The primary goal of each enterprise is to make a revenue. This may be accomplished by getting new prospects and or by retaining the present buyer base. Getting new prospects is troublesome and largely costly. An organization’s best choice is to retain its present buyer base, glad current prospects also can market an organization via phrase of mouth.
Buyer churn merely refers back to the probability of a buyer leaving an organization/cease patronising an organization’s services and products.
On this challenge, we goal to assist a big telecommunications firm(Vodafone) to have the ability to predict if a buyer will keep or go away their buyer base. It will assist the corporate determine prospects who’re more likely to go away and if attainable devise methods to vary their minds into staying to benefit from the firm’s merchandise.
Buyer churn can happen as a consequence of varied causes, resembling poor community high quality, unsatisfactory customer support, aggressive pricing, or the provision of higher alternate options. Figuring out potential churners early on will 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 info, and buyer gender. By leveraging this information, I goal to construct predictive fashions utilizing machine studying algorithms to determine patterns and indicators of buyer churn and buyer more likely to churn. I’ve chosen the highest 8 classification fashions (In keeping with Chat GPT):
– Logistic_regression
– Decision_tree
– Random_forest
– Support_vector
– KNN (KNeighborsClassifier)
– Gradient_boost
– Naive_bayes
– XGBoost
The dataset for this challenge was acquired from totally different sources (Microsoft SQL server and web sites). The info didn’t want a lot when it comes to cleansing. The 2 coaching datasets had been concatenated instantly since they’d the identical columns.
Cleansing accomplished for this dataset was largely changing values, the 5 lacking values in complete prices had been changed with values from month-to-month prices
The dataset is reasonably imbalanced, the Sure values within the goal column had been about 75% of the dataset in opposition to about 25 % for the No values.
Distribution of churn by Cost methodology
Distribution in Churn column
Violinplot utilizing churn and TotalCharges
Earlier than I prepare the machine studying fashions, function engineering is required, it performs an important function in extracting related info from the uncooked information. on this challenge we solely drop one column(Buyer ID) from the dataset and go away all different columns since I consider they’ve good info the fashions can be taught from.
This dataset was fairly clear and didn’t require a lot cleansing, all I needed to do was impute just a few 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 accomplished as a result of largely various things are accomplished to numbers and textual content.
A easy imputer was used for the explicit columns to fill lacking values utilizing probably the most frequent within the column, whereas a regular scaler was used to scale down the numeric values as a result of giant customary deviation within the complete prices column. I opted in opposition to 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, resembling logistic regression, choice 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 understood.
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 strategies 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 resembling The confusion matrix (accuracy, precision, recall, and F1-score). These metrics assist assess how successfully the fashions can predict buyer churn. I would like 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 carried out. These methods could embody customized gives, 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 amassing new information, fashions might be retrained periodically and incorporate new options or algorithms as wanted.
Within the fiercely aggressive telecom trade, buyer churn can have a big impression on an organization’s backside line. By leveraging machine studying classification fashions, resembling logistic regression, choice bushes, and random forests, Vodafone can predict buyer churn with cheap accuracy. These predictive fashions allow Vodafone to implement focused retention methods, thus lowering churn charges and enhancing buyer satisfaction.
As know-how advances and extra subtle machine studying strategies emerge, telecom corporations 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.
