For more details on how this solution is built, visit the solution guide in GitHub.
A typical retail business collects customer data through a variety of channels, including web-browsing patterns, purchase behaviours, demographics and other session-based web data. Some of the data originates from core business operations, but other data must be pulled and joined from external sources like partners, manufacturers, public domain, etc.
Many businesses leverage only a small portion of the available data, but in order to maximise ROI, a business must integrate relevant data from all sources. Traditionally, the integration of external, heterogeneous data sources into a shared data processing engine has required significant effort and resources to setup. This solution describes a simple, scalable approach to integrating analytics and machine learning to predict customer purchasing activity.
The Customer 360 Profile solution addresses the above problems by:
- Uniformly accessing data from multiple data sources while minimising data movement and system complexity in order to boost performance.
- Performing ETL and feature engineering needed to use a predictive Machine Learning model.
- Creating a comprehensive customer 360 profile enriched by predictive analytics running across a distributed system backed by Microsoft R Server and Azure HDInsight.
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