analytics-funnel-attribution

name	analytics-funnel-attribution
description	Track acquisition and retention funnels for skill usage and workflow completion
risk	medium
source	.agents-phase6
date_added	"2026-03-29T00:00:00.000Z"
usage	Use when implementing Phase 6 scale, interoperability, and analytics initiatives
avoid	Do not deploy without compatibility checks, rollback strategy, and measurable SLOs
mandates	["define measurable success criteria and failure budgets","include platform-specific acceptance gates","include data privacy and governance checks"]
response	assess baseline, design minimal implementation, validate measurable targets, document rollback plan

analytics-funnel-attribution

Overview

Track user acquisition, engagement, and retention funnels for game features—measuring skill adoption, workflow completion, and feature impact on business metrics. This skill enables data-driven iteration on framework quality and community engagement.

Key Capabilities

1. Funnel Tracking

• Event Stream: Track skill usage, workflow starts/completions, feature interactions
• Session Management: Identify user cohorts and session lifecycles
• Retention Curves: Measure D1, D7, D30 retention for features
• Convertion Tracking: Skill discovery → adoption → maintenance pipeline
• Churn Analysis: Identify users at risk and feature abandonment patterns

2. Attribution Modeling

• Multi-Touch Attribution: Credit skill exposure across multiple touchpoints
• First/Last-Click Models: Simple and complex attribution for causal inference
• Cohort Analysis: Compare feature adoption across user segments
• A/B Testing Framework: Controlled experiments on skill variants
• Incrementality Testing: Measure true impact vs. correlation

3. Analytics Infrastructure

• Event Validation: Schema validation, deduplication, late arrival handling
• Real-time Dashboards: Live funnel metrics with <5min latency
• Batch Processing: Daily/weekly cohort analysis and retention calculations
• Data Privacy: GDPR compliance with anonymization and consent tracking
• Sampling: Efficient large-scale processing via intelligent sampling

4. Insights & Reporting

• Automated Alerts: Anomaly detection on key metrics (drop >20%)
• Cohort Insights: Generation of insights via statistical testing
• Custom Reports: Self-service dashboards for different stakeholder needs
• Export APIs: CSV/JSON export for external analysis
• Predictive Models: Churn prediction, lifetime value estimation

Implementation Pattern

// Pseudo-code: Event tracking and funnel analysis
namespace SkillAnalytics {
    public class SkillEventTracker {
        public void TrackSkillDiscovery(string skillId, string userId, string source) {
            var evt = new Event {
                EventType = "skill_discovered",
                SkillId = skillId,
                UserId = userId,
                Source = source,
                Timestamp = DateTime.UtcNow
            };
            EventStore.Log(evt);
        }

        public void TrackSkillAdoption(string skillId, string userId, string workflowId) {
            EventStore.Log(new Event {
                EventType = "skill_adopted",
                SkillId = skillId,
                UserId = userId,
                WorkflowId = workflowId,
                Timestamp = DateTime.UtcNow
            });
        }

        public async Task<FunnelMetrics> ComputeFunnel(
            string skillId,
            TimeSpan period,
            CancellationToken ct = default)
        {
            var discovered = await EventStore.Count(
                eventType: "skill_discovered",
                skillId: skillId,
                period: period
            );

            var adopted = await EventStore.Count(
                eventType: "skill_adopted",
                skillId: skillId,
                period: period
            );

            var completed = await EventStore.Count(
                eventType: "workflow_completed",
                skillId: skillId,
                period: period
            );

            return new FunnelMetrics {
                Discovered = discovered,
                Adopted = adopted,
                AdoptionRate = (double)adopted / discovered,
                Completed = completed,
                CompletionRate = (double)completed / adopted
            };
        }
    }
}

Mandates

• Consent-First: Track only GDPR-compliant events with explicit user consent
• Privacy by Design: No PII in event stream, anonymized user IDs
• Accuracy Thresholds: Metrics must be 99%+ accurate for decision-making
• Latency Targets: Real-time dashboards update within 5 minutes
• Retention Policy: Raw events deleted after 90 days, aggregates kept for 2 years

Best Practices

1. Event Schema First: Design events before implementation, validate strictly
2. Track Behaviors, Not Identities: Focus on what users do, not who they are
3. Correlation ≠ Causation: Use proper statistical testing for causal claims
4. Sample Judiciously: Balance accuracy with cost through intelligent sampling
5. Iterate on Metrics: Review and refine KPIs quarterly based on learnings

Key Metrics

Metric	Target	Alert Threshold
Skill Discovery → Adoption	>30%	<20%
Workflow Completion Rate	>80%	<60%
Feature Retention (D7)	>50%	<30%
Event Processing Latency	<1min	>5min
Data Freshness	<5min	>15min

Resources

• Event Schema Design Guide
• Attribution Model Comparison
• GDPR Compliance Checklist
• Example: examples/funnel-dashboard-config.json