The Value of Mining (Big) Data - Data-Driven Marketing Conference

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It was a pleasure to kick-off Marketing Magazine's Data-Driven Marketing Conference (August 2013).

These are the slides from my presentation in which I talk about the the key challenges of "big data" and present both academic and brand-based case studies on how data, bigger data, and BIG data can:

1. Drive brand and consumer insights
2. Evaluate if marketing messages are working
3. Better target marketing communications efforts
4. Become a company asset to market
5. Allow for continued experimentation

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The Value of Mining (Big) Data - Data-Driven Marketing Conference

  1. 1. The Value of Mining (Big) Data… Without Scaring Your Customers Matthew Quint Director, Center on Global Brand Leadership Columbia Business School gsb.columbia.edu/globalbrands @mattquint Data Driven Marketing Conference – August 20, 2013 [NOTE: Images for Prof. Netzer, Everyday Health, The Weather Company, and Target, have hyperlinks to video talks on the topic!]
  2. 2. …and not everything that counts can be counted - Prof. William Bruce Cameron Not everything that can be counted counts…
  3. 3. Data Bigger Data BIG Data Small No integration Unit collected Large Some integration Firm collected Massive Heavy integration Firm and external
  4. 4. All marketers want to be DATA-DRIVEN Believe successful brands use data to drive marketing decisions 91% But many are NOT COLLECTING the data they need say their own company’s data are collected too infrequently 39% Marketing ROI in the Era of Big Data: 2012 BRITE-NYAMA Marketing Measurement in Transition Study David Rogers and Prof. Don Sexton, Columbia Business School
  5. 5. TOO LITTLE TOO INFREQUENT NOT SHARED NOT SPECIFIC DON’T PERSONALIZE
  6. 6. “The evidence is clear: Data-driven decisions tend to be better decisions. In sector after sector, companies that embrace this fact will pull away from their rivals.” - Erik Brynjolfsson and Andrew McAfee, MIT (Harvard Business Review)
  7. 7. Five key CHALLENGES of (Big) Data
  8. 8. Everywhere
  9. 9. Unstructured
  10. 10. Needs cleaning
  11. 11. Storage and processing
  12. 12. Privacy and security
  13. 13. Case studies on THE VALUE of (Big) Data
  14. 14. 1. Gain insights on brands or consumers 2. Understand what messaging works 3. Better target your communications 4. Your data becomes an asset to market 5. Continue experimenting
  15. 15. Brand and consumer INSIGHTS from (Big) Data
  16. 16. Brand insights Prof. Oded Netzer
  17. 17. Edmunds.com sedan forum <Brand>Honda</Brand> <Model>Honda Accord</Model> <Model>Toyota Camry</Model> <Brand>Toyota </Brand> <Term>Best</Term> <Term>Sedans</Term> <Term>Competent</Term> <Term>Price</Term> <Term>Love</Term> <Term>Best selling</Term> <Term>Best</Term> Honda Accords and Toyota Camrys are nice sedans, but hardly the best car on the road (for many people). It's just that they are very compentant in their price range. So, a love fest of the best selling may not tell you what is "best". Text mining
  18. 18. Network analysis
  19. 19. MODEL SENTRA COROLLA CIVIC Commonalities Differentiators Economy | Small-car | Subcompact | Compact Power Performance College Mileage Plastic parts Mom/Daughter VTEC Engine Hatchback Mud guards Edmunds.com brand sentiment
  20. 20. Consumer insights
  21. 21. MESSAGING EFFECTIVENESS from (Big) Data
  22. 22. AmericanLuxury Messaging effectiveness Prof. Oded Netzer
  23. 23. Based on JD Power PIN Data Brand-switching map
  24. 24. 0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 1/1/2001 4/1/2001 7/1/2001 10/1/2001 1/1/2002 4/1/2002 7/1/2002 10/1/2002 1/1/2003 4/1/2003 7/1/2003 10/1/2003 1/1/2004 4/1/2004 7/1/2004 10/1/2004 1/1/2005 4/1/2005 7/1/2005 10/1/2005 1/1/2006 4/1/2006 7/1/2006 10/1/2006 Lift Import Luxury American Brands Linear (Import Luxury) Linear (American Brands) Brands mentioned alongside Cadillac AMERICAN brands LUXURY imports
  25. 25. 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1/1/2001 4/1/2001 7/1/2001 10/1/2001 1/1/2002 4/1/2002 7/1/2002 10/1/2002 1/1/2003 4/1/2003 7/1/2003 10/1/2003 1/1/2004 4/1/2004 7/1/2004 10/1/2004 1/1/2005 4/1/2005 7/1/2005 10/1/2005 1/1/2006 4/1/2006 7/1/2006 10/1/2006 1/1/2007 4/1/2007 7/1/2007 10/1/2007 1/1/2008 4/1/2008 7/1/2008 10/1/2008 Lift Import Luxury American Linear (Import Luxury) Linear (American) Brands traded-in for Cadillac AMERICAN brands LUXURY imports
  26. 26. Demonstrate ROI
  27. 27. TARGETING improvements from (Big) Data
  28. 28. Better ad targeting
  29. 29. Cross-Platform Campaign Ratings
  30. 30. MARKET YOUR OWN (Big) Data
  31. 31. Stores care about the weather
  32. 32. Get acquired because of data
  33. 33. Continue EXPERIMENTING
  34. 34. A/B Testing for Obama Campaign
  35. 35. DON’T FREAK OUT your customers
  36. 36. Charles Duhigg, “How Companies Learn Your Secrets,” The New York Times (Feb 16, 2012) Target’s predictive analytics
  37. 37. Tracking your whereabouts
  38. 38. “I keep saying that the sexy job in the next 10 years will be statisticians. And I’m not kidding.” - Hal Varian, chief economist at Google.
  39. 39. The data scientist 1.Quantitative 2.Technical 3.Curious and creative 4.Skeptical 5.Communicative and collaborative
  40. 40. Questions? Matthew Quint Director, Center on Global Brand Leadership Columbia Business School matthew@globalbrands.org Data Driven Marketing Conference – August 20, 2013

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