Difference between revisions of "Analysis of User and Merchant Dropoff for Sugar App - Project Overview"

Latest revision as of 22:02, 17 April 2016

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 our sponsor 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, our sponsor needs to minimise user and merchant attrition, and retain vital segments of both groups in order to preserve the network effect of its 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, our sponsor 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

Determine the relationship between Redemption and time of day
Determine the relationship between User Growth and Revenue
Determine if the 75/25 rule holds for user i.e. 25% of the Users contribute more than 75% of the revenue

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
Finding the areas that has the Users with highest spending
Develop a method to analyse cannibalisation rates
Validate cannibalisation methodology using real world data

Two-Sided Market

Detecting Network effect if present
Solving the chicken-or-egg problem - i.e. which group, Users and Merchants, add more value to Revenue?

Scope of Project

The scope of our project includes the following:

Data collection - Collating datasets from our sponsor's SQL database
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 merchant performance (redemption rate) using funnel plot analysis
Analysis of the network effect in a two-sided market using time-series analysis
Analysis of the cannibalization effect between outlets using geospatial analysis
Refinement - Get client feedback and refine the model
Derive recommendations and solutions for Sugar

Project Deliverables

Project Proposal
Mid-term Report
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.

@@ Line 36: / Line 36: @@
 <!------- Details ---->
 ==<div style="background: #95A5A6; line-height: 0.3em; font-family:helvetica;  border-left: #6C7A89 solid 15px;"><div style="border-left: #FFFFFF solid 5px; padding:15px;font-size:15px;"><font color= "#F2F1EF"><strong>Introduction and Project Background</strong></font></div></div>==
-In this day and age of rapid modernization, local businesses have had a very hard time competing with large franchise chains such as Walmart, 7-11, Giant etc. Customers of local businesses also demand that local businesses deliver a high quality product (38%), yet offer the lowest price possible (38%) (The Consumer Barometer Survey, 2015). The lack of economies of scale, as well as the high infrastructure and marketing costs have led to the closure of many local businesses.
-As such, there have been a rise of various local discount websites such as Groupon, Deal.com.sg, Lazada and Qoo10, which offer high quality products at low prices; and are frequented by many Singaporeans (42%) (The Consumer Barometer Survey, 2015). The growing trend suggests that there may be room for more growth in this area, which is where Sugar steps in.
-==<div style="background: #95A5A6; line-height: 0.3em; font-family:helvetica;  border-left: #6C7A89 solid 15px;"><div style="border-left: #FFFFFF solid 5px; padding:15px;font-size:15px;"><font color= "#F2F1EF"><strong>Introduction of Sugar</strong></font></div></div>==
+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.
-Sugar is an interactive city guide that seeks to encourage a culture of exploration in Singapore and helping local small businesses get discovered.
-Currently, Sugar operates in 3 countries – Hong Kong, Jakarta and Singapore. It originated from Shanghai where it has experienced tremendous success of over 250,000 users a day and hence, the founders has decided to expand its operations to Singapore in early 2014.
+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.
-Sugar has discovered that product quality and price is essential in a user making a purchase decision. Thus, as a city guide with its location-based features, it hopes to revolutionise the online shopping industry, by providing high quality products, at the lowest price, at the most convenient locations for the user.
+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.
-There are two main stakeholders: Merchants and Users as Sugar exists as a platform in a two-sided market. Like many other two-sided markets, it connects users and merchants and earn a premium for connecting these two groups by charging the transactions made between the two groups.
-Sugar’s merchants are mainly small local businesses in Singapore. It has a large variety, including cafes, small restaurants, bars, hair salons, gyms, gift shops. The benefits for merchants is advertising to users that are in close proximity to them. As mentioned before, convenience is an important factor for a purchase decision and thus, Sugar is leveraging on this aspect.
-Users derive benefits from the discounted deals on the app. For example, Sugar can offer a deal which offers a 50% discount on truffle fries at a restaurant near potential users. Since price is highlighted as an extremely important factor in a purchase decision, this can entice existing users and new users to check out deals on Sugar app. The location-based feature also provides them with ample opportunities to explore hidden gems and new establishments in their vicinity.
-==<div style="background: #95A5A6; line-height: 0.3em; font-family:helvetica;  border-left: #6C7A89 solid 15px;"><div style="border-left: #FFFFFF solid 5px; padding:15px;font-size:15px;"><font color= "#F2F1EF"><strong>Differentiation from Other Apps</strong></font></div></div>==
-What differentiates Sugar from the rest of the apps are 2 features: '''Location-Based Recommendations''' and '''Skimming Mechanism'''.
-'''Location-Based Recommendations'''
-Sugar offers location-based advertising for local businesses while simultaneously offering attractive deals to users. As a heuristic, the app uses location information provided by the user to recommend deals. Location can be set by the user or by the in-built GPS system.
-'''Skimming Mechanism'''
-Another important feature of the app is the Skimming Mechanism. It allows each user to reduce the price of an item by 20 cents, hence “skimming the price”. This gives the app word-of-mouth potential as users are motivated to reduce the price further by persuading their friends to skim the price of an item. For example, if a person can get 4 other friends, the 5 of them can reduce the price of an item by $1 for everyone.
 ==<div style="background: #95A5A6; line-height: 0.3em; font-family:helvetica;  border-left: #6C7A89 solid 15px;"><div style="border-left: #FFFFFF solid 5px; padding:15px;font-size:15px;"><font color= "#F2F1EF"><strong>Business Problems and Motivations</strong></font></div></div>==
-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.
+As our sponsor 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, our sponsor needs to minimise user and merchant attrition, and retain vital segments of both groups in order to preserve the network effect of its platform. This is done by enhancing the app experience for both users and merchants.
-'''Unredeemed Vouchers'''
+'''Merchant Retention and Acquisition'''
-One issue Sugar faces is the issue of unredeemed vouchers. When a user applies for a voucher, there is a 7-day redemption period before it expires. Many users allow the vouchers expire. Maximising the voucher redemption rate will benefit both merchants and users, by ensuring that a faster turnaround time for products that users want, and higher profits for the merchant. This will enhance the user experience for both groups.
+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.
+To build a successful application, our sponsor 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.
-'''Merchant Retention'''
-Not all merchants are the same. There is a large variation between the popularity of individual merchants. Some merchants experience uptake daily whereas some have little to none at all. As a result, many of the latter may choose to drop out after a period of time. Since Sugar is in a two-sided market, a high attrition rate of merchants may result in users also ceasing usage. As Sugar has limited resources to reach out and engage merchants, high value targets can be identified to optimise the effort in reaching out to retain merchants. Further analysis can be finding out the main factors for merchant attrition.
 ==<div style="background: #95A5A6; line-height: 0.3em; font-family:helvetica;  border-left: #6C7A89 solid 15px;"><div style="border-left: #FFFFFF solid 5px; padding:15px;font-size:15px;"><font color= "#F2F1EF"><strong>Project Objectives</strong></font></div></div>==
 '''Merchants'''
-*Identifying star merchants (performing better than expected) and laggard merchants (performing worse than expected)
-**Based on Revenue and Redemption Rate
-*Identifying time-series patterns (e.g. day of week and hour) and grouping merchants with similar redemption behavior together
-'''Items'''
+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 star items (performing better than expected) and laggard items (performing worse than expected)
-**Based on Impressions, Clicks and Price
+*Identifying over-performing merchants (star merchants) and under-performing merchants (laggard merchants)
-*Identifying time-series patterns (e.g. day of week and hour) and grouping items with similar redemption behavior together - drill down to product level
+*#Visualize and compare the performance between merchants in the same group
+*#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
+* Determine the relationship between Redemption and time of day
-*#Length
+* Determine the relationship between User Growth and Revenue
-*#Recency
+* Determine if the 75/25 rule holds for user i.e. 25% of the Users contribute more than 75% of the revenue
-*#Frequency
-*#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'''
@@ Line 104: / Line 74: @@
 *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
+*Finding the areas that has the Users with highest spending
 *Develop a method to analyse cannibalisation rates
+*Validate cannibalisation methodology using real world data
+'''Two-Sided Market'''
+*Detecting Network effect if present
+*Solving the chicken-or-egg problem - i.e. which group, Users and Merchants, add more value to Revenue?
 ==<div style="background: #95A5A6; line-height: 0.3em; font-family:helvetica;  border-left: #6C7A89 solid 15px;"><div style="border-left: #FFFFFF solid 5px; padding:15px;font-size:15px;"><font color= "#F2F1EF"><strong>Scope of Project</strong></font></div></div>==
 The scope of our project includes the following:
-*Data collection - Collating datasets from Sugar’s SQL database, Flurry and Localytics analytics dashboard
+*Data collection - Collating datasets from our sponsor's SQL database
 *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 merchant performance (redemption rate) using funnel plot analysis
-*Analysis of survival curves for merchant and users
+*Analysis of the network effect in a two-sided market using time-series analysis
+*Analysis of the cannibalization effect between outlets using geospatial analysis
 *Refinement - Get client feedback and refine the model
-*Extrapolation of survival curve for forecast
 *Derive recommendations and solutions for Sugar
 ==<div style="background: #95A5A6; line-height: 0.3em; font-family:helvetica;  border-left: #6C7A89 solid 15px;"><div style="border-left: #FFFFFF solid 5px; padding:15px;font-size:15px;"><font color= "#F2F1EF"><strong>Project Deliverables</strong></font></div></div>==
 * Project Proposal
-* Mid-term Presentation
 * Mid-term Report
-* Final Presentation
 * Final Report
 * Project Poster
 ==<div style="background: #95A5A6; line-height: 0.3em; font-family:helvetica;  border-left: #6C7A89 solid 15px;"><div style="border-left: #FFFFFF solid 5px; padding:15px;font-size:15px;"><font color= "#F2F1EF"><strong>References</strong></font></div></div>==
-Deciding Factor in Selecting a Local Business. (n.d.). Retrieved January 10, 2016, from https://www.consumerbarometer.com/en/insights/?countryCode=SG
+[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
-Benneyan JC, Lloyd RC, Plsek PE: Statistical process control as a tool for research and healthcare improvement. Qual Saf Health Care 2003, 12:458-464.
-Caffrey, J., & Isaacs, H. H. (1971). Estimating the Impact of a College or University on the Local Economy.
-Clarke, G. P., & Hayes, S. (2006). GIS and retail location models. Geomarketing: Methods and Strategies in Spatial Marketing, 165-186.
-Daoud, R. , Amine, A. , Bouikhalene, B. , Lbibb, R. (2015). 'Customer Segmentation Model in E-commerce Using Clustering Techniques and LRFM Model: The Case of Online Stores in Morocco'. World Academy of Science, Engineering and Technology, International Science Index 104, International Journal of Computer, Electrical, Automation, Control and Information Engineering, 9(8), 1905 - 1915.
-Larivière, B., & Van den Poel, D. (2004). Investigating the role of product features in preventing customer churn, by using survival analysis and choice modeling: The case of financial services. Expert Systems with Applications,27(2), 277-285.
-Lu, J., & Park, O. (2003). Modeling customer lifetime value using survival analysis—an application in the telecommunications industry. Data Mining Techniques, 120-128.
-Öner, Ö. (2014). Retail location.
-Portela, S., & Menezes, R. Modeling Customer Churn: An Application of Duration Models.
-Rochet, J. C., & Tirole, J. (2004). Two-sided markets: an overview (Vol. 258). IDEI working paper.
-Schubert, S., & Lee, T. (2011). Time Series Data Mining with SAS® Enterprise Miner™ (1st ed.). SAS. Retrieved from https://support.sas.com/resources/papers/proceedings11/160-2011.pdf
-Singstat.gov.sg,. (2016). Statistics Singapore - Services Survey Series 2014 - Food and Beverage Services. Retrieved 27 February 2016, from http://www.singstat.gov.sg/statistics/visualising-data/storyboards/sss-food-and-beverage-services/
-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
-The Consumer Barometer Survey 2014/2015 (2015). Retrieved January 10, 2016, from https://www.consumerbarometer.com/en/insights/?countryCode=SG
-Van den Poel, D., & Lariviere, B. (2003). Customer attrition analysis for financial services using proportional hazard models. European Journal of Operational Research, 157(1), 196-217.
-Woodall DH: The Use of Control Charts in Health-Care and Public-Health Surveillance. J Qual Technol 2006, 38(2):89-104.
-Wood, S., & Browne, S. (2007). Convenience store location planning and forecasting-a practical research agenda. International Journal of Retail & Distribution Management, 35(4), 233-255.
-Zhang, G., & Chen, Y. (2007). An Integrated Data Mining and Survival Analysis Model for Customer Segmentation. In Integration and Innovation Orient to E-Society Volume 1 (pp. 88-95). Springer US.
+[2] Rochet, J. C., & Tirole, J. (2004). Two-sided markets: an overview (Vol. 258). IDEI working paper.

Difference between revisions of "Analysis of User and Merchant Dropoff for Sugar App - Project Overview"

Latest revision as of 22:02, 17 April 2016

Contents

Introduction and Project Background

Business Problems and Motivations

Project Objectives

Scope of Project

Project Deliverables

References

Navigation menu

Personal tools

Namespaces

Variants

Views

More

Search

Navigation

Tools