ANLY482 AY2017-18T2 Group26 Project Overview

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Geospatial Analysis

Geospatial Analysis is the technique of using geospatial data – from mobile devices, location sensors, social media, etc – to build maps, graphs, statistics and analytical models to make complex relationships understandable. The benefits of using geospatial analysis is that it is a step above regular analytical insights; more engaging and more understandable and recognizable, it helps managers move from hindsight to foresight and develop location-based targeted solutions. Focussing on this aspect of geospatial analysis, we aim to come up with a method that takes into consideration past location data, and its impact on other aspects of the business, to help optimize future location based decision making.

(Referenced: Geospatial Analytics The three-minute guide. (2012). Retrieved from https://www2.deloitte.com/content/dam/Deloitte/global/Documents/Deloitte-Analytics/dttl-analytics-us-ba-geospatial3minguide.pdf)

Company PQR

PQR is a Singapore based company with over 100 branches spread across Singapore as well as a growing online presence. They have a pronounced focused on providing aid to the community. Their employees are committed to helping the community albeit the elderly, challenged youth or the environment. The company itself, contributes over 60% of their profits to the betterment of the community each year.

Motivation & Objectives

PQR has been facing a road block while optimizing their sales targets, in order to meet their demand and serve their customers better. They have conquered central Singapore and need a smart method to decide on their sales targets considering the anomalies of each branch location. By analyzing mobile data and its impact on profits and sales, they would like to optimally predict their branch wise sales so that they can set realistic sales targets. Our project will use mobile data and find its relationship with the financial performance of PQR branches all over Singapore. Using this, we aim to find the optimal weightage of variables (mobile data) to consider, allowing us to suggest the most accurate targets possible.

Therefore, our objectives are: To learn the correlations between variables from mobile data, and financial data pertaining to PQR. We will use their given model to do so. To develop an equation that weighs these variables in a way that optimizes the financial outcome of potentially suggested branches. We aim to create our own model that builds on their existing one but includes our insights. To create a dashboard that summarizes these relationships and behaves like an interactive visualization of our formed equation. This is in order to give managers an overview of the most influential variables and their effect on the financial outcome which will help them set accurate sales targets.