The Role of Consumer Behavioural Factors in Customer Retention

This report investigates the role of consumer behavioural factors in influencing customer retention, highlighting the significance of retaining customers for organizational success. It emphasizes that customer retention is more cost-effective than customer acquisition and explores how machine learning techniques can minimize customer churn in the fintech sector. Various models, such as SVM, Random Forest, and Naïve Bayes, are discussed in terms of their application in predicting customer behaviour and improving customer retention strategies. The report also notes the increasing adoption of advanced technologies by banks to remain competitive in the fintech landscape, stressing the importance of providing structured quality service and customer satisfaction to foster long-term customer commitments.

CUSTOMER RETENTION
The role of the consumer
behavioural factors in influencing
customer retention

Aim of customer retention
 The customer retention lead to the
actions and activities taken by the
organization to reduce the number of
customer defections (Keramati, Ghaneei
and Mirmohammadi, 2016).
 The aim of customer retention is to help
an organization in retaining as many
customers as possible. The customer
retention is faster and costs seven times
less than the customer acquisition

The role of machine
learning
 The customer churn can be minimized by
using machine learning techniques.
 The customer churn is stated to the
condition when the existing customer stops
using services and buying products of the
organization (Ennew, Binks, and Chiplin,
2015).
 The machine learning is enabling the fintech
sector. It prevents fraud, manages risk,
improves chatbot customer experience,
network security and more

The use of various models
 Various models like SVM, Random Forest
or Naïve Bayes makes use of the existing
customers.
 These models expand the predictive
performance and outcome in the
evaluation of good and bad customers
(Ennew, Binks, and Chiplin, 2015).

Continued…
 The SVM is the supervised learning model
linked with learning algorithms which
analyse the data utilised for classification
and regression analysis.
 The random forest is a collaborative
learning technique which supports both
classification and regression. It
encompasses the concept of a single
classification tree by increasing various
classification trees in the training phase
(Qasem, Abukhadijeh and Aladham,
2016).

Continued…
 Naive Bayes is a categorized technique
based on Bayer’s theorem. It embraces
the idea of complete variables
independence as the absence of one
feature can lead the absence of other
feature.
 The banks are leading in announcing the
latest technologies to compete in the
current fintech domain (Pansari and
Kumar, 2017).

References
 Pansari, A. and Kumar, V., 2017. Customer
engagement: the construct, antecedents, and
consequences. Journal of the Academy of Marketing
Science, 45(3), pp.294-311.
 Qasem, M.F., Abukhadijeh, M.A. and Aladham,
M.A.A., 2016. Customer Relationship Management
and Customer Retention in Jordanian
Bank. International Business Research, 9(9), pp.41-
50.
 Keramati, A., Ghaneei, H. and Mirmohammadi, S.M.,
2016. Developing a prediction model for customer
churn from electronic banking services using data
mining. Financial Innovation, 2(1), p.10.

Continued…
 Ennew, C.T., Binks, M.R. and Chiplin, B., 2015.
Customer satisfaction and customer retention:
An examination of small businesses and their
banks in the UK. In Proceedings of the 1994
Academy of Marketing Science (AMS) Annual
Conference (pp. 188-192). Springer, Cham.
 Ennew, C.T., Binks, M.R. and Chiplin, B., 2015.
Customer satisfaction and customer retention:
An examination of small businesses and their
banks in the UK. In Proceedings of the 1994
Academy of Marketing Science (AMS) Annual
Conference (pp. 188-192). Springer, Cham.