Bank98 Customer Arrival Patterns: A Data-Driven Branch Analysis

This case study analyzes customer arrival data from Bank98 to understand traffic patterns and address long waiting lines. The analysis reveals that weekdays see peak traffic between 11am-2pm and 4pm-5pm, while Saturdays are busiest from 11am-12pm. Fridays also experience high customer volume. The study recommends aligning staffing levels with these peak periods to improve customer service and reduce congestion. The data-driven approach aims to optimize resource allocation and enhance the overall customer experience at Bank98 branches. Desklib provides access to this and other solved assignments for students.

DATA ANALYSIS

DATA ANALYSIS
INTRODUCTION
The biggest concern for consumers currently is the quality of products and services
(Kiechel, 2010). Presently, the main determinants of sales for businesses (product availability
and price) have been replaced by quality. The lack of proper transport and communication
channels in the past meant that, whichever brand was able to make their products available in the
market had the highest sales. The technological advancements in both transport and
communication have made product availability a weaker determinant of sales (Cameron, 2014).
Technological advancements have also made it possible for businesses to offer
competitive prices in the market. Therefore, price can no longer be regarded as a differentiating
factor between brands. This implies that consumers do not solely look at price as the determinant
of whether they purchase a product or not (Besanko, Dranove, & Shanley, 2012).
Consumers therefore consider quality and by extension convenience where procuring any
service or product. Businesses hence need to ensure that the consumers convenience is
prioritized in product and service provision (Laudon & Guercio, 2014). Data analysis provides a
useful tool when it comes to solving business problems (Galit, Peter, Inbal, & Nitin, 2018;
Witten, 2011; Oscar, 2009). Through data analysis, the ability of decision makers to make better
informed decisions is increased (Albright & Winston, 2014).
The cause(s) and potential solution(s) to consumer inconvenience can thus be determined
by analyzing relevant data. This study aims at determining the potential causes and solutions for
long waiting lines at the branches of Bank98. The long waiting lines present an inconvenience to
the customers of the bank and could have a negative impact on the sales.
2

DATA ANALYSIS
DATA ANALYSIS
From Figure 1: Number of Customers for Mondays to Thursdays below, the consumer
trends (in terms of number) appear to be similar for Mondays, Tuesdays, Wednesdays and
Thursdays. The four days have on average the joint lowest number of customers throughout a
day. The highest number of customers visit the bank between 11am and 2pm and from 4pm to
5pm. These represent the two periods with the highest traffic in customers for Mondays,
Tuesdays, Wednesdays and Thursdays.
Figure 1: Number of Customers for Mondays to Thursdays
The plot in Figure 2: Number of Customers for Fridays represent the trend in number of
customers on Fridays. The overall shape of the plot resembles that of the trend in number of
customers for Mondays, Tuesdays, Wednesdays and Thursdays. However, the two trends in
Figure 1: Number of Customers for Mondays to Thursdays and Figure 2: Number of Customers
for Fridays differ in terms of magnitude.
3

DATA ANALYSIS
More customers visit the bank on Fridays than on Mondays, Tuesdays, Wednesdays and
Thursdays. Between the 9am and Noon, only Saturdays have a higher number of customers than
Fridays as seen in Figure 3: Number of Customers for Saturdays. From Noon to 5pm, Fridays
have the highest number of customers.
The highest number of customers visit the bank between 11am and 2pm and from 4pm to
5pm on Fridays. These represent the two periods with the highest traffic in customers for
Fridays. For Saturdays, the highest number of customers visit the bank from 11 to noon as seen
in Figure 3: Number of Customers for Saturdays.
Figure 2: Number of Customers for Fridays
4

DATA ANALYSIS
Figure 3: Number of Customers for Saturdays
CONCLUSION AND RECOMMENDATIONS
We can conclude that on weekdays the busiest periods of the day are between 11am and
2pm and from 4pm to 5pm. Therefore, Bank98 should commit most of their staff to serving
customers during these periods of the day. On Saturdays, the busiest period in from 11am to
Noon, the bank should also commit most of their staff to serving the customers during this
period.
We also conclude that Fridays and Saturdays are the busiest days for Bank98. Hence,
days wise, the staff should be more committed to serving customers during these two days than
other days.
5

DATA ANALYSIS
REFERENCES
Albright, C. S., & Winston, W. L. (2014). Business Analytics: Data Analysis & Decision
Making. New York: Cengage Learning.
Besanko, D., Dranove, D., & Shanley, M. (2012). Economics of Strategy. New York: John Wiley
& Sons.
Cameron, T. B. (2014). Using Responsive Evaluation in Strategic Management. Strategic
Leadership Review, 22-27.
Galit, S., Peter, B. C., Inbal, Y., & Nitin, P. R. (2018). Data Mining for Business Analytics (1st
ed.). John Wiley & Sons, Inc.
Kiechel, W. (2010). The Lords of Strategy (2nd ed.). New York: Havard Business Press.
Laudon, K. C., & Guercio, T. C. (2014). E-commerce. Business. Technology. Society (1st ed.).
Chicago: Pearson.
Oscar, M. (2009). A data mining and knowledge discovery process model (1st ed.). Vienna: Julio
Ponce.
Witten, I. H. (2011). Data Mining: Practical Machine Learning Tools (3rd ed.). Sydney :
Morgan Kaufmann.
6