VibeOps MCP

{ "id": "MOD-003-FEAT-006-SPIKE-002", "title": "Investigate machine learning models for deal close probability prediction", "description": "Explore whether machine learning models can improve forecast accuracy beyond simple stage-based probability. Research what data features are most predictive of deal closure, evaluate ML frameworks, and determine if we have sufficient historical data for training.", "featureId": "MOD-003-FEAT-006", "status": "planned", "questions": [ { "question": "Do we have sufficient historical deal data (volume and quality) to train a reliable ML model?", "answered": false, "answer": "" }, { "question": "Which features are most predictive of deal closure (deal size, age, activity frequency, contact engagement, etc.)?", "answered": false, "answer": "" }, { "question": "What ML approaches are most suitable (logistic regression, random forest, gradient boosting, neural networks)?", "answered": false, "answer": "" }, { "question": "How much would ML-based forecasting improve accuracy compared to current stage-based method?", "answered": false, "answer": "" }, { "question": "What are the infrastructure and maintenance requirements for deploying an ML model in production?", "answered": false, "answer": "" } ], "timebox": { "duration": 2, "unit": "weeks", "startDate": "2025-03-10T09:00:00Z", "endDate": "2025-03-24T17:00:00Z" }, "objectives": [ "Analyze historical deal data to assess data quality and volume (need 1000+ closed deals minimum)", "Perform feature engineering and correlation analysis to identify predictive features", "Build prototype models using at least 3 different algorithms", "Compare model accuracy against baseline stage-based probability method", "Estimate infrastructure costs and ongoing maintenance effort", "Provide go/no-go recommendation with detailed justification" ], "findings": { "summary": "", "recommendations": [], "risks": [], "nextSteps": [] }, "assignee": "", "artifacts": [], "metadata": { "createdAt": "2025-03-07T14:00:00Z", "updatedAt": "2025-03-07T14:00:00Z", "createdBy": "robert.kim", "tags": [ "deals", "forecasting", "machine-learning", "research" ] } }