[Paper Review] Customer Profiling, Segmentation, and Sales Prediction using AI in Direct Marketing

Mahmoud SalahEldin Kasem, Mohamed A. Hamada+1 more|arXiv (Cornell University)|Feb 3, 2023

Customer churn and segmentation11 citations

TL;DR

The paper proposes an AI-driven data preprocessing and modeling framework to develop customer profiles, segment customers, and predict sales in direct marketing, using RFM analysis and boosting trees.

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.

Motivation & Objective

• Motivate the need for AI-driven customer profiling in a competitive, data-rich direct marketing context.
• Develop a preprocessing and modeling framework to create customer profiles and forecast sales.
• Evaluate segmentation strategies and predictive accuracy to support marketing decision-making.
• Leverage RFM analysis to assess client value and a boosting tree for sales prediction.

Proposed method

• Introduce a data mining preprocessing pipeline to build a customer profiling system.
• Use RFM-analysis to evaluate client capital and a boosting tree for prediction.
• Discuss the role of customer segmentation methods and algorithms to improve predictive accuracy.
• Incorporate references to deep learning and AI techniques as applicable to profiling and loyalty programs.
• Provide methodological discussion on data preparation, feature engineering, and model selection.

Figure 1 : Radial basis neural network

Experimental results

Research questions

• RQ1 How can AI-based preprocessing and RFM analysis be used to profile customers for direct marketing?
• RQ2 What segmentation methods and algorithms yield higher prediction accuracy for sales forecasting?
• RQ3 Can boosting tree models effectively predict sales based on customer profiles and RFM-derived features?
• RQ4 What role do loyalty programs and customer lifetime value play in profiling and targeting decisions?

Key findings

• The study results in the creation of a customer profile and a forecast for goods sales.
• RFM-analysis is used to evaluate client capital and inform pricing/ targeting decisions.
• Boosting tree methods are highlighted as a core predictive technique in the framework.
• The work underscores the importance of segmentation methods and algorithms for improving prediction accuracy.
• The paper discusses integration of AI techniques to enhance advertising efficiency and loyalty-oriented strategies.

Figure 2 : Processes of Customer Segmentation