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A Data-Driven Customer Profiling Method for Offline Retailers.

Huahong Zuo1, Sike Yang2, Hailong Wu3

  • 1Wuhan Chuyan Information Technology Co., Ltd., Wuhan 430050, China.

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|June 27, 2022
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Summary
This summary is machine-generated.

This study introduces a data-driven approach using big data technology to enhance offline retail sales. It employs an incremental RFM model, TGI analysis, and LSTM for customer profiling and purchase prediction, improving marketing strategies.

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Area of Science:

  • Data Science
  • Retail Analytics
  • Machine Learning

Background:

  • Offline retailers face challenges in leveraging big data for sales improvement.
  • Traditional customer value segmentation methods can be inefficient and static.

Purpose of the Study:

  • To propose a data-driven customer profile modeling method for offline retailers.
  • To enhance sales and marketing strategies through advanced analytics.
  • To enable dynamic customer segmentation and accurate purchase prediction.

Main Methods:

  • Developed an incremental Recency, Frequency, Monetary (RFM) model for efficient, dynamic customer valuation.
  • Utilized the Target and Index (TGI) model to analyze commodity preferences across customer segments.
  • Implemented a Long Short-Term Memory (LSTM) based model for predicting future customer purchasing behavior.

Main Results:

  • The incremental RFM model demonstrated an 80% time saving compared to traditional methods.
  • The proposed LSTM prediction model achieved 59.32% accuracy in forecasting customer purchases.
  • The integrated approach provides a more efficient and accurate method for retail strategy optimization.

Conclusions:

  • The data-driven customer profiling method significantly enhances offline retail transformation.
  • Dynamic segmentation and predictive modeling lead to optimized marketing and retail strategies.
  • This approach offers a scalable and effective solution for improving sales in the retail sector.