Group04 Proposal

R-CsI: An R- Con Sumer Insights Business Application to better understand Customers

Proposal

Poster

Application

Report

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 address fundamental issues, such as attrition or develop better strategies to attract new customers.

This project aims to discover insights on the customer segments that differ in important ways associated with product interest, market participation, or response to marketing efforts. This will be done through the analysis of two data-set samples. Upon understanding the differences among consumer groups as well as developing a better understanding of the patterns and hidden relationships in the 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 and/or classification

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 : Regression to predict response or potential high value customers

Regression analysis is a broad term for a set of statistical methodologies used to predict a response variable (also called a dependent, criterion, or outcome variable) from one or more predictor variables (also called independent or explanatory variables). In general, regression analysis can be used to identify the explanatory variables that are related to a response variable, to describe the form of the relationships involved, and to provide an equation for predicting the response variable from the explanatory variables.

Regression, as a statistical business analytic tool, can be a powerful data mining technique to acquire better understanding of patterns and hidden relationships in the data for businesses in customer strategies.

Overview of Dataset

Customer Campaigning dataset

This data-set include consumer demographics, coverage and product related information. Marketing personnel can use data-sets with such information to understand their customer base, what they want and what drives them, so as to be able to market more effectively to their customers.

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
Threejs	Provide 3-dimensional visualization
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)