Predictive Model for Purchase Likelihood

Business loses budget when it cannot predict which users are truly ready to buy. We develop ML models for conversion prediction that account for behavioral signals and clickstream to accurately estimate purchase probability. Our team delivers a turnkey project—from data audit to CRM integration—ensuring a reliable solution that scales with your business.
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Predictive Model for Purchase Likelihood
Medium
~1-2 weeks

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Latest works

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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.