Group11 proposal

Proposal

Poster

Application

Research Paper

Background

Google Analytics is a suite of analytical tools to provide insights on website access to aid businesses decisions. It allows businesses to profile their website visitors and how they interact with the content. It provides Analytics Intelligence for quick answers to common metrics, numerous online reports on audience, advertising, acquisition, behaviour, conversion and user flow, and data analysis with data filtering, manipulation, segmentation and visualization features. A paid version "Google Analytics 360" provides more advanced eCommerce features on which users are likely to convert to customers and how best to use the marketing dollars.

Motivation

Google Analytics delivers a ton of insights into the users visiting the website. However, the visualizations are limited to line charts, bar charts, pie charts, highlight tables and geo maps. Also, besides the use of totals, averages and proportions, there is no available statistical analysis and inference of the data. One possible reason for such approach could be due to the mass target audience nature of the tool, as it could be difficult to make advanced visualizations and statistical analysis easily understood by the common users.

The third motivation stamps from the fact that Google Analytics is a hugely popular tool with good data management capabilities. This allows further analysis and visualization of the data outside the platform and in a repeatable manner that may have widespread benefits.

Fig1: GA Audience Overview with metrics and basic pie chart

Fig2: GA user insights

Project Objectives

The project aims to delivery a R-Shiny app that provides:

1. better interactivity in user interface design;
2. visualization of key audience, behaviour and performance insights;
3. statistical analysis and inferences on key audience, behaviour and performance data; and
4. workflow for export and import of Google Analytics data.

Proposed Scope and Methodology

1. Analysis of Google Analytics schema to understand the data structure, metadata and table relationships
2. Analysis of Google Analytics data management features to support export of data
3. Analysis of R data management features and plugins to support import of data
4. Sourcing of sample data for analysis and testing
5. Analysis of existing Google Analytics features and shortfalls for enhancements
6. Design of enhanced UI, visualizations, statistical analysis and workflow
7. R-Shiny app development and testing
8. Demonstration of R-Shiny app
9. Pilot run with live data

Project Timeline

Challenging...