A New Approach for Construction of Geodemographic Segmentation Model and Prediction Analysis

Abstract

Customer retention is invariably the top priority of all consumer businesses, and certainly it is one of the most critical challenges as well. Identifying and gaining insights into the most probable cause of churn can save from five to ten times in terms of cost for the company compared with finding new customers. Therefore, this study introduces a full-fledged geodemographic segmentation model, assessing it, testing it, and deriving insights from it. A bank dataset consisting 11,000 instances, which consists of 10,000 instances for training and 10,000 instances for testing, with 14 attributes, has been used, and the likelihood of a person staying with the bank or leaving the bank is computed with the help of logistic regression. Base on the proposed model, insights are drawn and recommendations are provided. Stepwise logistic regression methods, namely, backward elimination method, forward selection method, and bidirectional model are constructed and contrasted to choose the best among them. Future forecasting of the models has been done by using cumulative accuracy profile (CAP) curve analysis.

A Hybrid Approach Using Oversampling Technique and Cost‐Sensitive Learning for Bankruptcy Prediction

“…Machine learning and data mining [1][2][3][4][5][6][7][8][9], which is the process of learning in order to look for patterns in observations or data and make better decisions in the future based on the training samples, is widely used in various fields such as cybernetics [10][11][12][13][14], engineering [15][16][17][18], bioinformatics [19], medical informatics [20], economics [21][22][23][24][25][26][27], etc. Especially in economics, there are many issues for optimizing profits in the business such as customer lifetime value modeling (CLVM), churn customer modeling (CCM), dynamic pricing, customer segmentation, recommendation systems, etc.…”

“…Dynamic pricing models [25] are for flexibly pricing products based on several factors such as the level of interest of the target customer, demand of the market at the time of purchase, and whether the customer has engaged with a marketing campaign. Meanwhile, customer segmentation models [26,27] group customers into personas based on specific variations among them using several clustering and classification algorithms. Recommendation systems are another major way by which machine learning proves its business value.…”

Establishment and Validation of The Axillary Lymph Node Burden Using Cone-Beam Computerized Tomography and Ultrasound-Based Prediction Models in T1-2 Breast Cancer Patients

“…Then, a bidirectional elimination method was applied to modify the model by repeatedly adding in and removing features until an optimal equation was developed (22,23). Finally, multivariable logistic regression analysis was used to select statistically meaningful parameters and t the best prediction model.…”

“…Clinical and pathological features were selected with a bidirectional selection method to reduce complexity and downsize predictive parameters. The AIC (Akaike information criterion) was applied in model construction (22). Models corresponding to the minimum AIC values were selected for construction.…”

Improving Electric Energy Consumption Prediction Using CNN and Bi-LSTM

“…With the development of data, internet as well as computing power of computers, machine learning and deep learning [1,2] have been used in many areas such as construction [3][4][5], cybernetic [6,7], economic [8][9][10][11] and medical [12,13] to help professionals save time and effort. Utilizing machine learning in economic, Hoang et al [14] introduced a full-fledged geo-demographic segmentation model for identifying and gaining insights of the most probable cause of churn for a bank dataset. Meanwhile, Le et al [8][9][10][11] developed several machine learning models for dealing with imbalance data problem to forecast the bankruptcy in South Korea.…”