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Project Description
Opportunity Scoring for Salesforce predicts the probability of success for each sales deal in the sales pipeline. It is built using the Microsoft Cortana Intelligence Suite.

Opportunity Scoring uses a machine learning model trained on historical sales data to predict the  probability that the deal will be won. E2e solution is built using Microsoft Advanced Analytics solution - Cortana Intelligence suite.

Solution deploy a plug-in in your Sales Force instance.  E2E solution including ML models, webjobs are deployed in Azure and connected to SalesForce instance.

Training and prediction are implemented with Azure Storage and Azure Machine Learning, orchestrated by Azure Scheduler, and executed within an Azure Web Job.

1. The installer provides an advanced solution reference architecture that a IT developer can customize to build customized end-to-end solutions.

2. The Documentation for Opportunity scoring for Sales Force has step-by-step instructions to guide you through the installation.

3. Once installed you will see three Machine learning models in Azure subscription, plus web services.  Installer also create retraining and scoring web jobs in your Azure Machine Learning subscription. 

4. You will also see customization applied to your instance to allow users to refresh their scores, view explanations why the opportunity got its score and view the history of previous scoring of opportunities.

       Please note - you will only see insight if feature contribution for that row is above certain threshold.

The solution will configure a connection between and your Azure subscription's services. Once set up, the solution will copy opportunity data into your Azure subscription, score your opportunities, and write the resulting scores and reason codes back in entities created during setup.

See a video of Installer and install ML Model & solution built using Microsoft Cortana Intelligence.

If you use, Microsoft Dynamics CRM - you can deploy similar solution for Dynamics CRM here

Last edited Oct 16, 2016 at 2:11 AM by prashdesh, version 7