This document describes services from Multivariate Algorithms Inc. to analyze web log data and visitor behavior in three main points: 1. They convert web logs into sessionized visitor records and analyze page requests to determine what content each visitor accessed and their discovery paths through the website. 2. As an example, they performed an analysis for Abbott Laboratories that showed how TV advertisements drove increases in website traffic and how most visitors engaged with specific product pages. 3. Their sessionization and analysis provides more accurate traffic counts and visitor behavior than other tools by accounting for all log file records and identifying non-human traffic that can inflate numbers.

1. Webservices from Multivariate Algorithms Inc.

2. • 30-40% of WebTrends, Atlas DMT and Double-Click data is ‘garbage’
– Not ‘sessionized’ - Not sourced by ‘requesting IP address’
– No integration with external databases
– No fraud control “Lost in reports and definitions”
• We converts web logs into databases of sessionized records and then
dissect information strings which accurately determine, by IP address,
exactly what each visitors requested at your web site, AND what
responders to online advertising actually did during their PAID web site
visits. We called it organizing logs into “Data-Driven Discovery Paths”.
• We can program to automatically correlate this information with past
survey behavior and/or other collected historical data
– Matches for registered users of TheInfoTool
– one-line product users
Multivariate Algorithm’s Cognos Cube™

3. Abbott Laboratories Case Study
• Two DTC- Pharma applications
– Client did not believe that the online audience:
• Was large enough to justify significant online budget,
• Thought visitors were ‘tire kickers’ (not qualified prospects)
• And questioned whether or not maintaining both branded and unbranded sites
is necessary
– Wanted to know what happened online after launch of first DRTV
television commercials
Multivariate Algorithm’s Cognos Cube™

4. Abbott Laboratories Case Study
• Average visits per week: 7,913
• Average unique visits per week: 6,265
• High: 10,323
• Low: 6,108
• Average visit length: 6 min 12 sec
• Median visit length: 2 min 40 sec
• Branded DRTV launch in late March
drove visits up through April
• Humira email survey blast in mid-
May increased traffic slightly
Weekly Humira.com Visit Trends for 2004
0
2,000
4,000
6,000
8,000
10,000
12,000
1/4/2004
1/18/2004
2/1/2004
2/15/2004
2/29/2004
3/14/2004
3/28/2004
4/11/2004
4/25/2004
5/9/2004
5/23/2004
6/6/2004
6/20/2004
7/4/2004
7/18/2004
8/1/2004
#ofVisits
Visits Unique Visitors

5. Top 5 Most Visited Pages
#1 About Humira #2 Considering Humira #3 Common Questions
Asked
#4 Professional
#5 Patient Programs

6. RA.com – 2004 Weekly Visit Trends
Weekly RA.com Visit Trends for 2004
0
2,000
4,000
6,000
8,000
10,000
12,000
14,000
1/4/2004
1/18/2004
2/1/2004
2/15/2004
2/29/2004
3/14/2004
3/28/2004
4/11/2004
4/25/2004
5/9/2004
5/23/2004
6/6/2004
6/20/2004
7/4/2004
7/18/2004
8/1/2004
#ofVisits
Visits Unique Visitors
• Average visits per week: 8,098
• Average unique visits per week: 6,502
• High: 10,829
• Low: 6,414
• Average visit length: 32 min 54 sec
• Median visit length: 2 min 30 sec
• Reuters feed update in early January
drove visits up significantly
• Homepage redesign in mid-April
increased traffic for a few weeks
• RA Catalog launch in mid-May with
media blitz increased traffic the week of
launch

7. Top 5 Most Visited Pages
#1 RA In Depth #2 Drug Guide #4 How Serious is RA?
#3 OA versus RA #5 Interactive Drug Tool

8. Profile of Online Registrants

9. BEFORE & AFTER DRTV LAUNCH
QUALIFIED LEADS USING ANOTHER BIOLOGIC
0
50
100
150
200
250
300
Qual Lead 57 102 319
3/1 - 3/21 3/22 - 5/21 5/22 - 7/31
Multivariate Algorithm’s Cognos Cube™

10. BEFORE AND AFTER DRTV LAUNCH
RA.com QUALIFIED LEADS USING ANOTHER BIOLOGIC
0
10
20
30
40
50
Qual Lead 4 23 41
3/1 - 3/21 3/22 - 5/21 5/22 - 7/31
Multivariate Algorithm’s Cognos Cube™

11. • John Kerry online media ROI analysis
Forget Clicks and Qualified Leads
…. Its Dollars
Tracking Online Contributions for A Presidential Candidate
Multivariate Algorithm’s Cognos Cube™

12. John Kerry Campaign Online Media Analysis Case Study
Each user can access a remote desktop that interacts directly with each
cube allowing for drill downs on Placement => Size => Content
Impressions => Clicks => Cost by source => Actual Contribution => ROI
Multivariate Algorithm’s Cognos Cube™

13. Custom OLAP Views of your Sessionized Weblogs
By Lifestage and Income Groups
Message Comparisons

14. •The auditable basis of this presentation is the data found within
weblogs collected by your hosting webserver.
• Typically Apache and IIS extended format log files.
• These files contain time stamped downloads made by the
webserver to a requesting browser.
• This information is strictly organized by the time stamp
• We “Sessionize by ‘requesting IP address’ sourced .
• Establishing discovery paths and one hit requestors
• Tracking cookies and adserving
• Custom data base tagging and merging
•Appending zip code level demographics
• matching back to product users and survey responders
Multivariate Algorithm’s Cognos Cube™

15. 1.) Raw Weblogs from Website Server: Chronologically ordered Requests
Multivariate Algorithm’s Cognos Cube™

16. 2.) Requests Ordered by Sessions
Multivariate Algorithm’s Cognos Cube™

17. 3.) Ordered requests within a “sessionized” visit
Multivariate Algorithm’s Cognos Cube™

18. 4.) Custom Coding on that sessionized Database
Complete accountability remains with the data as
29,437 records found in web log file and
29,340 records found in sessionized file
Multivariate Algorithm’s Cognos Cube™

19. 5.) Development of “Discovery Paths” within a session
Multivariate Algorithm’s Cognos Cube™

20. 6.) WebTrends Versus Multivariate Sessionizer
Multivariate Algorithm’s Cognos Cube™

21. Attempted Hacking that increases Visits and Dwell time

22. Attempted Hacking that increases Visits and Dwell time

23. Multivariate Sessionizing Algorithms provides
Total accountability of ALL traffic
Uses every record from each log file to create sessions
These sessions track “Discovery Paths” used by visitors
Can quickly identify Bad traffic that WebTrends counts
Get critically accurate counts and dwell times
Insure that Flash components get loaded and viewed
Harvest information into the corporate data bases
Go beyond reports that confuse and mislead owners
Multivariate Algorithm’s Cognos Cube™