Given the challenging industry dynamics, managing the customer base to reduce churn should be among any senior telecom executive’s highest priorities. Our work with MVNOs and ETCs in the US reveals that those companies that implement a comprehensive, analytics-based approach to base management can reduce their churn by as much as 15%.
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The reasons that lead to customer churn can be numerous, coming from poor service quality, delay on customer support, prices, new competitors entering the market, and so on. Usually, there is no single reason, but a combination of events that somehow culminated in customer displeasure.
Every churned customer leaves good clues about where you left to be desired. Identifying these clues leads to meaningful insights and we use this to train our Churn Prediction Models, learn from the past, and have strategic information at hand to improve future experiences. It is all about machine learning.
The analysis starts with Exploratory Data Analysis where the aim is to identify patterns that yield to customer churn. In the example below, the customer Lifespan (in months) is represented by the feature Tenure and customer churn is represented by the feature Churn, which is the target variable of this example. The bar chart below provides a good insight on how churn is distributed across the customer lifespan. In the below example, we can see that the largest majority of customers cancel or do not renew their subscription in the first month, totaling 20.3% of customers that defect. Reasons for this high rate can be bad first experience, trial periods, or prepaid accounts that expire automatically if no top-up is done within a predefined time period.
We develop Machine Learning algorithms that are used to build analytical models which use historical data to build a model, which can predict the value of the outcome variable in new data where that value is not known. A good machine learning model can accurately predict the value of an outcome variable and thus help with quick decisions in the process workflow.
Predictive analytics uses an outcome variable, which, in the churn prediction case, is the churn indicator variable, for building the predictive model. The typical steps that we follow to build a Machine Learning Model:
The first step at this stage is to create an appropriate custom database. Data is collected from different sources (both internal and external sources). Data in our example dataset will have features like:
Data preparation typically involves the following tasks:
There are a few supervised algorithms available. Each algorithm differs in nature and produce different results based on the given data set. We choose the appropriate algorithms based on 2 factors: i. Nature of the data, and ii. The problem that we are solving. Below are the algorithms that we use for Churn Model Construction:
The Model building activity involves construction of machine learning algorithms that can learn from historical data and make predictions or decisions on unseen data. Once our Churn Model is built, it is trained with training dataset, then validated with validation dataset, while fine tuning hyper parameters, and finally tested with test dataset. At each stage, chosen performance metric is observed to get desired performance level.
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