Analysis of User and Merchant Dropoff for Sugar App Methodology
| Background | Data Source | Methodology |
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Introduction
The key aim of this project is to tackle the three problems above and 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 will make adjustments to the pre-existing methods of survival analysis due to the limitations of our scenario.
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 on 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 like predicting churn for a fixed subscription, multiple survival curves are needed to have a complete picture of the user's journey.
Tools Used
- Sequel Pro: Connection to Sugar's SQL database
- Flurry: Sugar's analytics dashboard
- Localytics: Sugar's analytics dashboard
- SAS JMP: Analysis of data
Analysis
We will perform the analysis in 4 stages:
Stage 1 : Exploratory Analysis
As with most papers, we will start off with an exploratory analysis into the the behavior of the users.
We will seek to answer the following questions:
- Who are the active users & merchants?
- Who are the non-active users & merchants?
- What is the trend from 2013 to 2015?
- What are the most popular deals?
This is done via merging of users, merchants, campaigns and orders table and doing frequency counts and trends based on the table’s content, in order to spot any outliers or notable trends.
Stage 2: Cluster Analysis
We will then attempt to cluster the users/merchants into different groups using their categories, purchase history, activity level, geolocation (for starters). Each user or merchant will be assigned a cluster number.
Stage 3: Multiple Survival Curves
Each cluster will then be analysed with a survival curve with a corresponding event of the user journey funnel.
The events include:
- First Installation
- First Skimming
- First Purchase
- First Redemption
- Repeated purchases
Stage 4: Refining and extrapolating
After survival analysis, we may refine the user segments to have better differentiation between the groups. From the results, we will attempt to generate recommendations and identify high value targets, as well as their attrition probability for Sugar.
Lastly, if possible, we can extrapolate the data to do forecasting and calculate the lifetime value for users and merchants.
