Retail Consumer Traffic Multiple Factors Analysis and Forecasting Model Based on Sparse Regression

The rapid development of O2O business has increased the competition among offline shops in China. Accurate prediction of the shop’s customer traffic can help the stores to change the strategy of sales timely and improve their competitiveness. Customer traffic forecast is more than a problem of time...

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Published inGreen, Pervasive, and Cloud Computing pp. 489 - 494
Main Authors Zheng, Zengwei, Du, Junjie, Zhou, Yanzhen, Sun, Lin, Huo, Meimei, Wu, Jianzhong
Format Book Chapter
LanguageEnglish
Published Cham Springer International Publishing 2019
SeriesLecture Notes in Computer Science
Subjects
Consumer traffic forecasting
Multiple factor
Sparse regression
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Abstract The rapid development of O2O business has increased the competition among offline shops in China. Accurate prediction of the shop’s customer traffic can help the stores to change the strategy of sales timely and improve their competitiveness. Customer traffic forecast is more than a problem of time series. In fact, customer traffic for the next period is related to some external factors except for historical traffic. In this paper, the external factors affecting the customer traffic are analyzed using sparse coding, and we propose a sparse regression forecasting model with these external factors. The obtained results show that these external factors have varying degrees of impact on consumer traffic, and the prediction accuracy is significantly improved after considering these factors.
AbstractList The rapid development of O2O business has increased the competition among offline shops in China. Accurate prediction of the shop’s customer traffic can help the stores to change the strategy of sales timely and improve their competitiveness. Customer traffic forecast is more than a problem of time series. In fact, customer traffic for the next period is related to some external factors except for historical traffic. In this paper, the external factors affecting the customer traffic are analyzed using sparse coding, and we propose a sparse regression forecasting model with these external factors. The obtained results show that these external factors have varying degrees of impact on consumer traffic, and the prediction accuracy is significantly improved after considering these factors.
Author Sun, Lin
Zhou, Yanzhen
Wu, Jianzhong
Du, Junjie
Huo, Meimei
Zheng, Zengwei
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Snippet The rapid development of O2O business has increased the competition among offline shops in China. Accurate prediction of the shop’s customer traffic can help...
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StartPage 489
SubjectTerms Consumer traffic forecasting
Multiple factor
Sparse regression
Title Retail Consumer Traffic Multiple Factors Analysis and Forecasting Model Based on Sparse Regression
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