ETE 2013: Going Big with Big step at a time


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ETE 2013: Going Big with Big step at a time

  1. 1. ETE: Going Big with Big Data… …one step at a time Anita Garimella Andrews 2 April 2013
  2. 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. 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. 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• MS Computer Science – Stanford University• BA Politics – New York University• Love numbers. Hate endless (and needless) discussions. Constantly iterating.
  5. 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. 6. Everybody’s Doing It BIG. They are Winning.
  7. 7. Are they really?
  8. 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. 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. 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. 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. 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. 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. 14. First, Do an Assessment…Quickly• Goals• Team capabilities• Sources of data• Tools for reporting• Opportunities
  15. 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. 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. 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 questions1. 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. 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. 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. 20. Moving the Dial• What should you do NOW? IDENTIFY People THIS Good Low Data KPIs Tools
  21. 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. 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. 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. 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. 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. 26. Thank you!Anita Garimella AndrewsGM, A&ODelphic