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#39. Predictive Models with BigQuery ML

Predictive analytics is no longer just for data scientists. With BigQuery ML, marketers, analysts, and RevOps teams can build and deploy machine learning models using simple SQL—right inside their existing data warehouse.

In this guide, we’ll explore how to build and use predictive models with BigQuery ML to forecast conversions, identify churn risk, optimize campaigns, and drive data-driven decisions.

Why Use BigQuery ML for Predictive Modeling?

BigQuery ML enables you to:

  • Train models directly on massive datasets.
  • Avoid complex data exports and ETL to external tools.
  • Use SQL syntax to create, evaluate, and deploy models.
  • Easily integrate with Looker Studio, Power BI, and marketing platforms.

Common Use Cases for Marketing & RevOps

1. Predict Lead Conversion:

Use behavioral and demographic data to score leads based on likelihood to convert.

2. Churn Prediction:

Forecast which customers are at risk of leaving based on product usage, support data, or engagement patterns.

3. Campaign ROI Forecasting:

Estimate how different channels or audiences will respond to new marketing campaigns.

4. Product Recommendation Models:

Suggest next-best content, features, or upsell opportunities based on user behavior.

Step 1: Prepare Your Training Data

Key Inputs:

  • Behavioral signals: sessions, email opens, page views, feature usage.
  • Demographics: industry, job title, company size.
  • Funnel stage or outcome: converted, churned, purchased.

Ensure your dataset includes a label column (e.g., converted, churned) for supervised models.

Step 2: Train a Model in BigQuery ML

Example: Logistic regression model for lead conversion.

CREATE OR REPLACE MODEL `project.dataset.lead_model`
OPTIONS(model_type='logistic_reg') AS
SELECT
  industry,
  sessions,
  email_opens,
  webinar_attended,
  converted
FROM `project.dataset.leads_data`

BigQuery ML supports models like:

  • Logistic regression
  • Linear regression
  • Time series forecasting
  • K-means clustering
  • XGBoost (via remote models)

Step 3: Evaluate Model Performance

After training, review:

ML.EVALUATE(MODEL `project.dataset.lead_model`,
  (SELECT * FROM `project.dataset.test_data`))

Metrics include:

  • Accuracy, precision, recall (for classification)
  • Mean squared error (for regression)
  • ROC AUC for model strength

Step 4: Score New Records with Predictions

Once validated, use the model to score leads or customers:

SELECT *,
  predicted_label,
  predicted_probability
FROM
  ML.PREDICT(MODEL `project.dataset.lead_model`,
    (SELECT * FROM `project.dataset.new_leads`))

Send scores to your CRM (e.g., Salesforce, HubSpot) or visualize them in Looker Studio.

Step 5: Visualize and Activate Predictions

Connect scored data to dashboards and workflows:

  • Prioritize sales outreach by predicted score.
  • Trigger nurture campaigns based on probability tiers.
  • Visualize prediction accuracy and distribution across segments.

Use Looker Studio to show lead scoring trends, churn risk heatmaps, or campaign ROI forecasts.

Final Thoughts

BigQuery ML brings powerful predictive analytics into the hands of marketers and RevOps teams—no coding required. With fast modeling, easy deployment, and seamless integration, you can turn data into forward-looking strategy.

Next Steps

In upcoming articles, we’ll explore:

  • Blending BigQuery ML Predictions with Attribution Data
  • Automated Forecasting Dashboards for Marketing & Sales
  • Custom Machine Learning Pipelines with dbt + BigQuery

Stay tuned for smarter modeling workflows!

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