Solution architecture: Optimize marketing with machine learning

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 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 machine learning helps improve both sales leads and revenue generation and can provide strong ROI for your marketing investment.

In this solution, SQL Server R Services brings the compute to the data by running R on the computer that hosts the database.

Deploying to Azure

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

Deploying to Azure
Optimize Marketing with Machine Learning | Microsoft Azure Diagram showing three connected icons. At the center is the icon for SQL Database, which stores the campaign and lead data. To the left, connected by a mutual arrow, is Machine Learning, which helps make predictions based on that data. To the right of SQL Database, connected by a one-way arrow, is Power BI, which visualizes the data through an interactive dashboard. Power BI SQL Database Machine Learning

Implementation guidance

Products Documentation

SQL Database

 		SQL Server stores the campaign and lead data. R-based analytics provide training and predicted models and predicted results for consumption using R.

Machine Learning

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

Power BI

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

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