The Reactive to Proactive transition has provoked the enterprise paradigm of ‘predicting events’ to deal with the future effectively. Where eleventh-hour action may fail to support successful outcomes, predictions help, as in the case of predicting customer churn and improving customer retention.
A negative experience, better offer from competition or an unresolved complaint can set ‘Customer Churn’ into action. But there’s more to churn than these obvious scenarios that spring to mind – what matters to keep customers under fold is the need to read hidden customer signals that may guide enterprises to predict customer attrition.
As Bertie Wooster who turned to Jeeves for dire need of intelligence, in the Wodehousian classics, enterprises rest faith on Data and Machine Learning Models to acquire insightful predictions needed to prevent customer churn and increase customer lifetime value.
With Machine learning leading the churn prediction way, what rises in relevance is the use of appropriate ML algorithm to predict and prevent customer chum. Among machine learning models used for churn prediction, does Logistic Regression score over others as the right ML algorithm for the customer churn scenario?
The answer lies in taking a closer look at the goal of the churn prediction exercise, delving into the data and insight imperatives to accomplish the set goal.
Looking at Insight-Approach Connection
What are the insights you want to acquire?
That’s one question that initiates the task of weighing and using the appropriate ML algorithm for building the churn prediction model. As the insight journey begins with data, the prelude to data collection is about nailing down the final objective that an enterprise wants to accomplish in forecasting customer attrition. Making the decision on what insights that need to be acquired is well supported by two most popular approaches covering:
Classification comes into play when the objective is to find if a customer is a likely churner or not – Delving deeper, classification also infuses predictions in knowing if a customer would buy a product or whether a customer transaction is fraudulent among other scenarios. Detecting unusual customer behaviour, by way of anomaly detection, is also a churn problem that calls for the use of classification technique.
Regression becomes the sought technique when enterprises want to unearth factors leading to churn. It is about finding out how different data variables produce an impact on the target variable. And more precisely, it is about unearthing how many variables influence the target variable.
Evaluating if other ML models stay in contention
As the use of ML algorithm hinges on the churn prediction goal that we chase, it calls for evaluating other ML models in accomplishing the goal with the right ML model. Keeping Logistic Regression in contrast with other machine learning models is more about gauging appropriateness of Logistic regression across churn prediction goals and in fulfilling the prime purpose of the churn prediction exercise.
Is it Churn or no churn?
If the goal is to predict ‘Churn or no churn’, it becomes a classification problem wherein Logistics regression scores high through easy facilitation of feature-output relationships. Logistic Regression becomes the ideal-fit in the way it makes it easy to deal with dependent variable that is categorical and other independent variables. On the flip side, this linear classifier may not produce good performance on non-linear data or when the exercise deals with high-dimensional data.
Why Churn and where to focus?
When the question arises as to what probably is the prime influencer of churn or to analyse other probable causes, Decision Tree is a good fit and scores over Logistic Regression. With the Decision Tree model, estimating propensity of customers to churn is not the only insight to be acquired as the model also throws light on significant features that play a pivotal role in improving customer retention.
What are the patterns?
When the goal is to find patterns and predict churn, Support Vector Machine (SVM) rises in relevance as the churn prediction model. When high-dimension data comes into picture and when there are more features to deal with, SVM fits in by facilitating observations through high-dimensional space and creating ideal hyperplane demarcations between instances comprising different classes.
What’s the probability?
In this case, where we set out to find the probability of a customer churning or staying, Naïve Bayesian raises into a best-fit algorithm. Every possible reason for churn can be taken for evaluation and a probability score for that specific reason triggering churn can be attached through Naïve Bayesian algorithm.
While Logistic Regression is a popularly used machine leaning algorithm for churn prediction, accomplishing the goal of the churn prediction exercise, nailing down the sort of insights and answers to be acquired from the exercise and the data available for executing the exercise are some key pointers that help nail down the right machine learning algorithm for building churn prediction model.