Answers to your questions about the BlueConic AI Workbench.
Q: How can I get started with AI Workbench?
Q: I'm new to Jupyter and Python. Where can I learn more?
A: Here are some helpful guides for working with Jupyter notebooks:
- Jupyter/IPython Notebook Quick Start Guide
- DataCamp's Jupyter Notebook Tutorial
- Dataquest tutorial: Jupyter Notebook for Beginners
Q: Where can I find the AI Workbench API reference?
A: See the AI Workbench API page for reference documentation.
Q: I'm a developer working in a Jupyter notebook, and I don't see the changes that another user has applied to my notebook code.
A: While your notebook (kernel) is running, you and your colleague can each work on your own version of the same notebook. If your colleague opens your notebook, makes changes, and saves the notebook, you would need to refresh your browser window in order to see the updated version of the notebook
If you leave a notebook without saving, then the next time you reopen the notebook, the last saved version of the notebook is loaded. If your kernel is terminated (manually or automatically) the latest saved version will be shown when you open the notebook again.
Q: Are Python notebooks included with BlueConic or do they have to be acquired separately?
A: A number of Python notebooks will be included out-of-the-box for BlueConic customers in a later phase of the release. Anyone in your organization who knows Python will be able to make changes to these out-of-the-box notebooks; alternatively, you can import your own model into BlueConic from an online library or by creating a custom one.
Q: Can we use time range for the RFM responsively as well as explicitly?
A: The RFM example notebook retrieves all orders for the last year. This time range can be configured as a parameter of the model. It also calculates recency as the maximum of "10 – the number of months that have passed since the customer last purchased."
A marketer could configure those 10 months as another parameter. Different companies will benefit from a completely different recency formula based on the cadence of purchase or conversion events in their business. With the BlueConic AI Workbench, it only takes a few lines of Python code (the current calculation use 3 lines of code) to completely change the way the value is calculated in a way that makes more sense for your organization.
Q: Do the CLV and RFM models work well with ‘high-cost, low-frequency’ products? How low frequency is low frequency? 1x purchase a year?
A: The CLV model tries to determine a certain purchase cadence. In the case of low-frequency product purchases, it can use weeks or months instead of days to calculate the summary data.
One thing to keep in mind with low-frequency products is that you should ensure that the chosen observation period is large enough to observe multiple purchases in order to deliver the most accurate calculation.
As for how low is low-frequency, it's going to depend on how your business defines low frequency. The great thing about AI Workbench is that you can adjust the models to fit your business needs. Keep in mind that machine learning needs to have a robust set of data to work with in order to come up with conclusions. So, if you are trying to do an RFM model with limited data because you only have transaction data for a single purchase a year and a small customer base, you may need to choose a different type of model for your use case. For instance, you might want to find a model that will look at common attributes of customers to predict the buying behavior of prospects.
Q: How does AI Workbench handle data for records that are not in the database yet?
A: AI Workbench runs on top of the full BlueConic database, including both anonymous and known data. In AI Workbench, you can also use data that’s not available in the profile, such as weather data or transactions from Shopify or Magento.
Q: Are there any plans to add the R programming language?
A: There aren't any concrete plans right now, but that's not to say we won't add it in the future.