Access and Analyze Rich Customer Data

Enhance the productivity and effectiveness of your data scientists by giving them access to the rich analytics data collected and maintained in Evergage.

Evergage's Data Science Workbench provides companies with a means of accessing the in-depth behavioral data, contextual information and explicit survey responses – that Evergage tracks and maintains on each and every visitor, customer and account. Data scientists can explore the data, create visualizations, execute data transformations, and run numerical simulations and statistical models. Output from these analyses and models can, in turn, be brought back into Evergage as profile attributes so those insights can be used for improving real-time personalization efforts.

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With Evergage's Data Science Workbench, data scientists can access rich customer, catalog and transactional data – natively collected by Evergage – in order to explore the data, run numerical simulations and conduct statistical modeling using a pre-configured cluster.

Rich Customer Data
With Evergage’s Data Science Workbench, companies can access a treasure trove of rich customer data. For every visitor, customer and account who engages with your website or mobile app, Evergage maintains a unified customer profile where engagement details as well as a contextual understanding of a visitor’s behavior is stored. Profiles may also include explicit data collected from surveys and attribute data passed from external sources into Evergage’s customer data platform (CDP).

Access Catalog & Transactional Data
Evergage also tracks detailed engagement statistics at the product and/or content level as well as transactional data such as downloads, purchases, adds-to-cart, etc. Using the Data Science Workbench, this information can be accessed by data scientists and analyzed to identify trends and patterns related to their product and/or content catalogs. For example, among many possible applications, a retail-focused data scientist could apply models to this data to identify seasonality patterns within specific geographies.

Pre-Configured Environments
Users of the Data Science Workbench are provided with a dedicated cluster, where they can access Evergage’s data through a safe and secure read-only proxy. The cluster is pre-installed with a suite of familiar tools which run on top of Apache Spark. Apache Zeppelin provides a notebook in which Python, R and Scala can be used together, sharing data across languages. Additionally, libraries allow for data to be pulled from Evergage into Spark DataFrames in a clear and well-documented manner.

Leverage Your Model Output in Evergage
Not only are your data scientists able to create custom models using the Evergage-housed data, they are also provided a pathway to integrate those learnings back into Evergage. This occurs by uploading the results of your modeling as custom attributes. For instance, you could flag particular customers as “high value,” or “seasonable buyers” or “likely to churn” based on your analysis.

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