Difference between revisions of "Analysis of User and Merchant Dropoff for Sugar App Time Series"
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− | + | <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>Abstract</strong></font></div></div> | |
− | + | 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. | 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. |
Revision as of 19:26, 17 April 2016
Mid-Term | Finals |
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Funnel Plot Analysis | Time Series Analysis | Geospatial Analysis |
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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.
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 |
Contents
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