Use Machine Learning to predict customers you might lose – Part 1

“Customer satisfaction is worthless. Customer loyalty is priceless.”

Jeffrey Gitomer

Business is becoming increasingly competitive these days and getting new customers increasingly difficult. The wisest thing to do in this cut-throat scenario is to hold on to your existing customer base while trying to develop new business. Realistically, no matter how hard it tries, every organisation still loses a percentage of its customers every year to the competition. This process of losing customers is called Churn.

Progressive organisations take churn seriously, they want to know in advance that approximately how many customers they are going to lose this year and what is causing the churn. Having an insight into customer churn at least gives an organisation an opportunity to proactively take measures to control the churn before it is too late and the customer is gone.

Two pieces of information help the most when it comes to minimising the churn

1. Which customers are we going to lose this year

2. What are the biggest drivers of customer churn

The answers to the above questions often are hidden in the customer data itself but revealing these answers out of swathes of data is an art – rather a science called Data Science. With recent advances in some practical Data Science techniques like Machine Learning getting these answers is becoming increasingly feasible even for small scale organisations who do not have the luxury of a Data Science team. Thanks to services like Azure Machine Learning which are trying to democratise these advanced techniques to a level such that even a small scale customer can leverage them to solve their business puzzles.

Let me show you how your Dynamics CRM can leverage the powerful Machine Learning cortex to get some insights into the key drivers of customer churn. In this blog series, we will build a machine learning model that will answer the questions regarding churn. I have divided the series into four parts as below

Part 1 – Introduction

Part 2 – Creating a Machine Learning model

Part 3- Integrate the model with Dynamics CRM

Part 4 – Gaining insights within Dynamics CRM

I will take the example of a Telecom organisation but the model can be extended any kind of organisation in any capacity and from any industry.

Scenario

Let us say there is a Telecom company called TelcoOrg which uses Microsoft CRM 2016 and they have an entity called Telecom Customer that stores their telco profile. Such profile may include some data regarding a customer mobile plan, phone usage, demography and reported satisfaction.

Understanding the features

In data science projects, it is crucial to understand the data points (called features). You need to carefully select those features that are relevant to the problem at hand, some the features also need to be engineered and normalised before they start generating some information gain. Below are the features that we will be using in this scenario of our Churn problem

Let me quickly explain the features so that we can understand the information contained in them

The screenshot below shows the information from the Telecom Customer entity. The section highlighted in blue are predictions based on Azure Machine Learning web services. Whenever any of the fields on this CRM form changes, the WS updates its prediction scores based on the record’s data. I will provide details later during the series as to how I built this integration.

Below is a screenshot of some of these records

We will achieve the following business benefits using Azure Machine Learning

1. Customers who are predicted to be at a higher risk of leaving (churn) can be flagged, so the customer retention teams can get it touch with them to proactively address their concerns in a bid to retain them

2. Find what factors affect churn the most i.e. out of all these fields we will determine what fields are more likely to make a leave than others

3. We will also get insights into some business rules that dictate churn i.e. the drivers

I hope you understand the problem now and find it interesting so far. Let us meet in next part of the blog where I will show how a machine learning model is created.