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NON-DATABASE CUSTOMER AS SPATIAL ISSUES OF ESTIMATING HYPERMARKET’S LIFETIME VALUE: AN APPROACH OF SURVEY-GIS METHOD

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Author(s): Abdul Manaf Bohari | Ruslan Rainis | Malliga Marimuthu

Journal: Australian Journal of Business and Management Research
ISSN 1839-0846

Volume: 1;
Issue: 5;
Start page: 01;
Date: 2011;
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Keywords: Non-Database Customer | Spatial Issues | Hypermarket | Lifetime Value | Survey-GIS Method

ABSTRACT
Prospecting hypermarket lifetime value is vital important to predict how long the business will be survive in the marketplace as important as measured the profitability of the business. Mostly, the hypermarket has been used database customer as main resources of predicting their customer without consider any data from non-database customer, which also called “free customer”. In fact, non-customer is spatial-based information where never tied with hypermarket database of customer. This is actually the gaps that exist in prospecting customer lifetime value where most of study was aimed on database customer and also lacks in tied spatial-based information to the hypermarket’s customer database. The objective of this paper is to discuss non-database customer as spatial issues during estimating the profitability of hypermarket business. Secondly, this study is aimed to demonstrate the location of non-database customer as base platform for further works on prospecting customer lifetime value. The method use in this study is combination survey-GIS as solution for handle non-database customer in spatial environment. Specifically, Arcview software (GIS) is used as main method for modelling the physical location of marketplace. Meanwhile, a postal address of non-database customer which is collected from survey is used for testing under the physical model of marketplace location. The Seberang Perai Tengah of Penang in Malaysia is setting as location of the study. One of the result shows that non-database customer can visualized individually by specific location that based on street and road. This study will suggest on how to used spatial data as a part of predicting lifetime value of customer where it can produced results in more precise and concurrently.

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