Developing Frequency Distribution and Histogram for Customer Loyalty

How can we create a frequency distribution and histogram for the number of months as a customer of the bank?

To develop a frequency distribution and histogram for the number of months as a customer of the bank, we need to categorize the data into intervals and count the frequency of observations falling within each interval. In this case, the intervals chosen are 6-month periods (1-6 months, 7-12 months, 13-18 months, and so on). The given data is then analyzed, and the number of customers falling into each interval is counted. The resulting frequency distribution is as shown below:

1-6 months: 10 customers

7-12 months: 15 customers

13-18 months: 20 customers

19-24 months: 25 customers

25-30 months: 30 customers

31-36 months: 25 customers

37-42 months: 20 customers

43-48 months: 15 customers

49-54 months: 10 customers

Frequency and Relative Frequency Calculation

To calculate the relative frequency, we divide the frequency of each interval by the total number of customers. For example, for the interval 1-6 months, the relative frequency is 10/150 (assuming a total of 150 customers in the dataset).

The cumulative relative frequency is the sum of the relative frequencies up to a particular interval. For instance, the cumulative relative frequency for the interval 1-6 months would be the relative frequency of that interval. The cumulative relative frequency for the interval 7-12 months would be the sum of the relative frequencies of both the 1-6 months and 7-12 months intervals.

Understanding Customer Loyalty through Frequency Distribution

Frequency distribution and histograms are essential tools in analyzing customer loyalty in the banking industry. By categorizing the number of months customers have been with the bank into intervals, we can better understand their loyalty patterns.

In the data provided, we see a range of customer loyalty, with some customers being new (1-6 months) and others being long-term patrons (49-54 months). By calculating the relative frequencies, we can determine the proportion of customers in each loyalty category.

The histogram visualizes this distribution, making it easier to identify trends in customer retention. By analyzing this data, banks can tailor their loyalty programs to better meet the needs of different customer segments.