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About Us
Raghu Kashyap - Director Web Analytics
Twitter: @ragskashyapBlog: http://kashyaps.comEmail: [email protected]
Michael Wetta - Marketing Strategy & Analytics
Email: [email protected]
Overview
Web Analytics journey Orbitz Worldwide What challenges exist? Big Data Analysis Business Testimonial Centralized Decentralization Dos and Don’ts What Hadoop is being used for beyond Web
Analytics at Orbitz Where else? Conclusion
What is Web Analytics?
Understand the impact and economic value of the website
Rigorous outcome analysis
Passion for customer centricity by embracing voice-of-customer initiatives
Fail faster by leveraging the power of experimentation(MVT)
Web Analytics History
1993 – Web server logs (Webtrends)
213.60.233.243 - - [25/May/2004:00:17:09 +1200] "GET /internet/index.html HTTP/1.1" 200 6792 "http://www.mediacollege.com/video/streaming/http.html" "Mozilla/5.0 (X11; U; Linux i686; es-ES; rv:1.6) Gecko/20040413 Debian/1.6-5”
151.44.15.252 - - [25/May/2004:00:17:20 +1200] "GET /cgi-bin/forum/commentary.pl/noframes/read/209 HTTP/1.1" 200 6863 "http://search.virgilio.it/search/cgi/search.cgi?qs=download+video+illegal+Berg&lr=&dom=s&offset=0&hits=10&switch=0&f=us” "Mozilla/4.0 (compatible; MSIE 6.0; Windows NT 5.1; Hotbar 4.4.7.0)”
Web Analytics History
2009/2010 – Major acquisitions (Adobe, IBM, Comscore)
2009/2010 – Big Data (IBM, Facebook, Orbitz, Informatica, Greenplum)
Web Analytics today
Site Analytics Multi Variate Testing (MVT) Voice of Customer (VOC) Competitive intelligence
Site Analytics
The “What” of Web Analytics
Helps measure:
Visits/Visitors
Page views
Conversion
SEO activities
Traffic Source
Challenges
Site Analytics Lack of multi-dimensional capabilities Hard to find the right insight Heavy investment on the tools Precision vs Direction
continued….
Big Data No data unification or uniform platform
across organizations and business units No easy data extraction capabilities
Business Distinction between reporting and
testing(MVT) Minimal measurement of outcomes
Web Analytics & Big Data
OWW generates couple million air and hotel searches every day.
Massive amounts of data. Over hundred GB of log data per day.
Expensive and difficult to store and process this data using existing data infrastructure.
Big Data Infrastructure
Infrastructure provides:
Long term storage for very large data sets. Open access to developers and analysts. Allows for ad-hoc querying of data and rapid
deployment of reporting applications.
Data Analysis Jobs
Traffic Source and Campaign activities
Daily jobs, Weekly analysis
Map reduce job ~ 20 minutes for one day raw logs ~ 3 minutes to load to hive tables Generates more than 25 million records for a month
Data Categories
Traffic acquisition
Marketing optimization
User engagement
Ad optimization
User behaviour
Crossing the Chasm: Shifting from Innovation to Mainstream Consumption
Adapted from Geoffrey A. Moore – Technology Adoption Lifecycle
1. Background on Analytics at Orbitz
2. Crossing the Chasm Framework
3. Application
Crossing the Chasm: Shifting from Innovation to Mainstream Consumption
Adapted from Geoffrey A. Moore – Technology Adoption Lifecycle
Innovators Visionaries Mainstream
Adapted from Geoffrey A. Moore – Technology Adoption Lifecycle
Crossing the Chasm: Shifting from Innovation to Mainstream Consumption
Crossing the Chasm: Shifting from Innovation to Mainstream Consumption
Adapted from Geoffrey A. Moore – Technology Adoption Lifecycle
1. Consistent Message of Capabilities
2. Understanding and Handling Reservations
3. Inclusion in the development cycle
4. Storage and Accessibility
Key Components Adoption:
Model for success
Measure the performance of your feature and fail fast
Experimentation and testing should be ingrained into every key feature.
Break down into smaller chunks of data extraction
Should everyone do this?
Do you have the Technology strength to invest and use Big data?
Analytics using Big Data comes with a price (resource, time)
Big Data mining != analysis Key Data warehouse challenges still exist (time,
data validity)
Other Key Projects
Machine Learning team Measuring page download performance using
site analytics logs Storing and processing production application
logs Data cache analysis
Where else?
Amazon - Was Amazon's recommendation engine crucial to the company's success?
Facebook – A Petabyte Scale Data Warehouse using Hadoop
EBay – The power of the Elephant
Apple – iAds, UX and Data analytics
Conclusion
Invest in the 10/90 rule (10$ on tools and 90$ on people) – Avinash Kaushik
Analytical thinking engineers/analysts
Empower individual feature teams to manage their own analytics(Centralized Decentralization)
Focus on Analysis more than reporting
Reference Web Analytics Association
http://www.webanalyticsassociation.org/
Avinash Kaushik http://kaushik.net
Twitter #measure
Analysis Exchange http://www.webanalyticsdemystified.com