1. Problem: BBVA has a problem with customer churn. It’s asking you to help identify which custoemrs might churn and come up with a creative way to prevent them from moving to another bank. 2. Using dataset: churn.csv, create a predictive model that will inform BBVA of a future marketing campaign for a new banking product. 3. Combine this with your own primary (qualitative) research on why people switch banks.4. Make a recommendation for how the product team could evolve their product to prevent these customers from moving to another bank. Include a high-level (low-fidelity) prototype concept of what a campaign could look like.
Submission format: An .ipynb notebook of your analysis and model• Your presentation in .pdf• A video between 5 and 10 minutes long explaining your quant and qual (hence presentation speaker notes)
I woud add my slides after I get an a price
Elenora-Final-Retake MCXI 2021 Final Re-Take Assessment Data Driven Innovation You have been hired by BBVA to help them reduce customer churn You are an Innovation Consultant! 1. Problem: BBVA has a problem with customer churn. It’s asking you to help identify which custoemrs might churn and come up with a creative way to prevent them from moving to another bank. 2. Using dataset: churn.csv, create a predictive model that will inform BBVA of a future marketing campaign for a new banking product. 3. Combine this with your own primary (qualitative) research on why people switch banks. 4. Make a recommendation for how the product team could evolve their product to prevent these customers from moving to another bank. Include a high-level (low-fidelity) prototype concept of what a campaign could look like. 5. https://www.bbva.es/ BBVA: Reduce Customer Churn https://raw.githubusercontent.com/iamctodd/MCXI/main/churn.csv https://raw.githubusercontent.com/iamctodd/MCXI/main/churn.csv https://www.bbva.es/ BBVA Data Dictionary Input Variables: 1 - RowNumber (numeric) 2 - CustomerId : (numeric) unique customer Identifier 3 - Surname : (text) Last Name of Customer 4 - CreditScore (numeric) credit score - See Credit Score range explanation here: https://www.creditkarma.com/advice/i/credit-score-ranges 5 - Geography: Country (categorical: 'Germany','France','Spain') 6 - Gender: has housing loan? (categorical: ‘Female','Male','unknown') 7 - Age: (numeric) 8 - Tenure: (numeric) How long has this customer been with BBVA 9 - Balance: (numeric) how much money (in €) they have as a bank account balance 10 - NumOfProducts: (numeric) how many accounts they have with BBVA (could mean, Loans, Savings, Credit Cards, Checking, etc) 11 - HasCrCard: (categorical: 0= no, 1 = yes) - Do they have a Credit Card? 12 - IsActiveMember: (categorical: 0= no, 1 = yes) - Are they active within the past 60 days? 13 - EstimatedSalary: (numeric) Their estimated annual salary Target variable: 14 - y - Did the customer churn? (binary: ‘1= yes’,’0 = no') https://raw.githubusercontent.com/iamctodd/MCXI/main/churn.csv RowNumber,CustomerId,Surname,CreditScore,Geography,Gender,Age,Tenure,Balance,NumOfProducts,HasCrCard,IsActiveMember,EstimatedSalary,Exited https://www.creditkarma.com/advice/i/credit-score-ranges a .pdf and a .ipynb file! 1. You will email the files (you can zip them if you like) to
[email protected] by midnight Sunday 2-May-2021. 2. Submit three files: • An .ipynb notebook of your analysis and model • Your presentation in .pdf • A video between 5 and 10 minutes long explaining your quant and qual analysis plus solution(s). 3. The assignment is due at midnight on Sunday, 2-May–2021 4. Please name files: name_final_retake… Example: joseph_final_retake.pdf joseph_final_retake.ipynb Presentation & Submission Details mailto:
[email protected] Points determined as follows (similar to posted via CoL). Grading Rubric