BlueConic Launches AI Workbench for Marketing Teams

BlueConic, a customer data platform (CDP) provider, has released AI Workbench, which allows marketers to leverage their unified profile databases to build and deploy models.

Because it is fully integrated into the BlueConic CDP, customers can also test models within the same environment and then deploy the models in real time to enrich profiles with intelligent customer scores, enable smarter segmentation, and activate marketing based on the output. Additionally, marketers can select from a gallery of models , such as customer lifetime value, propensity to buy/churn/etc., uplift, and lookalike, and then deploy them right from the interface.

"Our customers are already using machine learning in BlueConic for predictive behavioral scoring and one-to-one recommendations, but now we've added a solution for both marketers and data scientists who want to build, import, or select their own models," said Bart Heilbron, CEO of BlueConic, in a statement. "Due to the close proximity of the model to the unified profile database that is also connected to the marketer's activation channels, data scientists can now build and deploy a model in minutes and days rather than weeks or months so marketers can build smarter segments for immediate activation."

With AI Workbench, data scientists, BI professionals, and marketers will benefit from access to unified customer data for modeling, including both anonymous and known customer data; and a library of embedded Jupyter notebooks in AI Workbench, allowing models to be scheduled to run at specified cadences to update profiles and associated segments that can then be passed to activation channels via BlueConic connections.

"For the last year, we've been testing AI Workbench across dozens of our customers' datasets in multiple industries," said Bart Leusink, data science engineer at BlueConic, in a statement. "Using BlueConic's database for machine learning enables our customers to realize the full potential of both their machine learning models and the data scientist responsible for building them."