AI Workbench combines the power of machine learning with the rich profile data in the BlueConic customer data platform. Marketing teams can use machine learning to analyze their data, gain new insights, and further enrich individual profiles.
Using built-in Jupyter notebooks, data scientists can build and train machine learning models to analyze BlueConic data and return new, richer data to user profiles that can be used for segmenting and other CDP use cases.
Watch the video: Introducing the BlueConic AI Workbench
AI Workbench use cases
With AI Workbench, you can apply the power of machine learning to your BlueConic data. You can use AI Workbench to run machine learning models 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.
- Examine propensity for customers on a time-based contract or subscription to 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.
AI Workbench architecture
AI Workbench is an integral part of BlueConic and uses the Jupyter notebook UI for building and training machine learning models on BlueConic data, which can be used for making predictions on other profiles. See the AI Workbench API page for reference documentation.
Contact your BlueConic Customer Success Manager to get started using AI Workbench.
Your instance of AI Workbench uses a shared resource pool with other BlueConic customers. Note that BlueConic does not share data across customers; you only share resources such as memory and computing power.
Existing BlueConic customers can use AI Workbench free of charge as long as your total data operations remains smaller than the amount that is allotted to your subscription. If you intend to use this feature, contact your Customer Success Manager at firstname.lastname@example.org to ensure your subscription can accommodate your intended usage. We'll discuss your requirements and upgrade your subscription as necessary.
Visualizing your AI Workbench results
You can view the results of your AI Workbench notebooks on BlueConic Dashboards. Visualize the results of your machine learning algorithms using the AI Workbench Notebook insight to see the results of all notebook cells or the Notebook insight (single cell) to visualize only one cell's results.
Notebooks can be added to BlueConic privacy and consent Objectives, allowing for privacy management of the information that is being picked up. When a notebook is related to an objective, only profiles that have given consent to at least one of the related objectives will be returned to the notebook. As a result, a notebook will only process profiles that have consented to at least one of the objectives that the notebook is linked to.
- Getting Started with AI Workbench
- Scheduling and running AI Workbench notebooks
- Optimizing AI Workbench notebook performance
- Python API reference documentation, also available inside Jupyter notebooks in AI Workbench.
- Jupyter/IPython Notebook Quick Start Guide
- For up-to-date Python reference materials, select Help > Python Reference from the notebook menu bar.