Data foundations
ML scoring ingests historical activity, firmographics, behavior, and sentiment to build features that predict conversion probability. Continuous feedback loops improve accuracy over time.
Algorithms that work
Gradient Boosting Machines (GBMs) often outperform in noisy B2B datasets. Neural networks excel when signals are high-volume and multi-modal. Logistic regression remains effective for interpretable baselines.
Non-obvious drivers
Models uncover hidden patterns—reply sentiment, engagement sequences, and role dynamics—missed by manual qualification.
Business results
Shorter cycles, stronger forecast, higher demo-to-close. Marketing prioritization improves and SDRs focus where impact is highest.
Conclusion
ML replaces intuition with empirical accuracy. With clean data and feedback, conversion prediction becomes a reliable revenue lever.