My name is Chandoo. My mission is to make you awesome in Excel and Power BI. I do this by sharing Excel & Power BI tutorials, examples, tips, videos and articles on this website. I live in Wellington, New Zealand with my beautiful wife Jo & our twins Nishanth & Nakshatra. Take a minute to browse various topics of the site to see how I can help you.
In the previous parts of this case study, we understood what kind of complaints were made and where they came from (states). For the customer satisfaction scorecard, let’s focus on individual companies.
This is part two of our three part series on how to analyze half a million customer complaints. Read part 1 here.
Analyzing Regional Trends
As introduced in part 1, our complaints dataset has geographical information too. We know the state & zip code for each complaint. Please note that zip codes are partial or missing for a 10% of the data.
In this article, let’s explore three ways to analyze regional trends.
How would you analyze data when you have lots of it? That is the inspiration for this series.
Let’s meet our data – Finance Industry Consumer Complaints
As part of open data initiatives, US government & Consumer Financial Protection Bureau maintain a list of all consumer complaints made against financial institutions (banks, credit unions etc.) You can download this data from the catalog page here. I have obtained the data on 1st of February, 2016. The download has 513,824 records. Each row contains one complaint.
In this and next two parts of the series, we are going to analyze these half a million complaints to find insights.
Most advanced Excel users know that slicers are cool. Today, let’s learn how to use slicers to create an awesome selection mechanism for your dashboards and forms.
Power BI is the most exciting thing to happen for your data since spreadsheets. If you are looking for a new skill to learn this year, I highly recommend Power BI. Check out below tutorials and get started today.
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Soulful Sista
Excel Sister
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You are helping the world!
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Accountant
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Thank you for everything you do.
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