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Web analytics

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Presentación para una sesión introductoria de Analítica Web

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Web analytics

  1. 1. MÓDULO 1. Asignatura 3. Técnicas de Análisis de Datos y Explotación de Datos. TEMA. Analítica Web. (Fernando Tricas García. Universidad de Zaragoza) MASTER IN BIG DATA & BUSINESS INTELLIGENCE
  2. 2. An´alisis de redes sociales y anal´ıtica web. Fernando Tricas Garc´ıa Departamento de Inform´atica e Ingenier´ıa de Sistemas Universidad de Zaragoza http://webdiis.unizar.es/~ftricas/ http://campusvirtual.nextibs.com/ ftricas@unizar.es
  3. 3. Anal´ıtica web Fernando Tricas Garc´ıa Departamento de Inform´atica e Ingenier´ıa de Sistemas Universidad de Zaragoza http://webdiis.unizar.es/~ftricas/ http://campusvirtual.nextibs.com/ ftricas@unizar.es
  4. 4. Index Some motivation Some generalities Some Definitions Qualitative Data Testing and Experimentation Social, mobile, video, ...
  5. 5. Some time ago
  6. 6. Some time ago
  7. 7. Some time ago
  8. 8. On meausrement
  9. 9. On meausrement
  10. 10. Definition Web analytics is the measurement, collection, analysis and reporting of web data for purposes of understanding and optimizing web usage. WAA Standards Committee. “Web analytics definitions.”Washington DC: Web Analytics Association (2008).
  11. 11. Definition Web Analytics 2.0 (1) the analysis of qualitative and quantitative data from your website and the competition, (2) to drive a continual improvement of the online experience that your customers, and potential customers have, (3) which translates into your desired outcomes (online and offline). Avinash Kaushik http://www.kaushik.net/avinash/rethink-web-analytics-introducing-web-analytics-20/
  12. 12. Web Analytics
  13. 13. Clicks What Why?
  14. 14. Web Analytics 2.0
  15. 15. Clickstream Tools in house: collecting, storing, processing, analyzing out: collecting and analyzing
  16. 16. Multiple Outcomes Analysis Increase revenue Reduce cost Improve customer satisfaction/loyalty
  17. 17. Experimentation and Analysis (the Why) Testing Trying Experimentation
  18. 18. Voice of Customer Surveys Lab usability testing Remote usability testing Card sorting
  19. 19. Competitive Intelligence Information about direct and indirect competitors Your performance against competitors
  20. 20. How?
  21. 21. Too much?
  22. 22. The 10/90 Rule If your have a budget of $100 to make smart decisions about your websites . . . invest $10 in tools and vendor implementation and spend $90 on Analysts with big brains.
  23. 23. The 10/90 Rule Websites are massively complex Tools are only about the data Complex world Tribal knowledge (unwritten rules, missing metadata, actions,...)
  24. 24. Some Definitions Jason Burby, Angie Brown & WAA Standards Committee. ‘Web Analytics Definitions’ Web Analytics Association. (2007)
  25. 25. Building Blocks Terms
  26. 26. Some definitions [Page] A page is an analyst definable unit of content.
  27. 27. Some definitions [Page] A page is an analyst definable unit of content. Flash, AJAX, media files, downloads, documents, and PDFs?
  28. 28. Some definitions [Pageviews] The number of times a page (an analyst-definable unit of content) was viewed. Vendors do make different distinctions in deciding what should be counted. Consult your tool provider for more information on your implementation.
  29. 29. Some definitions [Visit/Sessions] A visit is an interaction, by an individual, with a website consisting of one or more requests for an analyst-definable unit of content (i.e. “page view”). If an individual has not taken another action (typically additional page views) on the site within a specified time period, the visit session will terminate. Visit → Several pageviews Representation of the interaction of the visitor with the site
  30. 30. Some definitions [Unique Visitors] The number of inferred individual people (filtered for spiders and robots), within a designated reporting timeframe, with activity consisting of one or more visits to a site. Each individual is counted only once in the unique visitor measure for the reporting period. Authentication, either active or passive, is the most accurate way to track unique visitors. Their activity will be over-represented unless they are de-duplicated. Blocked cookies! Related → New Visitor, Repeat Visitor (reporting period), Return Visitor (previous periods)
  31. 31. Visit Characterization
  32. 32. Some definitions [Entry Page] The first page of a visit. First page in the visit regardless of how the sessions are calculated
  33. 33. Some definitions [Landing Page] A page intended to identify the beginning of the user experience resulting from a defined marketing effort. Landing pages are often optimized for specific keywords, audiences, or calls to action
  34. 34. Some definitions [Exit Page] The last page on a site accessed during a visit, signifying the end of a visit/session. In a tabbed or multi-window browser environment it should still be the final page accessed that is recorded as the Exit Page though it cannot be definitively known that this was the last page the visitor viewed.
  35. 35. Some definitions [Visit Duration] The length of time in a session. Calculation is typically the timestamp of the last activity in the session minus the timestamp of the first activity of the session.
  36. 36. Some definitions [Referrer] The referrer is the page URL that originally generated the request for the current page view or object. Internal Referrer External Referrer Search Referrer Visit Referrer (session) Original Referrer (all visits)
  37. 37. Some definitions [Click-through] Number of times a link was clicked by a visitor. Click-throughs are typically associated with advertising activities, whether external or internal to the site. Note that click-throughs measured on the sending side (as reported by your ad server, for example) and on the receiving side (as reported by your web analytics tool) often do not match.
  38. 38. Some definitions [Click-through Rate/Ratio] The number of click-throughs for a specific link divided by the number of times that link was viewed.
  39. 39. Some definitions [Page Views per Visit] The number of page views in a reporting period divided by number of visits in the same reporting period.
  40. 40. Content Characterization
  41. 41. Some definitions [Page Exit Ratio] Number of exits from a page divided by total number of page views of that page. Page exit ratio should not be confused with bounce rate, which is an indicator of single-page-view visits on your site. Page exit ratio applies to all visits regardless of length.
  42. 42. Some definitions [Single-Page Visits] Visits that consist of one page regardless of the number of times the page was viewed. For a single-page visit, the entry page and exit page are the same page.
  43. 43. Some definitions [Single Page View Visits (Bounces)] Visits that consist of one page-view.
  44. 44. Some definitions [Bounce Rate] Single page view visits divided by entry pages.
  45. 45. Conversion Metrics
  46. 46. Some definitions [Event] Any logged or recorded action that has a specific date and time assigned to it by either the browser or server. An example is counting page views per day. The event count gives the total number of page views loaded during the day, visit count is the number of visits (that downloaded at least one page view) during the day, and the visitor count gives the number of unique visitors (that downloaded at least one page view) that visited the site during the day.
  47. 47. Some definitions [Conversion] A visitor completing a target action.
  48. 48. Great Metrics Uncomplex Relevant Timely Instantly Useful
  49. 49. Technologies Logs
  50. 50. Technologies Javascript Tags
  51. 51. Technologies Cookies Transient vs. Persistent. First Party vs. Third Party. Exception for Third Party Cookies. (Add servers?) Deletion and Rejection Privacy
  52. 52. Best practices 1: Tag all your pages. 2: Tags go last (customers come first :). 3: Tags should be inline. 4: What’s your unique page definition? 5: Use cookies intelligently (they are delicious). (Source attributes, Page attributes, User attributes) 6: Javascript wrapped links might be a issue. 7: Redirects, be aware of them. 8: Validate data is being captured correctly. 9: Don’t forget Flash, Flex, RIA, RSS, Videos etc.
  53. 53. Some Questions How many Visitors are coming to my website? Long-term focus, trends,... Where are Visitors coming from? Referring URLs, Search Keywords What do I want Visitors to do on the website? What is it for? What are Visitors actually doing? Top entry pages, top viewed pages, Site overlay (click density) analysis, Abandonment analysis.
  54. 54. Examples of Actionable Outcome KPIs Conversion Rate Average Order Value Days & Visits To “Purchase”. Visitor Loyalty & Visitor Recency. Task Completion Rate. Share of Search.
  55. 55. Different sizes, different objectives
  56. 56. I’m not an e-commerce! Visitor loyalty Visitor Recency Lenght of Visit Depth of Visit
  57. 57. Qualitative Data
  58. 58. Lab usability testing Are the users able to finish a given task? You do not need many people (8 – 12) Live system, beta version, paper prototype
  59. 59. Preparing tasks Identify critical tasks Create scenarios to test them Identify success Identify the adequate users Compensation Recruiting Test the process before the actual test
  60. 60. Conducting the test Welcome Think aloud exercise Read the task aloud Pay attention (verbal, non-verbal, ...) You can ask more questions Thanks
  61. 61. Analyzing data As soon as possible, debrief session Take time to note trends and patterns Do a deep dive to identify problems Make recommendations to fix the problems Identify points of failure Make concrete recommendations Prioritize (Urgent, Important, Nice to Have, ...)
  62. 62. Repeat
  63. 63. Alternatives Remote studies Outsourced studies Surveys
  64. 64. Examples of emerging user research options Competitive Benchmarking Studies http://www.userzoom.com/ Rapid Usability Tests http://www.fivesecondtest.com/ Online Card-Sorting Studies http://optimalsort.com/ Artificially Intelligent Visual Maps http://www.feng-gui.com/
  65. 65. Testing and Experimentation A/B Testing: showing different versions of a web page Pro’s: Cheap, easy and energizing, contrasting Con’s: Difficult to control, Limiting
  66. 66. Testing and Experimentation Multivariate Testing: change dynamically what modules show up on the page, where they show up and to which traffic Pro’s: doing a lot very quickly, continuos learning methodology Con’s: it is easy to optimize crap quickly, complex experiences Pro’s: Cheap, easy and energizing, contrasting Con’s: Difficult to control, Limiting
  67. 67. Testing and Experimentation Testing and Experimentation: the ability to change the entire site experience of the visitor using capability of your site platform Pro’s: Wow!, very focused, powerful results Con’s: needs platform support, it takes longer, more work Pro’s: Cheap, easy and energizing, contrasting Con’s: Difficult to control, Limiting
  68. 68. Actionable ideas Your first test is “Do or Die” Don’t get caught in the Tool/Consultant Hype Be open (were you wrong?) Start with a Hypothesis Establish goals and make them evaluation criteria Test for and Measure mutiple outcomes Source your Tests in customer pain Analyze data and communicate learnings Two must haves: evangelism and expertise
  69. 69. Social, mobile, video. The data challenge Consumption off-site (feed readers, aggregator sites, mobile (apps), ...) We need more info! (feed subscribers?) From Conversion rate to Conversation rate
  70. 70. Anlyzing mobile data Log-Based solutions Packet-Sniffing solutions Tag-based solutions (Javascript or image) Measuring: How many visits Sources Screen resolutions Search engine keywords How long Conversions
  71. 71. Blogs Raw Author Contribution (do I deserve to be successful?) Posts per month Content created Audicence Growth (Is anyone reading?) Conversation rate Citation (other pages, Twitter, ...) Cost of blogging Benefit Comparative value, direct value, nontraditional value, unquantifiable value
  72. 72. Twitter Growth in Number of Followers Message Amplification (RT) Click Through Rates and Conversions Conversation Rate Engagement - Reach - Velocity - Demand - Network Strength - Activity Facebook?
  73. 73. Video Video consumption (and location) Attention and Audience Engagement Social Engagement Tracking Viralness
  74. 74. References Avinash Kaushik. ‘Web Analytics 2.0’ http://www.webanalytics20.com/ ‘Occam’s Razor’ http://www.kaushik.net/avinash/

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