Lilly Tech Systems
Optimization & Analytics
Measuring, testing, and continuously improving AI-powered nurture sequences through advanced analytics, multi-armed bandit testing, attribution modeling, and automated optimization loops.
Key Nurture Metrics
| Metric | What It Measures | Target |
|---|---|---|
| Sequence Completion Rate | % of leads who complete the full nurture path | 40-60% (adaptive sequences often improve this) |
| MQL Conversion Rate | % of nurtured leads reaching MQL threshold | 15-30% improvement over baseline |
| Time to MQL | Days from entry to MQL qualification | AI nurturing should reduce by 20-40% |
| Engagement Score Trend | Average engagement change across the sequence | Positive trend indicates effective nurturing |
| Nurture-Influenced Revenue | Revenue from deals that touched nurture sequences | Track percentage of pipeline attributed to nurturing |
AI-Powered Testing
AI transforms A/B testing from manual experiments into continuous, automated optimization:
- Multi-Armed Bandit: Instead of fixed 50/50 splits, AI dynamically allocates traffic to winning variants, maximizing results during the test period.
- Multivariate Testing: AI tests combinations of subject lines, content blocks, CTAs, and send times simultaneously to find the optimal combination.
- Contextual Bandits: Different variants win for different segments. AI learns which variant works best for each lead profile and serves accordingly.
- Continuous Optimization: Tests never truly "end." The AI keeps learning and adapting as audience behavior and preferences shift over time.
Attribution for Nurture Sequences
Touch Attribution
Measure the contribution of each nurture email/touchpoint to eventual conversion. Identify which content pieces are most influential in the sequence.
Path Analysis
Map the most common and most effective journeys through your nurture sequences. Discover which paths lead to fastest conversion.
Revenue Attribution
Connect nurture touchpoints to closed revenue. Understand the ROI of each nurture track, content piece, and AI optimization.
Incrementality
Run holdout tests to measure the true incremental impact of AI nurturing versus no nurturing or traditional sequences.
Continuous Improvement Framework
- Weekly: Review email-level metrics (open rates, click rates, unsubscribes). Identify and replace underperforming content.
- Monthly: Analyze sequence-level conversion rates and time-to-MQL. Compare AI-optimized vs. baseline performance.
- Quarterly: Retrain lead scoring models with fresh conversion data. Review and update content library. Evaluate new AI capabilities.
- Annually: Reassess nurture strategy alignment with business goals. Audit data quality and integration health.