Difference between revisions of "ANLY482 AY2017-18 T1 Group1: Project Overview"
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Our project sponsor, eatigo, currently has 2 million registered users. However, reservation and reactivation rates remain low as only 30% of eatigo's subscribers have ever made a reservation. Further, even amongst this 30%, each customer makes only 1.6 bookings on average per month. Our sponsor believes that there is higher potential, given their presence in markets such as Singapore and Thailand, both of which are amongst the top 3 spenders in South-East Asia when it comes to dining out. | Our project sponsor, eatigo, currently has 2 million registered users. However, reservation and reactivation rates remain low as only 30% of eatigo's subscribers have ever made a reservation. Further, even amongst this 30%, each customer makes only 1.6 bookings on average per month. Our sponsor believes that there is higher potential, given their presence in markets such as Singapore and Thailand, both of which are amongst the top 3 spenders in South-East Asia when it comes to dining out. | ||
| − | Through our project, we plan on helping eatigo improve its vendor listing and customer bookings. This will be done by utilizing their reservation data to reveal booking patterns across different | + | <br /> |
| + | Eatigo’s business model is such that it earns revenue when customers show up for their booking. For every customer that does show up, Eatigo receives a certain fee from the vendors. Therefore, it is crucial for eatigo to have a good vendor listing (so customers are interested in making their booking through eatigo) and ensure that customers are aware of these vendor listings and discounts available (so customers are incentivized to make their booking through eatigo). | ||
| + | <br /> | ||
| + | Through our project, we plan on helping eatigo improve its vendor listing and customer bookings. This will be done by utilizing their reservation data to reveal booking patterns across different neighborhoods and types of customers. This is meant to improve their currently existing yield management intelligence | ||
<div style="background:#AE0000; line-height:0.3em; font-family:montserrat; font-size:120%; border-left:#FFE2C0 solid 15px;"><div style="border-left:#fff solid 5px; padding:15px;"><font color="#fff"><strong>'''Objectives''' | <div style="background:#AE0000; line-height:0.3em; font-family:montserrat; font-size:120%; border-left:#FFE2C0 solid 15px;"><div style="border-left:#fff solid 5px; padding:15px;"><font color="#fff"><strong>'''Objectives''' | ||
Revision as of 16:14, 14 January 2018
Our project sponsor, eatigo, currently has 2 million registered users. However, reservation and reactivation rates remain low as only 30% of eatigo's subscribers have ever made a reservation. Further, even amongst this 30%, each customer makes only 1.6 bookings on average per month. Our sponsor believes that there is higher potential, given their presence in markets such as Singapore and Thailand, both of which are amongst the top 3 spenders in South-East Asia when it comes to dining out.
Eatigo’s business model is such that it earns revenue when customers show up for their booking. For every customer that does show up, Eatigo receives a certain fee from the vendors. Therefore, it is crucial for eatigo to have a good vendor listing (so customers are interested in making their booking through eatigo) and ensure that customers are aware of these vendor listings and discounts available (so customers are incentivized to make their booking through eatigo).
Through our project, we plan on helping eatigo improve its vendor listing and customer bookings. This will be done by utilizing their reservation data to reveal booking patterns across different neighborhoods and types of customers. This is meant to improve their currently existing yield management intelligence
As described, the overall objective of our project is to help eatigo increase number of bookings per customer and potentially attract more vendors. With this in mind, at this stage, our aim is to conduct an Exploratory Data Analysis (EDA) to understand the vendor choice, booking and redemption patterns of eatigo customers across its 6 regions, and 700 restaurants.
From our EDA, we would like to answer the following questions from our datasets :
1. Analysis of Vendor Data
- Variation of booking patterns across cuisines
- Variation of booking patterns across neighbourhoods
- Variation of peak and downtime across restaurants
- Understanding the potential of optimizing discounts provided by restaurants within the same neighbourhood
2. Analysis of Customer (Transaction & Redemption) Data:
- Variations of booking patterns across customers that have made bookings previously
- The average gap between the time booking is made and the time customers have to show up at the restaurant
- The customer clusters by restaurant type, location, timing and discount preferences
The current objectives may be subjected to further changes after we have obtained and look at the actual data.
References:
“Singapore Among Top Spenders, Asia Pacific Survey” (http://www.todayonline.com/singapore/singapore-among-top-spenders-asia-pacific-dining-survey)
