AI Yield Optimization Campaign System
Orchestrates 16 specialized AI agents executing a 7-phase pipeline with parallel processing. Combines predictive analytics, competitor intelligence, social media monitoring, and autonomous optimization to deliver personalized interventions with measurable ROI tracking.
Problem Statement
The challenge addressed
Solution Architecture
AI orchestration approach
Campaign Configuration - Scenario setup and target selection
Agent Execution Pipeline - Multi-agent processing status
Campaign Results - Student recommendations and interventions
Executive Summary - Yield improvement and ROI metrics
AI Agents
Specialized autonomous agents working in coordination
Campaign Orchestrator Agent
Coordinating 15+ specialized agents across a complex yield optimization workflow requires intelligent task distribution, dependency management, and phase-based execution control.
Core Logic
Serves as the central coordinator managing 7-phase execution pipeline. Handles agent initialization, task distribution based on dependencies, parallel execution within phases, and result aggregation. Broadcasts execution events, manages timeout policies (30s default), and enforces confidence thresholds (0.7 minimum). Tracks comprehensive execution metrics and generates executive summaries.
Student Data Collection Agent
Student data exists across disparate systems (SIS, CRM, engagement platforms) making holistic analysis impossible. Manual data gathering is slow and incomplete.
Core Logic
Connects to multiple data sources with 120ms average latency. Retrieves comprehensive student profiles including academic records, financial status, engagement metrics, and communication history. Applies data normalization, quality checks, and filtering based on target criteria. Supports geographic, program, and risk-level filtering for campaign segmentation.
Student Behavior Analysis Agent
Understanding student engagement patterns and predicting enrollment intent requires sophisticated behavioral analysis that manual review cannot accomplish at scale.
Core Logic
Uses Claude 3 Sonnet for engagement pattern recognition and activity trend detection. Analyzes portal logins, email opens, link clicks, event attendance, and document submissions. Calculates engagement scores (0-100) and identifies behavioral signals indicating enrollment intent or dropout risk. Generates trend analysis and behavioral insights.
Communication Sentiment Agent
Student communications contain valuable sentiment signals about enrollment intent and concerns, but manual analysis of thousands of emails and messages is impractical.
Core Logic
Employs BERT-based NLP for sentiment extraction from student communications. Performs emotion classification (positive/negative/neutral), intent detection, and concern identification. Generates sentiment scores (-100 to 100) with confidence levels. Identifies specific concerns (financial, academic, social) requiring targeted intervention.
Enrollment Risk Assessment Agent
Identifying students at risk of not enrolling requires multi-factor analysis combining academic, financial, behavioral, and competitive factors that exceed human analytical capacity.
Core Logic
Implements XGBoost model for multi-factor risk calculation. Categorizes students into CRITICAL, HIGH, MEDIUM, LOW, and MINIMAL risk levels using weighted factor analysis. Considers engagement decline, financial concerns, competitor applications, and communication gaps. Generates threshold alerts for immediate intervention on critical-risk students.
Enrollment Yield Prediction Agent
Forecasting individual enrollment probability enables resource prioritization but requires ensemble machine learning models and real-time feature processing unavailable in traditional systems.
Core Logic
Runs ensemble ML models with 35ms average latency. Calculates yield probability (0-100%) using features including engagement score, financial fit, academic match, geographic proximity, and competitor threat. Provides confidence intervals, feature importance analysis, and peer comparison percentiles. Supports what-if scenario modeling for intervention impact prediction.
Intervention Recommendation Agent
Selecting optimal interventions from 18+ options based on student profile, risk level, and budget constraints requires sophisticated matching beyond rule-based systems.
Core Logic
Uses GPT-4 Turbo for personalized intervention generation. Matches students with interventions (email, SMS, calls, campus visits, financial reviews, peer ambassadors) based on profile analysis. Calculates expected impact and ROI for each recommendation. Applies budget constraints and communication caps (3 emails, 2 SMS, 1 call per student) while maximizing intervention effectiveness.
Personalized Content Generation Agent
Creating personalized emails, SMS messages, and call scripts for thousands of students with individual concerns and interests requires scalable AI-powered content generation.
Core Logic
Leverages GPT-4 Turbo for multi-format content creation. Generates personalized emails addressing specific student concerns, SMS campaigns with optimal timing, and call scripts with talking points. Achieves 85-92% personalization scores by incorporating student interests, program details, and engagement history. Supports A/B variant generation for testing.
Communication Channel Optimizer Agent
Determining the optimal communication channel (email, SMS, phone, mail) and timing for each student requires analysis of historical response patterns and preference signals.
Core Logic
Analyzes communication preference indicators and historical response data with 700ms execution time. Calculates optimal contact windows (day/time) based on engagement patterns. Recommends channel mix (email, SMS, phone) weighted by student responsiveness. Respects opt-outs and FERPA compliance requirements while maximizing response probability.
Campaign Execution Planning Agent
Translating intervention recommendations into executable campaign plans with proper sequencing, scheduling, and approval workflows requires systematic execution planning.
Core Logic
Creates comprehensive execution plans with intervention schedules, touchpoint sequences, and timeline management. Generates human approval requirements for high-confidence-threshold interventions. Coordinates with CRM systems for campaign deployment. Tracks budget allocation by channel and monitors execution status.
Competitor Intelligence Agent
Understanding competitor positioning, aid packages, and student overlap provides strategic advantage but requires continuous market monitoring unavailable to enrollment teams.
Core Logic
Tracks competitor institutions with threat level classification (CRITICAL/HIGH/MEDIUM/LOW). Monitors market position, yield rates, average aid packages, and student overlap percentages. Calculates win rates from historical data. Generates counter-strategies with specific implementation recommendations for competing against each institution.
Social Media Intelligence Agent
Student discussions on social media reveal enrollment intent, concerns, and competitor sentiment, but monitoring multiple platforms at scale exceeds human capacity.
Core Logic
Uses GPT-4o for multi-platform social media analysis (Twitter, Instagram, TikTok, LinkedIn, Reddit, Facebook). Tracks total mentions, sentiment scores, and trending topics. Identifies influencer mentions and viral content. Monitors competitor sentiment comparisons. Alerts on negative sentiment spikes requiring rapid response.
A/B Testing Optimization Agent
Running effective A/B tests on enrollment campaigns requires proper experiment design, statistical analysis, and autonomous winner selection that manual processes cannot deliver consistently.
Core Logic
Employs Bayesian optimization engine for experiment design and analysis. Creates variant tests for messaging, timing, and channel selection. Performs statistical significance analysis with automatic winner selection. Enables autonomous optimization without manual intervention. Supports multi-armed bandit approaches for continuous improvement.
Enrollment Forecasting Agent
Strategic enrollment planning requires accurate forecasting that accounts for seasonal patterns, external factors, and multiple scenarios—capabilities beyond spreadsheet-based projections.
Core Logic
Combines Prophet and XGBoost models for enrollment forecasting. Generates multi-scenario predictions (optimistic, baseline, pessimistic, aggressive intervention). Analyzes seasonal factors (application deadlines, deposit dates) and external factors (economic conditions, demographic shifts, competitive landscape). Provides confidence-bounded forecasts with strategic recommendations.
Student Intent Detection Agent
Identifying students' enrollment decision timeline and dropout risk requires analysis of behavioral signals across multiple touchpoints that traditional systems cannot aggregate.
Core Logic
Implements LSTM model for behavioral signal analysis. Processes intent signals (portal activity, email engagement, event attendance, FAFSA actions, deposit page views, competitor research). Calculates intent scores (0-100) with signal strength classification. Predicts decision timelines and dropout risk scores. Identifies students requiring immediate attention.
Autonomous Intervention Scheduler Agent
Scheduling interventions across thousands of students while avoiding communication fatigue, respecting time zones, and handling conflicts requires autonomous coordination.
Core Logic
Provides real-time scheduling with conflict resolution and fatigue prevention algorithms. Respects communication caps and time zone preferences. Handles scheduling conflicts through priority-based resolution. Monitors intervention effectiveness and adjusts timing based on response patterns. Enables batch scheduling for campaign launches.
Worker Overview
Technical specifications, architecture, and interface preview
System Overview
Technical documentation
Tech Stack
6 technologies
Architecture Diagram
System flow visualization