ANLY482 AY2016-17 T2 Group21 : PROJECT OVERVIEW
Contents
Project Overview
Dressabelle is a leading online fast-fashion retailer in Singapore. Having established themselves online since 2012, Dressabelle has also expanded to 6 physical stores in malls around Singapore. Dressabelle set themselves apart with not one but two fresh fashion collection every week. Their mission is for women to feel confident about themselves when they dress in their clothes. With affordable pricing they want women to know they don’t have to blow a couple hundred for a dress that doesn’t compromise on style or quality.
Business Problems
Being in the fast-fashion industry, Dressabelle launches new fashion collection each week, the fast-paced nature of their merchandising, logistics and marketing has given little room to explore how data could aid their decision process.
For merchandising, know what to purchase, how much to purchase are important business decisions that have to be made on a regular basis. Dressabelle lacks a data-driven feedback loop on which products have done well and which can be done better.
Part and parcel of marketing is to attract new and old customers, dressabelle faces two main challenges. First, they are unclear how well are their marketing efforts are paying off, current marketing investments into Google and Facebook do not have clear return of investments. Second, it is crucial to identify which customers to target their marketing efforts into, current practice includes promotions to 'lost' customers.
Motivation
Dressabelle aims to be a leading player in the fashion industry, this require them to kept in the know of the latest fashion trends, streamline their internal operations and as well as understand their customers. The use of analytics for merchandising and marketing will give them a competitive advantage over other players in the market.
Project Objectives
The aim of this project is to help Dressabelle with understanding sales patterns to ultimately improve sales:
1. Merchandising decisions - Breaking down product sales by product attributes, allowing merchandiser to purchase more of what performs well.
2. Marketing analytics - Identifying cost & profit effective marketing mediums. Customer segmentation for effective targeted marketing.
Project Scope
Data collection - Gather transactions, web analytics data from Dressabelle
Data preparation - Cleaning data and anonymising/censoring data
Preliminary data analysis - Generation of association rules and contextualising it with product information.
Product breakdown - Understanding sales by product and it's attributes
Cohort analysis - Grouping customers by various traits such as by date of first purchase, category of purchase and channels of aquisition.
Stakeholders
Project Supervisor: Prof Kam Tin Seong, Associate Professor of Information Systems; Senior Advisor, SIS
Sponsor: Gwen Ang, Marketing Manager, Dressabelle
