PIG E. Bank

Objective

The PIG E. Bank wanted to understand the factors behin them losing clients. I used a data mining mechanism, build a decision tree to predict which clients might leave the bank.

Data

Client Data set

Techniques applied

Data sorting, filtering and cleaning, Grouping & sumarizing data, Descriptive analysis, Data Mining, Building a decision tree as a data mining algorithm

Tools

Analysis

Key Questions

Which risk factors contribute to a client's likelihood to leade Pig E. Bank?

Results

The factors inactivity, female and being under 40 years old contribute to a high probability of a customer to leave the bank.

Further Insights

Recommendations for further analysis

  • Top three factors that lead to clients leaving: The top three factors are active membership, gender and age. Therefore PIG E. Bank should focus on clients, who fullfill these factors.
  • Activity: They could create a bonus or loyalty programm where clients get points for being active that they can redeem for bonuses.
  • Age: They could give people under 40 special discounts or interest rates to retain these clients.
  • Gender: They can find out which financial topics are particularly interesting for women and build a specific program based on them in order to lose fewer women and gain more clients.