Address the growing trend of card cancellations at a major credit card company.
Identify key patterns and trends contributing to the problem.
Propose strategic actions to mitigate losses.
Identify the groups of customers most likely to cancel cards.
Understand the reasons why customers cancel cards.
Propose recommendations to reduce cancellation rates.
🤷♀️ Who?
Target Audience: A credit card company managers and executives.
🕵️ What?
Set of customer data from a large credit card company.
📅 When?
Data analysis was conducted based on 2022 data.
🌎 Where?
Large credit card company located in Brazil.
🤔 Why?
The credit card company is concerned about rising card cancellation rates.
If you wish to delve into the discoveries related to the 5 W's, feel free to explore the resources available in this repository.
Your ideas and contributions are welcome!
You can access the databases directly Here.
Data was initially loaded into a Python environment.
Columns were selectively chosen and renamed for relevance.
Data types were standardized for consistency and analysis.
Null or missing values were identified and addressed.
Data validation and cleansing were performed using Pandas and Pyspark.
The insights are a result of powerful analysis tools:
Pandas
NumPy
Pandera
Seaborn
Matplotlib
Plotly.express
Data analysis revealed the following key insights and findings:
The card cancellation rate is 16%.
The majority of customers who canceled their cards are women.
Most customers who canceled the card used the blue card.
Most customers who canceled the card had an annual income of less than 40 thousand.
Most customers who canceled their card had a 36-month relationship with the bank.
Most customers who canceled their card experienced 2 to 3 months of annual inactivity.
Most customers who canceled their cards were in the ranges with the lowest limits.
The analysis reveals distinct patterns influencing card cancellations.
Personalized strategies, revised fees and benefits, and improved customer interactions are imperative.
Implementation of these recommendations can enhance customer retention and competitiveness in the credit card sector.
📊🔄👥Retention Strategies: execution of recommended strategies adapted to specific customer segments.
📊🔍📉Monitoring and evaluation: monitoring of the impact of implemented strategies.
🗣️🔄🛠️Feedback: establishment of channels for customer feedback, aiming to further refine strategies.
📊🌐📚Exploring additional data sources: Incorporating external data sources for more comprehensive analysis.
Thank you for exploring my project! Feel free to delve into our detailed analyses and insights.
Your feedback is valuable.
Maria Betânia Nunes.
