Geostatistical modeling in the presence of interaction between the measuring instruments, with an application to the estimation of spatial market potentials

TL;DR

This paper introduces a novel geostatistical model to estimate spatial market potential from sales data, accounting for instrument interactions and missing data, demonstrated through a case study in Bergamo, Italy.

Contribution

The paper develops a new geostatistical modeling approach that handles instrument interaction and missing data in spatial potential estimation.

Findings

Identified high-potential areas for newsstand placement.

Estimated total market volume of the city.

Validated the model with real sales data from Bergamo.

Abstract

This paper addresses the problem of recovering the spatial market potential of a retail product from spatially distributed sales data. In order to tackle the problem in a general way, the concept of spatial potential is introduced. The potential is concurrently measured at different spatial locations and the measurements are analyzed in order to recover the spatial potential. The measuring instruments used to collect the data interact with each other, that is, the measurement at a given spatial location is affected by the concurrent measurements at other locations. An approach based on a novel geostatistical model is developed. In particular, the model is able to handle both the measuring instrument interaction and the missing data. A model estimation procedure based on the expectation-maximization algorithm is provided as well as standard inferential tools. The model is applied to the estimation of the spatial market potential of a newspaper for the city of Bergamo, Italy. The estimated spatial market potential is eventually analyzed in order to identify the areas with the highest potential, to identify the areas where it is profitable to open additional newsstands and to evaluate the newspaper total market volume of the city.