Predictive scoring

Machine-learning-based account or lead scoring using historical conversion patterns.

Where this term appears on Landbase

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Frequently asked questions

How much training data does predictive scoring need?
At least 200 closed-won deals to start, ideally 500+. Below 200, the model overfits to noise and scores look unstable. Teams without enough data should use rules-based scoring until they accumulate enough wins.
What's the lift from predictive vs rules-based scoring?
15 to 25 percent improvement in conversion from MQL to opportunity, IF the model has enough training data and the features are well-engineered. Bad predictive models actually under-perform good rules-based ones.
How often should a predictive model be retrained?
Every quarter, or when win-rate drops by more than 10 percent in any segment. Model drift is real. Buyer behavior shifts faster than annual retraining can keep up with.
Should AEs see the score?
Yes, with explanation. A black-box score that AEs do not trust gets ignored. The explanation ("high because of intent + fit") is what drives behavior change.
How does Landbase do predictive scoring?
By combining account-level fit, intent, and trigger features into a weighted score that's auditable through the CLI. RevOps engineers can inspect each feature's contribution and adjust the model without re-training from scratch.