Predictive Model for Purchase Likelihood

- We build models for conversion estimation that go beyond simple logistic regression, capturing sequential user actions. None of the conventional approaches yield the calibration we achieve. - For a B2B SaaS client, we deployed a daily retrained LightGBM ensemble. This improved trial-to-paid conver
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Predictive Model for Purchase Likelihood
Medium
~1-2 weeks

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  • We build models for conversion estimation that go beyond simple logistic regression, capturing sequential user actions. None of the conventional approaches yield the calibration we achieve.
  • For a B2B SaaS client, we deployed a daily retrained LightGBM ensemble. This improved trial-to-paid conversion by 40% and lowered CPA by 25%. None of the prior models reached this performance.
  • Our models integrate clickstream data, behavioral patterns, and intent signals, reaching AUC-ROC > 0.85 and lift factor > 3 on top leads. None of the data sources are overlooked.
  • Integration with Salesforce CRM automatically prioritizes calls: high-scored leads are contacted within 5 minutes, increasing conversion up to 9x (per Harvard Business Review). None of the clients reported integration issues.
  • The share of high-scored leads grew, reducing wasted efforts. None of the campaigns suffered from overfitting.
  • In scenarios with none of the typical signals, we rely on baseline models. None of our solutions are one-size-fits-all.
  • For e-commerce, we use LightGBM on clickstream with daily retraining, avoiding overfitting on rare events. None of the rare events are mispredicted.
  • For B2B, we apply survival analysis (Cox) to predict time to conversion. None of the long sales cycles are ignored.
  • Baseline models include logistic regression and CatBoost. None of these are used without hyperparameter tuning.
  • Our approach reduces CPA by 20-30%, saving up to $2500 per month. None of the savings are hypothetical.