BlueConic AI Workbench

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.

You can use AI Workbench to run machine learning models with data from BlueConic profiles, connections, timeline events, and listeners.

AI Workbench use cases

With AI Workbench, you can apply the power of machine learning to your BlueConic data:

  • 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 the BlueConic AI Workbench.

Learn more about Jupyter and Python

New to the Jupyter environment? Here are some helpful guides for working with Jupyter notebooks:

Getting started with AI Workbench

BlueConic users with the “AI Workbench” permission enabled (Settings > Roles) can use AI Workbench to apply machine learning models to BlueConic profiles and data.

  1. To get started, select AI Workbench from the BlueConic navigation bar.
    AI Workbench UI opens, and here is where you add, delete, and manage Jupyter notebooks.
  2. Select Add notebook and choose a new notebook type, for example "API examples notebook" to get started. These examples contain sample Python code, and also a basic simple notebook with some visualizations.
  3. Provide a name for your notebook at the top of the page.
    If you're familiar with Jupyter, you'll recognize the menu, toolbar commands, and code cells from the notebook interface. See the AI Workbench API page for reference documentation.
    Here's an example notebook showing an application that calculates customer lifetime in days, based on profile properties that store first and last visit dates.
    Saving and deleting notebooks: Use the Save menu to save, save as, or delete a notebook in AI Workbench.

    Running notebook code: To execute the selected notebook cell, select Run on the notebook toolbar, or press Shift+Enter.

    Kernels: The Jupyter notebooks you create inside the BlueConic AI Workbench are connected to a kernel through the Jupyter UI.  A Python3 kernel executes the notebook code and returns results. The kernel and the notebook remain active while you are actively working in them. The kernel will be automatically terminated after 36 hours of inactivity, or when a BlueConic user manually terminates them. Also, a kernel will be automatically terminated when a notebook is opened without running one or more cells (once you navigate to another notebook).

    Privacy management: In the right-hand side bar, you can see a list of the privacy and consent management Objectives and other related items for this notebook. Select Add to objective to add this notebook in an existing privacy objective.

  4. Return to AI Workbench to see the list of notebooks.

    The notebook icon shows each notebook's status:

    • Green icons indicate that the kernel is active and has not been terminated.
    • Grey icons indicate that a notebook is not running.
  5. To see all the running kernels, select Running notebook kernels from the Save menu in AI Workbench.
    The Running notebook kernels window appears, showing all running notebooks and the amount of memory in use and free.
    AI_Workbench-Running_Notebook_kernels.pngMemory: At the bottom of the window, you can see how much memory is available and in use. You need at least 100MB of RAM available to start a new notebook and kernel.
  6. To terminate a kernel, select its name, and select Terminate kernel(s).
    Jupyter kernels that have been idle for 36 hours will automatically be terminated.

Resource usage

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 We'll discuss your requirements and upgrade your subscription as necessary.

Privacy management

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.

API reference for AI Workbench 

You can find API reference information in the AI Workbench example notebook. Select Add notebook in the Jupyter notebook editor of AI Workbench.

See the BlueConic Python API reference documentation.

Learn more