1. W E B A N L Y T I C S
Step
Change
Web Analytics in
Jaisri Chety

2. Participate
This is a highly interactive session; request all of you to participate with questions, challenges & solutions…

3. Web Analytics 1.0 Click Stream data Visits Visitors Geo Targeting
Average time spent
Funnel conversion
Landing page optimization
Conversion rates….
In Brief we were looking at the What, When & where questions

4. What did we miss?

5. Advent of Web 2.0 KPIs sans insight User generated content
Content distribution through Rss & Xml
Rich internet applications
Non traditional browsers like iPhone, BlackBerry.
KPIs sans insight
Demand for more insights rather than aesthetically presented numbers/ Ratios.
Achieving marketing ROI with onsite optimisation & behavior targeting

6. Change in how Web Analytics is perceived by SEM

7. Large gap in off-site and on-site spending…
Resources
Off-site
Resources
marketing
Affiliate programs
Behavioral Targeting
Paid search management
Banner advertising
Call center referrals
Search Engine Optimization
Offline marketing to web
In-store Web promotion $
On-site
Resources
On-site
Resources
Registration optimization
Site testing
Web analytics
Usability testing
On-site
Resources
Large Investment Gap

8. On-site engagement determines conversion success
Campaign Traffic
marketing
Affiliate programs
Behavioral Targeting
Referred Traffic
Paid search management
Banner advertising
Call center referrals
Direct Traffic
Search Engine Optimization
Offline marketing to web
In-store Web promotion
Off-site
Marketing
Spending
Critical
Engagement
Layer
Campaign Landing Pages
Home Page
On-site
Experience
Determines
Conversion Rate
Product Category Pages
Attrition losses
Conversion Process
Successful conversion
$$$

9. How automated 1 to 1 targeting works:
Visitor arrives at your website
CMS (Serves content)
Visitor Profile Repository
Call goes out to Visitor Profile
Repository
Optimal content decision
sent to CMS
Content library
build profile
First-time visitor
Self-learning
Predictive Modeling Engine
retrieve profile
Repeat visitor

10. Offline Customer Variables
If we could answer a few questions, we could determine what page to serve to each customer
Highly
predictive anonymous
visitor profile
What is this visitor
doing now?
What
have they done
before?
Where is this visitor
Located?
What is
their online
experience like?
Offline Customer Variables
How did this visitor
arrive here?
Have they already
expressed
what they want?
When is this visit
occurring?
How frequently &
recently have
they visited? 10

11. What data is used to select the relevant offer?
Customer/prospect
New/return visitor
Previous Visit pattern
Tools usage
Previous Product interests
Searches
Previous online purchases
Previous Campaign exposure
Previous Campaign responses
Site Behaviour Variables
IP address
Country of origin
Time zone
Operating system
Browser type
Screen resolution
Environment Variables
Offline Customer Variables
Referring domain
Campaign ID
Affiliate
PPC
Natural search
Search keywords
Direct/bookmark
Referrer Variables
Temporal Variables
Time of day
Day of week
Recency
Frequency 11

12. Lloyds TSB Initial Page

13. Profile A

14. Profile B

15. Profile C

16. Profile D

18. Targeting on the secure logoff page18

19. Temporal targeting
3:15 PM

20. 1:45 AM

21. Why Web Analytics 2.0
is the inevitable response to the changing Internet
A reflection that:
Page views are becoming less relevant as a fundamental measure on some sites
Quantitative data alone doesn’t tell us enough about visitor engagement
The browser wars are starting over again, this time on mobile devices
Available reporting mechanisms are increasingly inadequate
The nature of measurement is changing rapidly

22. Web Analytics 2.0 is
the analysis of qualitative and quantitative data from your website and the competition,
to drive a continual improvement of the online experience that your customers, and potential customers have,
which translates into your desired outcomes (online and offline).

25. Arrive at Insight Clickstream — Typical web analytics.
Multiple Outcomes Analysis — All those objective outcomes need to be measured to see if the site is really driving the desired outcomes.
Experimentation & Testing — In it’s simplest form, this means A/B testing the design of your website, including text, graphics, buttons, banner ads, everything. 
Voice of the Customer — The results can be tied back to analytics data and may reveal customers’ true motivations.
Competitive Analysis — Your competitors may be running campaigns or launching products/features that are impacting your site’s performance (could be either up or down).

26. Customer Experience Management
The core value of CEM systems is the ability to capture and report on every interaction a visitor has with a site.
It is highly diagnostic as it helps to determine whether the abandonment was audience or application related.
Pinpoints the true source of the problem

27. Customer is still the king
Hence understanding the customer/ visitor behaviour through both quantitative & qualitative ways are critical.
Tools such as CEM, VOC & Click Stream give us a complete view of our customer behaviour.

29. Web 3.0 The real problem we would all eventually face is
Web 3.0 will be about mobile computing
All the same problems …
On smaller screens …
With different usability challenges …
Potentially without JavaScript and cookies …
But Web 3.0 will create unique opportunities
Every request for information could be tied to a good unique ID
Every request for information could be coupled with a geographic location

30. Web Analytics 3.0
Some new questions we’ll be able to ask with Web Analytics 3.0!
Which of our stores was the visitor in or near when they came to our site?
What offers do we have in the visitor’s neighborhood at work or at home?
Can the visitors location or demographic profile be used to disambiguate search?
Which ads work best based on the visitors phone browsing platform and time of day?
What message would be most appropriate given time of day, geographic location, and observed visitor behavior?
Web 3.0 will bring advertisers and marketers closer than ever to their customers
And how will we help them take advantage of these new opportunities

31. Source
Improving Customer Acquisition through Analytics - Brent Hieggelke
CUSTOMER EXPERIENCE MANAGEMENT ND WEB ANALYTICS From KPIs to Customer Transactions - Eric Peterson
Multiplicity: Succeed Awesomely At Web Analytics 2.0! - Avinash Kaushik

32. Questions
I would also cover the 3 step changes in detail in my blog - web-scapes.blogspot.com
If you want any clarification or want to post questions on the same please feel free to post it as comments in the blog as above or mail me at
Thank You