Real-Time Journey Generation System for User Engagement
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Solution Overview
Problem
Existing systems for generating workflows to engage users on online platforms are inefficient, leading to ineffective marketing campaigns in a competitive environment.
Innovation Solution
A computer-implemented method that generates personalized journeys for engaging users in real-time by analyzing user data, past events, and live interactions using machine learning algorithms to identify patterns and optimize engagement times and channels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional workflow automation systems are used to engage users, then the system structure is simple, but the engagement effectiveness and personalization capability are insufficient
Solution Approach 1:
The system segments users into different groups based on their behavior patterns, preferences, and characteristics. By dividing the user base into distinct segments, the system can apply personalized engagement strategies to each segment, thereby improving personalization capability without overwhelming system complexity through targeted rather than universal personalization
Solution Approach 2:
The system dynamically changes engagement parameters such as timing, channel, and content based on real-time user data and historical patterns. Machine learning models adjust these parameters automatically, enabling high adaptability while the automation handles the complexity of parameter optimization without requiring manual system reconfiguration
2Productivity
If manual user engagement strategies are used, then the system complexity is low, but the productivity and real-time response capability are insufficient
Solution Approach 1:
The system employs machine learning models that automatically analyze user data, predict optimal engagement timing and channels, and execute personalized campaigns without human intervention. This self-service capability dramatically improves productivity and real-time response, while the automated nature of the system manages complexity internally rather than requiring proportional human resources
Solution Approach 2:
The system continuously collects feedback from user interactions and uses this data to refine its engagement strategies through machine learning. This feedback loop enables the system to improve engagement efficiency over time while the automated feedback processing handles the complexity of data analysis and strategy adjustment
3Reliability
If generic marketing campaigns are sent to all users, then the system operation is simple, but the engagement success rate and user experience are poor
Solution Approach 1:
The system applies different engagement strategies, content, and timing to different user segments based on their specific characteristics and behaviors. This local quality approach improves engagement success rates by tailoring messages to each segment's preferences, while the automated segmentation and targeting processes manage the data processing complexity
4Adaptability or versatility
If real-time personalized engagement is implemented, then the user experience and engagement effectiveness are improved, but the data processing requirements and computational resources increase
Solution Approach 1:
The system performs preliminary actions by pre-segmenting users and pre-analyzing their preferences before engagement campaigns are launched. Machine learning models prepare engagement strategies in advance based on historical data, enabling real-time personalization during execution while reducing computational burden during the actual engagement moment
Solution Approach 2:
The system applies personalization selectively to the most critical engagement parameters rather than optimizing every aspect of each interaction. By focusing computational resources on the most impactful personalization dimensions, the system achieves effective real-time personalization while managing computational resource consumption
Data Source
AI summary
The present disclosure provides a method and system for generating a plurality of journeys for engaging a plurality of users in real-time. The system receives a first set of data associated with the plurality of users. In addition, the system fetches a second set of data associated with a plurality of past events on a plurality of platforms through one or more communication devices. Further, the system obtains a third set of data associated with a plurality of live events. Furthermore, the system analyzes the first set of data, the second set of data and the third set of data using one or more machine learning algorithms. Moreover, the system generates the plurality of journeys for engaging the plurality of users through a plurality of channels. Also, the system creates one or more goals for each of the plurality of journeys of the plurality of platforms.