The document discusses WebTrends data integration and access capabilities through examples from Internet Broadcasting. It describes how Internet Broadcasting used WebTrends data and custom reports to optimize their Distributed Ad Products, gaining insights that doubled revenue. It also discusses how CRM and email integration allows using WebTrends data for targeted messaging and gaining a comprehensive view of customers. Finally, it provides an overview of WebTrends new REST APIs for analytics data access.

1. WebTrends Data Access and Integration
John DeFoe
Vice President of Solution Services
Guest Speaker
Tom Masterman
Internet Broadcasting

2. Agenda
• Explore the possibilities of data access and integration through real
world integration and data access examples
– Site optimization – feed WebTrends data back to your site for
higher clickthroughs
– Email targeting/remarketing– increase conversion by
automating remarketing to specific targeted segments.
– CRM – get a comprehensive view of your customer and create
targeted leads based on site engagement from the missing
channel in your CRM system.

3. Open Exchange with WebTrends Connect

4. Optimizing Distributed Ad Products
Tom Masterman
Internet Broadcasting

5. Agenda
• About Internet Broadcasting
• The “DAP” dilemma
• Instant insights with custom reports
• Optimization through automated data integration
• Future plans & lessons learned

6. About Internet Broadcasting
• Leading provider of Web sites, content and advertising revenue
solutions to the world’s largest and most successful media
companies
– Partners include Hearst-Argyle, Post-Newsweek, McGraw-Hill, Cox
Television, Meredith Broadcasting, Telemundo and CNN
• Ad network of 500+ local media properties
– 40-50mm monthly UVs, 93% U.S. market reach
• Worked with WebTrends for 5 years
– Began with ’04 Olympics
– Rolled out WebTrends across network in ’06

7. The “DAP” Dilemma
• Distributed Ad Products (DAP)
Premium paid links – “Links We Like”
–
Strategic advertisers
–
IB controls & customizes the content
–
Runs on media sites across the U.S.
–
• Business issue
Limited click data via DART
–
Difficult to extract and interpret reports
–
Content selected by gut instinct
–
Impossible to price the value of the product
–

8. Instant Insights
• Leveraged WebTrends custom reporting
– Impressions tracked using existing tag
– Also began tracking click-throughs
– Created click-through reports using custom
reports and calculated measures
– Automated translation file uploaded daily to
translate values from our CMS
• Instant insights
– Clicks and CTR for:
Advertiser
•
Link title
•
Link position
•
Site, section, page
•

9. Data Integration: Why We Did It
• Limitations of the UI
– Tons of great data, but difficult to derive deep
insights
– Content editor was “cherry picking” just the
top performing links
• Needed to connect to external cost per
click data
• Wanted the data to end up in Excel for
analysis

10. Data Integration: What We Did
• Import data from WebTrends using variety of automated tools
– Making the transition from manual exports, to scheduled reports, to
APIs
• Set up automated script:
– Parse the WebTrends data
– Marry it to external metrics
– Organize in a way more relevant to editor

11. Data Integration: Samples

12. Data Integration: The Results
• Click-through and revenue generation
quot;Links We Likequot; $ RPM $
doubled within a month
$ Revenue Per 1000 Impressions $
• Vastly outperformed Google AdSense
• Plateau occurred once editor ran out of
obvious insights
• Down economy has presented an
ongoing challenge with click-through
conversion
• Continue to outperform AdSense
• New optimization reports have greatly
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improved workflow, saving hours of work
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13. Future Plans
• Currently producing daily corporate scorecard
leveraging WebTrends APIs
– Populating key WebTrends metrics in to IB data
archive
– Automatically pulling data out of archive in to report
• Next steps with DAP tracking
– Integrate data in to rules based engine leveraging
APIs
– Completely automated optimization and
performance reporting

14. Lessons Learned
• Maximize custom report & calculated measured capabilities
• Don’t try to solve every reporting need in the UI
• Investment in APIs integration has a rapid payoff

15. Email Integration
Delivering Precise and Relevant Marketing Messages

16. Audience Participation
• How many of you track the effectiveness (conversions, visit
duration..) of your Email Campaigns?
• How many of you integrate Analytics data (Conversions, Visit
Duration..) with Email Data (bounces, sents..)?
• How many of you send targeted/remarketing email campaigns based
on behavioral data?

17. Level 1 - Insight
• Campaign Tracking: Allow users to easily see the email campaign
response data in WebTrends
• Make decisions based on performance

18. Level 2– Comprehensive View
• Email partner campaign metrics “sent”, “bounced” and other metrics
alongside WebTrends stats
• Make BETTER decisions with comprehensive insight

19. Level 3– Taking Action
• Targeting/Remarketing –Provide email partner visitor lists based on
predefined/custom segments
• Drive performance (lift) with precise and relevant messages based
on behavioral data
Report DB
WebTrends Data Scheduler
ODDB Email Partner
Analytics (SFTP)

20. Example Remarketing Analytics Report

21. Data Scheduler Setup

22. Data Schedule Details

23. Data Scheduler Setup Example

24. Taking Action Prerequisites
• WebTrends Product
– Analytics. Yes Analytics!
• Data Scheduler
– SFTP
• Custom Reports
• Dedicated Profile
– OR
– Marketing Warehouse
• Named Queries
• NOTE: Best if requirements involve Score, complex queries or significant scale
• Data Capture
– Visitor ID (Obfuscated recommended)
– Conversions or other remarketing criteria tagged

25. CRM Integration
Similar Approach as Email Remarketing
Report DB
WebTrends Data Scheduler CRM
ODDB
Analytics (SFTP)

26. CRM Integration
• Data augmentation and enrichment of web behavior at the
VISITOR level
• Enable actionability around contact/lead opportunities for
sales and/or marketing
• Provide insight into other areas of focus (interest or concern)
that a lead or contact may have
– X-sell/up-sell opportunities
– Support/Cust Service issues
• Acquistion is only part of the puzzle, focus on the
'understand, maintain & grow' areas of the customer
relationship.

27. SFDC Example – Summary and Detailed Insight

28. Other Examples of Data Access/Integration
Scorecards/Dashboards
•
Data Warehouse Feed
•
Top searched terms on Site
•
Top viewed products on site
•
Most viewed/emailed Articles
•
Partners (ForeSee Results, Optimost…)
•
• Tell us about your data integrations on the developer community
– developer.webtrends.com

29. Keys to Integration Success
Understanding both systems and data points.
•
Define a starting point / objective and stick to it.
•
Pilot and plan for iteration
•
Document and cross departmental collaboration
•
Executive buy in
•

30. New REST based Web Services
What are the new analytics web services?
• These new services augment, not replace, current ODBC and Web
Services technology.
Even easier to use
•
Allow for trending capabilities
•
Make it easy to integrate into Excel
•
Increased performance
•
Provide developer support (examples, documentation, community)
•
Public Beta
• Begins Tue April 7
• Developer site, http://developer.webtrends.com

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WebTrends and
John DeFoe and
Tom Masterman Internet Broadcasting
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32. QUESTIONS?
Tom Masterman John DeFoe
Director, Research & Metrics VP of Solution Services
Internet Broadcasting WebTrends
tmasterman@ibsys.com john.defoe@webtrends.com
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33. Appendix
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34. Appendix

35. Web Services REST Interface
Easily composed URLs that allow users to access data as XML,
JSON or Excel formats
Example:
https://server/v1/profile/Zedesco/report/KeyMetricsSummary?period=2008.m01&format=xml

36. Methods
get report data for period
retrieves report data for a given profile for a given time period
get report definition
returns the report definition for the given report ID
list reports for profile
lists the reports defined for a profile
list profiles
provides a list of profiles for an account
list profile time periods
returns the time periods for a given profile

37. Report Parameters
format
JSON, Excel, or XML
period
Time period of interest, expressed as year, month, day or trend length
measures
Measures to return
dimension
The report dimension name (for example, page)
search
String within a dimension name; returns only rows containing that string
range
Number of rows to return
totalsonly
Set to true to return totals only

40. URL Builder
Prototype
New REST format
foundation of
creating a app like a
“URL Builder”.
Example, “List
Profiles” is a request,
“List Reports” is a
request, “List
Measures”, etc.