Telco Customer Churn
Emeka Anthony Nwabueze
2024-04-05
Set working directory
Read my dataset
telco_cust_churn <- read.csv("WA_Fn-UseC_-Telco-Customer-Churn.csv")Introduction
Harnessing the power of data analytics is critical for firms looking to understand and reduce customer turnover, which is a major challenge in today’s competitive environment. In this exploratory study, we go into a large dataset (7043 observations of 21 variables) encompassing customer attributes, service subscriptions, and demographic information to gain significant insights into churn behavior. I hope to provide meaningful recommendations for startups looking to improve client retention tactics and operational efficiencies by employing advanced visualization techniques.
- Objectives:
Analyse the distribution of churn within the dataset to determine the frequency and degree of customer attrition.
Investigate the correlation between turnover and important demographic characteristics like gender, age, and household composition.
Investigate how contract type, payment method, and service subscriptions affect churn rates.
Visualize the monthly and total charges for churned and retained clients to find viable customer retention pricing options.
Provide enterprises with meaningful data and ideas for improving client retention and optimizing profitability.
Loading neccessary libraries
##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
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## filter, lag## The following objects are masked from 'package:base':
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## intersect, setdiff, setequal, unionExploring the data
head(telco_cust_churn)## customerID gender SeniorCitizen Partner Dependents tenure PhoneService
## 1 7590-VHVEG Female 0 Yes No 1 No
## 2 5575-GNVDE Male 0 No No 34 Yes
## 3 3668-QPYBK Male 0 No No 2 Yes
## 4 7795-CFOCW Male 0 No No 45 No
## 5 9237-HQITU Female 0 No No 2 Yes
## 6 9305-CDSKC Female 0 No No 8 Yes
## MultipleLines InternetService OnlineSecurity OnlineBackup DeviceProtection
## 1 No phone service DSL No Yes No
## 2 No DSL Yes No Yes
## 3 No DSL Yes Yes No
## 4 No phone service DSL Yes No Yes
## 5 No Fiber optic No No No
## 6 Yes Fiber optic No No Yes
## TechSupport StreamingTV StreamingMovies Contract PaperlessBilling
## 1 No No No Month-to-month Yes
## 2 No No No One year No
## 3 No No No Month-to-month Yes
## 4 Yes No No One year No
## 5 No No No Month-to-month Yes
## 6 No Yes Yes Month-to-month Yes
## PaymentMethod MonthlyCharges TotalCharges Churn
## 1 Electronic check 29.85 29.85 No
## 2 Mailed check 56.95 1889.50 No
## 3 Mailed check 53.85 108.15 Yes
## 4 Bank transfer (automatic) 42.30 1840.75 No
## 5 Electronic check 70.70 151.65 Yes
## 6 Electronic check 99.65 820.50 Yessummary(telco_cust_churn)## customerID gender SeniorCitizen Partner
## Length:7043 Length:7043 Min. :0.0000 Length:7043
## Class :character Class :character 1st Qu.:0.0000 Class :character
## Mode :character Mode :character Median :0.0000 Mode :character
## Mean :0.1621
## 3rd Qu.:0.0000
## Max. :1.0000
##
## Dependents tenure PhoneService MultipleLines
## Length:7043 Min. : 0.00 Length:7043 Length:7043
## Class :character 1st Qu.: 9.00 Class :character Class :character
## Mode :character Median :29.00 Mode :character Mode :character
## Mean :32.37
## 3rd Qu.:55.00
## Max. :72.00
##
## InternetService OnlineSecurity OnlineBackup DeviceProtection
## Length:7043 Length:7043 Length:7043 Length:7043
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
##
## TechSupport StreamingTV StreamingMovies Contract
## Length:7043 Length:7043 Length:7043 Length:7043
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
##
## PaperlessBilling PaymentMethod MonthlyCharges TotalCharges
## Length:7043 Length:7043 Min. : 18.25 Min. : 18.8
## Class :character Class :character 1st Qu.: 35.50 1st Qu.: 401.4
## Mode :character Mode :character Median : 70.35 Median :1397.5
## Mean : 64.76 Mean :2283.3
## 3rd Qu.: 89.85 3rd Qu.:3794.7
## Max. :118.75 Max. :8684.8
## NA's :11
## Churn
## Length:7043
## Class :character
## Mode :character
##
##
##
## colSums(is.na(telco_cust_churn))## customerID gender SeniorCitizen Partner
## 0 0 0 0
## Dependents tenure PhoneService MultipleLines
## 0 0 0 0
## InternetService OnlineSecurity OnlineBackup DeviceProtection
## 0 0 0 0
## TechSupport StreamingTV StreamingMovies Contract
## 0 0 0 0
## PaperlessBilling PaymentMethod MonthlyCharges TotalCharges
## 0 0 0 11
## Churn
## 0- Customer characteristics include gender, senior citizenship status, relationship status, dependent status, tenure, and a variety of service subscriptions.
- Numeric variables such as tenure, monthly charges, and total charges have a wide range of values, indicating probable variation in client behavior.
- The ‘Churn’ variable specifies whether a client has churned or not, with no missing values found.
- There are 11 missing values in the ‘TotalCharges’ field that may necessitate imputation or additional inquiry. This initial assessment lays the framework for further investigation to find trends and insights into customer turnover behavior.
Count and remove NA in the dataset
na_count <- sum(is.na(telco_cust_churn))
telco_cust_churn <- na.omit(telco_cust_churn)Removed 11 ‘na’ left with 7032 observations of 21 variables
Visualize Churn distribution
The plot reveals that the majority of customers in the dataset did not churn (labelled as “No”), as evidenced by the greater count on the x-axis for “No” churn. Conversely, the number of churned customers (labelled as “Yes”) is substantially lower. This suggests an imbalance in the dataset, with a higher number of non-churned consumers than churned customers. Understanding this distribution is critical for effectively modelling and predicting churn, because imbalanced datasets can affect model performance. Further examination and potential rebalancing methods will be required to effectively solve this issue.
Visualizing Churn by Contract type
The data illustrates that customers with lengthier contracts are more likely to continue with the service, but those with month-to-month contracts are more likely to depart. This shows that committing to a lengthier contract may increase customer loyalty, allowing the company to keep them for a longer period of time. You can see why certain companies, such as EE, would want to commit you to a lengthier term.
Visualizing Churn by Gender
The data shows that gender has no significant effect on churn rates, as both males and females have similar numbers for churn and no churn. However, regardless of gender, lengthier contracts have a stronger influence on minimizing churn. This shows that contract type has a greater impact on client retention than gender.
Visualizing distribution of Monthly Charges by Churn
Customers with lower monthly rates are less likely to churn, as evidenced by the higher bars near the bottom end of the x-axis. Customers with higher monthly charges are more likely to churn, as evidenced by the drop in bars as monthly charges rise. This shows that pricing has the potential to influence client retention
When considering churn status, customers who do not churn tend to have lower monthly prices, whereas churned customers have higher monthly charges. This emphasizes the relevance of price tactics in retention of customers initiatives.
Visualizing distribution of Total Charges by Churn
Customers with lower total charges are more prone to churn, as indicated by the higher bars near the bottom of the the horizontal axis Customers with larger total charges are less likely to churn, as evidenced by a decrease in bars as total charges rise. This implies that higher-spending customers are more loyal and less likely to churn.
When evaluating churn status, churned customers typically have lower overall charges, whereas non-churned customers have higher total charges. This shows that customer loyalty may be influenced by overall expenditure, with higher-spending consumers demonstrating stronger loyalty.
Throughout the entire study, it is clear that some trends emerge regarding the customer attrition. Lets take a look:
Demographic Factors: Gender does not appear to have a substantial impact on turnover rate, since male and female consumers exhibit identical churn tendencies. Other demographic characteristics, such as contract type, may affect churn.
Contract Type: Customers on month-to-month contracts are more likely to leave than those on one- or two-year contracts. This implies that longer contract periods increase client retention rates.
Monthly payments: Customers with smaller monthly payments are more likely to leave, whilst those with higher rates are more loyal. This means that customers who pay more for their services are less likely to depart.
Total Charges: As with monthly charges, customers with lower total charges are more likely to churn, whilst those with higher total charges are less likely. This suggests that overall spending is correlated with customer loyalty.
By combining these insights, organisations can better comprehend the factors that influence customer churn and modify their strategy to increase retention rates. To promote the customer engagement and loyalty, businesses can concentrate on providing value-added services or offering incentives for extended term commitments.