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2 results on '"customer personality analysis"'

1. Customer Personality Analysis for Churn Prediction Using Hybrid Ensemble Models and Class Balancing Techniques

Author

Noman Ahmad, Mazhar Javed Awan, Haitham Nobanee, Azlan Mohd Zain, Ansar Naseem, and Amena Mahmoud

Subjects
Customer personality analysis, machine learning, generative adversarial networks, SMOTE, Electrical engineering. Electronics. Nuclear engineering, TK1-9971
Abstract

Today’s businesses rely heavily on focused marketing to improve their chances of growing and keeping their consumer base. Internet behemoths like Google and Facebook have expanded their business models around targeted advertisements that support business growth. Customer personality identification helps for churn prediction for companies. This problem arises in several companies where customer leaves companies for many reasons. This gap leads to conduct study for customer personality analysis. The collected dataset was highly imbalanced in nature. Two class balancing approaches CTGAN (Conditional tabular Generative adversarial networks) and SMOTE (Synthetic minority oversampling technique) has been utilized to equalize the both classes. There are three ensemble approaches such as bagging, boosting and stacking have been utilized for modeling purpose bagging approach uses Random Forest (RF) boosting utilizes XGBoost (XGB), Light Gradient Boosting Machine (LGBM) and ADA Boost (ADA B). The proposed Hybrid Model HSLR comprises of RF, XGB, ADA Boost, LGBM approaches as base classifiers and LR as a Meta classifier. Three testing independent set, k-fold with 5 and 10 folds have been utilized. To evaluate the performance of classifiers evaluation metrics such as Accuracy score, Precision, Recall, F1 score, MCC and ROC score have been utilized. The SMOTE generated data has shown results as compare with CTGAN generated data. The SMOTE approach has shown the highest results of 94.06, 94.23, 94.28, 94.05, 88.13 and 0.984 as accuracy score, Precision, recall, F1, MCC and Roc score respectively.

Published
2024
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2. Using Unsupervised Machine Learning Methods for Analyzing Customer Habits

Author

Grujić Ostojić, Petra Dunja and Pintar, Damir

Subjects
customer segmentation, hijerarhijsko aglomerativno grupiranje, eksploratorna analiza podataka, customer personality analysis, the K-Means algorithm, TEHNIČKE ZNANOSTI. Računarstvo, grupiranje, exploratory data analysis, customer behavior analysis, analiza navika kupaca, analiza osobnosti kupaca, analiza glavnih komponenata, TECHNICAL SCIENCES. Computing, segmentacija kupaca, marketing, hierachical agglomerative clusering (HAC), algoritam K-sredina, algoritam K-medoida, unsupervised machine learning, the K-Medoids algorithm, nenadzirano strojno učenje, Principal Component Analysis (PCA), clustering
Abstract

Osnovni zadatci nenadziranog strojnog učenja su grupiranje, procjena gustoće, otkrivanje anomalija i stršećih vrijednosti i smanjenje dimenzionalnosti. Nenadzirano strojno učenje ima veliku primjenu u marketingu. Danas to područje obiluje podatcima pomoću kojih tvrtke mogu razumjeti potrebe kupaca i steći njihovu naklonost, a time i dugoročni profit. U ovom je radu naglasak na metodama grupiranja kupaca na temelju njihovih karakteristika i potrošačkih navika. Na odabranom podatkovnom skupu provedena je eksploratorna analiza, reducirana dimenzionalnost te su primijenjeni algoritmi K-sredina, K-medoida i algoritam hijerarhijskog grupiranja (HAC). Uočeno je da na ovom podatkovnom skupu algoritam K-sredina dao bolje rezultate od algoritma K-medoida: dobivene 3 grupe su bile homogenije. Na temelju razlika između grupa kupaca tvrtka bi mogla personalizirati buduću marketinšku kampanju. Unsupervised machine learning models are utilized for four main tasks: clustering, density estimation, anomalies and outliers detection, and dimensionality reduction. Nowadays, there's a lot of data that can be used to satisfy customer needs and sustain a profitable business. Therefore, unsupervised machine learning plays a major role in marketing. The main focus of this paper is customer clustering techniques. Before performing customer segmentation based on their characteristics and purchase behavior, an exploratory analysis of chosen dataset has been done. Clustering algorithms that were used in this paper are the K-means algorithm, the K-medoids algorithm, and hierarchical agglomerative clustering (HAC). It turned out that the K-means algorithm outperformed the K-medoids algorithm on this dataset: 3 obtained clusters were more homogeneous. Based on differences among clusters company could personalize its future marketing campaign.

Published
2022

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