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Dive into the world of Money Market where everything we talk big numbers πŸ’΅πŸ’Έ . Join forces with me in creating a exciting dashboards for Credit Card Company πŸ¦πŸ’³ that will help them weekly analyze revenue trends and customer patterns. This Power BI dashboard reveals the secrets hidden within the digits. πŸš€βœ¨

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Vaibhav-Xo/Credit-Card-Transaction-And-Customer-Dashboard-Report

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Credit-Card-Transaction-And-Customer-Dashboard

Screenshot 2024-07-05 175711

Dashboards:

Screenshot 2024-07-05 193246 Screenshot 2024-07-05 193310

Project Objective:

Create a dashboard that will help a Credit Card Company generate weekly reports that showcases the trends that contrubute to their revenue and will help them to plan strategies to increase their profit and attract new customers.

About Dataset:

This datasets consist transaction and customer information record of 10000 customers of a credit card company for year 2023. The dataset consist of well over 10000 records in each csv file spread across 18 fields in credit_card_transaction file and 15 fields in credit_card_customer file. The fields in credit_card_transaction include (Client_Num, Card_Category, Annual_Fee, Activation_30_Days, Customer_Acq_Cost, Week_Start_Date, Week_Num, Qtr, current_year, Credit_Limit, Total_Revolving_Bal, Total_Trans_Amt, Total_Trans_Ct, Avg_Utilization_Ratio, Use_Chip, Exp_Type, Interest_Earned, Delinquent_Acc). Similarly the credit_card_customer include (Client_Num, Customer_Age, Gendet, Dependent_Count, Education_Level, Marital_Status, State_cd, Zipcode, Car_Owner, House_Owner, Personal_Loan, Contact, Customer_Job, Income, Cust_Satisfaction_Score). Both of the datasets are clean and do not contain any null values.

Initial Insights Drawn:

  • 1] The Revenue from Q1,Q2,Q3,Q4 is as such 14M, 13.8M, 14.2M, 13.3M.
  • 2] The company generated a whooping revenue of 55 Million with transaction amount of 45 Million and intrest earned 8 Million in 2023.
  • 3] Blue card user contribute 46M in revenue followed by Silver with 6M, Gold 2M , Platinum 1M.
  • 4] In terms of expendeture type Bills generate 14M followed by Entertenment 10M, Fuel 9M, Grocesrry 9M, Food 8M, Travel 6M.
  • 5] Revenue by job description Businessmen 17M, White Collor 10M, Self Employed 8M, Govt 8M, Blue Collor 7M, Retires 5M.
  • 6] Swipe is the most use method of payment and generate revenue of 35M.
  • 7] By Gender Males generate 30M in revenue and female 25M.
  • 8] Customer with High income generate 29M, Mid 16M, Low 10M.
  • 9] Age group 40-50 is highest revnue generator with 25M.
  • 10] Texas, New York and California are top contributing cities with revenue 13M each.

Remember, data analysis is an ongoing adventure. So grab your coffee β˜•, investigate further, and keep drawing those insights! 🚨 Feel free to expand upon this conclusion or add any additional findings you discover. Happy analyzing! πŸ˜ŠπŸ±β€πŸ‘“

About

Dive into the world of Money Market where everything we talk big numbers πŸ’΅πŸ’Έ . Join forces with me in creating a exciting dashboards for Credit Card Company πŸ¦πŸ’³ that will help them weekly analyze revenue trends and customer patterns. This Power BI dashboard reveals the secrets hidden within the digits. πŸš€βœ¨

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