constel analytics a visual tool for web analytics

Constel AnalyticsA visual tool for web analytics

Index

• Background & review

• Constel Analytics

• Evaluation

• Live Demonstration

• Discussion & conclusion

Motivation & aims

• Assumption: Web analytics is underexploited

• A possible culprit ? Data visualization

• Thus, this project aimed at:• Discovering if web analytics tools are late in adopting advanced

visualizations

• Building an application that would help understand a complex information that other tools don’t support

Background & reviewPart I of V

“Web analytics is the measurement, collection, analysis and reporting of internet data for purposes of understanding and

optimizing web usage.”

–Wikipedia, « Web Analytics - Wikipedia, the free encyclopedia»

http://en.wikipedia.org/wiki/Web_analytics

Web analytics: a definition

Web analytics: a definition (2)

• Measurement: define a strategy, find which data much be collected

• Collection: get the data from the website, either by page-tagging or logfile analysis

• Analysis: compute dimensions and values out of the raw data

• Report: display the data in a comprehensive way

• Data visualization is closely related to web analytics, as the reports generated must be clear for everyone.

Data visualization: a definition

• A field of research dedicated to the way of presenting data in a comprehensive fashion, focusing on both functionality and esthetics.

• Visualizations’ users can be trying to do 10 different activities with a visualization.

• Those 10 activities can be used during the development of a visualization and during its evaluation. We relied on them for this project.

“Advanced” visualizations?

Review: selected tools

• We selected 20 tools, while more than 40 were considered

• They were reviewed according to several criteria: nature, targets, data storage, collection method, visualizations, …

• Google Analytics

• Shinystat

• Clicky

• Woopra

• Adobe Marketing

Cloud

• IBM Coremetrics &

Unica

• AWStats

• comSCore Digital

Analytix

• AT Internet

• Crazy Egg

• Mint

• Mouseflow

• Piwik

• WebPUM

• Yandex.Metrica

• Web Usability

Probe

• Labroche, Lesot &

Yaffi’s Web Usage

Mining and

Visualization Tool

• WebQuilt

• Open Web

Analytics

• Webtrends

Analytics

Review: our final taxonomy

An interesting finding…• Academic projects use Network diagrams to represent their

data

• Commercial web analytics tools do not

Constel AnalyticsPart II of V

Constel Analytics : aims & design

• The main purpose of this first version of Constel Analytics is to display the interactions between the pages of a website, using a network diagram

• The selected layout is Force-directed layout, as it clusters nodes naturally instead of asking for precomputing

• In order to be used by many websites, Constel Analytics relies on Google Analytics’ data

• The application was thought to be easy to deploy

Constel Analytics Workflow

Constel Analytics: technologies

• Google Analytics was selected as Data Source:

• Complete APIs with over 200 metrics and dimensions

• Authentification through Oauth 2.0

• Data-Driven Documents (D3) was selected for the visualization

• Provide a Force-layout using three or more forces

• PHP and the Framework Symfony were selected for backend

• Popular technologies available to everyone

• Use of Twig as a template engine

Constel Analytics: techs & workflow

Constel Analytics: User Interface

• Four parts :

• Global navigation menu

• Visulization's parameters

• Visualization and tools

• Advanced tools

Constel Analytics: tools & features

• Timelapse: possibility to see four graphs for different periods, in order to see an evolution over time.

• Filter: possibility to show the visualization only for specific dimension's values.

• Segments: possibility too show the visualization only for a specific segment.

• Toolbox: magnifying glass, path finder, closer related node, minimal weight slider, zoom, ...

EvaluationPart III of V

Setting up the evaluation

• Qualitative evaluation instead of quantitative

• 2 use cases:

• «Course offerings» of University of Fribourg

• Main portal of HEP of Canton Vaud

• Evaluation took place in four steps:

• Discussion about the websites’ characteristics and aims, access to the data

• Performance tests and correction of initial bugs

• Familiarization of the evaluators with Constel Analytics

• Interview and live demonstration of Constel Analytics

Description of the use cases

Course offerings of Unifr• Description of the course offerings of the

University

• Aimed at students

• Average traffic: ~800 different pages in 20 days

• Deployment on the University’s servers

• 3 evaluators : Nicolas Frétigny, Samuel Crausaz and Serge Keller

HEP of Canton Vaud• Main institutional portal of the High School

• Various audiences (students, teachers, employees, …)

• High traffic : +1’000 different pages in 20 days

• Deployment on a dedicated shared hosting

• 2 evaluators : Barbara Fournier and Bertrand Mure

Main visualizations for the use cases

Course offerings of Unifr HEP of Canton Vaud

Performance evaluation

Course offerings of Unifr• Configuration worked well

• Different pages had the same title, thus appeared as one node on the graph

• Processing time was reasonable

• Deployment failed because of Symfony’s requirements

HEP of Canton Vaud• Configuration worked well

• All nodes had the same color because of the URIs of the pages

• Processing time was too high (exceeding 30 seconds)

• Deployment failed because of outdated servers

Interviews

Course offerings of Unifr• Lasted 1h30

• Evaluators made 3 observations for each feature (12 in total)

• Some of the observations might be wrong

HEP of Canton Vaud• Lasted 1h00

• Evaluators made 7 observations out of the 12 expected

• Mostly expected observations, nothing new or surprising

Interview’s wrap-up

• Usability issues

• Low resolution screens have scrolling issues

• Nodes' color and position change each time you reload a page

• There are no indications that logarithmic scales are used for nodes' size, bars' height and minimal weight slider

• Intensity of links should be visible easily

• Relevance of the tool

• Globally satisfied, Unifr evaluators found interesting clues

• A good way to have a first glance at a website

• Doesn't provide enough concrete, quantitative information

• Can mislead

Live demonstrationPart IV of V

Live demonstration

Course offerings• You know the website, don’t you.

• http://studies.unifr.ch

• Follow the link

• http://bit.ly/1huXZmH

Nid du Phénix• Doesn’t appear in the report, so it’s

whole new

• http://web.archive.org/web/20090205190810/http://nid-du-phenix.ch/

• Follow the link

• http://bit.ly/1f11d4V

We’re going to compare the use of Constel Analytics with two different websites

Discussion & conclusionPart V of V

Limitations of Constel Analytics

• Memory loss : the graph cannot keep in mind actual visitors' path, it just displays first level interactions between pages.

• Usability issues : the need to scroll is an impediment for both new and returning users. Understanding this application can take a while.

• Not enough quantitative data : the lack of quantitative information doesn't allow Constel Analytics to provide new, unexpected findings.

• Require a few more exploration tools : as it stands now, information given by the application isn't very helpful for a large website.

Short-term improvements

Information management

• UI improvement: saving place for more quantitative information

• Detection of groups of pages

• Categorization of the interactions

New features• Sitemap comparison

• Visits typology

• Other sources of Data

Conclusion

• This thesis stated that current web analytics tools tend to underuse visualizations of data

• Constel Analytics filled a gap by using data from a public tool and displaying them with a relevant Force-directed graph

• In the future, the application could be improved

• By using better algorithms to cluster pages and visitors (Visual Analytics)

• By proposing more esthetically pleasant and immediately understandable visualizations (Communication)

The End

• Thank you for your attention

• Any questions ?

Got enough time ?Then you'll take a few more slides, won't ya?

Force-directed layout ?

• Force-directed layout applies at least 2 forces over nodes

• Repelling force : each pair of nodes of the graph is repelled according to a given function

• Attracting force : each connected nodes are attracted to each other according to a given function

• This process is repeated iteratively

• D3 uses a third force : gravity, which keeps nodes within a sphere for better space optimization

• Ofher forces can be applied thanks to D3's Force-layout

Unifr Interview

Main visualization

Segments Filters Timelapse

Three main areas observed

More German-speakers in new visitors

China is second after Western countries

Late subscription pages do not appear

Sections are well divided

Tablet and mobile traffics is mostly similar to desktop traffic

Swiss visitors speak more French than German

There were more French speakers during the first few months

Bachelor students don't care about Master courses

English part is almost merged with French and German for Referal traffic

Spain and Italy have a strong majority of French

Size of the english part varies according to the calendar

HEP of Canton Vaud Interview

• Findings…

Main visualization

Segments Filters Timelapse

Global structure matches the expectations

Student's part is less important with the new visitors segment

Visitors from France are less interested by course offerings

Freinet event generated a lot of traffic

Few interactions between the branches

Accès rapide isn't used to navigate through the website

Burkina Faso and Mozambique did not generate a lot of views