Campaign Optimization with SQL Server

> **Note:** You can read more about this solution and deployment guides in the [Campaign Optimization solution](https://github.com/Microsoft/r-server-campaign-optimization) published on GitHub. > **Required preliminary agreement:** You need to accept the Terms of Use for the Data Science Virtual Machine on your Azure Subscription before you deploy this VM the first time. Click [here](https://portal.azure.com/#blade/Microsoft_Azure_Marketplace/LegalTermsSkuProgrammaticAccessBlade/legalTermsSkuProgrammaticAccessData/%7B%22product%22%3A%7B%22publisherId%22%3A%22microsoft-ads%22%2C%22offerId%22%3A%22standard-data-science-vm%22%2C%22planId%22%3A%22standard-data-science-vm%22%7D%7D) to agree to these terms. ## Overview When a business launches a marketing campaign to interest customers in new or existing product(s), they often use a set of business rules to select leads for their campaign to target. Machine learning can be used to help increase the response rate from these leads. This solution demonstrates how to use a model to predict actions that are expected to maximize the purchase rate of leads targeted by the campaign. These predictions serve as the basis for recommendations to be used by a renewed campaign on how to contact (for example, e-mail, SMS, or cold call) and when to contact (day of week and time of day) the targeted leads. The solution presented here uses simulated data from the insurance industry to model responses of the leads to the campaign. The model predictors include demographic details of the leads, historical campaign performance, and product-specific details. The model predicts the probability that each lead in the database makes a purchase from a channel, on each day of the week at various times of day. Recommendations on which channel, day of week and time of day to use when targeting users are based then on the channel and timing combination that the model predicts will have the highest probability a purchase being made. The Microsoft Marketing Campaign Optimization solution is a combination of a Machine learning prediction model and an interactive visualization tool, PowerBI. The solution is used to increase the response rate to a campaign by recommending the channel to contact (for example, e-mail, SMS, or cold call) as well as when to contact (day of week and time of day) targeted leads for use in a new campaign. The solution uses simulated data, which can easily be configured to use your own organization’s data, to model the acquisition campaign response. The model uses predictors such as demographics, historical campaign performance and product details. The solution predicts the probability of a lead conversion from each channel, at various times of the day and days of the week, for every lead in the database. The final recommendation for targeting each lead is decided based upon the combination of channel, day of week and time of day with the highest probability of conversion. The solution has been modeled after a standardized data science process, where the data preparation, model training and evaluation can be easily done by a data scientist and the insights visualized and correlated to KPIs by marketing via Power BI visualization. Read more about this solution, including step-by-step instructions on how to deploy it, at the [Campaign Optimization Website](https://microsoft.github.io/r-server-campaign-optimization/). ## Pricing Your Azure subscription used for the deployment will incur consumption charges on the services used in this solution, approximately $4.12(USD)/hour for the default VM. >Please ensure that you stop your VM instance when not actively using the solution. Running the VM will incur higher costs. > >**Please delete the solution if you are not using it.** ## Disclaimer ©2017 Microsoft Corporation. All rights reserved. This information is provided "as-is" and may change without notice. Microsoft makes no warranties, express or implied, with respect to the information provided here. Third party data was used to generate the Solution. You are responsible for respecting the rights of others, including procuring and complying with relevant licenses in order to create similar datasets.