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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>Introduction</strong></font></div></div>==
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| − | The key aim of this project is to tackle the three problems above and increase the voucher redemption rate, customer retention rate and merchant retention rate.
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| − | 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.
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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>Limitations</strong></font></div></div>==
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| − | 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.
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| − | 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.
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| − | Our third limitation is that Sugar is an ecommerce app, which takes customer through a sales funnel. There are a few main stages of a customer’s journey, and users can drop off at any point:
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| − | <div align="center">'''Installation > Skimming > First Purchase > Redemption > Second Purchase'''</div>
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| − | 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.
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| − | 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 customer’s journey.
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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>== |
| − | <ol>
| + | 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. |
| − | <li>Sequel Pro: Connection to Sugar's SQL database</li>
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| − | <li>Flurry: Sugar's mobile analytics dashboard</li>
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| − | <li>Localytics: Sugar's geolocational analytics dashboard</li>
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| − | <li>SAS JMP: Analysis of data</li>
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| − | </ol>
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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>==
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| − | We will perform the analysis in 4 stages:
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| − | <u>'''Stage 1 : Exploratory Analysis'''</u><br>
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| − | As with most papers, we will start off with an exploratory analysis into the the behavior of the users.
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| − | We will seek to answer the following questions: | |
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| − | *Who are the active users & merchants?
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| − | *Who are the non-active users & merchants?
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| − | *What is the trend from 2013 to 2015?
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| − | *What are the most popular deals?
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| − | 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.
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| − | <u>'''Stage 2: Cluster Analysis'''</u><br>
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| − | 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.
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| − | <u>'''Stage 3: Multiple Survival Curves'''</u><br>
| + | 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. |
| − | Each cluster will then be analysed with a survival curve with a corresponding event of the customer journey funnel.
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| − | The events include:
| + | For geospatial analysis, we have decided to use the QGIS software as it is open source, with a large amount of documentation and plugins available in the market. It is also the preferred software of choice for the Geospatial class in our university, which allowed us to access to more resources, namely the teaching materials, as well as the experience of our fellow university peers. |
| − | *First Installation
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| − | *First Skimming
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| − | *First Purchase
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| − | *First Redemption
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| − | *Repeated purchases
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| − | <u>'''Stage 4: Refining and extrapolating'''</u><br> | + | ==<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>Methodology</strong></font></div></div>== |
| − | After survival analysis, we may refine the customer 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.
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| − | Lastly, if possible, we can extrapolate the data to do forecasting and calculate the lifetime value for users and merchants.
| + | [[File:SugarMethodology.jpg|center|700px]] |
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.
For geospatial analysis, we have decided to use the QGIS software as it is open source, with a large amount of documentation and plugins available in the market. It is also the preferred software of choice for the Geospatial class in our university, which allowed us to access to more resources, namely the teaching materials, as well as the experience of our fellow university peers.
