AI Workbench CDP use cases with BlueConic
With the BlueConic AI Workbench, you can apply the power of artificial intelligence and machine learning to your first-party customer data. You can use AI Workbench to run machine learning models and make smarter predictions with data from BlueConic profiles, connections, timeline events, and listeners.
Calculate predictive customer scores based on combinations of profile properties: customer lifetime value (CLV); recency, frequency, monetary value (RFM); or propensity to buy, click, or churn.
Use clustering to discover new customer segments.
Compare different machine learning models to find the optimal algorithm for examining or enriching customer data. For example, you can use the Advanced A/B testing notebook to find the best-performing dialogue for your marketing goals.
Customer timeline events, which can be imported via BlueConic connections, can be used to train a machine learning model for calculating new scores for other profiles.
Use probabilistic or fuzzy matching to find matching profiles in your profile database.
Create new AI-based data visualizations in Python.
Best practices for optimizing AI models for first-party customer data
Hear from BlueConic's resident expert on Best Practices for Optimizing Your AI-Powered First-Party Data Strategy.
Contact us
Contact your Customer Success Manager to learn more about applying AI modeling to your first-party customer data in BlueConic using AI Workbench.
Learn more about AI-driven CDP use cases
Python API reference documentation, also available inside Jupyter notebooks in AI Workbench.
For up-to-date Python reference materials, select Help > Python Reference from the notebook menu bar.