A website is just like a store You have millions of people visiting you every day and shopping on your site 3.So where does Analytics fit here? 4. Web Analytics is the invisible shopper who goes around the store and watches everyone's behavior.5. Web Analytics takes the behavioral attributes and helps business with insights! 6. Know the travel details such as how many travelers, what kind of travelers, any preferred carrier or hotels? 7. Understand the shopping patterns. Does he want to shop only on weekends or else only on Thursdays. 8. Focus on Visit Patterns. How many times does he come to the site before he buys anything 9. Learn the page navigation. I.e does he see 100 pages every time he comes or does he know exactly what to look at
Big Old Elephant: http://wallszone.com/wallpapers/Big_Old_Elephant_HD_1080P_1920x1080_2916.jpg
1. Last year at EMetrics I had interesting tweet exchanges with few folks. In essence we were talking about the importance of visitor level granularity of data and how it will impact personalization.Here I have 3 use cases which is being enabled through Big Data at OWW.2. Our CEO affectionately calls this EFX – We use Hadoop to analyze the attributes from Site Analytics, Internal logs(Consists of multiple application logs and NOT just weblogs), MVT logs. All these in essence will funnel our regression models. One of the key wins from the Machine Learning team was to analyze, build and implement the recommendation engine for out hotel search. The data was from our Site Analytics, and some other internal application logs. was analyzed using MR Hadoop jobs. The results we saw was astonishing. 7% interaction rate37% had a likely chances to continue deep in the funnel2.6% increase in booking path engagement.The beauty of this is the Big Data Analytics is fed into a machine and it learns and changes as time progresses. 3. PPC bidding based on Site Analytics data. EX in turn funneling out PPC channels. The results are very encouraging. Helps us with regression analysis4. The final use case is where we learned that Mac users tend to in general spend more money on our sites
We perform around 100s of tests on our site. We need to analyze the consumer patterns and behaviors which will enable us to make the consumer experience very goodOne of the test we perform is around the search results. Based on the visitor we usually alter the search results order to make it more personal to them.This enables the user to find the right hotel results very easilyImagine you as a user take a yearly vacation in Honololu Hawaii and always stay at the same hotel. If we know this information about you we can serve you better to show that hotel at the top.We also do a lot of testing around layout, colors, button placementEventually lot of this data flows through our Hadoop infrastructure which will enable us to to perform modeling exercise and analysis of our control and test groups.
1. We faced organizational resistance to deploying Hadoop.Not from management, but from other technical teams.Required persistence to convince them that we needed to introduce a new hardware spec to support Hadoop.CTO was a big believer and provided technical guidance with Big Data. This helped a lot in making this a success at organization. Its not everyday you have CMO and GVP asking their team members to get data out of Hadoop
1. Here are some key learning's from our experience and some thoughts for you to consider 2. If you have the strength of technology go for it. 3. This needs heavy investment from time and resource perspective 4. Like I mentioned many times data without analysis is worthless 5. Senior Leadership buy in is a must. We had huge support from our CTO, CEO and CMO6. Data Governance is a must7. Lastly if you want to succeed then you need to fight the tough battles and make the tough choices.
Big Data Analytics from a Practitioners View
Big Data Analytics from
a Practitioners view
About Raghu Kashyap
Areas of Responsibility
Data Insights Group (Site analytics,
Competitive Intelligence, Big Data)
Orbitz India, supporting Analytics
and BI teams
US, Europe, Australia(APAC)
Director – Data Insights Group
Strong background with technology(13
years) passion and experience with
analytics(4 years) and big data (3.5
Masters in Computer Science
Golf, traveling, helping non-profit
organizations, spending time with my
wife and 2 boys
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.
Love Thy Hadoop
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
Show me the money
EFX – Every Friggin X
PPC bidding efficiencies
MAC vs. PC
Marketing Channel optimization
Hotel Rate Cache optimization
Data is collected as part of RCDC.
Includes every live rate search (aka
burst) performed by our hotel stack.
Raw data: ~200 GB, compressed, 108
Extraction: <40 GB compressed, 109
Analyze behavioral and Test data from our
DWH Log analysis
• Analysis of Greenplum DB logs within Hadoop
to analyze the data usage patterns.
• Impact analysis
• Hadoop usage for the last 30 days of DB log
HIPPO is your best friend
• Expect organizational resistance from
• You can do wonders in the analytics area if
you get buy in.
Analytics using Big Data comes with a price.
Senior Leadership buy in
I can't tell you the key to success, but the key
to failure is trying to please everyone." -Ed
How to capitalize on Big Data?
Learn from people who have already
DO NOT reinvent the wheel
Buy v/s Build balance
Build once and leverage mulitple
Go where clients don’t want to go or
cant go in terms of execution.
What matters to Practitioners?
Things change dramatically in the
world of analytics
Being Agile is very important
Dashboards and Reports can take
you only to a certain level
Buy in from key groups is important
Grow business and impress Boss