ANLY482 AY2016-17 T2 Group3: HOME/Interim
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Contents
Overview
Project Background & Motivation
Vanitee was officially launched in May 2015, in an attempt to bridge the gap between customers and independent beauty professionals. Typically, beauty professionals that are listed on the platform are emerging and independent beauty artists. To put it simply, they are professionals who want to grow their brand and customer base. By providing such a platform, Vanitee is able to help them showcase what they do best.
However, this does not mean that there are no competitors. Competitors include brick and mortar shops in local neighbourhoods and even bigger beauty brands with chain stores such as Jean Yip Group. Even though these are physical stores, they still pose as a threat as customers can still choose to go to these stores instead of using Vanitee to engage a beauty professional. Hence, Vanitee does not want to stop at just providing a platform for these beauty professionals and for customers to engage them. Furthermore, with an increasing number of professionals and customers coming on board, evaluating their performance so far becomes much more imperative.
Firstly, to further the success of their application, Vanitee has to place emphasis on attracting new customers as well as retaining their existing customers. Many customers might have become dormant after just one booking. Hence, analysis can be done to find out why they have turned dormant and identify possible solutions to attract them to make the next booking.
Secondly, in response to these dormant customers, Vanitee currently has an extensive loyalty program (as shown in Figure 1) in place that offers customers credits, gems as well as campaign codes with every booking made. However, one issue they face is the lack of understanding of how consumers utilize these in-app resources. Also, they wish to understand the effectiveness of such a loyalty program in encouraging customers to make repeated bookings in the future.
Project Objectives
Hence, by utilizing the data from their current application’s database, we would wish to discover meaningful and informative insights which will allow Vanitee to better retain their customers and beauty professionals and understand the effectiveness of their current loyalty program. To achieve the above mentioned, we have set the following objectives:
Customers
- To determine the customer segmentation (different groups of customers) from the current booking patterns. Which customers are stagnant? Which customers are actively using the app?
- To understand customers’ behaviour. When was the last time a customer used the app? How frequent does a customer use the app? How much does a customer spend on average?
- To evaluate the effectiveness of using campaign codes to ensure customers repeat their bookings
- To understand how customers are using credits and gems (refer to Figure 2), whether they are accumulating before use or using them in their next booking
- To determine the Customer Lifetime Value (CLV) by campaign (which promotional campaign drives the highest value customer?) To which campaign do customers react to more? Do customers respond more to campaigns giving discounts in dollar amounts (e.g. $20 off) or to percentage amounts (e.g. 20% off)? Which customers react and respond more to campaigns, credits and gems?
- Which services generate the most profits?
Beauty Professionals
- To determine if there is any correlation on what makes beauty professionals more attractive to customers (based on the following hypothesis).
- Are beauty professionals more attractive if they have a higher chat response rate?
- Are beauty professionals more attractive if they have a greater variety of services?
- Are beauty professionals more attractive if they offer less expensive services compared to other professionals?
- Are beauty professionals more attractive if they offer services on non-working days or hours?
Data Integration and Filtering
Data Collection
To facilitate our data analysis, Vanitee has provided us with access to their current MongoDB database on the cloud. The database contains numerous tables such as customers, beauty professionals, bookings, campaigns etc. Our team has decided to use two full years worth of data which ranges from Jan 2015 to Dec 2016.
Extracted Tables
Different types of data are currently represented by different tables in the database. In this case, there were a total of 59 tables for us to utilize. After exploring each table and its suitability for analysis, we eventually narrowed down to the following 7 tables:
Bookings
A row in this table represents a specific booking of a Customer with a Beauty Professional. The detailed description of the main columns in this table is as follows:
Campaigns
A row in this table represents a specific campaign (marketing initiative). The detailed description of the main columns in this table is as follows:
Categories
A row in this table represents a specific category that can be used to classify services. The detailed description of the main columns in this table is as follows:
Users
A row in this table represents a specific user that has an account on the application (either as a customer or professional). The detailed description of the main columns in this table is as follows:
Customers
A row in this table represents a specific customer in relation to a specific beauty professional. The detailed description of the main columns in this table is as follows:
Professionals
A row in this table represents a specific beauty professional. The detailed description of the main columns in this table is as follows:
Services
A row in this table represents a specific service offered by a beauty professional. The detailed description of the main columns in this table is as follows:
Challenges
Data Cleaning and Exploration
Issues
Duplicate Values
Missing Values
Changes in Business Model
Findings
Users, Customers & Professionals
Bookings
Services
Campaigns
Revised Methodology
Cluster Analysis
Survival Analysis
Revised Scope of Work
Project Timeline
Revised Work Plan
