Difference between revisions of "Group04 Proposal"
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=Project Motivation= | =Project Motivation= | ||
| − | + | With the fast advancement of technologies, such as the digital transformation, Internet of Things (IoT), cloud computing, these make available huge amount of data about consumer behaviour, and about transactions, event activities and influencing factors that provide visibility into performance and behavioural decision across a variety of industries and consumer channels.<br/> | |
| + | As customers have different needs and wants, they have 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 promotion. | ||
=Overview of Dataset= | =Overview of Dataset= | ||
Revision as of 11:51, 21 November 2018
R-CsI: An R-ConSumerInsights Business Application to better understand Customers
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Contents
Background
testing
Project Motivation
With the fast advancement of technologies, such as the digital transformation, Internet of Things (IoT), cloud computing, these make available huge amount of data about consumer behaviour, and about transactions, event activities and influencing factors that provide visibility into performance and behavioural decision across a variety of industries and consumer channels.
As customers have different needs and wants, they have 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 promotion.
Overview of Dataset
(1) Customer Campaigning dataset – This dataset includes customers' demographics, coverage and product related information. Marketing managers used dataset with such information to understand their customers base, what they want and what drives them, so as to be able to market effectively to their customers.
(2) Dunnhumby dataset – The Complete Journey (https://www.dunnhumby.com/sourcefiles) 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.
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)
