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The Smart Data Workbench is an incredible step in opening up complex machine learning and data analytic algorithms to users who know their data really well, but don't have the expertise to write machine learning algorithms or have the money to hire data scientist teams. You can select from a number of "Smart Data" creators to do amazing things with your data, like segmenting customers by fields like total spend and demographics, find relationships between different data sources and fields, clustering data together based any type of performance metric, and many more use cases.

Example Use Cases

General Clustering: Grouping things like text, date, and numerical fields in such a way that objects in the same group (called a cluster) are more similar to each other than to those in other groups.

Performance Clustering: When you have multiple fields that indicate the performance of almost any concept, show what is similar between high-performing rows, and low-performing rows.

Correlation: The term "correlation" refers to a mutual relationship or association between quantities. In almost any business, it is useful to express one quantity in terms of its relationship with others.

Multi-Field Customer Segmentation: Select up to four data fields to cluster or segment your customers together. This isn't just filtering by one field, the machine learning algorithms clusters data together from multiple fields to give you an in-depth view of your data.

Segment Customers by Age Range or Gender Fields: A filtering function that can be combined with other types of "Smart Data" jobs, like showing groups of active / high-value customers clustered together by age, gender or location.

Customer Churn Grouping: Select up to 4 fields (things like gender, age, purchase history, location) to produce lists of customers that help you identify why they might be in churn.

And Many More!


General Smart Data / Machine Learning Examples


Micro Segmentation Smart Data Examples


Churn Analytics Examples