ANLY482 AY2017-18T2 Group12: Project Overview
Background | Data Source | Methodology |
---|
Sponsor Introduction and Project Background
ARTistique is an arts events company established in 2008, driven by a mission to spread passion towards the arts and culture in Singapore. They offer a range of arts and cultural events and within the past decade, they have managed to capture a sizeable following of Singaporeans of varying levels of expertise in arts appreciation.
One of ARTistique's long term objective is to engage the wider populace through their arts and cultural programmes. However, participation rates has remained dismal despite ARTistique’s many initiatives. Therefore over the last few years, ARTistique has conducted numerous surveys to better understand consumers’ preference for the arts scene. Ultimately, ARTistique is interested in using their survey databank to find a sustainable way to broaden their membership base.
Project Motivation
Despite ARTistique’s consistent efforts to generate interest in arts and culture, their programmes continues to receive little attention from the general public. ARTistique has also traditionally always relied heavily on complimentary events to boost participation numbers and awareness amongst the general public. As such, while this initiative was effective in establishing a stronghold for ARTistique in the arts service sector, it results in slim profit margins which is unsustainable in the long run.
Moving forward, ARTistique is interested in leveraging on their existing membership base while changing their business model to include a higher proportion of paid arts events and activities. This will enable the business to become more sustainable in the long run as well as to go beyond customer acquisition to focus on customer retention. Therefore, they have engaged Team Flair to uncover relevant insights in achieving this long-term goal.
Scope of Project
The scope of the project includes the following phases:
Phase 0: Discovery
Phase 1: Data Preparation
Phase 2: Data Exploration & Model Planning
Phase 3: Data Analysis & Model Building
Phase 4: Communication & Deliverables