Customer Profiling, Segmentation, and Sales Prediction using AI in Direct Marketing

• URL: http://arxiv.org/abs/2302.01786v1
• Date: Fri, 3 Feb 2023 14:45:09 GMT
• Title: Customer Profiling, Segmentation, and Sales Prediction using AI in Direct Marketing
• Authors: Mahmoud SalahEldin Kasem, Mohamed Hamada, Islam Taj-Eddin
• Abstract summary: This paper proposes a data mining preprocessing method for developing a customer profiling system to improve sales performance. The main result of this study is the creation of a customer profile and forecast for the sale of goods.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In an increasingly customer-centric business environment, effective communication between marketing and senior management is crucial for success. With the rise of globalization and increased competition, utilizing new data mining techniques to identify potential customers is essential for direct marketing efforts. This paper proposes a data mining preprocessing method for developing a customer profiling system to improve sales performance, including customer equity estimation and customer action prediction. The RFM-analysis methodology is used to evaluate client capital and a boosting tree for prediction. The study highlights the importance of customer segmentation methods and algorithms to increase the accuracy of the prediction. The main result of this study is the creation of a customer profile and forecast for the sale of goods.

Related papers

• F-FOMAML: GNN-Enhanced Meta-Learning for Peak Period Demand Forecasting with Proxy Data [65.6499834212641]
  We formulate the demand prediction as a meta-learning problem and develop the Feature-based First-Order Model-Agnostic Meta-Learning (F-FOMAML) algorithm. By considering domain similarities through task-specific metadata, our model improved generalization, where the excess risk decreases as the number of training tasks increases. Compared to existing state-of-the-art models, our method demonstrates a notable improvement in demand prediction accuracy, reducing the Mean Absolute Error by 26.24% on an internal vending machine dataset and by 1.04% on the publicly accessible JD.com dataset.
  arXiv  Detail & Related papers  (2024-06-23T21:28:50Z)
• Emulating Full Client Participation: A Long-Term Client Selection Strategy for Federated Learning [48.94952630292219]
  We propose a novel client selection strategy designed to emulate the performance achieved with full client participation. In a single round, we select clients by minimizing the gradient-space estimation error between the client subset and the full client set. In multi-round selection, we introduce a novel individual fairness constraint, which ensures that clients with similar data distributions have similar frequencies of being selected.
  arXiv  Detail & Related papers  (2024-05-22T12:27:24Z)
• An explainable machine learning-based approach for analyzing customers' online data to identify the importance of product attributes [0.6437284704257459]
  We propose a game theory machine learning (ML) method that extracts comprehensive design implications for product development. We apply our method to a real-world dataset of laptops from Kaggle, and derive design implications based on the results.
  arXiv  Detail & Related papers  (2024-02-03T20:50:48Z)
• Modeling the Telemarketing Process using Genetic Algorithms and Extreme Boosting: Feature Selection and Cost-Sensitive Analytical Approach [0.06906005491572399]
  This research aims at leveraging the power of telemarketing data in modeling the willingness of clients to make a term deposit. Real-world data from a Portuguese bank and national socio-economic metrics are used to model the telemarketing decision-making process.
  arXiv  Detail & Related papers  (2023-10-30T08:46:55Z)
• Customer Churn Prediction Model using Explainable Machine Learning [0.0]
  Key objective of the paper is to develop a unique Customer churn prediction model which can help to predict potential customers who are most likely to churn. We evaluated and analyzed the performance of various tree-based machine learning approaches and algorithms. In order to improve Model explainability and transparency, paper proposed a novel approach to calculate Shapley values for possible combination of features.
  arXiv  Detail & Related papers  (2023-03-02T04:45:57Z)
• FilFL: Client Filtering for Optimized Client Participation in Federated Learning [71.46173076298957]
  Federated learning enables clients to collaboratively train a model without exchanging local data. Clients participating in the training process significantly impact the convergence rate, learning efficiency, and model generalization. We propose a novel approach, client filtering, to improve model generalization and optimize client participation and training.
  arXiv  Detail & Related papers  (2023-02-13T18:55:31Z)
• Estimating defection in subscription-type markets: empirical analysis from the scholarly publishing industry [0.0]
  We present the first empirical study on customer churn prediction in the scholarly publishing industry. The study examines our proposed method for prediction on a customer subscription data over a period of 6.5 years. We show that this approach can be both accurate as well as uniquely useful in the business-to-business context.
  arXiv  Detail & Related papers  (2022-11-18T01:29:51Z)
• Towards Revenue Maximization with Popular and Profitable Products [69.21810902381009]
  A common goal for companies marketing is to maximize the return revenue/profit by utilizing the various effective marketing strategies. Finding credible and reliable information on products' profitability is difficult since most products tends to peak at certain times. This paper proposes a general profit-oriented framework to address the problem of revenue based on economic behavior, and conducting the 0n-shelf Popular and most Profitable Products (OPPPs) for the targeted marketing.
  arXiv  Detail & Related papers  (2022-02-26T02:07:25Z)
• Characterization of Frequent Online Shoppers using Statistical Learning with Sparsity [54.26540039514418]
  This work reports a method to learn the shopping preferences of frequent shoppers to an online gift store by combining ideas from retail analytics and statistical learning with sparsity.
  arXiv  Detail & Related papers  (2021-11-11T05:36:39Z)
• Predicting seasonal influenza using supermarket retail records [59.18952050885709]
  We consider supermarket retail data as a proxy signal for influenza, through the identification of sentinel baskets. We make use of the Support Vector Regression (SVR) model to produce the predictions of seasonal flu incidence.
  arXiv  Detail & Related papers  (2020-12-08T16:30:43Z)
• Dynamically Tie the Right Offer to the Right Customer in Telecommunications Industry [0.0]
  This work presents a conceptual model by studying the significant campaign-dependent variables of customer targeting. The outcomes of customer segmentation of this study could be more meaningful and relevant for marketers.
  arXiv  Detail & Related papers  (2020-10-18T16:44:51Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.