Anticipating Churn with Big Data & Machine Learning

Anticipating churn has become a strategic issue for operators: understanding your customers' departure signals allows you to act in advance and strengthen their loyalty.

And we know that in the ultra-competitive telecommunications market, losing a customer (churn) is expensive. Replacing a subscriber can cost up to five times more than retaining one.

So, rather than piling up promotional offers and manual reminders, Big Data and Machine Learning technologies offer a new approach: anticipating churn before it occurs.

By analyzing usage behaviors (calls, data, interactions) and weak signals, we identify at-risk customers. Marketing can then focus its retention efforts where they really matter.

I. Churn is a major strategic issue (Anticipating churn)

Before thinking about solutions, it's crucial to understand why churn is such a central challenge for telecom companies. Here are the key aspects to consider.

1. A ubiquitous phenomenon

In a sector as competitive as telecoms, customer loss is not an isolated incident, but a daily reality that directly impacts revenue. This constant trend of customer departures is forcing operators to rethink their business strategies.

2. A high cost for the company

Keeping an existing customer is generally much cheaper than attracting a new one. Yet, many companies still invest heavily in customer acquisition, at the expense of retention efforts, which can be ineffective in the long run.

3. A key indicator of business health

The churn rate acts as a thermometer for business performance. Its evolution provides information on the company's ability to retain customers, and an increase can jeopardize the gains made by acquisition campaigns.

4. Target the right customers

With an often immense customer base, it's essential to identify those who are truly at risk of churn. This knowledge allows marketing efforts to be focused on the most at-risk segments, thus optimizing return on investment.

II. How to build a predictive churn model? (Anticipate churn)

To anticipate customer departures, it is essential to rely on a model capable of assessing the likelihood that a subscriber will leave the service. This allows targeted actions to be put in place in advance.

1. Key Technologies

The construction of such a model relies on robust technical tools:

• Apache Spark : a powerful engine for processing very large quantities of data, in real time or in batch mode, guaranteeing speed and scalability.
• XGBoost : a machine learning algorithm recognized for its effectiveness in handling complex data and producing accurate predictions.
• MLeap : a solution to make the model compatible with different platforms, facilitating its integration into web or mobile applications.
• Play Framework & Scala : technologies used to develop an intuitive interface where managers can view the results of predictions in real time.

2. Data used

The model is based on a wide range of historical data collected from customers:

• Subscription duration, a key indicator of loyalty.
• Detailed usage of calls and data, segmented by time of day (day, evening, night) and internationally.
• Interactions with customer service often reveal signals of dissatisfaction.
• The availability of specific services such as voicemail or international plan.
• Contextual variables such as the customer's geographic location.

This data makes it possible to precisely identify habits and behaviors, which are essential for detecting the first signs of attrition.

3. Processing and modeling

To transform this raw data into a high-performance predictive model, several steps are necessary:

• Pretreatment : cleaning, transformation and selection of the most relevant variables.
• Dimensionality Reduction (PCA) : data simplification by reducing redundancy to improve the quality of predictions.
• Training the XGBoost model : supervised learning based on historical data, so that the model identifies predictive factors of churn.
• Export and deployment : the model is then exported and integrated into interactive applications, allowing marketing teams to score customers in near real time.
  

III. Marketing results and benefits (Anticipating churn)

Once the predictive model is operational, it brings several concrete benefits for marketing teams.

1. Personalized risk scores

Each customer is assigned a score that precisely quantifies their likelihood of churn. This individualized measurement allows for a detailed assessment of the level of risk.

2. Segmentation précise

Using these scores, customers are classified into distinct groups: high risk, medium risk, or low risk. This segmentation facilitates a better understanding of the customer profiles to retain.

3. Targeted actions

Marketing can then direct its campaigns towards customers identified as the most vulnerable, adapting messages and offers to their specific situation.

4. Optimization of resources

By targeting only high-risk customers, the company avoids unnecessarily dispersing its efforts and budget, thus improving return on investment.

5. Influence sociale et affinement des prédictions

Taking into account social networks and interactions between customers, in particular the contagion effect of churn within a group, further enriches the accuracy of the model and strengthens the retention strategy.

Ultimately, anticipating churn with Big Data and Machine Learning tools offers marketing a real compass to better understand customer behavior, take targeted action and develop their loyalty.