customer churn

(1%) Describes and motivates a real-world problem where data science may provide helpful insights. Your description should be easily understood by a casual reader and include citations to motivating sources or relevant information (e.g. news articles, further reading links … Wikipedia makes for a poor reference but the links it cites are usually promising).

This project proposal is aimed to tackle the issue of sustainable business strategy focused around banks and credit care companies. One of the banking and credit industry's biggest problems is customer attrition. Customer churn rates have been a persistent issue because it is difficult to implement solutions in an ultra-competitive environment. According to Forbes, US-based credit card providers average a 20% annual churn rate, meaning that each year they lose one fifth of their existing customers due to various reasons. For not only credit card providers, a 20% churn rate is a major red indicator for the business. Many experts believe that fixing the issue of churn rate is more effective than spending efforts on customer acquisition. One McKinsey & Company study estimates that efforts to reduce churn rate can increase earnings by up to 9%. Churn rate is an obstacle in many industries, however it impacts credit card providers the hardest. One method to resolve high levels of attrition include applying predictive analytics to react to attrition more effectively.

References:

Why Retaining Customers For Banks Is As Important As Winning New Ones - Forbes Article

A Smarter Way To Reduce Customer Churn - Forbes Article

(1%) Explicitly load and show your dataset. Provide a data dictionary which explains the meaning of each feature present. Demonstrate that this data is sufficient to make progress on your real-world problem described above.

import pandas as pd

pd.read_csv('bank_data.csv')

# data dictionary for features in the dataset
features = {"CLIENTNUM":"Client ID",
            "Attrition_Flag":"Existing or former customer",
            "Customer_Age":"Age",
            "Gender":"Gender",
            "Dependent_count":"Number of dependents",
            "Education_Level":"Highest level of education",
            "Marital_Status":"Married, Single, Unknown",
            "Income_Category":"Less than 40k, 40k-60k, other",
            "Card_Category":"Card types: Blue, Silver, Gold, Platinum",
            "Months_on_book":"Relationship with bank by month",
            "Months_Inactive_12_mon":"Number of months inactive over last 12 months",
            "Contacts_Count_12_mon":"Number of contacts over last 12 months",
            "Credit_Limit":"Credit limit",
            "Total_Revolving_Bal":"Total revolving balance",
            "Avg_Open_To_Buy":"Open to buy a credit line (Average TTM)",
            "Total_Amt_Chng_Q4_Q1":"Change in transaction amount from Q4 to Q1",
            "Total_Trans_Amt":"Total transaction amount (TTM)",
            "Total_Trans_Ct":"Total number of transactions (TTM)",
            "Total_Ct_Chng_Q4_Q1":"Change in total number of transactions from Q4 to Q1",
            "Avg_Utilization_Ratio":"Credit card spending limit utilization percentage"}

	CLIENTNUM	Attrition_Flag	Customer_Age	Gender	Dependent_count	Education_Level	Marital_Status	Income_Category	Card_Category	Months_on_book	...	Months_Inactive_12_mon	Contacts_Count_12_mon	Credit_Limit	Total_Revolving_Bal	Avg_Open_To_Buy	Total_Amt_Chng_Q4_Q1	Total_Trans_Amt	Total_Trans_Ct	Total_Ct_Chng_Q4_Q1	Avg_Utilization_Ratio
0	768805383	Existing Customer	45	M	3	High School	Married	$60K - $80K	Blue	39	...	1	3	12691.0	777	11914.0	1.335	1144	42	1.625	0.061
1	818770008	Existing Customer	49	F	5	Graduate	Single	Less than $40K	Blue	44	...	1	2	8256.0	864	7392.0	1.541	1291	33	3.714	0.105
2	713982108	Existing Customer	51	M	3	Graduate	Married	$80K - $120K	Blue	36	...	1	0	3418.0	0	3418.0	2.594	1887	20	2.333	0.000
3	769911858	Existing Customer	40	F	4	High School	Unknown	Less than $40K	Blue	34	...	4	1	3313.0	2517	796.0	1.405	1171	20	2.333	0.760
4	709106358	Existing Customer	40	M	3	Uneducated	Married	$60K - $80K	Blue	21	...	1	0	4716.0	0	4716.0	2.175	816	28	2.500	0.000
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
10122	772366833	Existing Customer	50	M	2	Graduate	Single	$40K - $60K	Blue	40	...	2	3	4003.0	1851	2152.0	0.703	15476	117	0.857	0.462
10123	710638233	Attrited Customer	41	M	2	Unknown	Divorced	$40K - $60K	Blue	25	...	2	3	4277.0	2186	2091.0	0.804	8764	69	0.683	0.511
10124	716506083	Attrited Customer	44	F	1	High School	Married	Less than $40K	Blue	36	...	3	4	5409.0	0	5409.0	0.819	10291	60	0.818	0.000
10125	717406983	Attrited Customer	30	M	2	Graduate	Unknown	$40K - $60K	Blue	36	...	3	3	5281.0	0	5281.0	0.535	8395	62	0.722	0.000
10126	714337233	Attrited Customer	43	F	2	Graduate	Married	Less than $40K	Silver	25	...	2	4	10388.0	1961	8427.0	0.703	10294	61	0.649	0.189

10127 rows × 21 columns

This data is sufficient to make progress on the proposal's main topic as it provides both background details about each client and specific details regarding their credit history with the bank. This dataset also has over 10k records which provides a more effective scope for the application of a classifier.

link to data souce

(1%) Write one or two sentences about how the data will be used to solve the problem. Earlier in the semester, we won’t have studied the Machine Learning methods just yet but you should have a general idea of what the ML will set out to do.

We will use classification given parameters such as age, income, previous relationship with the bank, credit limit, utilization, and more to determine which existing customers are likely to become inactive in the near future. Doing so will allow us to gain more insight into the ideal parameters that determine the potential for inactiveness and which existing customers are more prone to becoming inactive in order to make strategic decisions for the credit cart provider.