Difference between revisions of "Analysis of User and Merchant Dropoff for Sugar App Time Series"

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Our client is a city guide discovery application that brings users and merchants together through geo-located offers. The objective of this research is to examine merchant performance via redemption rates. The results are displayed using funnel plots, a useful tool for displaying unbiased information on performance outcomes when comparing entities within a group. The funnel plot shows a high amount of overdispersion where there is a large number of outlying merchants. By further analyzing under-performing and over-performing merchants separately, the analysis shows that there is also a large variation in outlying redemption rates within each group. To investigate the underlying reasons, we conducted exploratory data analysis. Merchant and product category are shown to be significant contributors to a merchant’s redemption rate. These findings will help our client set benchmarks for individual merchants and develop interventions to help merchants increase their performance.  
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A two-sided market is defined as a platform enabling two groups of end-users to interact with each other while the platforms charges for transactions made between the groups. In such a market, each additional User should attract more Merchants and vice versa, a.k.a. the network effect. While many papers on two-sided markets exist, little of them test for network effects for ecommerce. This paper tests the presence of network effects and solves the chicken-or-egg problem by examining the relationship of Users, Merchants and Revenue. This is done by a unique dataset provided by a location-based deals mobile app.  
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Aggregating transactional data into 97 rows of weekly number of Users, Merchants and Revenue, we constructed three multiple linear regression models on time series data to test the hypotheses. Results show that network effects are not present. Additional Merchants are associated with increased Users. However, additional Users are not associated with additional Merchants. Solving the chicken-or-egg problem, results show that additional Users, not Merchants, are associated with more Revenue. From this, we derived two recommendations for the app. Firstly, they can allocate more resources on user acquisition than merchant acquisition. Secondly, the app should attempt to increase its network effect via reducing information asymmetry. On top of that, we created a prediction model that is able to predict the end-of-month revenue with a R-squared value of 0.87 given user data.  
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Revision as of 19:25, 17 April 2016

Home

 

Project Overview

 

Findings

 

Project Documentation

 

Project Management

Mid-Term Finals
Funnel Plot Analysis Time Series Analysis Geospatial Analysis

Abstract

A two-sided market is defined as a platform enabling two groups of end-users to interact with each other while the platforms charges for transactions made between the groups. In such a market, each additional User should attract more Merchants and vice versa, a.k.a. the network effect. While many papers on two-sided markets exist, little of them test for network effects for ecommerce. This paper tests the presence of network effects and solves the chicken-or-egg problem by examining the relationship of Users, Merchants and Revenue. This is done by a unique dataset provided by a location-based deals mobile app.

Aggregating transactional data into 97 rows of weekly number of Users, Merchants and Revenue, we constructed three multiple linear regression models on time series data to test the hypotheses. Results show that network effects are not present. Additional Merchants are associated with increased Users. However, additional Users are not associated with additional Merchants. Solving the chicken-or-egg problem, results show that additional Users, not Merchants, are associated with more Revenue. From this, we derived two recommendations for the app. Firstly, they can allocate more resources on user acquisition than merchant acquisition. Secondly, the app should attempt to increase its network effect via reducing information asymmetry. On top of that, we created a prediction model that is able to predict the end-of-month revenue with a R-squared value of 0.87 given user data.


Business Motivations and Objectives

Literature Review

Methodology

Data


Data Preparation


Tools Used


Constructing the Population Regression Model


Method for Hypothesis 1

Method for Hypothesis 2

Method for Hypothesis 3

Results

Hypothesis 1: Merchant Growth(IV) is associated with User growth(DV)


Hypothesis 2: User Growth(IV) is associated with Merchant growth(DV)


Hypothesis 3: Revenue Growth is a function of User and Merchant Growth

Discussion

Implications

Prediction Model

Univariate Prediction Model

Multivariate Prediction Model

Conclusion

References

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