Difference between revisions of "Analysis of User and Merchant Dropoff for Sugar App Time Series"
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| style="padding:0.3em; font-family:helvetica; font-size:100%; border-bottom:2px solid #626262; border-left:2px #66FF99; background: #18e2a1; text-align:left;" width="20%" | <font color="#000000" size="3em"><strong>Hypothesis 2: User Growth(IV) is associated with Merchant growth(DV)</strong><br></font> | | style="padding:0.3em; font-family:helvetica; font-size:100%; border-bottom:2px solid #626262; border-left:2px #66FF99; background: #18e2a1; text-align:left;" width="20%" | <font color="#000000" size="3em"><strong>Hypothesis 2: User Growth(IV) is associated with Merchant growth(DV)</strong><br></font> | ||
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Revision as of 19:18, 17 April 2016
| Mid-Term | Finals |
|---|
| Funnel Plot Analysis | Time Series Analysis | Geospatial Analysis |
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Contents
Abstract
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.
| 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
