Description
Overview
You are a business analyst at Financial Charm Bank. You have been asked to submit a report to your vice president (VP) analyzing the characteristics of customers who have left and are no longer using the banking services. You have been given a customer churn data set that contains details regarding current and past customers, such as their demographics, financial details, and their use of banking services.
You need to analyze this data, generate visualizations for customers who are not using the banking services, and summarize their key characteristics, such as their credit score, salaries, and demographics.
For this report, you will create visual representations of the data in Power BI and analyze the results.
Prompt
Create a report to summarize your findings of customer churn. Using visualization methods of your choice, create three graphs that show the credit scores, salaries, and demographics of those customers who have left your banking institution. Be sure to include the relevant screenshots in your report.
In the report to the VP, include the following criteria:
Create appropriate visualizations to help answer questions about customer characteristics. Use Power BI to create three data visualizations to show the credit scores, estimated salaries, and demographics.
Choose the graphs or charts you believe are best suited to represent the given data.
Use proper labels and include any descriptive statistics.
Include a screenshot of each visualization in your final analysis
Justify your choice of visualization method.
Describe the purpose and benefit of using each visualization method.
Summarize your conclusions from each visual.
Describe patterns and trends for each customer characteristic within the data set.
What to Submit
Submit a 2- to- 3-page Microsoft Word report using double spacing, 12-point Times New Roman font, and one-inch margins. This report should include references cited in APA format. Consult the Shapiro Library APA Style Guide for more information on citations.
Unformatted Attachment Preview
RowNumberCustomerIdSurname CreditScoreGeography Gender
1 15634602 Hargrave
619 France
Female
2 15647311 Hill
608 Spain
Female
3 15619304 Onio
502 France
Female
4 15701354 Boni
699 France
Female
5 15737888 Mitchell
850 Spain
Female
6 15574012 Chu
645 Spain
Male
7 15592531 Bartlett
822 France
Male
8 15656148 Obinna
376 Germany Female
9 15792365 He
501 France
Male
10 15592389 H?
684 France
Male
11 15767821 Bearce
528 France
Male
12 15737173 Andrews
497 Spain
Male
13 15632264 Kay
476 France
Female
14 15691483 Chin
549 France
Female
15 15600882 Scott
635 Spain
Female
16 15643966 Goforth
616 Germany Male
17 15737452 Romeo
653 Germany Male
18 15788218 Henderson
549 Spain
Female
19 15661507 Muldrow
587 Spain
Male
20 15568982 Hao
726 France
Female
21 15577657 McDonald
732 France
Male
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636 Spain
Female
23 15699309 Gerasimov
510 Spain
Female
24 15725737 Mosman
669 France
Male
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846 France
Female
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577 France
Male
27 15736816 Young
756 Germany Male
28 15700772 Nebechi
571 France
Male
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574 Germany Female
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411 France
Male
31 15589475 Azikiwe
591 Spain
Female
32 15706552 Odinakachukwu 533 France
Male
33 15750181 Sanderson
553 Germany Male
34 15659428 Maggard
520 Spain
Female
35 15732963 Clements
722 Spain
Female
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Female
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Male
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804 Spain
Male
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850 France
Male
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Female
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Female
44 15755196 Lavine
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Female
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Female
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776 Germany Female
Age
Tenure
42
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31
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51
61
49
61
32
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2
1
8
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7
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3
10
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1
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6
8
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70349.48
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637 Germany
550 Germany
776 Germany
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788 France
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9
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0
5
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1
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1
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4
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696
