Analysis of User and Merchant Dropoff for Sugar App - Project Overview

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Background		Data Source		Methodology

Introduction and Project Background

As of 2016, Singapore has the highest smartphone penetration rate in the world at 88%, ahead of other countries such as Hong Kong, Australia and the U.S. [1]. Most smartphones today are able to use GPS track the user’s location, opening up opportunities for location-based marketing.

Our sponsor seeks to take advantage of this by offering local deals tailored to the user’s location using a mobile app. It helps local small businesses such as cafes, restaurants, and small retail shops get discovered and market to users close in proximity. Whenever the user opens the app, they can see a list of discounted items from merchants nearby.

By definition, our sponsor exists as a platform in a two-sided market, enabling two groups of end-users to come “on board” by charging one or both sides [2]. Examples include Alibaba, eBay, Groupon, Uber and many more e-commerce companies. Like many other two-sided markets, it connects users and merchants and earns a premium for connecting these two groups by charging the transactions made between the two groups.

Business Problems and Motivations

As Sugar is a relatively young startup in Singapore, it has not yet attained a critical mass of user and merchant numbers. As such, user growth and user experience is vitally important. To reach this critical mass, Sugar needs to minimise user and merchant attrition, and retain vital segments of both groups in order to preserve the network effect of Sugar’s platform. This is done by enhancing the app experience for both users and merchants.

Merchant Retention and Acquisition

Redemption rate will be used as our main performance indicator for measuring merchant performance since the only channel for generating revenue is via upsell. This is the main factor of merchant attrition as merchants with little to zero uptake will perceive the application as a waste of time and choose to drop off. Since our sponsor is in a two-sided market, a high attrition rate of merchants may result in users also ceasing usage. For our sponsor, there is a strong need to retain and attract merchants who are performing well and to improve or perhaps eliminate merchants who are not.

User Retention

To build a successful application, Sugar needs to grow as fast as possible while retaining existing users. Many users have become dormant or stop buying after the first purchase. Analysis can be applied to find out why they have turned dormant/ stopped buying and identify solutions that may be able to attract them.

Project Objectives

Merchants The objective of this paper is to show how the funnel plot may be more accurate and unbiased in comparing the performance between entities in a group. The following are some objectives we hope to achieve in our research study.

• Identifying over-performing merchants (star merchants) and under-performing merchants (laggard merchants)
  1. Visualize and compare the performance between merchants in the same group
  2. Set benchmarks for individual merchants
• Examine underlying factors that affect redemption rate of merchants

Users

• Identifying star users (performing better than expected) and normal users using LRFM
  1. Length
  2. Recency
  3. Frequency
  4. Monetary
• Cluster users based on that
• Apply survival analysis from installation to purchase (e.g. which type of users are more likely to purchase only x times before falling off the app?)

Geospatial

• Determining the relationship between user location and merchant location (i.e. do users really go to places near them or are they more willing to travel to visit certain merchants?)
• Determining the relationship between redemption rate and merchant location
• Identifying popular areas and unpopular areas (i.e. the proportion of merchants in an area should be proportion to the number of orders it receive)
• Recommending potential locations for new merchants
• Develop a method to analyse cannibalisation rates

Scope of Project

The scope of our project includes the following:

• Data collection - Collating datasets from Sugar’s SQL database, Flurry and Localytics analytics dashboard
• Data preparation - Setting and censoring the data. We will also be filtering users and merchants based on location, where we will only focus only on the Singapore region
• Analysis of voucher redemption rates using survival analysis
• Analysis of survival curves for merchant and users
• Refinement - Get client feedback and refine the model
• Extrapolation of survival curve for forecast
• Derive recommendations and solutions for Sugar

Project Deliverables

• Project Proposal
• Mid-term Presentation
• Mid-term Report
• Final Presentation
• Final Report
• Project Poster

References

[1] The Connected Consumer Survey 2014/2015. (2015). Retrieved January 10, 2016, from https://www.consumerbarometer.com/en/graph-builder/?question=M1&filter=country:united_states,china,hong_kong_sar,korea,malaysia,singapore,australia

[2] Rochet, J. C., & Tirole, J. (2004). Two-sided markets: an overview (Vol. 258). IDEI working paper.