Answers to your questions about the BlueConic AI Workbench.
Q: How can I get started with AI Workbench?
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. Please contact your Customer Success Manager at email@example.com if you intend to use this feature, to ensure your subscription can accommodate your intended usage. We'll discuss your requirements and upgrade your subscription as necessary.
Q: What is the Data Scientist role in BlueConic?
A: BlueConic uses roles-based permissions, and you need to have the BlueConic role of Data Scientist to create and run AI Workbench notebooks.
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: Can developers use external libraries in the BlueConic AI Workbench?
A: Yes, you can install external libraries using pip. Libraries cannot be installed in AI Workbench if they are incompatible with Python 3, or if they require a specific native library to be installed.
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: BlueConic provides out-of-the-box prebuilt AI notebooks with working CLV models and RFM models so you can calculate CLV (customer lifetime value) and RFM (recency, frequency, monetary value). Your marketing team can use these AI marketing models out of the box, without writing any code.
In addition, anyone in your organization who knows Python will be able to customize these out-of-the-box notebooks. You can import your own model into BlueConic from an online library or by creating a custom one.
Q: Can I use time range for the RFM model responsively as well as explicitly?
A: AI Workbench comes with an out-of-the-box, prebuilt RFM notebook.
The RFM example notebook retrieves all orders for the last year. Marketing teams can set this time range as a parameter of the model using the AI Workbench UI. The notebook 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 AI models work well with ‘high-cost, low-frequency’ products? How low frequency is low frequency? 1x purchase a year?
A: The CLV AI marketing model tries to determine a certain purchase cadence. In the case of low-frequency product purchases, the BlueConic CLV model 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.
Learn more about the CLV and RFM notebooks for AI marketing.
Q: What is predictive analytics?
A: Predictive analytics models look at existing customer data to make predictions about future customer behavior or events. For example, with AI Workbench, you can use calculate a customer's lifetime value, or CLV, based on a their past spending behavior. You can also calculate RFM scores that measure recency, frequency, and monetary value to create better customer segments to target customers based on past behavior or spending.
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.
Q: Once I build my CLV segments, can I only use them for web personalization? or can I use them in other channels like email and Google?
A: You can use your CLV Segment across channels once you’ve created them. Because your models are connected to your profile database in BlueConic which are connected to your marketing technologies, you have the ability to apply the same segment across all channels. This is a great example of why having a unified, “single” customer view is so valuable in terms of efficiency.