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Solution architecture: Predictive marketing campaigns with machine learning and Spark

Marketing campaigns are about more than the message being delivered; when and how that message is delivered is just as important. Without a data-driven, analytical approach, campaigns can easily miss opportunities or struggle to gain traction.

Through machine learning informed by historical campaign data, this solution architecture helps predict customer responses and recommends an optimized plan for connecting with your leads—including the best channel to use (by email, SMS, a cold call, etc.), the best day of the week, and the best time of the day.

Optimizing your campaigns with predictive marketing helps improve both sales leads and revenue generation and can provide strong ROI for your marketing investment.

This architecture enables efficient handling of big data on Spark with Microsoft R Server.

Deploy to Azure

Use the following pre-built template to deploy this architecture to Azure

Deploy to Azure

View deployed solution

Dashboard Machine Learning HDInsight Blob Storage

Implementation guidance

Products/Description Documentation

Apache Spark for Azure HDInsight

Microsoft R Server on HDInsight Spark clusters provides distributed and scalable machine learning capabilities for big data, combining the power of R Server and Apache Spark.

Power BI

Power BI provides an interactive dashboard with visualization that uses data stored in SQL Server to drive decisions on the predictions.


Azure Storage stores campaign and lead data.

Machine Learning Studio

Machine Learning helps you easily design, test, operationalize, and manage predictive analytics solutions in the cloud.

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