The primary purpose of each enterprise is to make a revenue. This may be completed by getting new prospects and or by retaining the present buyer base. Getting new prospects is tough and largely costly. An organization’s best choice is to retain its present buyer base, glad current prospects also can market an organization by way of phrase of mouth.
Buyer churn merely refers back to the chance of a buyer leaving an organization/cease patronising an organization’s services and products.
On this undertaking, we purpose to assist a big telecommunications firm(Vodafone) to have the ability to predict if a buyer will keep or depart their buyer base. It will assist the corporate establish prospects who’re more likely to depart and if doable devise methods to alter their minds into staying to benefit from the firm’s merchandise.
Buyer churn can happen as a consequence of varied causes, similar to poor community high quality, unsatisfactory customer support, aggressive pricing, or the supply of higher alternate options. Figuring out potential churners early on may also help telecom suppliers take proactive measures to retain these prospects. That is the place machine studying comes into play.
Vodafone collects huge quantities of buyer information, together with billing strategies, contract sorts, information utilization, billing data, and buyer gender. By leveraging this information, I purpose to construct predictive fashions utilizing machine studying algorithms to establish patterns and indicators of buyer churn and buyer more likely to churn. I’ve chosen the highest 8 classification fashions (In response to Chat GPT):
– Logistic_regression
– Decision_tree
– Random_forest
– Support_vector
– KNN (KNeighborsClassifier)
– Gradient_boost
– Naive_bayes
– XGBoost
The dataset for this undertaking was acquired from completely different sources (Microsoft SQL server and web sites). The info didn’t want a lot by way of cleansing. The 2 coaching datasets had been concatenated instantly since they’d the identical columns.
Cleansing completed for this dataset was largely changing values, the 5 lacking values in whole costs had been changed with values from month-to-month costs
The dataset is reasonably imbalanced, the Sure values within the goal column had been about 75% of the dataset towards about 25 p.c for the No values.
Distribution of churn by Fee methodology
Distribution in Churn column
Violinplot utilizing churn and TotalCharges
Earlier than I prepare the machine studying fashions, characteristic engineering is required, it performs a vital position in extracting related data from the uncooked information. on this undertaking we solely drop one column(Buyer ID) from the dataset and depart all different columns since I consider they’ve good data the fashions can study from.
This dataset was fairly clear and didn’t require a lot cleansing, all I needed to do was impute a couple of lacking values
I additionally encoded the goal y variables with a label encoder.
After the preliminary cleansing, the information was separated into categorical and numeric pipelines. The separation was completed as a result of largely various things are completed to numbers and textual content.
A easy imputer was used for the specific columns to fill lacking values utilizing probably the most frequent within the column, whereas a typical scaler was used to scale down the numeric values because of the massive commonplace deviation within the whole costs column. I opted towards my most well-liked sturdy scaler as a result of I had no outliers in my dataset.
A one-hot encoder was additionally utilized to remodel all categorical values to numeric to arrange it for the information for the fashions.
# Numerical pipeline to work on numeric columns
num_pipeline = Pipeline(steps=[
('num_scaler', StandardScaler()), #Standard scaler is used because there are no outliers in our dataset
])
cat_pipeline = Pipeline(steps=[
('cat_imputer', SimpleImputer(strategy='most_frequent')), # Simple imputer will impute missing values with the modes of the corresponding columns
('cat_encoder', OneHotEncoder()),
])#Preprocessor makes use of the the num and pipelines as its steps
preprocessor = ColumnTransformer(transformers=[
('num_pipeline', num_pipeline, num_col),
('cat_pipeline', cat_pipeline, cat_col)
])
I’m utilizing varied classification algorithms, similar to logistic regression, resolution bushes, random forests, gradient boosting, and assist vector machines, to construct churn prediction fashions. These fashions are skilled utilizing historic buyer information, the place the churn standing of every buyer is thought.
The coaching course of includes dividing the dataset into coaching and analysis units. The coaching set is used to coach the mannequin, and the validation set is used to judge its efficiency and fine-tune hyperparameters. I later used methods like cross-validation and grid search to optimize the fashions and guarantee higher efficiency.
As soon as the fashions are skilled, they’re evaluated utilizing efficiency metrics similar to The confusion matrix (accuracy, precision, recall, and F1-score). These metrics assist assess how successfully the fashions can predict buyer churn. I would favor to strike a steadiness between figuring out churners precisely with out overwhelming the system with false positives.
The first aim of churn prediction fashions is to allow Vodafone to take proactive measures to retain prospects who’re at excessive danger of churning. As soon as potential churners are recognized, focused retention methods might be applied. These methods might embody customized provides, reductions, improved customer support, or tailor-made advertising campaigns to deal with particular ache factors and incentivize prospects to stick with Vodafone.
Buyer habits and preferences evolve over time, so it’s important for companies to repeatedly replace and enhance their churn prediction fashions. By monitoring the efficiency of the fashions and accumulating new information, fashions might be retrained periodically and incorporate new options or algorithms as wanted.
Within the fiercely aggressive telecom business, buyer churn can have a big affect on an organization’s backside line. By leveraging machine studying classification fashions, similar to logistic regression, resolution bushes, and random forests, Vodafone can predict buyer churn with cheap accuracy. These predictive fashions allow Vodafone to implement focused retention methods, thus decreasing churn charges and enhancing buyer satisfaction.
As expertise advances and extra refined machine studying methods emerge, telecom firms will proceed to refine their churn prediction fashions. With a proactive method to buyer retention, telecom suppliers can construct long-lasting relationships with their prospects and keep forward within the extremely dynamic and aggressive market.