Group06 Proposal

Group 6: Buy More! - Maximizing Revenues

Overview

Proposal

Poster

Application

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Project Motivation

Google and Temasek Holdings have forecasted that the Singapore’s e-commerce market will grow to S$7.5bn within the next 10 years. With this growth, the amount of data available for analysis in this area will also grow rapidly. This presents unique opportunities to find out how retailers can maximize the value of their data.

Objective

Our objective for this project is to push the boundaries on market and customer analytics. We want to challenge ourselves as to how transaction data for retailers can be visualized and various machine learning techniques can be adopted to generate insights in this area.

Data Source and Preparation

We will use an e-commerce dataset from the UCI Machine Learning Repository that contains transactional data between 2010 and 2011. The company mainly sells unique all-occasion gifts and most of their customers are wholesalers. Below is a data dictionary of the available fields within the dataset:

Field	Description
InvoiceNo	Invoice number. Nominal, a 6-digit integral number uniquely assigned to each transaction. If this code starts with letter 'c', it indicates a cancellation.
StockCode	Product (item) code. Nominal, a 5-digit integral number uniquely assigned to each distinct product.
Description	Product (item) name. Nominal.
Quantity	The quantities of each product (item) per transaction. Numeric.
InvoiceDate	Invoice Date and time. Numeric, the day and time when each transaction was generated.
UnitPrice	Unit price. Numeric, Product price per unit in sterling.
CustomerID	Customer number. Nominal, a 5-digit integral number uniquely assigned to each customer.
Country	Country name. Nominal, the name of the country where each customer resides.

 

Approach

The application will be built primarily based on the R Shiny framework. The key focus areas will be:

• Visualization of bipartite networks (i.e. between customers and products)
• Visualization of clustering/segmentation of customers (RFM Model)
• Visualization of popular products through text analytics

Some examples of visualizations are as follows:

Visualization of bipartite networks

Visualization of clustering/segmentation of customers

We intend to create the feature to allow users can visualize clustering results interactively. It helps to give more meaningful labels to different clusters. And eventually it brings insightful understanding and segmentation to the customers.

Visualization of products popularity

It is very important for retailers to understand customer preferences for different products, Natural Language Processing will be conducted to explore the popularity of products. By understanding what are the most and least popular products and the properties of the popular products, the retail will be able to make smarter decisions towards the customer’s preference, thus improve the revenues. Different visualization techniques such as bar chart, word cloud and etc will be used to ease the understanding of the insights discovered.

Selection of Tools

Based on our preliminary assessment, we will utilize the following libraries for the development of the R Shiny dashboard: tidyr, dplyr, ggplot2, igraph, htmlwidgets, networkD3, mclust, shiny, shinyTime, shinydashboard, shinythemes, sjmisc, readxl, stringr, data.table, dummies, sjPlot, car, DT, reshape2, sqldf, igraph, etc.

References

1. Webber, R. (2013). The evolution of direct, data and digital marketing. Journal of Direct, Data and Digital Marketing Practice, 14(4), 291–309. https://doi.org/10.1057/dddmp.2013.20
2. You, Z., Si, Y.-W., Zhang, D., Zeng, X., Leung, S. C. H., & Li, T. (2015). A decision-making framework for precision marketing. Expert Systems with Applications, 42(7), 3357–3367. https://doi.org/10.1016/j.eswa.2014.12.022