Analysis of User and Merchant Dropoff for Sugar App Time Series
| 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
Two-Sided market
The definition of a two-sided market is broadly about getting two sides “on board”, but such a definition may not be restrictive enough (Rochet & Tirole, 2004). Current definition of multi-sided platforms may be too inclusive or broad (Hagiu & Wright, 2015). However, 2 key features are present in multi-sided platforms and not in other businesses: (1) enabling direct intercommunication between two or more sides, (2) each side has a relationship with the platform (Hagiu & Wright, 2015). Rochet & Tirole(2004) defined it as a two-sided market if the platform can change the volume of transactions by charging one side of the market and subsidizing the price paid by the other side by an equal amount.
For networks effects, it can be positive or negative, cross-sided or same sided or cross side. Same-sided network effects refer to effects that affect the group it originates from, for example, an additional fax machine makes the whole network more valuable for everyone who owns a fax machine. Cross-sided network effects are when one side of a multi-sided market affects the other side and it can be either positive or negative. An example of negative cross-sided network effects can be found in the media industry, where the advertisers will exert a negative effect on the number of users because they are averse to additional advertisements (Reisinger, 2004). A positive cross-sided network effect is one where one group fuel demands of the other in a positive manner, creating a virtuous cycle (Eisenmann, Parker & Van Alstyne, 2006).
Various papers have demonstrated network effects in two-sided markets. However, current papers on two-sided markets usually explore pricing choices whereas papers on network effects usually look into adoption by users and network size (Rysman, 2009). Furthermore, Rysman (2009) found that papers on two-sided markets focused more on media, payment systems, and matching markets while the papers on network effect look into technology and telecommunications market. Kim, Lee & Park(2012) tested the existence of cross-sided network effects using a novel approach by examining the advantage of the incumbent(Groupon) over the new entrant(Living Social). Rysman (2007) found the existence of a positive feedback loop between consumer and merchant using data on payment card, suggesting cross-sided network effects. However, from our research, few or no papers test network effects for ecommerce mobile apps. Furthermore, papers testing network effects on online platforms usually do not have direct data on User & Merchants. Lastly, few papers have used a time-regression model to examine network effects.
As such, this paper will utilize a unique ecommerce data set to test the existence of cross-sided network effects with empirical User and Merchant data using a time-regression model approach.
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
