Posted in Machine Learning

Dynamics CRM: Use Machine Learning to calculate Happiness Index

Right now there is a  fantastic opportunity for the CRM systems to get intelligent by leveraging the smart capabilities offered by Machine Learning (referred as ML hereafter). As the markets around the world become increasingly competitive, I reckon it will be the smartness of their CRM systems that will keep certain companies ahead of others. CRM 2016 is out and Microsoft is working hard to put intelligence into their flagship business solution by integrating Azure Machine Learning offerings with Dynamics CRM.


What is Machine Learning

In my opinion, the key notion of machine learning is to be able to make sense out of the data, a sense that can drive timely business decisions. More and more business are adopting ML in a bid to strengthen their customer base and to stay ahead of their competitors.


How does ML work. Is it Magic?


Courtesy: ironarchive

Every magic has a reasoning behind it and ML is no exception. The ML Systems do it by classifying data, learning from past trends, getting intelligent over time and eventually by making predictions based on scoring results achieved after massive number crunching.

The purpose of this post is not to talk about ML and its concepts but to rather show it in action especially its integration with Dynamics CRM. There are numerous resources on the internet that you can leverage to understand ML.


Take control over Machine Learning

You may already be aware of certain functionalities where Dynamics CRM is already using ML capabilities like matching KBs to cases, recommending products to cross-sell and generating sentiment analysis reports from within Social Listening. When I look at these solutions, their core ML engines are not exposed to users, they are not customisable. You input data (from CRM), call the ML service and get output, but you have no control over how that output is generated.

What if a customer wants full control of the ML engine:

  • they want to choose what algorithm to use
  • they want to train the system with their own data
  • their data scientists want to define the parameters

so that the outcome of the integration is more personalised (and accurate).

These are the objectives that I will attempt to achieve through this blog series, absolute control over the ML engine.


Sounds exciting ??

download (1)

Courtesy: hungrysojourner


Lets begin !!



I will put together a solution that will show how we can integrate Dynamics CRM 2016 with Azure Machine Learning Services. Throughout the series, the focus will be poised more towards an end-to-end working integration, rather than the perfection of the ML algorithms and accuracy of predictions. In other words, in this first cut we will have a solution that works; may not be a perfect solution.  You however will have full opportunity to improve it  i.e. you can choose a better algorithm, better training model to improve the ML capabilities.


Below is the series

Dynamics CRM: Use Machine Learning to calculate Happiness Index

Part 1 – The Business Problem & Solution Architecture

Part 2 – Training the Machine Learning model

Part 3 – Deploying the Machine Learning solution

Final – Customising CRM and Integration

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