Case Study 2– How a manager used Analytics to get insights from the data to improve customer
Industry – Banking and Financial Services
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Business Problem – The manager has data of its customers who have taken loan from the
institution. The data has customers demography information along with customers loan details. She wishes to explore the data and find out what is the typical loan amount of its customers.
The manager approaches the analytics team with the problem and shares the data with the team.
The analytics team explores the data to treat the data for missing values and outliers and create
dummy variables for the categorical data. The team comes out with visualization. One of the
visualization is shown below –
This graph is the density plot and on x-axis we have the loan duration and y-axis represents the density. We observe that the data is grouped into two different loan tenures.
The analytics team then does statistical analysis, first to check if the data follows a normal
distribution or not. In this case, the data is not following the normal distribution. The team applies
Chebychev’s theorem to draw conclusion from the data. In contrast to the empirical rule of 68–95–
99.7, under Chebyshev’s inequality a minimum of 75% of values must lie within two standard
deviations of the mean and 89% within three standard deviations to draw inferences. Using the
findings, the analytics team submits a report highlighting the maximum number of loan amount in
the range for the two tenure groups. The manager uses these details to improve on the customer