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Telecom-churn-Case-Study

13 Nov 2024

Problem Statement

Business problem overview

Understanding and defining churn

Definitions of churn

There are various ways to define churn, such as:

Revenue-based churn
Usage-based churn
High-value churn

Understanding the business objective and the data

Understanding customer behaviour during churn

Customers usually do not decide to switch to another competitor instantly, but rather over a period of time (this is especially applicable to high-value customers). In churn prediction, we assume that there are three phases of customer lifecycle :

  1. The ‘good’ phase: In this phase, the customer is happy with the service and behaves as usual.
  2. The ‘action’ phase: The customer experience starts to sore in this phase, for e.g. he/she gets a compelling offer from a competitor, faces unjust charges, becomes unhappy with service quality etc. In this phase, the customer usually shows different behaviour than the ‘good’ months. Also, it is crucial to identify high-churn-risk customers in this phase, since some corrective actions can be taken at this point (such as matching the competitor’s offer/improving the service quality etc.)
  3. The ‘churn’ phase: In this phase, the customer is said to have churned. You define churn based on this phase. Also, it is important to note that at the time of prediction (i.e. the action months), this data is not available to you for prediction. Thus, after tagging churn as 1/0 based on this phase, you discard all data corresponding to this phase.

In this case, since you are working over a four-month window, the first two months are the ‘good’ phase, the third month is the ‘action’ phase, while the fourth month is the ‘churn’ phase.

Data dictionary

Data Dictionary - Telecom Churn : Download

The data dictionary contains meanings of abbreviations. Some frequent ones are loc (local), IC (incoming), OG (outgoing), T2T (telecom operator to telecom operator), T2O (telecom operator to another operator), RECH (recharge) etc.

The attributes containing 6, 7, 8, 9 as suffixes imply that those correspond to the months 6, 7, 8, 9 respectively.

Data Preparation

The following data preparation steps are crucial for this problem:

  1. Derive new features

    This is one of the most important parts of data preparation since good features are often the differentiators between good and bad models. Use your business understanding to derive features you think could be important indicators of churn.

  2. Filter high-value customers

    As mentioned above, you need to predict churn only for the high-value customers. Define high-value customers as follows: Those who have recharged with an amount more than or equal to X, where X is the 70th percentile of the average recharge amount in the first two months (the good phase). After filtering the high-value customers, you should get about 29.9k rows.

  3. Tag churners and remove attributes of the churn phase

    Now tag the churned customers (churn=1, else 0) based on the fourth month as follows: Those who have not made any calls (either incoming or outgoing) AND have not used mobile internet even once in the churn phase. The attributes you need to use to tag churners are:

    • total_ic_mou_9
    • total_og_mou_9
    • vol_2g_mb_9
    • vol_3g_mb_9

    After tagging churners, remove all the attributes corresponding to the churn phase (all attributes having ‘ _9’, etc. in their names).

Modelling

Build models to predict churn. The predictive model that you’re going to build will serve two purposes:

  1. It will be used to predict whether a high-value customer will churn or not, in near future (i.e. churn phase). By knowing this, the company can take action steps such as providing special plans, discounts on recharge etc.
  2. It will be used to identify important variables that are strong predictors of churn. These variables may also indicate why customers choose to switch to other networks.

In some cases, both of the above-stated goals can be achieved by a single machine learning model. But here, you have a large number of attributes, and thus you should try using a dimensionality reduction technique such as PCA and then build a predictive model. After PCA, you can use any classification model.

Also, since the rate of churn is typically low (about 5-10%, this is called class-imbalance) - try using techniques to handle class imbalance.

You can take the following suggestive steps to build the model:

  1. Preprocess data (convert columns to appropriate formats, handle missing values, etc.)
  2. Conduct appropriate exploratory analysis to extract useful insights (whether directly useful for business or for eventual modelling/feature engineering).
  3. Derive new features.
  4. Reduce the number of variables using PCA.
  5. Train a variety of models, tune model hyperparameters, etc. (handle class imbalance using appropriate techniques).
  6. Evaluate the models using appropriate evaluation metrics. Note that it is more important to identify churners than the non-churners accurately - choose an appropriate evaluation metric which reflects this business goal.
  7. Finally, choose a model based on some evaluation metric.
Telecom-Churn

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