Analysis of customer churn in online spheres: methods, models and strategies

The primary and most frequently used tool for analyzing historical data and forecasting future churn is a predictive model. This model predicts which customers are most likely to churn and why, helping companies take measures to retain such customers.

Various analytical methods, including machine learning, statistical algorithms, and, most importantly, data analysis, are used to create a predictive churn model. The foundation of such a model lies in processing large volumes of customer information, their purchases, platform activity, support interactions, and other factors.

The main stages of creating a predictive churn model include:

1. Data collection.
2. Data preparation.
3. Feature selection for classification and indicators.
4. Model selection and training.
5. Model testing and evaluation.

Data collection for the churn prediction model is a key stage that begins with defining the information necessary for analysis.

The first step involves determining the parameters and variables that may influence customer churn. This may include customer information (demographic data, purchase history, duration of service usage, frequency of product usage), their activity (interaction with services, website visits, app usage), and any other information related to customer behavior and churn. Various sources, such as customer databases, CRM systems, payment information, user activity logs, customer surveys, data from social networks, and more, are used to collect data. It's important to consider legislation and privacy policies when collecting data.

The collected data often contains noise and errors. During the data cleansing stage, data undergoes a process of filtering, deduplication, error correction, and filling in missing values. Additionally, the data may be in different formats and needs to be standardized. This could involve recoding categorical variables, scaling numerical data, or creating new features based on existing ones.

During the process of building the database for training the churn prediction model, data is selected and prepared for use in training the churn prediction model. This may involve dividing the data into training and test sets. Data processing includes various data processing techniques such as outlier filtering, dimensionality reduction, feature selection, etc. The main goal is to prepare the data for use in the model.

The next step is selecting features for the churn prediction model, which is an important process that affects the model's effectiveness and generalization ability. Here are some steps and methods that can be used:

1. Correlation analysis: measuring the degree of linear dependence between features and the target variable (in this case, churn). Features with high correlation to churn are usually considered important.
2. Feature importance selection: many machine learning algorithms can provide an estimation of the importance of each feature in the context of the model. For example, Random Forest has a “feature importances” attribute, which allows for the assessment of each feature's importance.
3. Variance analysis: examining feature variances. Features with low variance may not carry much information and might be excluded.
4. Dependency analysis: creating plots and conducting dependency analysis between each feature and the target variable. This might include scatter plots, box plots, histograms, and other visualizations.
5. Outlier processing: Upon detecting outliers, decisions can be made to remove or transform them. Some methods like trimming or replacing outliers with median values can be applied.
6. Statistical analysis: using statistical methods to determine feature importance. This might involve t-tests, variance analysis, and other methods.
7. Regularization Methods. In some models, such as linear regression, regularization methods (e.g., L1 and L2 regularization) are applied, penalizing or excluding specific features, making feature selection automatic.
8. Cross-validation. Evaluating the model on multiple different datasets helps understand which features work better on different data segments.

It's also important to remember the business context and the subject area. For instance, in some cases, non-intuitive features might be crucial for explaining certain aspects of customer churn. Feature selection is an iterative process, and decisions about excluding or including features might change depending on model results and data changes.

When moving to the stage of analyzing dependencies between variables, it's crucial to understand the data structure's importance and identify significant patterns. Various methods are employed to search for and analyze dependencies.

Correlation analysis. A commonly used method that measures the degree of linear dependency between two variables. Pearson's or Spearman's correlation coefficients help determine the strength and direction of the relationship between variables.

Covariance. Indicates the degree to which two variables change together. However, covariance doesn't consider variable scales and might not always be informative.

Correlation matrix. One of the most frequently used methods. It creates a matrix showing correlations between corresponding variables, allowing for a quick assessment of correlated variables.

Dependency analysis on the target variable. If you have a target variable (e.g., churn), evaluate which variables have the most significant influence on this target variable using various statistical tests or machine learning methods.

Scatter matrix. For datasets with multiple features, a scatter matrix helps explore dependencies between each feature pair.

Regression analysis. Utilizing regression models to assess the impact of one or more independent variables on the dependent variable.

Analyzing dependencies helps identify crucial factors that may impact the target variable or other critical aspects in the data, which is essential for further churn prediction model training.

Understanding the type of problem being solved is crucial: whether it's a classification, regression, clustering, or other tasks. Next, ensure that the obtained data fits the chosen model's requirements. For instance, some models require feature scaling. Explore various model types suitable for the specific task – for example, for classification tasks: logistic regression, random forest, gradient boosting, etc.

Moving on to the model training step, divide the data into training (usually 70-80% of the data) and test sets (remaining 20-30%) for model training and performance evaluation. Carry out necessary data transformations, such as scaling, encoding categorical features, and handling missing values. Use the training data to train the model. This stage involves tuning parameters to fit the data.

Using different metrics depending on the task type (classification, regression, etc.) is logical. For example, for classification tasks: accuracy, recall, F1-score, ROC-AUC, etc., and for regression tasks: mean squared error (MSE), coefficient of determination and others. Cross-validation is often applied at this stage. It involves splitting data into multiple parts, training the model on different combinations of training and test data. Cross-validation helps assess how well the model generalizes across different data sets.

The stage requiring maximum involvement from retention professionals is interpreting results. Analyzing why the current model makes specific decisions and which features have the most significant impact on predictions helps understand how the model decides and which factors it considers.

Remember, testing and evaluating the model are iterative processes that may require multiple cycles to achieve optimal performance and generalization.

Effectively applying the model improves retention strategies aimed at customer retention and increasing loyalty. Observable increases in the number of customers, reduced churn, and improved customer experience all demonstrate the significant benefits of data analysis and predictive model creation.

Therefore, companies aiming to improve customer relationships and enhance business efficiency should actively use analytical methods and models to optimize retention strategies. Further research in this area may lead to even more precise and efficient methods of preventing customer churn and strengthening long-term relationships with customers.