SlideShare uma empresa Scribd logo
1 de 67
Baixar para ler offline
Web Analytics & Customer Analysis




 By Jean-François (JF) Bélisle, MSc, PhD©
               Jean-François Bélisle, 2012 ©
Who are you man?



                           Director – Consulting Services
                                            @ K3 Media

B.Sc. Economics, Université de Montréal
M.Sc. Marketing, HEC Montréal
Award of Achievement, Web Analytics, University of British Columbia
Ph.D. Studies, Marketing & Computational Stats, McGill University
Executive training in Customer Analytics, University of Pennsylvania (Wharton)

10/2/2012                        Jean-François Bélisle, 2012 ©                   2
For which companies have you worked man?




10/2/2012            Jean-François Bélisle, 2012 ©   3
… and some clients of K3?




10/2/2012          Jean-François Bélisle, 2012 ©   4
K3 Certifications




                            Pay-Per-Click
            Programming                                  Design   Analytics
                               (SEM)

                5                 4                           2     12




10/2/2012                     Jean-François Bélisle, 2012 ©                   5
K3 Strategic Alliances




10/2/2012        Jean-François Bélisle, 2012 ©   6
Game Plan

1.      Analytics Quick Intro
2.      Web Analytics: Getting Started
3.      Data Gathering
4.      Key Terms in Web Analytics
5.      KPIs
6.      Strategic Issues
7.      Other Methods
8.      Some Resources




10/2/2012                     Jean-François Bélisle, 2012 ©   7
Section 1 – Analytics Quick Intro




10/2/2012              Jean-François Bélisle, 2012 ©   8
1.1 Analytics - What is it?




                        Analytics

The application of computer technology, operational research, and
  statistics to solve problems in business and industry.




10/2/2012                 Jean-François Bélisle, 2012 ©         9
1.2 Analytics – in 1990

 In 1990, what came to people’s mind when someone said
                         analytics?

                                           Boring
                  Ugly
Geeky
            Useless
                   Incomprehensible
    Hard
                                           Worthless

10/2/2012                Jean-François Bélisle, 2012 ©   10
1.3 Analytics – in 2012

In 2012, what comes to people’s mind when someone says
                        analytics?

                                                   Cool
                          Sexy
Common
                 Useful
                          Understandable
    Accessible
                                                   Gold

10/2/2012                        Jean-François Bélisle, 2012 ©   11
1.4 Analytics & « Moneyball »




10/2/2012            Jean-François Bélisle, 2012 ©   12
1.5 Analytics & Data




10/2/2012        Jean-François Bélisle, 2012 ©   13
1.6 Data = Analytics?


Companies have lots and lots of data…



The problem




how to make sense of these data?


10/2/2012             Jean-François Bélisle, 2012 ©   14
1.7 Analytics - Managerially




     You can’t manage what you
           can’t measure!


10/2/2012           Jean-François Bélisle, 2012 ©   15
1.8 Analytics – Managerially (2)




     You can’t manage what you
           don’t measure!


10/2/2012             Jean-François Bélisle, 2012 ©   16
1.9 Analytics -> Managerial Insights?




            Garbage In, Garbage Out

10/2/2012                 Jean-François Bélisle, 2012 ©   17
1.10 How Data Becomes Managerial Insights?


                     Objectives

                            Data

                Analytics (e.g. KPIs)

                      Decision


10/2/2012            Jean-François Bélisle, 2012 ©   18
Section 2 – Web Analytics: Getting Started




10/2/2012                   Jean-François Bélisle, 2012 ©   19
2.1 Big Names in Web analytics

International:
• Avinash Kaushik
• Bryan Eisenberg
• Jim Sterne
• Eric Peterson
• Jim Novo
• Alex Langshur

Quebec:
• Stéphane Hamel
• Jacques Warren




10/2/2012              Jean-François Bélisle, 2012 ©   20
2.2 Web Analytics Tools Ranking




10/2/2012             Jean-François Bélisle, 2012 ©   21
2.3 Google Analytics or IBM Coremetrics




            Free                        vs.                          Pay (8K to 12K per yr, base)
            Public (Google Server)      vs.                          Private data (Own Server)
            No service                  vs.                          Dedicated service
            Basic Features              vs.                          Advanced Features
            Aggregated data             vs.                          Individual data
            No Integration              vs.                          Multiple integrations

10/2/2012                            Jean-François Bélisle, 2012 ©                                  22
2.4 Google Analytics Premium or IBM Coremetrics




                           Or




10/2/2012         Jean-François Bélisle, 2012 ©   23
Section 3 – Data Gathering




10/2/2012           Jean-François Bélisle, 2012 ©   24
3.1 Cookies

Cookies: “A cookie is a piece of text that a Web server can store on a
  user's hard disk. Cookies allow a website to store information on a
  user's machine (computer, smartphone, console) and later
  retrieve it. The pieces of information are stored as name-value
  pairs.” (Marshall Brain, www.HowStuffworks.com)




10/2/2012                   Jean-François Bélisle, 2012 ©           25
3.2 Cookies & Google Analytics

Google Analytics ->"page tag“ = Google Analytics Tracking Code
  (GATC)

GATC: Snippet of JavaScript code that the user adds onto every page
  of his or her website. This code collects visitor data and sends it to
  a Google data collection server as part of a request for a web
  beacon (Taken from wikipedia.org).

In addition to transmitting information to a Google server, the GATC
   sets first party cookies on each visitor's computer.



10/2/2012                    Jean-François Bélisle, 2012 ©             26
3.3 Cookies & Google Analytics (2)




            CTRL + U on Chrome or Firefox




10/2/2012                             Jean-François Bélisle, 2012 ©   27
3.4 Cookies’ Usefulness

Through cookies, a company can know:

      How many visitors came;

      How many new visitors vs. returning visitors;

      How many times a visitor has visited the website.




10/2/2012                   Jean-François Bélisle, 2012 ©   28
3.5 Problems with Cookies
3 major problems with cookies:

1. Many users may share a machine;

2. Cookies can be erased;

3. Many users connect to a website using different machines
   (iPhone, Desktop Computer, Laptop).




10/2/2012                   Jean-François Bélisle, 2012 ©     29
Section 4 – Key Terms in Web Analytics




10/2/2012                 Jean-François Bélisle, 2012 ©   30
4.1 Basic Key Terms

Visit or session: When a user views a page or a series of web pages
   viewed in sequence during a specified time.

Unique visitors: A user or group of users who have the same IP
  address, which views a page or a consecutive series of web pages.
  The same visitor may visit more than once the same website, but
  it is always the same visitor.




10/2/2012                 Jean-François Bélisle, 2012 ©          31
4.2 Other Basic Key Terms


Page views: Refers to the number of times a Web page is displayed
  in a web browser.

Returning visitors: Refers to one or more users who visit a website
  for the second time or more, with the same IP address.




10/2/2012                 Jean-François Bélisle, 2012 ©          32
4.3 Keep On Going Man …




10/2/2012          Jean-François Bélisle, 2012 ©   33
4.4 Last Batch of Key Terms

Home page: Page you defined as the “root” of your website.

Landing page: Page where users enter your website.

Conversion: When a user reaches a target set by the company (e.g.
  the user buys your product, the user subscribes to your
  newsletter)




10/2/2012                 Jean-François Bélisle, 2012 ©        34
Section 5 – Introduction to KPIs




10/2/2012             Jean-François Bélisle, 2012 ©   35
5.1 KPIs – A definition
KPIs (Key Performance Indicators): Financial and nonfinancial
  measures or parameters used to help an organization define and
  measure their success in terms of progress towards achieving
  their objectives.

How to proceed:
 Advocates the use of ratios, percentages and averages rather than
  raw data.
 Advocates the use as a lever of tachometers, thermometers and
  projections, rather than pie charts and bar graphs.
 Provides a temporal context and identifies the changes rather
  than presenting data tables.
 Influence the decisions of a company.
10/2/2012                 Jean-François Bélisle, 2012 ©          36
5.2 KPIs vs. Raw Data

• 100 people have purchased products on your website last month.

• So what? In which context?

• 100 people on 10 000 visitors -> Conversion rate of 1%.

• 100 people compared to the 200 of last month -> Decreased in
  the number of buyers by 50%.




10/2/2012                  Jean-François Bélisle, 2012 ©         37
5.3 Method by Objectives


                Type of website

                   Objectives

                          KPIs

                    Decision


10/2/2012          Jean-François Bélisle, 2012 ©   38
5.4 Types of Websites

Types of web sites

1. Content

2. Marketing

3. Sales

4. Support



10/2/2012                 Jean-François Bélisle, 2012 ©   39
5.5 Objectives
KPIs should answer managerial objectives which are SMART.

1. Specific

2. Measurable

3. Achievable

4. Really Useful

5. Time Dependent

10/2/2012                 Jean-François Bélisle, 2012 ©     40
5.6 KPIs Really useful?




   « Any KPI that, when it changes suddenly and unexpectedly, does
    not inspire someone to send an email, pick up the phone or take a
           quick walk to find help, is not a KPI worth reporting »

                          – Eric T. Peterson –
10/2/2012                   Jean-François Bélisle, 2012 ©          41
5.7 One Objective, one KPI

Types of objectives:

1.   Related to revenue sources
2.   Related to cost
3.   Related to loyalty
4.   Related to traffic
5.   Related to conversion funnel
6.   Etc …




10/2/2012                    Jean-François Bélisle, 2012 ©   42
5.8 Types of KPIs
KPIs related to:

1. Averages

2. Percentages

3. Ratio

4. Rates




10/2/2012             Jean-François Bélisle, 2012 ©   43
5.9 Some KPIs

Brief selection:
1. Bounce Rate
2. Average Cost per Conversion
3. Average Order Value
4. Percent Revenue from New Returning Visitors and Customers
5. Order Conversion Rate
6. Order Conversion Rate per campaign
7. Average Time to Respond to Email Inquiries
8. Cart Completion Rate
9. Checkout Start Rate
10. Form Completion Rate

10/2/2012                Jean-François Bélisle, 2012 ©         44
5.10 Importance of Presentation




                               Vs.




10/2/2012             Jean-François Bélisle, 2012 ©   45
5.11 Some Tips for Presentation

1. Run comparisons over time

2. Use colors and arrows

3. Always show the percentage change from one period to another

4. Establish guidelines

5. Set clear goals



10/2/2012                  Jean-François Bélisle, 2012 ©      46
5.12 Presentation Format



                  Excel Sheets

                             Or

                   Dashboards




10/2/2012          Jean-François Bélisle, 2012 ©   47
5.13 Conversion Funnels & GA

Conversion Funnels: Method for identifying each step closer to a
   user’s conversion on a website.




             http://www.youtube.com/watch?v=IibCs23EuiE

10/2/2012                 Jean-François Bélisle, 2012 ©       48
5.14 GA Multi-Channel Funnels




            http://www.youtube.com/user/googleanalytics#p/u/17/Cz4yHOKE5j8


10/2/2012                          Jean-François Bélisle, 2012 ©             49
5.15 GA Segmentation




            http://www.youtube.com/watch?v=yvkvMjPJXmM


10/2/2012                 Jean-François Bélisle, 2012 ©   50
Section 6 – Strategic Issues




10/2/2012           Jean-François Bélisle, 2012 ©   51
6.1 HiPPOs




            Highest Paid Person’s Opinion



10/2/2012          Jean-François Bélisle, 2012 ©   52
6.2 Number of KPIs for each type of strategist
The higher the person in the company’s hierarchy:
• The less time he/she has;
• The more interest is in KPIs related to ROI;
• The more the number of KPIs presented
  should be lower.

Number of KPIs for each type of strategist:
• Senior strategists: 2 to 5
• Middle Class strategists: 5 to 7
• Tactical resources: 7 to 10



10/2/2012                  Jean-François Bélisle, 2012 ©   53
Section 7 – Nirvana of Methods




10/2/2012             Jean-François Bélisle, 2012 ©   54
7.1 Triangulation of Methods
Three methods to gather data

1. Web Analytics
2. A/B Testing
3. Usability tests




10/2/2012                Jean-François Bélisle, 2012 ©   55
7.2 A/B Testing: Definition


When you test several different versions of a Web site (an
 advertisement, email, etc ...)




… and you take the version that gives you the best results from your
  dependent variable perspective (i.e. conversion rates, registration
  rates, etc ...)
10/2/2012                   Jean-François Bélisle, 2012 ©           56
7.3 A/B Testing: An Example

  1                                        2




                                            Inscrivez-vous maintenant!




10/2/2012           Jean-François Bélisle, 2012 ©                        57
7.4 Multivariate Testing: Definition
The process by which more than one component of a website may
be tested in a live environment. It can be thought of in simple terms
as numerous A/B tests performed on one page at the same time. A/B
tests are usually performed to determine the better of two content
variations; multivariate testing can theoretically test the
effectiveness of limitless combinations.




10/2/2012                   Jean-François Bélisle, 2012 ©           58
7.5 A/B Testing: Tools



    Google Analytics      IBM Coremetrics                    Adobe Omniture




    Content experiments   Marketing Center                   Test & Target module in
    module in Google      module in Coremetrics              Adobe Omniture
    Analytics




10/2/2012                    Jean-François Bélisle, 2012 ©                             59
7.6 Usability Tools – Morae




            http://www.youtube.com/watch?v=gTfdeUGEc3E




10/2/2012                  Jean-François Bélisle, 2012 ©   60
7.7 Usability Testing: Tealeaf

   Tealeaf's customer experience management (CEM) solutions empower
   companies to optimize ebusiness by eliminating the obstacles that block
   successful conversions or completion of business processes.




10/2/2012                      Jean-François Bélisle, 2010 ©                 61
7.8 Usability Testing: Tealeaf (2)




10/2/2012              Jean-François Bélisle, 2010 ©   62
7.9 Usability Testing: Tealeaf (3)




            http://www.tealeaf.com/products/customer-behavior-analysis-
                                 suite/cximpact.php
                               (Watch CEM Overview)


10/2/2012                           Jean-François Bélisle, 2010 ©         63
Section 8 – Some Resources




10/2/2012           Jean-François Bélisle, 2012 ©   64
8.1 Some Readings

                    Web Analytics Demystified (Free)
                                     &
            The Big Book of Key Performance Indicators (Free)
                                     By
                             Eric T. Peterson
      http://www.webanalyticsdemystified.com/content/index.asp


                           Web Analytics 2.0
                                 By
                           Avinash Kaushik

10/2/2012                  Jean-François Bélisle, 2012 ©         65
8.2 Some Readings (Cont’ed)

                        Always be Testing
                                  By
                         Bryan Eisenberg



            Advanced Web Metrics with Google Analytics
                              By
                         Brian Clifton




10/2/2012                Jean-François Bélisle, 2012 ©   66
Hope you Enjoyed and Have a Good Night
                          Everyone!




                    Jean-François Bélisle, MSc, PhD©
                 LinkedIn: www.linkedin.com/in/jfbelisle
                    Twitter: www.twitter.com/jfbelisle
                      Website: http://jfbelisle.com

10/2/2012                    Jean-François Bélisle, 2012 ©   67

Mais conteúdo relacionado

Semelhante a analytics presentation

Introduction to Social Media for Business Use 291112
Introduction to Social Media for Business Use 291112Introduction to Social Media for Business Use 291112
Introduction to Social Media for Business Use 291112
WMG, University of Warwick
 
Corporate Partnership in sustainable development initiatives
Corporate Partnership in sustainable development initiativesCorporate Partnership in sustainable development initiatives
Corporate Partnership in sustainable development initiatives
Xavier Heude
 
The Data Effect: Canadian Big Data & Analytics Update - Dr. Alison Brooks Dir...
The Data Effect: Canadian Big Data & Analytics Update - Dr. Alison Brooks Dir...The Data Effect: Canadian Big Data & Analytics Update - Dr. Alison Brooks Dir...
The Data Effect: Canadian Big Data & Analytics Update - Dr. Alison Brooks Dir...
CityAge
 

Semelhante a analytics presentation (20)

Marketing to connected communities: from play to stay
Marketing to connected communities: from play to stayMarketing to connected communities: from play to stay
Marketing to connected communities: from play to stay
 
Business Intelligence Webinar
Business Intelligence WebinarBusiness Intelligence Webinar
Business Intelligence Webinar
 
DYNDEC Company Overview
DYNDEC Company OverviewDYNDEC Company Overview
DYNDEC Company Overview
 
Why Things Go Awry
Why Things Go AwryWhy Things Go Awry
Why Things Go Awry
 
Buyers guide 5 steps to selecting member management software
Buyers guide 5 steps to selecting member management softwareBuyers guide 5 steps to selecting member management software
Buyers guide 5 steps to selecting member management software
 
MBASmart - DYNDEC Assessment Partner
MBASmart - DYNDEC Assessment PartnerMBASmart - DYNDEC Assessment Partner
MBASmart - DYNDEC Assessment Partner
 
Introduction to Social Media for Business Use 291112
Introduction to Social Media for Business Use 291112Introduction to Social Media for Business Use 291112
Introduction to Social Media for Business Use 291112
 
Corporate Partnership in sustainable development initiatives
Corporate Partnership in sustainable development initiativesCorporate Partnership in sustainable development initiatives
Corporate Partnership in sustainable development initiatives
 
The Data Effect: Canadian Big Data & Analytics Update - Dr. Alison Brooks Dir...
The Data Effect: Canadian Big Data & Analytics Update - Dr. Alison Brooks Dir...The Data Effect: Canadian Big Data & Analytics Update - Dr. Alison Brooks Dir...
The Data Effect: Canadian Big Data & Analytics Update - Dr. Alison Brooks Dir...
 
Henri Barthel (GS1)
Henri Barthel (GS1)Henri Barthel (GS1)
Henri Barthel (GS1)
 
NHS Greenfield Business Intelligence
NHS Greenfield Business IntelligenceNHS Greenfield Business Intelligence
NHS Greenfield Business Intelligence
 
Agile BI with Data Virtualization (session 2 from Packed Lunch Webinar Series)
Agile BI with Data Virtualization (session 2 from Packed Lunch Webinar Series)Agile BI with Data Virtualization (session 2 from Packed Lunch Webinar Series)
Agile BI with Data Virtualization (session 2 from Packed Lunch Webinar Series)
 
SMX Landing Page Optimisation
SMX Landing Page OptimisationSMX Landing Page Optimisation
SMX Landing Page Optimisation
 
SugarCon 2012 Presentation
SugarCon 2012 PresentationSugarCon 2012 Presentation
SugarCon 2012 Presentation
 
Implementing SugarCRM for Australia's premiere business network
Implementing SugarCRM for Australia's premiere business networkImplementing SugarCRM for Australia's premiere business network
Implementing SugarCRM for Australia's premiere business network
 
Create and execute a Social Strategy - Workshop
Create and execute a Social Strategy - WorkshopCreate and execute a Social Strategy - Workshop
Create and execute a Social Strategy - Workshop
 
How DSW Redefined Shrink and EBR to Drive Loss Prevention Success
How DSW Redefined Shrink and EBR to Drive Loss Prevention SuccessHow DSW Redefined Shrink and EBR to Drive Loss Prevention Success
How DSW Redefined Shrink and EBR to Drive Loss Prevention Success
 
GE
GEGE
GE
 
Recticel Strategy and Trading Update
Recticel Strategy and Trading UpdateRecticel Strategy and Trading Update
Recticel Strategy and Trading Update
 
Litebi Truebi - Cloud Business Intelligence for your Department
Litebi   Truebi - Cloud Business Intelligence for your DepartmentLitebi   Truebi - Cloud Business Intelligence for your Department
Litebi Truebi - Cloud Business Intelligence for your Department
 

Mais de Pinny

2012 Online marketing-cmis542
2012 Online marketing-cmis5422012 Online marketing-cmis542
2012 Online marketing-cmis542
Pinny
 
Mc gill leveraging social media for your job search
Mc gill leveraging social media for your job searchMc gill leveraging social media for your job search
Mc gill leveraging social media for your job search
Pinny
 
Session #4 2011
  Session #4 2011  Session #4 2011
Session #4 2011
Pinny
 
Session 1 2012, CMIS542
Session 1 2012, CMIS542Session 1 2012, CMIS542
Session 1 2012, CMIS542
Pinny
 
Session #2 2012
Session #2 2012Session #2 2012
Session #2 2012
Pinny
 
Ethics
EthicsEthics
Ethics
Pinny
 

Mais de Pinny (8)

2012 Online marketing-cmis542
2012 Online marketing-cmis5422012 Online marketing-cmis542
2012 Online marketing-cmis542
 
Search engine optimization (seo) workshop 2
Search engine optimization (seo) workshop 2Search engine optimization (seo) workshop 2
Search engine optimization (seo) workshop 2
 
Mc gill leveraging social media for your job search
Mc gill leveraging social media for your job searchMc gill leveraging social media for your job search
Mc gill leveraging social media for your job search
 
Session #4 2011
  Session #4 2011  Session #4 2011
Session #4 2011
 
Session 1 2012, CMIS542
Session 1 2012, CMIS542Session 1 2012, CMIS542
Session 1 2012, CMIS542
 
Session #2 2012
Session #2 2012Session #2 2012
Session #2 2012
 
Mcgill social media cmis542
Mcgill social media cmis542 Mcgill social media cmis542
Mcgill social media cmis542
 
Ethics
EthicsEthics
Ethics
 

analytics presentation

  • 1. Web Analytics & Customer Analysis By Jean-François (JF) Bélisle, MSc, PhD© Jean-François Bélisle, 2012 ©
  • 2. Who are you man? Director – Consulting Services @ K3 Media B.Sc. Economics, Université de Montréal M.Sc. Marketing, HEC Montréal Award of Achievement, Web Analytics, University of British Columbia Ph.D. Studies, Marketing & Computational Stats, McGill University Executive training in Customer Analytics, University of Pennsylvania (Wharton) 10/2/2012 Jean-François Bélisle, 2012 © 2
  • 3. For which companies have you worked man? 10/2/2012 Jean-François Bélisle, 2012 © 3
  • 4. … and some clients of K3? 10/2/2012 Jean-François Bélisle, 2012 © 4
  • 5. K3 Certifications Pay-Per-Click Programming Design Analytics (SEM) 5 4 2 12 10/2/2012 Jean-François Bélisle, 2012 © 5
  • 6. K3 Strategic Alliances 10/2/2012 Jean-François Bélisle, 2012 © 6
  • 7. Game Plan 1. Analytics Quick Intro 2. Web Analytics: Getting Started 3. Data Gathering 4. Key Terms in Web Analytics 5. KPIs 6. Strategic Issues 7. Other Methods 8. Some Resources 10/2/2012 Jean-François Bélisle, 2012 © 7
  • 8. Section 1 – Analytics Quick Intro 10/2/2012 Jean-François Bélisle, 2012 © 8
  • 9. 1.1 Analytics - What is it? Analytics The application of computer technology, operational research, and statistics to solve problems in business and industry. 10/2/2012 Jean-François Bélisle, 2012 © 9
  • 10. 1.2 Analytics – in 1990 In 1990, what came to people’s mind when someone said analytics? Boring Ugly Geeky Useless Incomprehensible Hard Worthless 10/2/2012 Jean-François Bélisle, 2012 © 10
  • 11. 1.3 Analytics – in 2012 In 2012, what comes to people’s mind when someone says analytics? Cool Sexy Common Useful Understandable Accessible Gold 10/2/2012 Jean-François Bélisle, 2012 © 11
  • 12. 1.4 Analytics & « Moneyball » 10/2/2012 Jean-François Bélisle, 2012 © 12
  • 13. 1.5 Analytics & Data 10/2/2012 Jean-François Bélisle, 2012 © 13
  • 14. 1.6 Data = Analytics? Companies have lots and lots of data… The problem how to make sense of these data? 10/2/2012 Jean-François Bélisle, 2012 © 14
  • 15. 1.7 Analytics - Managerially You can’t manage what you can’t measure! 10/2/2012 Jean-François Bélisle, 2012 © 15
  • 16. 1.8 Analytics – Managerially (2) You can’t manage what you don’t measure! 10/2/2012 Jean-François Bélisle, 2012 © 16
  • 17. 1.9 Analytics -> Managerial Insights? Garbage In, Garbage Out 10/2/2012 Jean-François Bélisle, 2012 © 17
  • 18. 1.10 How Data Becomes Managerial Insights? Objectives Data Analytics (e.g. KPIs) Decision 10/2/2012 Jean-François Bélisle, 2012 © 18
  • 19. Section 2 – Web Analytics: Getting Started 10/2/2012 Jean-François Bélisle, 2012 © 19
  • 20. 2.1 Big Names in Web analytics International: • Avinash Kaushik • Bryan Eisenberg • Jim Sterne • Eric Peterson • Jim Novo • Alex Langshur Quebec: • Stéphane Hamel • Jacques Warren 10/2/2012 Jean-François Bélisle, 2012 © 20
  • 21. 2.2 Web Analytics Tools Ranking 10/2/2012 Jean-François Bélisle, 2012 © 21
  • 22. 2.3 Google Analytics or IBM Coremetrics Free vs. Pay (8K to 12K per yr, base) Public (Google Server) vs. Private data (Own Server) No service vs. Dedicated service Basic Features vs. Advanced Features Aggregated data vs. Individual data No Integration vs. Multiple integrations 10/2/2012 Jean-François Bélisle, 2012 © 22
  • 23. 2.4 Google Analytics Premium or IBM Coremetrics Or 10/2/2012 Jean-François Bélisle, 2012 © 23
  • 24. Section 3 – Data Gathering 10/2/2012 Jean-François Bélisle, 2012 © 24
  • 25. 3.1 Cookies Cookies: “A cookie is a piece of text that a Web server can store on a user's hard disk. Cookies allow a website to store information on a user's machine (computer, smartphone, console) and later retrieve it. The pieces of information are stored as name-value pairs.” (Marshall Brain, www.HowStuffworks.com) 10/2/2012 Jean-François Bélisle, 2012 © 25
  • 26. 3.2 Cookies & Google Analytics Google Analytics ->"page tag“ = Google Analytics Tracking Code (GATC) GATC: Snippet of JavaScript code that the user adds onto every page of his or her website. This code collects visitor data and sends it to a Google data collection server as part of a request for a web beacon (Taken from wikipedia.org). In addition to transmitting information to a Google server, the GATC sets first party cookies on each visitor's computer. 10/2/2012 Jean-François Bélisle, 2012 © 26
  • 27. 3.3 Cookies & Google Analytics (2) CTRL + U on Chrome or Firefox 10/2/2012 Jean-François Bélisle, 2012 © 27
  • 28. 3.4 Cookies’ Usefulness Through cookies, a company can know:  How many visitors came;  How many new visitors vs. returning visitors;  How many times a visitor has visited the website. 10/2/2012 Jean-François Bélisle, 2012 © 28
  • 29. 3.5 Problems with Cookies 3 major problems with cookies: 1. Many users may share a machine; 2. Cookies can be erased; 3. Many users connect to a website using different machines (iPhone, Desktop Computer, Laptop). 10/2/2012 Jean-François Bélisle, 2012 © 29
  • 30. Section 4 – Key Terms in Web Analytics 10/2/2012 Jean-François Bélisle, 2012 © 30
  • 31. 4.1 Basic Key Terms Visit or session: When a user views a page or a series of web pages viewed in sequence during a specified time. Unique visitors: A user or group of users who have the same IP address, which views a page or a consecutive series of web pages. The same visitor may visit more than once the same website, but it is always the same visitor. 10/2/2012 Jean-François Bélisle, 2012 © 31
  • 32. 4.2 Other Basic Key Terms Page views: Refers to the number of times a Web page is displayed in a web browser. Returning visitors: Refers to one or more users who visit a website for the second time or more, with the same IP address. 10/2/2012 Jean-François Bélisle, 2012 © 32
  • 33. 4.3 Keep On Going Man … 10/2/2012 Jean-François Bélisle, 2012 © 33
  • 34. 4.4 Last Batch of Key Terms Home page: Page you defined as the “root” of your website. Landing page: Page where users enter your website. Conversion: When a user reaches a target set by the company (e.g. the user buys your product, the user subscribes to your newsletter) 10/2/2012 Jean-François Bélisle, 2012 © 34
  • 35. Section 5 – Introduction to KPIs 10/2/2012 Jean-François Bélisle, 2012 © 35
  • 36. 5.1 KPIs – A definition KPIs (Key Performance Indicators): Financial and nonfinancial measures or parameters used to help an organization define and measure their success in terms of progress towards achieving their objectives. How to proceed:  Advocates the use of ratios, percentages and averages rather than raw data.  Advocates the use as a lever of tachometers, thermometers and projections, rather than pie charts and bar graphs.  Provides a temporal context and identifies the changes rather than presenting data tables.  Influence the decisions of a company. 10/2/2012 Jean-François Bélisle, 2012 © 36
  • 37. 5.2 KPIs vs. Raw Data • 100 people have purchased products on your website last month. • So what? In which context? • 100 people on 10 000 visitors -> Conversion rate of 1%. • 100 people compared to the 200 of last month -> Decreased in the number of buyers by 50%. 10/2/2012 Jean-François Bélisle, 2012 © 37
  • 38. 5.3 Method by Objectives Type of website Objectives KPIs Decision 10/2/2012 Jean-François Bélisle, 2012 © 38
  • 39. 5.4 Types of Websites Types of web sites 1. Content 2. Marketing 3. Sales 4. Support 10/2/2012 Jean-François Bélisle, 2012 © 39
  • 40. 5.5 Objectives KPIs should answer managerial objectives which are SMART. 1. Specific 2. Measurable 3. Achievable 4. Really Useful 5. Time Dependent 10/2/2012 Jean-François Bélisle, 2012 © 40
  • 41. 5.6 KPIs Really useful? « Any KPI that, when it changes suddenly and unexpectedly, does not inspire someone to send an email, pick up the phone or take a quick walk to find help, is not a KPI worth reporting » – Eric T. Peterson – 10/2/2012 Jean-François Bélisle, 2012 © 41
  • 42. 5.7 One Objective, one KPI Types of objectives: 1. Related to revenue sources 2. Related to cost 3. Related to loyalty 4. Related to traffic 5. Related to conversion funnel 6. Etc … 10/2/2012 Jean-François Bélisle, 2012 © 42
  • 43. 5.8 Types of KPIs KPIs related to: 1. Averages 2. Percentages 3. Ratio 4. Rates 10/2/2012 Jean-François Bélisle, 2012 © 43
  • 44. 5.9 Some KPIs Brief selection: 1. Bounce Rate 2. Average Cost per Conversion 3. Average Order Value 4. Percent Revenue from New Returning Visitors and Customers 5. Order Conversion Rate 6. Order Conversion Rate per campaign 7. Average Time to Respond to Email Inquiries 8. Cart Completion Rate 9. Checkout Start Rate 10. Form Completion Rate 10/2/2012 Jean-François Bélisle, 2012 © 44
  • 45. 5.10 Importance of Presentation Vs. 10/2/2012 Jean-François Bélisle, 2012 © 45
  • 46. 5.11 Some Tips for Presentation 1. Run comparisons over time 2. Use colors and arrows 3. Always show the percentage change from one period to another 4. Establish guidelines 5. Set clear goals 10/2/2012 Jean-François Bélisle, 2012 © 46
  • 47. 5.12 Presentation Format Excel Sheets Or Dashboards 10/2/2012 Jean-François Bélisle, 2012 © 47
  • 48. 5.13 Conversion Funnels & GA Conversion Funnels: Method for identifying each step closer to a user’s conversion on a website. http://www.youtube.com/watch?v=IibCs23EuiE 10/2/2012 Jean-François Bélisle, 2012 © 48
  • 49. 5.14 GA Multi-Channel Funnels http://www.youtube.com/user/googleanalytics#p/u/17/Cz4yHOKE5j8 10/2/2012 Jean-François Bélisle, 2012 © 49
  • 50. 5.15 GA Segmentation http://www.youtube.com/watch?v=yvkvMjPJXmM 10/2/2012 Jean-François Bélisle, 2012 © 50
  • 51. Section 6 – Strategic Issues 10/2/2012 Jean-François Bélisle, 2012 © 51
  • 52. 6.1 HiPPOs Highest Paid Person’s Opinion 10/2/2012 Jean-François Bélisle, 2012 © 52
  • 53. 6.2 Number of KPIs for each type of strategist The higher the person in the company’s hierarchy: • The less time he/she has; • The more interest is in KPIs related to ROI; • The more the number of KPIs presented should be lower. Number of KPIs for each type of strategist: • Senior strategists: 2 to 5 • Middle Class strategists: 5 to 7 • Tactical resources: 7 to 10 10/2/2012 Jean-François Bélisle, 2012 © 53
  • 54. Section 7 – Nirvana of Methods 10/2/2012 Jean-François Bélisle, 2012 © 54
  • 55. 7.1 Triangulation of Methods Three methods to gather data 1. Web Analytics 2. A/B Testing 3. Usability tests 10/2/2012 Jean-François Bélisle, 2012 © 55
  • 56. 7.2 A/B Testing: Definition When you test several different versions of a Web site (an advertisement, email, etc ...) … and you take the version that gives you the best results from your dependent variable perspective (i.e. conversion rates, registration rates, etc ...) 10/2/2012 Jean-François Bélisle, 2012 © 56
  • 57. 7.3 A/B Testing: An Example 1 2 Inscrivez-vous maintenant! 10/2/2012 Jean-François Bélisle, 2012 © 57
  • 58. 7.4 Multivariate Testing: Definition The process by which more than one component of a website may be tested in a live environment. It can be thought of in simple terms as numerous A/B tests performed on one page at the same time. A/B tests are usually performed to determine the better of two content variations; multivariate testing can theoretically test the effectiveness of limitless combinations. 10/2/2012 Jean-François Bélisle, 2012 © 58
  • 59. 7.5 A/B Testing: Tools Google Analytics IBM Coremetrics Adobe Omniture Content experiments Marketing Center Test & Target module in module in Google module in Coremetrics Adobe Omniture Analytics 10/2/2012 Jean-François Bélisle, 2012 © 59
  • 60. 7.6 Usability Tools – Morae http://www.youtube.com/watch?v=gTfdeUGEc3E 10/2/2012 Jean-François Bélisle, 2012 © 60
  • 61. 7.7 Usability Testing: Tealeaf Tealeaf's customer experience management (CEM) solutions empower companies to optimize ebusiness by eliminating the obstacles that block successful conversions or completion of business processes. 10/2/2012 Jean-François Bélisle, 2010 © 61
  • 62. 7.8 Usability Testing: Tealeaf (2) 10/2/2012 Jean-François Bélisle, 2010 © 62
  • 63. 7.9 Usability Testing: Tealeaf (3) http://www.tealeaf.com/products/customer-behavior-analysis- suite/cximpact.php (Watch CEM Overview) 10/2/2012 Jean-François Bélisle, 2010 © 63
  • 64. Section 8 – Some Resources 10/2/2012 Jean-François Bélisle, 2012 © 64
  • 65. 8.1 Some Readings Web Analytics Demystified (Free) & The Big Book of Key Performance Indicators (Free) By Eric T. Peterson http://www.webanalyticsdemystified.com/content/index.asp Web Analytics 2.0 By Avinash Kaushik 10/2/2012 Jean-François Bélisle, 2012 © 65
  • 66. 8.2 Some Readings (Cont’ed) Always be Testing By Bryan Eisenberg Advanced Web Metrics with Google Analytics By Brian Clifton 10/2/2012 Jean-François Bélisle, 2012 © 66
  • 67. Hope you Enjoyed and Have a Good Night Everyone! Jean-François Bélisle, MSc, PhD© LinkedIn: www.linkedin.com/in/jfbelisle Twitter: www.twitter.com/jfbelisle Website: http://jfbelisle.com 10/2/2012 Jean-François Bélisle, 2012 © 67