This document provides an overview of using data analytics to optimize business performance. It begins with introductions and defines "big data." It then discusses how most companies are not truly leveraging big data and challenges they face. The document recommends doing an initial assessment of goals, team capabilities, data sources, and tools. It suggests starting with a small, focused data set to quickly test the analytics process and prove value. Finally, it outlines two approaches to optimization: funnel optimization and "Russian doll" optimization, where differentiating characteristics of high-performing users or items are used to improve lower-performing ones in an iterative process. The key messages are doing analytics incrementally, interpreting data intelligently, and using insights to optimize measurable

1. ETE: Going Big with Big Data…
…one step at a time
Anita Garimella Andrews
2 April 2013

2. Today’s Talk
• A bit about me
• The “Big Data” myth
• What it takes to leverage data in your biz
• A couple of approaches to using data to optimize
• QUESTIONS

3. Should You Stay?
• If you love how your biz is using data, you should
probably leave 
• This is a “data for optimization” talk – not data for
market or product research
• Geared towards web or web-enabled businesses

4. A bit about me
• General Manager, Analytics & Optimization
– Founded Sepiida, an A&O consultancy in 2009 with clients including
Zynga and Haymarket Media – sold to Delphic in 2012
– Previously, VP E-commerce at Nutrisystem
– Dir of Program Management at Ingenio, sold to AT&T
YellowPages.com
• MS Computer Science – Stanford University
• BA Politics – New York University
• Love numbers. Hate endless (and needless) discussions.
Constantly iterating.

5. What is Big Data?
• Wikipedia’s Definition
In information technology, big data is a collection of data sets so large
and complex that it becomes difficult to process using on-hand
database management tools or traditional data processing
applications.
• Keep reading…
What is considered "big data" varies depending on the capabilities of
the organization managing the set, and on the capabilities of the
applications that are traditionally used to process and analyze the data
set in its domain.
• Big is in the Eye of the Beholder.

6. Everybody’s Doing It BIG. They are Winning.

7. Are they really?

8. Reality: All over the map
Multibillion dollar companies who
didn’t look at their Google
Analytics until this year
Angel-funded start-ups who are
tracking everything with
innovative reporting software

9. Did You Know…
• Size of company has little correlation to size of
dataset?
• Size of company has little correlation to facility with
data and analytics?
• Size of company has little correlation to current
status of analytics activities?
• Size of company has little correlation to where
future efforts should be focused?

10. Common Cultural Challenges
• Large company bureaucracy
– How many stifled data geeks do you have?
– How much lost revenue?
– Lots of boxes checked. But how many smarter, more
efficient decisions?
• Data mania
– Don’t lose sight of the forest for the trees
– In smaller companies, how does all the data actually
connect to the steps needed for growth?
– More data doesn’t mean more revenue

11. What do you DO with all that data?
• Using data to create  Creative Marketing
– Big new opportunities
• Loyalty program creation, Geo-targeting, etc.
– What data to look at is often unknown
• Using data to optimize  A&O
– Often, the metric that is suffering is known
– The data subset is typically easier to identify

12. What does it take?
• The right goals
• The right people
• The right tools
• The right perspective about the process
• “Right” is in the eye of beholder.
• What is YOUR environment?

13. Let’s define a few things
• Data is the activity being tracked in your system
• Reporting is the presentation of that data in
comprehensible, actionable ways
• Analytics is the smart interpretation of the data
through the reporting that creates measurable
improvements to the product offering
• Different companies do each of the above
differently and with different levels of accuracy,
efficiency, and beauty

14. First, Do an Assessment…Quickly
• Goals
• Team capabilities
• Sources of data
• Tools for reporting
• Opportunities

15. Assessment: Goals
• What specific metrics or KPIs do you want to
improve?
• What are the formulas for these?
– Need consistent definitions!
• What will move your Analytics practice forward?
– Think of A&O as sales and evangelization
– If you do it right, you’re the source of improvement for
other parts of the business

16. Assessment: People & Teams
• What are your strengths?
• Where are your holes?
• Answer is not always hiring
• If I could have only 2 people:
1. Technical person to query the database or produce
accurate reports
2. The “forest for the trees” business person

17. Assessment: Sources of data
• Bet you have LOTS of data
– Web traffic data
– Transactional databases
– Internal toolsets (often different DBs)
– Third party (email, CRM, etc.)
Key questions
1. How accurate are each of these?
2. How much of what you need are you actually tracking?
3. Which of these has the answers to your goals?

18. Short commercial break…
• Fight the impulse to “track everything”
– Technically painful
– Painful for business people
– You don’t need it to drive your business forward
– There is no glory in having lots of data. Size does NOT
matter here…

19. Assessment: Tools
• Collecting Data & Reporting
– GA vs. the rest (KISSMetrics, MixPanel, Omniture)
– GoodData, Domo, RJ Metrics
– Excel!
• There are no good analysis or analytics tools.
Yea, I said it.
Stop looking for them. It’s about people and practices.

20. Moving the Dial
• What should you do NOW?
IDENTIFY
People
THIS
Good Low
Data KPIs
Tools

21. What This Means
• It may not target the largest pool
• It may not even be web-based
• It may not be obvious
• It may FAIL
• Goal is to experiment with process, prove value and
get data-driven results quickly
• Data driven culture will come from doing data
driven things

22. What do you DO with ALL that data?
• Have perspective about the process
• It’s all iterative. It’s not sexy, but it drives the
numbers UP.
– And that gets teams excited, grows your capabilities,
increases confidence, and so on.
• Two approaches:
– Funnel optimization
– Russian Doll optimization

23. Russian Dolls Optimization
1. Determine
differentiating
characteristics
Decent Users of “A”
“Grade D”
2. Use that to
move more
Good Users “B’s” into “A”
“Grade C”
3. Repeat
Great Users
“Grade B” 4. Lessen the
Delta = Widen
the Base
Best Users
“Grade A”

24. Some other truths
• “Small” data sets are okay to work with
– Develop instincts that allow you to trust the data
• Don’t worry about what competitors using “big
data” are doing
– You don’t know what works/doesn’t in their product

25. Let me leave you with this…
D A T A
Synthesiz
Harness Optimize
e
The right data,
Iterative,
from the right Intelligent
measured
places – Interpretation
execution of
accurately & &
prioritized data-
actionably Insights
driven tactics
reported
Faster, Better, Decision-Making to Improve KPIs

26. Thank you!
Anita Garimella Andrews
GM, A&O
Delphic Digital
@agarimella
aandrews@delphicdigital.com