Difference between revisions of "Analysis of User and Merchant Dropoff for Sugar App Methodology"
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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>Tools Used</strong></font></div></div>== | ==<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>Tools Used</strong></font></div></div>== | ||
| − | + | JMP Pro will be used to perform exploratory analysis, funnel plot analysis and survival analysis. SAS JMP Pro is an analytical software that is able to handle large volumes of data efficiently, which is imperative since Sugar's data is too large to be handled by other software such as Microsoft Excel. Its built-in tools for survival analysis and funnel plot add-in will be extremely useful in our analysis. We are also very familiar with JMP Pro as we have utilised the software for many of our analytical modules such as Analytical Foundation. | |
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| − | + | In terms of time-series data-mining, we will be using SAS Enterprise Miner as its tool allows us to perform descriptive, predictive and time-series analysis on huge volumes of data. | |
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| − | + | QGIS will be used for mainly geospatial analysis. | |
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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>Analysis</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 | |
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| − | + | '''Items''' | |
| − | + | *Identifying star items (performing better than expected) and laggard items (performing worse than expected) | |
| + | **Based on Impressions, Clicks and Price | ||
| + | *Identifying time-series patterns (e.g. day of week and hour) and grouping items with similar redemption behavior together - drill down to product level | ||
| − | + | '''Users''' | |
| + | *Identifying star users (performing better than expected) and normal users using LRFM | ||
| + | *#Length | ||
| + | *#Recency | ||
| + | *#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''' | |
| + | *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?) | ||
| + | *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 | ||
Revision as of 12:31, 27 February 2016
| Background | Data Source | Methodology |
|---|
Introduction
The key aim of this project is to tackle the three problems above and subsequently increase the voucher redemption rate, user retention rate and merchant retention rate. For this study, we will only focus on Singapore users and merchants.
As the nature of our study differs in some ways to existing literature reviews, we face three main limitations. Hence, we will make adjustments to the pre-existing methods of survival analysis.
Limitations
Our first limitation is, unlike subscription-based services, Sugar provides the app to users for free. Users can use the app indefinitely or choose to uninstall it. However, at this present time, there is no way for Sugar to track their uninstallations. This means that Sugar has no way of telling when a user has dropped off for real.
Our second limitation is that Sugar belongs in a two sided market. In a two sided market, users and merchant affect each other. As such, dropouts on the user end can cause dropouts on the merchant end, and vice versa. Thus, our survival analysis may be confounded by the network effects.
Our third limitation is that Sugar is an ecommerce app, which takes users through a sales funnel. There are a few main stages of a user’s journey, and users can drop off at any point:
Sugar’s aim will be to move as many users as possible from the start to the end of the funnel in order to earn profits.
Therefore, it is not a straightforward analysis as predicting churn for a fixed subscription and it requires multiple survival curves to have a complete picture of the user’s journey.
Tools Used
JMP Pro will be used to perform exploratory analysis, funnel plot analysis and survival analysis. SAS JMP Pro is an analytical software that is able to handle large volumes of data efficiently, which is imperative since Sugar's data is too large to be handled by other software such as Microsoft Excel. Its built-in tools for survival analysis and funnel plot add-in will be extremely useful in our analysis. We are also very familiar with JMP Pro as we have utilised the software for many of our analytical modules such as Analytical Foundation.
In terms of time-series data-mining, we will be using SAS Enterprise Miner as its tool allows us to perform descriptive, predictive and time-series analysis on huge volumes of data.
QGIS will be used for mainly geospatial analysis.
Analysis
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
- Identifying star items (performing better than expected) and laggard items (performing worse than expected)
- Based on Impressions, Clicks and Price
- Identifying time-series patterns (e.g. day of week and hour) and grouping items with similar redemption behavior together - drill down to product level
Users
- Identifying star users (performing better than expected) and normal users using LRFM
- Length
- Recency
- 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
- 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?)
- 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
