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The number of clients for any company is undoubtedly an important parameter, and
the more clients, the higher the company's profit. There is no monopoly on the
provision of mobile services to customers, high-speed Internet access or cable
television, therefore, the so-called outflow of customers is possible for one reason or
another, most often associated with the transition to another telecommunications
company offering more favorable conditions from the point of view of customers ...
To ensure the stable operation of a telecommunications company, an analysis of the
customer base is necessary, for example, to ensure targeted promotions, as well as an
analysis of the reasons for customer churn, including with the ability to predict the
number of customers who have left and the reasons for switching to the services of
another telecommunications company. According to experts, attracting one new client
costs companies on average seven times more (in some cases, from 5 to 20 times
more) than retaining an existing one [
1-3
]. Reducing customer churn by 5% increases
the company's profits from 25% to 85% [
4
]. Therefore, understanding how exactly to
maintain customer engagement is a natural foundation for developing strategies for
customer retention.
In this regard, there is a number of scientific studies aimed both at predicting
customer churn and predicting their preferences in any specific services. For example,
in work [
5
], the authors try to understand the behavior of customers when paying
bills, using such machine learning methods as logistic regression, one rule and
support vector machines, they proposed an analytical approach to studying and
predicting the payment behavior of customers. As input for the analytical system, they
use: customer ID (a hashed unique number indicating each customer), action type
(such as SMS, IVR, phone calls, service cut related and legal actions), the date of the
action's execution, stage changer fl ag (such as payment through the banking system,
unpaid invoice occurrence, or action timer fl ag). The feasibility of using machine
learning methods is ensured by their ability to detect patterns in data of various
natures and universes [
6
].
In [
7
], on the basis of Call Data Records data, various types of customers are
determined, which subsequently affects the analysis of the possibility of their outflow
and the impact on other customers of the company. Authors include these types of
customers: follower, standard, leader, core and important customer. The authors note
that customers who interact with the Leader and Important categories are more likely
to churn after an influential member who also left the company. For clustering, the
authors used neural networks such as linear perceptron, multilayer perceptron and
networks with radial basis activation function. In the work of the authors [
8
], using
the random forest classifier, decision tree classifier, gradient-boosted tree classifier
and multiplexer perceptron, clients were segmented according to the
“time-frequencymonetary” approach, while “Time” characterizes the total of calls duration and
Internet sessions in a certain period of time, the "Frequency" parameter characterizes
the frequency of using services frequently within a certain period, and the "Monetary"
parameter is determined by the amount of money spent during a certain period.
It should be noted that there is often a situation where the assessment of the
forecast of customer churn is carried out according to completely different criteria.
For example, in [
9
], forecasting is based on the following information: region where
the customer lives; time since the customer joined the operator (in months); average
revenue; how long did the customer not pay the bills; amount that the customer is
overdue; number of times the service was disconnected, etc. In the study [
10
], the
following parameters are used to predict customer churn: state the US state, in which,
the customer resides, the remaining seven-digit phone number, total number of calling
minutes used during the day, the billed cost of daytime calls, total number of calling
minutes used during the nighttime, and a few others. Moreover, some of these criteria
may not be available to telcos, making it difficult to determine how, in practice, a
telco will forecast these models. It is also worth mentioning several studies [
11-12
] on
the use of customer analysis technologies in the banking sector, for example, to
predict the likelihood of outflow based on information related to the
sociodemographic parameters of customers, their activity in obtaining banking information,
information about their salaries, and etc.
There is no unambiguous option for the machine learning methods used, there are a
lot of them, it is almost impossible to choose an empirically suitable method, it is
necessary to conduct training, in a practical way, selecting the optimal architecture
and parameters of the machine learning method.
This paper presents the results of predicting the churn of customers of a
telecommunications company using machine learning methods, since the latter are
able to use statistical data, to determine dependencies between data of different
nature.
2
Preparation of training sample
The problem of predicting customer churn can be formulated as a binary classification
problem [
9
]. The solution to the problem is to classify customers with the
corresponding characteristics (parameters) x ∈ X to one of the two classes Y = {no
churn, churn}.
To solve such a problem, in this work, we used a dataset consisting of 21 columns
and 7043 rows made publicly available by IBM and containing information about
customers of a telecommunications company (available at
https://www.kaggle.com/blastchar/telco- customer-churn). Information about each
customer includes the following characteristics, which are identifying signs for
predicting churn (they are presented in Tables 1-3), and, of course, each customer is
assigned a unique customer identifier (customerID).
Also, data on the period of use of the services of a telecommunications company
were used as input information, the parameter is calculated in months (tenure); the
size of the client's monthly fee (MonthlyCharges) and the final amount of payments
for the entire period of work with the client (TotalCharges). For each set of input
information, consisting of 20 indicators, there is a predetermined output - Churn
characterizing whether the client left the telecommunications company with these
parameters or not.
No.
1
2
3
4
5
6
No.
1
2
3
4
5
6
7
8
9
Also, data on the period of use of the services of a telecommunications company
were used as input information, the parameter is calculated in months (tenure); the
size of the client's monthly fee (MonthlyCharges) and the final amount of payments
for the entire period of work with the client (TotalCharges). For each set of input
information, consisting of 20 indicators, there is a predetermined output - Churn
characterizing whether the client left the telecommunications company with these
parameters or not.
3
Modeling machine learning methods
The following machine learning methods have been selected for training, which have
proven themselves well in solving various classification problems: AdaBoost
adaptive boosting; Decision Tree - decisive trees; Extra Tree Classifier - random
trees; Gradient Boosting - gradient boosting; KNeighbors - k-nearest neighbors
method; Logistic Regression - logistic regression; Naive Bayes - naive Bayesian
classifier; Neural Network - neural networks; Random Forest - random forest method;
SVM - support vector machine; XGB - Gradient boosting on trees.
To assess the effectiveness of the models, the Area Under the Curve (AUC)
characteristic was chosen, a statistical indicator that is often used in machine learning
methods that determines the area bounded by a certain curve and an abscissa axis,
called the ROC curve (from receiver operating characteristic). The use of AUC for
binary classification problems is popular because of its simplicity, intuitive
interpretation [
9
], and also its use in the case of unbalanced datasets [
13
]. For a
random classifier, the AUC is 0.5, and for an ideal classifier, the AUC is 1 [
14
]. Since
there are only two options for a conclusion in the problem being solved (whether the
client leaves or not), it is the ROC analysis, which is a graphical method for assessing
the quality of the work of a binary classifier, that is most promising for assessing the
proposed method for predicting the churn of customers of a telecommunications
company. To construct the ROC curve, a pair of the following values is used:
sensitivity and specificity. The value "sensitivity" characterizes the share of
truepositive classifications in the total number of positive observations and is marked
along the vertical axis of the ROC-curve graph, and the value "specificity"
characterizes the proportion of true-negative classifications in the total number of
negative observations and is marked along the horizontal axis of the ROC-curve
graph. It should be noted that the higher the “sensitivity” value, the more reliably the
classifier recognizes positive examples, and the higher the “specificity” value, the
more reliable the classifier recognizes negative observations. Thus, the ROC curve
reflects the relationship between the probability of false alarms (proportion of
falsepositive classifications) and the probability of “correct detection” (proportion of
truepositive classifications). With an increase in sensitivity, the reliability of recognition
of positive observations increases (the probability of "missing a target" decreases), but
at the same time the probability of a false alarm increases. Table 4 shows the learning
outcomes of individual machine learning methods and their characteristics according
to the AUC metric.
Thus, as a result of the work carried out, the authors investigated 11 different machine
learning methods to solve the problem of predicting the churn of customers of a
telecommunications company. The best machine learning method was
LogisticRegression, which showed an AUC of 80.38%. The use of Logistic
Regression in conjunction with other machine learning methods within the ensemble
of machine learning methods allowed us to increase the forecasting quality to 81.37.
Further research by the authors will be devoted to identifying those identification
parameters that significantly affect the process of predicting customer churn in order
to use a smaller number of identification features with constant values of the forecast
quality.