Data Analytics Lifecycle Report: Case Study on Customer Retention

This report examines the data analytics lifecycle through the lens of a case study involving Yoyodyne Bank. The bank aims to enhance its Net Present Value and improve customer retention, specifically targeting a reduction in the churn rate. The report outlines the importance of a pilot program to test the effectiveness of new strategies, such as marketing campaigns and offers. It discusses data preparation techniques, utilizing daily transactions and customer account data to analyze churn rates and the impact of policy changes. The analysis highlights the benefits of building a data warehouse to support the marketing strategy and emphasizes the role of the pilot program in validating predictions and ensuring the success of the project. The report also references relevant research on big data analytics and its application in business operations and risk management, emphasizing the use of data for environmental performance evaluation.

Running head: DATA ANALYTICS LIFECYCLE BASED ON CASE STUDY
DATA ANALYTICS LIFECYCLE BASED ON CASE STUDY
Name of the Student
Name of the university
Author note

1DATA ANALYTICS LIFECYCLE BASED ON CASE STUDY
Pilot study has two different versions in the sphere of social science research that are
feasibility studies and trial run. The former refers to the study done in case of small scale
versions while the latter refers to the preparations done in case of major studies. A pilot study
is meant to test or try out a specific instrument related to research or before implementing a
new analytical technology (Li et al. 2015). One of the primary advantages of pilot program is
that it provides a prior warning about where exactly the research project can fail. In the given
case study of Yoyodyne Bank it is said that the bank wants to make improvements in its
policy of Net Present Value as well as the retention of its customers. The bank is interested in
establishing a marketing campaign that could help in reducing the churn rate by a value of
five percent. The bank has to find out whether retaining those specific customers be of any
help. The attrition rate of the customers is also to be analyzed by the bank and what could be
the possible ways in which these customers can be retained. Building a data warehouse to
support the marketing strategy is also one of the objectives of the bank. In the pilot program
the bank can find out the number of customers of the bank at the starting of the month and the
customers at the end of the months (Choi, Chan and Yue 2016). This data will in turn help in
finding out the churn rate of the customers of the bank. In order to find out the churn rate the
bank can make use of the data related to transaction volume along with the type of the key
predictors of the churn rates. Data preparation can be done based on the daily transactions of
the customers and the number of customers joining the bank. Running a pilot program will
help assess the benefits of the change in policy of Net Present Value, retaining the employees
as well. Data of the last five years will be helpful in making the data. The model chosen
should be supportive to the set of cash flows occurring at different times in order to improve
the Net Present Value (Song et al. 2018). For customer retention the bank can make use of
schemes and other offers which attract new customers as well retain the old ones. The daily
transactions and the newly opened bank accounts can be the source of data for the new

2DATA ANALYTICS LIFECYCLE BASED ON CASE STUDY
policy. The pilot program will help the bank in valid predictions regarding the churn so the
project desired to be implemented does not fail at any point (Storey and Song 2017). The
results of the pilot program can give a vivid idea whether the project planning is appropriate
for achieving the set goals of retaining customers and improving Net Present Value. The pilot
program is the only way to find out whether the model to be followed will be effective in
bringing in the required changes.

3DATA ANALYTICS LIFECYCLE BASED ON CASE STUDY
References
Choi, T.M., Chan, H.K. and Yue, X., 2016. Recent development in big data analytics for
business operations and risk management. IEEE transactions on cybernetics, 47(1), pp.81-92.
Li, J., Tao, F., Cheng, Y. and Zhao, L., 2015. Big data in product lifecycle management. The
International Journal of Advanced Manufacturing Technology, 81(1-4), pp.667-684.
Song, M.L., Fisher, R., Wang, J.L. and Cui, L.B., 2018. Environmental performance
evaluation with big data: Theories and methods. Annals of Operations Research, 270(1-2),
pp.459-472.
Storey, V.C. and Song, I.Y., 2017. Big data technologies and management: What conceptual
modeling can do. Data & Knowledge Engineering, 108, pp.50-67.