web analytics

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

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Asignatura 3. Técnicas de Análisis de Datos y Explotación de Datos.TEMA. Analítica Web. (Fernando Tricas García. Universidad de Zaragoza)


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Analisis de redes sociales y analıtica web.

Fernando Tricas Garcıa

Departamento de Informatica e Ingenierıa de SistemasUniversidad de Zaragoza



[email protected]

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Analıtica web

Fernando Tricas Garcıa

Departamento de Informatica e Ingenierıa de SistemasUniversidad de Zaragoza



[email protected]

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I Some motivation

I Some generalities

I Some Definitions

I Qualitative Data

I Testing and Experimentation

I Social, mobile, video, ...

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Some time ago

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Some time ago

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Some time ago

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On meausrement

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On meausrement

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Web analytics is the measurement, collection, analysisand reporting of web data for purposes of understandingand optimizing web usage.

WAA Standards Committee. “Web analyticsdefinitions.”Washington DC: Web Analytics Association (2008).

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Web Analytics 2.0(1) the analysis of qualitative and quantitative datafrom your website and the competition,(2) to drive a continual improvement of the onlineexperience that your customers, and potentialcustomers have,(3) which translates into your desired outcomes(online and offline).

Avinash Kaushikhttp://www.kaushik.net/avinash/rethink-web-analytics-introducing-web-analytics-20/

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Web Analytics

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Web Analytics 2.0

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I in house:collecting, storing, processing, analyzing

I out:collecting and analyzing

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Multiple Outcomes Analysis

I Increase revenue

I Reduce cost

I Improve customer satisfaction/loyalty

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Experimentation and Analysis (the Why)

I Testing

I Trying

I Experimentation

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Voice of Customer

I Surveys

I Lab usability testing

I Remote usability testing

I Card sorting

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Competitive Intelligence

I Information about direct and indirect competitors

I Your performance against competitors

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Too much?

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The 10/90 Rule

If your have a budget of $100 to make smart decisionsabout your websites . . . invest $10 in tools and vendorimplementation and spend $90 on Analysts with bigbrains.

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The 10/90 Rule

I Websites are massively complex

I Tools are only about the data

I Complex world

I Tribal knowledge (unwritten rules, missing metadata,actions,...)

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Some Definitions

Jason Burby, Angie Brown & WAA Standards Committee.‘Web Analytics Definitions’Web Analytics Association. (2007)

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Building Blocks Terms

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Some definitions

[Page]A page is an analyst definable unit of content.

Flash, AJAX, media files, downloads, documents, and PDFs?

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Some definitions

[Page]A page is an analyst definable unit of content.

Flash, AJAX, media files, downloads, documents, and PDFs?

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Some definitions

[Pageviews]The number of times a page (an analyst-definable unit ofcontent) was viewed.

Vendors do make different distinctions in deciding what should becounted. Consult your tool provider for more information on yourimplementation.

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Some definitions

[Visit/Sessions]A visit is an interaction, by an individual, with a websiteconsisting of one or more requests for ananalyst-definable unit of content (i.e. “page view”). If anindividual has not taken another action (typicallyadditional page views) on the site within a specified timeperiod, the visit session will terminate.

Visit → Several pageviewsRepresentation of the interaction of the visitor with the site

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Some definitions

[Unique Visitors]The number of inferred individual people (filtered forspiders and robots), within a designated reportingtimeframe, with activity consisting of one or more visitsto a site. Each individual is counted only once in theunique visitor measure for the reporting period.

Authentication, either active or passive, is the most accurate wayto track unique visitors.Their activity will be over-represented unless they arede-duplicated.Blocked cookies!Related → New Visitor, Repeat Visitor (reporting period),Return Visitor (previous periods)

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Visit Characterization

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Some definitions

[Entry Page]The first page of a visit.

First page in the visit regardless of how the sessions are calculated

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Some definitions

[Landing Page]A page intended to identify the beginning of the userexperience resulting from a defined marketing effort.

Landing pages are often optimized for specific keywords, audiences,or calls to action

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Some definitions

[Exit Page]The last page on a site accessed during a visit, signifyingthe end of a visit/session.

In a tabbed or multi-window browser environment it should still bethe final page accessed that is recorded as the Exit Page though itcannot be definitively known that this was the last page the visitorviewed.

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Some definitions

[Visit Duration]The length of time in a session. Calculation is typicallythe timestamp of the last activity in the session minusthe timestamp of the first activity of the session.

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Some definitions

[Referrer]The referrer is the page URL that originally generated therequest for the current page view or object.

I Internal Referrer

I External Referrer

I Search Referrer

I Visit Referrer (session)

I Original Referrer (all visits)

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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-throughsmeasured on the sending side (as reported by your ad server, forexample) and on the receiving side (as reported by your webanalytics tool) often do not match.

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Some definitions

[Click-through Rate/Ratio]The number of click-throughs for a specific link dividedby the number of times that link was viewed.

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Some definitions

[Page Views per Visit]The number of page views in a reporting period dividedby number of visits in the same reporting period.

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Content Characterization

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Some definitions

[Page Exit Ratio]Number of exits from a page divided by total number ofpage views of that page.

Page exit ratio should not be confused with bounce rate, which isan indicator of single-page-view visits on your site. Page exit ratioapplies to all visits regardless of length.

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Some definitions

[Single-Page Visits]Visits that consist of one page regardless of the numberof times the page was viewed.

For a single-page visit, the entry page and exit page are the samepage.

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Some definitions

[Single Page View Visits (Bounces)]Visits that consist of one page-view.

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Some definitions

[Bounce Rate]Single page view visits divided by entry pages.

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Conversion Metrics

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Some definitions

[Event]Any logged or recorded action that has a specific dateand time assigned to it by either the browser or server.

An example is counting page views per day. The event count givesthe total number of page views loaded during the day, visit countis the number of visits (that downloaded at least one page view)during the day, and the visitor count gives the number of uniquevisitors (that downloaded at least one page view) that visited thesite during the day.

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Some definitions

[Conversion]A visitor completing a target action.

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Great Metrics

I Uncomplex

I Relevant

I Timely

I Instantly Useful

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TechnologiesI Logs

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I Javascript Tags

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I Cookies

I Transient vs. Persistent.I First Party vs. Third Party.

I Exception for Third Party Cookies. (Add servers?)

I Deletion and RejectionI Privacy

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Best practices

I 1: Tag all your pages.

I 2: Tags go last (customers come first :).

I 3: Tags should be inline.

I 4: What’s your unique page definition?

I 5: Use cookies intelligently (they are delicious).(Source attributes, Page attributes, User attributes)

I 6: Javascript wrapped links might be a issue.

I 7: Redirects, be aware of them.

I 8: Validate data is being captured correctly.

I 9: Don’t forget Flash, Flex, RIA, RSS, Videos etc.

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Some Questions

I How many Visitors are coming to my website?Long-term focus, trends,...

I Where are Visitors coming from?Referring URLs, Search Keywords

I What do I want Visitors to do on the website?What is it for?

I What are Visitors actually doing?Top entry pages, top viewed pages, Site overlay (click density)analysis, Abandonment analysis.

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Examples of Actionable Outcome KPIs

I Conversion Rate

I Average Order Value

I Days & Visits To “Purchase”.

I Visitor Loyalty & Visitor Recency.

I Task Completion Rate.

I Share of Search.

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Different sizes, different objectives

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I’m not an e-commerce!

I Visitor loyalty

I Visitor Recency

I Lenght of Visit

I Depth of Visit

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Qualitative Data

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Lab usability testing

I Are the users able to finish a given task?

I You do not need many people (8 – 12)

I Live system, beta version, paper prototype

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Preparing tasks

I Identify critical tasks

I Create scenarios to test them

I Identify success

I Identify the adequate users

I Compensation

I Recruiting

I Test the process before the actual test

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Conducting the test

I Welcome

I Think aloud exercise

I Read the task aloud

I Pay attention (verbal, non-verbal, ...)

I You can ask more questions

I Thanks

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Analyzing data

I As soon as possible, debrief session

I Take time to note trends and patterns

I Do a deep dive to identify problemsI Make recommendations to fix the problems

I Identify points of failureI Make concrete recommendationsI Prioritize (Urgent, Important, Nice to Have, ...)

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I Remote studies

I Outsourced studies

I Surveys

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Examples of emerging user research options

I Competitive Benchmarking Studieshttp://www.userzoom.com/

I Rapid Usability Testshttp://www.fivesecondtest.com/

I Online Card-Sorting Studieshttp://optimalsort.com/

I Artificially Intelligent Visual Mapshttp://www.feng-gui.com/

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Testing and Experimentation

A/B Testing: showing different versions of a web page

I Pro’s: Cheap, easy and energizing, contrasting

I Con’s: Difficult to control, Limiting

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Testing and Experimentation

Multivariate Testing: change dynamically what modules show upon the page, where they show up and to which traffic

I Pro’s: doing a lot very quickly, continuos learning methodology

I Con’s: it is easy to optimize crap quickly, complex experiences

I Pro’s: Cheap, easy and energizing, contrasting

I Con’s: Difficult to control, Limiting

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Testing and Experimentation

Testing and Experimentation: the ability to change the entire siteexperience of the visitor using capability of your site platform

I Pro’s: Wow!, very focused, powerful results

I Con’s: needs platform support, it takes longer, more work

I Pro’s: Cheap, easy and energizing, contrasting

I Con’s: Difficult to control, Limiting

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Actionable ideas

I Your first test is “Do or Die”

I Don’t get caught in the Tool/Consultant Hype

I Be open (were you wrong?)

I Start with a Hypothesis

I Establish goals and make them evaluation criteria

I Test for and Measure mutiple outcomes

I Source your Tests in customer pain

I Analyze data and communicate learnings

I Two must haves: evangelism and expertise

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Social, mobile, video. The data challenge

I Consumption off-site (feed readers, aggregator sites, mobile(apps), ...)

I We need more info! (feed subscribers?)I From Conversion rate to Conversation rate

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Anlyzing mobile data

I Log-Based solutions

I Packet-Sniffing solutions

I Tag-based solutions (Javascript or image)


I How many visits

I Sources

I Screen resolutions

I Search engine keywords

I How long

I Conversions

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I Raw Author Contribution (do I deserve to be successful?)I Posts per monthI Content created

I Audicence Growth (Is anyone reading?)

I Conversation rate

I Citation (other pages, Twitter, ...)

I Cost of blogging

I BenefitComparative value, direct value, nontraditional value,unquantifiable value

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I Growth in Number of Followers

I Message Amplification (RT)

I Click Through Rates and Conversions

I Conversation Rate

Engagement - Reach - Velocity - Demand - Network Strength -ActivityFacebook?

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I Video consumption (and location)

I Attention and Audience Engagement

I Social Engagement

I Tracking Viralness

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I Avinash Kaushik. ‘Web Analytics 2.0’


I ‘Occam’s Razor’http://www.kaushik.net/avinash/

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