall system more manageable and easier to implement while delivering improved user experience through real-time adaptations.
Solution Approach 2:
The system introduces intermediary components such as behavior pattern databases and analysis engines that act as mediators between raw user data and interface adjustments. These intermediaries simplify the complexity by providing structured data formats and pre-computed patterns that can be quickly applied to improve user experience without requiring complex real-time computations at every interface level.
Data Source
AI summary
Systems and methods for customer journey prediction and recommendation are disclosed. In an embodiment, a computer system determines that a user activity for a customer journey has been performed by a user interacting with a platform. The computer system predicts a next user activity that will be performed by the user. The computer system determines a first probability that the predicted next user activity will lead to a positive outcome for the customer journey and a second probability that an alternative user activity will lead to the positive outcome. Based on the second probability being a threshold amount greater than the first probability, the computer system intervenes on the platform to direct the user to the alternative user activity. Related systems and methods are also disclosed.