scholarly journals Customer Segmentation Using Machine Learning

2021 ◽  
pp. 207-211
Author(s):  
Varad R Thalkar
Keyword(s):  
Machine Learning ◽  
Customer Segmentation ◽  
Business Decisions ◽  
Demand And Supply ◽  
Potential Customer ◽  
Customer Base ◽  
Customer Defection ◽  
Customer Segment

Customer Segmentation is the process of division of customer base into several groups called as customer segments such that each customer segment consists of customers who have similar characteristics. Segmentation is based on the similarity in different ways that are relevant to marketing such as gender, age, interests, and miscellaneous spending habits.The customer segmentation has the importance as it includes, the ability to modify the programs of market so that it is suitable to each of the customer segment, support in business decisions; identification of products associated with each customer segment and to mange the demand and supply of that product; identifying and targeting the potential customer base, and predicting customer defection, providing directions in finding the solutions.

Techniques and Methods That Help to Make Big Data the Simplest Recipe for Success

2019 ◽  
pp. 161-194
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Data Analysis ◽  
Statistical Analysis ◽  
Data Analytics ◽  
Big Data Analysis ◽  
Customer Segmentation ◽  
Learning Context ◽  
Feature Vectors

Data analytics has grown in a machine learning context. Whatever the reason data is used or exploited, customer segmentation or marketing targeting, it must be processed first and represented on feature vectors. Many algorithms, such as clustering, regression, classification, and others, need to be represented and clarified in order to facilitate processing and statistical analysis. If we have seen, through the previous chapters, the importance of big data analysis (the Why?), as with every major innovation, the biggest confusion lies in the exact scope (What?) and its implementation (How?). In this chapter, we will take a look at the different algorithms and techniques analytics that we can use in order to exploit the large amounts of data.

scholarly journals CUSTOMER SEGMENTATION IN PRIVATE BANKING SECTOR USING MACHINE LEARNING TECHNIQUES

2013 ◽  
Vol 14 (5) ◽  
pp. 923-939 ◽  
Author(s):  
Ion Smeureanu ◽  
Gheorghe Ruxanda ◽  
Laura Maria Badea
Keyword(s):  
Machine Learning ◽  
Character Recognition ◽  
Foreign Exchange Market ◽  
Banking Sector ◽  
Customer Segmentation ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Learning Techniques ◽  
Fraud Prediction ◽  
Private Banking

Machine learning techniques have proven good performance in classification matters of all kinds: medical diagnosis, character recognition, credit default and fraud prediction, and also foreign exchange market prognosis. Customer segmentation in private banking sector is an important step for profitable business development, enabling financial institutions to address their products and services to homogeneous classes of customers. This paper approaches two of the most popular machine learning techniques, Neural Networks and Support Vector Machines, and describes how each of these perform in a segmentation process.

A Study of the Beauty Commerce Customer Segment Classification and Application based on Machine Learning : Focusing on Untact Service

2020 ◽  
Vol 22 (4) ◽  
pp. 75-92
Author(s):  
Sang-Hyeak Yoon ◽  
◽  
Yoon-Jin Choi ◽  
So-Hyun Lee ◽  
Hee-Woong Kim
Keyword(s):  
Machine Learning ◽  
Customer Segment

Towards digital loyalty programs: insights from customer medium preference segmentation

2017 ◽  
Vol 45 (2) ◽  
pp. 195-210 ◽  
Author(s):  
Marco Ieva ◽  
Cristina Ziliani
Keyword(s):  
Latent Class ◽  
Customer Segmentation ◽  
Online Marketing ◽  
Loyalty Programs ◽  
Content Type ◽  
Customer Preference ◽  
Clustering Model ◽  
Multinomial Logit Regression ◽  
Customer Segment ◽  
Logit Regression Model

Purpose The purpose of this paper is to identify patterns of medium preference for loyalty programs (LPs) among members to support the case for segmenting customers based on their medium preference. Design/methodology/approach A survey of nearly 2,000 customers who are enrolled in at least one supermarket LP was employed. LP members are segmented based on a latent class clustering model and then profiled in terms of socio-demographic variables by means of a multinomial logit regression model. Findings Medium preference is heterogeneous and differs at the customer segment and at the LP touchpoint level. Five segments emerge which display different medium preference patterns. LP medium preference is associated with age, gender, affluency and number of different LPs the customer is enrolled in. Practical implications Retailers, e-tailers and brands can benefit from this customer segmentation when faced with the challenges of adding online features or migrating their LPs online. Marketers should differentiate their investment in online and offline LP touchpoints according to the medium preference for each LP touchpoint of the customer segments of interest. Originality/value Retailers, e-tailers and brands are today introducing online marketing strategies and tactics, such as LPs, that have been traditionally used offline. So far, however, they have failed to answer the question whether online and offline LPs and related touchpoints have the same preference among consumers. Literature on LPs has not explored customer preference for the LP medium or the consumer characteristics related to medium preference. This work is unique in providing an overview of medium preference for LPs and their touchpoints.

scholarly journals A machine-learning scraping tool for data fusion in the analysis of sentiments about pandemics for supporting business decisions with human-centric AI explanations

2021 ◽  
Vol 7 ◽  
pp. e713
Author(s):  
Swarn Avinash Kumar ◽  
Moustafa M. Nasralla ◽  
Iván García-Magariño ◽  
Harsh Kumar
Keyword(s):  
Machine Learning ◽  
Expert System ◽  
Data Fusion ◽  
High Impact ◽  
Business Decisions ◽  
Daily Routines ◽  
Web Scraping ◽  
Small Medium Enterprises ◽  
Medium Enterprises ◽  
Different Sources

The COVID-19 pandemic is changing daily routines for many citizens with a high impact on the economy in some sectors. Small-medium enterprises of some sectors need to be aware of both the pandemic evolution and the corresponding sentiments of customers in order to figure out which are the best commercialization techniques. This article proposes an expert system based on the combination of machine learning and sentiment analysis in order to support business decisions with data fusion through web scraping. The system uses human-centric artificial intelligence for automatically generating explanations. The expert system feeds from online content from different sources using a scraping module. It allows users to interact with the expert system providing feedback, and the system uses this feedback to improve its recommendations with supervised learning.

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