Estimating Shopping Center Visitor Numbers Based On Various Environmental Indicators

EasyChair Preprint 2968

Abstract

The value of data as a result of the rapid increase of data production is gaining importance in recent years both in Turkey and globally. As data gains importance, data mining also changes and develops. With the help of data mining, companies have started to determine their customer management strategies based on data models. The literature review in this field shows that many data models have been studied in the field of customer management. When a more detailed literature review is made, it is observed that the number of sources where demand estimation and location analysis applied together with the machine learning algorithms is very low. When the studies are analyzed on the basis of the sector, it is observed that the studies made for shopping centers are scarce. Within the scope of this study, a new model has been developed by combining location analysis and demand forecasting models that will estimate the number of customers for shopping malls in order to overcome this deficiency in the literature. This model was strengthened with estimation algorithms and tested to generalize this model to all shopping malls. In this study conducted through a large-scale technology and communications services provider company, it was found that environmental factors such as temperature, precipitation market variables and traffic density had a significant effect on the number of customers going to shopping centers.

Keyphrases: Customer Strategy, Location Analysis, Predictive Analysis, Regression, customer demand forecast, data model, machine learning, shopping mall

@booklet{EasyChair:2968,
  author    = {Cagatay Ozdemir and Sezi Çevik Onar and Selami Bagriyanik},
  title     = {Estimating Shopping Center Visitor Numbers Based On Various Environmental Indicators},
  howpublished = {EasyChair Preprint 2968},
  year      = {EasyChair, 2020}}