Group04 Proposal
R-CsI: An
R-
Con
Sumer
Insights Business Application to better understand Customers
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Contents
Abstract
Technology in today's world is advancing faster than ever before. With the concepts of digital transformation, the Internet of Things (IoT) and cloud computing becoming more and more prevalent, it has also become far easier to obtain and access large amounts of data on a variety of consumer activities in an ever-widening list of industries. By using various visual, statistical and data mining techniques on these data sets, businesses will be able to harness the power of hindsight with regards to customer behavior, allowing them to learn more about the activities, purchases or other transactions made by their customer base. Businesses will then be able to use the insights gleaned from data exploration and discovery to address fundamental issues, such as customer acquisition, development and retention.
This project aims to discover insights on the segments that exist in a selected retailer’s customer base, as well as identify groups of products that are highly associated during purchase. This will be done through the analysis of Dunnhumby - The Complete Journey dataset obtained from the Dunnhumby data science company that tracks the purchases of 2500 households over 2 years. Upon understanding the differences among consumer groups as well as developing a better understanding of the patterns and hidden relationships in the transactional data, it is our hope that businesses will be able to obtain invaluable insights into its customer profile, and can focus its efforts on developing more effective customer-based strategies.
Project Motivation
With the fast advancement of technology, such as digital transformation, Internet of Things (IoT), cloud computing, these made huge amounts of data about consumer behavior, transactions, event activities and influencing factors that provide visibility into performance and behavioral decisions across a variety of industries and consumer channels available today.
For our project, we intend to build a business application to allow users to perform business analysis (namely, exploratory, explanatory and predictive analysis) on the demographic and transaction data of their customers. The application will be built to achieve the following objectives:
- Provide good visualisation of raw data, variable and results by faceting and/or 3D view;
- Interactive selection of variables in formulation of scenario/business objectives; and
- User-friendly for non-statistician/layman.
Customer Analytics
Use case 1 : Segmentation by clustering (latent class analysis)
As different customers have different needs and wants, it is then logical to conclude that they will have their ow different reasons or drivers for buying products of the company. Therefore, customer segmentation is a very useful data mining technique to find groups of customers that differ in important ways associated with product interest, market participation, or response to marketing efforts. By understanding the differences among groups, a marketer can make better strategic choices about opportunities, product definition, and positioning, and can engage in more effective promotional efforts.
Use case 2 : Market Basket Analysis
Uncovers association between items by identifying items in transactions that frequently occur together. An Association Rules algorithm is used to analyze retail basket/transaction data.
Overview of Dataset
Dunnhumby dataset - The Complete Journey
Dunnhumby is a data science company that specializes in Customer Data Analytics. The “Dunnhumby – A Complete Journey” dataset is a collection of transaction data at household level over two years from a group of 2,500 households who are frequent shoppers at a retail chain. The amount of details captured goes down to individual purchases, specific items, item category, demographics and includes direct campaign details including coupons and redemptions made based on the purchases made. https://www.dunnhumby.com/sourcefiles
Application Libraries & Packages
| Package Name | Descriptions |
|---|---|
| shiny & shiny dashboard | Interactive web applications for data visualization |
| ggplot2 | High-quality graphs |
| Tidyverse: tidyr, dplyr, ggplot2 | Tidying and manipulating data for visualizing in ggplot2 |
| shinythemes | Apply themes to Shiny applications |
| ggthemr | Apply themes to ggplot2 plots |
| lubridate | Easily transform dates |
| Plotly | Provide graphics |
| ggraph | Provide graphics for clustering, regression |
| ggiraph | Provide interactive ggplot graphics |
| k Means Algorithms in R | Provide various k means algorithms in R |
| ISLR | Provide glm() for logistic regression |
References
Image credit to: Christopher Dombres (under a Creative Commons license)
