Case Study

Geospatial Analysis & Consumer Psychographics

One the largest banks with over 200 branches in the Middle East wanted to strategically assess its branch and ATM networks across key cities. In addition, the client sought psychographic and market segmentation of its current and potential customer base to better design is financial services products and services.

The client realised that future banking success depended on gaining a better understanding of current and prospective clients and multilevel segmentation of the market at a detailed geographical area level. In absence of any publicly available information of geospatial population characteristics, financial services behaviour and forecast trends, the client knew that it had to undertake a detailed primary research campaign to extract valuable insight for its business strategy.

The grmc solution

grmc was engaged by the client to undertake geospatial socio-demographic assessment across key cities where the client operated. Leveraging grmc primary research capabilities and advanced analytical tools we developed a detailed geospatial socio-demographic model that predicted population and income trends across 30 to 40 zones within each of the investigated cities. Our research study not only assessed current trends but also focused on future direction of population growth by area, giving consideration to new real estate developments and their absorption.

Results achieved

Population and consumer insights gained through multilevel segmentation at a granular community level helped in identifying current and future opportunities to serve the customers better and improve market share. Through our research and analysis the client was able streamline its ATM and branch network and significantly improve market penetration.

Combining granular geospatial socio-demographic data and customer financial services behaviour with real P&L data the client was able to optimise its service provision by focusing on products, services, branch & ATM networks that deliver the highest ROI. Moreover, our predictive analysis derived the likelihood of a customer purchasing each of the client's products; thus identifying product clusters that represent cross-selling opportunities. Our analysis provided the basis to shift resources from lower performing segments to those with the highest ROI.