AI Workbench combines the power of machine learning with the rich profile data in BlueConic. 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.
See AI Workbench in action
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 customer scores based on combinations of profile properties: customer life-time value (CLV), 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.
- 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.
- 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. If your AI Workbench use cases require additional resources, let us know via firstname.lastname@example.org. We'll discuss your requirements and upgrade your subscription as necessary.
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
- Insights to visualize AI Workbench results: Notebook insight (all cells), Notebook insight (single cell)
- 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.
- AI Glossary for Marketers