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Constel Analytics

Constel Analytics. A 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

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Constel Analytics

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  1. ConstelAnalytics A visualtool for web analytics

  2. Index Background & review ConstelAnalytics Evaluation Live Demonstration Discussion & conclusion

  3. Motivation & aims • Assumption: Web analytics is underexploited • A possible culprit ? Data visualization • Thus, thisprojectaimed at: • Discovering if web analyticstools are late in adoptingadvancedvisualizations • Building an application that would help understand a complex information that other tools don’t support

  4. Background & review Part I of V

  5. Web analytics: a definition “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

  6. Web analytics: a definition (2) • Measurement: define a strategy, findwhich data muchbecollected • Collection: get the data from the website, either by page-tagging or logfileanalysis • Analysis: compute dimensions and values out of the raw data • Report: display the data in a comprehensiveway • Data visualizationiscloselyrelated to web analytics, as the reports generated must beclear for everyone.

  7. 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’ userscanbetrying to do 10 differentactivitieswith a visualization. • Those 10 activitiescanbeusedduring the development of a visualization and duringitsevaluation. Werelied on them for thisproject.

  8. “Advanced”visualizations?

  9. Review: selectedtools • 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 VisualizationTool • WebQuilt • Open Web Analytics • WebtrendsAnalytics Weselected 20 tools, while more than 40 wereconsidered Theywerereviewedaccording to severalcriteria: nature, targets, data storage, collection method, visualizations, …

  10. Review: our final taxonomy

  11. An interestingfinding… Academic projects use Network diagramsto representtheir data Commercial web analytics tools do not

  12. ConstelAnalytics Part II of V

  13. ConstelAnalytics : aims & design The main purpose of this first version of ConstelAnalyticsisto display the interactions between the pages of a website, using a network diagram The selectedlayoutisForce-directedlayout, as it clusters nodesnaturallyinstead of asking for precomputing In order to beused by manywebsites, ConstelAnalytics relies on Google Analytics’ data The application wasthought to beeasy to deploy

  14. ConstelAnalytics Workflow

  15. ConstelAnalytics: 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

  16. ConstelAnalytics: techs & workflow

  17. ConstelAnalytics: User Interface • Four parts : • Global navigation menu • Visulization's parameters • Visualization and tools • Advanced tools

  18. ConstelAnalytics: tools& features • Timelapse: possibility to see four graphs for differentperiods, in order to see an evolution over time. • Filter: possibility to show the visualizationonly for specificdimension's values. • Segments: possibilitytoo show the visualizationonly for a specific segment. • Toolbox: magnifying glass, pathfinder, closerrelatednode, minimal weightslider, zoom, ...

  19. Evaluation Part III of V

  20. Setting up the evaluation • Qualitative evaluationinstead 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 evaluatorswithConstelAnalytics • Interview and live demonstration of ConstelAnalytics

  21. Description of the use cases Course offeringsof Unifr 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 • Description of the course offerings of the University • Aimed at students • Averagetraffic: ~800 different pages in 20 days • Deployment on the University’s servers • 3 evaluators : Nicolas Frétigny, Samuel Crausaz and Serge Keller

  22. Main visualizations for the use cases Course offerings of Unifr HEP of Canton Vaud

  23. Performance evaluation Course offerings of Unifr HEP of Canton Vaud Configuration workedwell All nodeshad the samecolorbecause of the URIs of the pages Processing time wastoo high (exceeding 30 seconds) Deploymentfailedbecause of outdated servers • Configuration workedwell • Different pages had the sametitle, thusappeared as one node on the graph • Processing time wasreasonable • Deploymentfailedbecause of Symfony’srequirements

  24. Interviews Course offerings of Unifr HEP of Canton Vaud Lasted 1h00 Evaluators made 7 observations out of the 12 expected Mostly expected observations, nothing new or surprising • Lasted 1h30 • Evaluators made 3 observations for each feature (12 in total) • Some of the observations might be wrong

  25. Interview’swrap-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

  26. Live demonstration Part IV of V

  27. Live demonstration We’regoing to compare the use of ConstelAnalytics withtwodifferentwebsites Course offerings Nid du Phénix Doesn’tappear in the report, soit’swhole new http://web.archive.org/web/20090205190810/http://nid-du-phenix.ch/ Follow the link http://bit.ly/1f11d4V • You know the website, don’tyou. • http://studies.unifr.ch • Follow the link • http://bit.ly/1huXZmH

  28. Discussion & conclusion Part V of V

  29. Limitations of ConstelAnalytics • 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.

  30. Short-termimprovements Information management New features Sitemap comparison Visits typology Other sources of Data • UI improvement: saving place for more quantitative information • Detection of groups of pages • Categorization of the interactions

  31. 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)

  32. The End Thank you for your attention Any questions ?

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

  34. 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

  35. Unifr Interview

  36. HEP of Canton Vaud Interview Findings…

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