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McKinsey: Understanding shifts in consumer behavior

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Big data is getting bigger, creating more challenges and opening more opportunities for businesses. This McKinsey presentation argues that CMOs and sales leaders need to take 5 actions: harness their data, put data at the heart of the organization,

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McKinsey: Understanding shifts in consumer behavior

  1. 1. June 2012 Le Web, London Any use of this material without specific permission of McKinsey & Company is strictly prohibited Faster than Real Time Understanding shifts in digital consumer behavior
  2. 2. McKinsey & Company | 1 So where does all this data come from? Exabytes of data … 1,200 1,000 400 800 200 600 0 1986 1993 2000 2007 2012 One exabyte = 4,000 x info in U.S. Library of Congress 95%+ now digital, vs. 25% in 2000
  3. 3. McKinsey & Company | 2 This trend will only accelerate: BIG and BIGGER data Internet users worldwide 2020 5 B 2010 1.9 B Digital information in the world – videos, photos, music, texts, etc. 53 zettabytes2020 800 exabytes2010 Mobile subscribers 2020 10 B 2010 5 B
  4. 4. McKinsey & Company | 3 The rise of personal mobile phones is a key driver of this trend, especially in emerging markets India internet traffic by type, desktop vs. mobile, 12/08–5/12
  5. 5. McKinsey & Company | 4 A smart phone now has computing power superior to the computers needed to send a man to the moon in 1969…
  6. 6. McKinsey & Company | 5 Data storage, exabytes 50 300 250 200 150 100 0 2007200019931986 Global installed, optimally compressed, storage Technology performance has increased exponentially, opening the door to big data … 1212 million instructions per second Overall 2010200019931986 This computational power is equivalent to almost 3 billion laptops Analog Digital
  7. 7. McKinsey & Company | 6 … and more sophisticated analytics that help make big data relevant for business decisions Example analytic methods Visualization tools Social network mappingPredictive modeling Neural networks
  8. 8. McKinsey & Company | 7 Big data is now better, quicker and cheaper Limited, one-shotvs. Massive amount of experimentation Laggingvs.Real-time Laggingvs. Sometimes ahead of time vs. Separate sets of non interoperable data Easy mash-up vs. Stated Behavioral- and intent-based Data in nodes vs. Separate
  9. 9. McKinsey & Company | 8 5 10 15 20 25 30 35 40 45 50 55 60 65 % of buyers in category Research online 0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 % of buyers in category Purchase online The second e-commerce “big bang” now touches every product category Still in store Grocery HH Products Digital battleground Furniture Footwear DIY Clothing Home Décor Health & Beauty Mobile phones Gone to digital Electronics DVD/Video Books Video Games Computer HW / SW Music
  10. 10. McKinsey & Company | 9 Online research 50% +22% 2012 2010 41% Percentage of respondents who used online research before buying % Internet users, Europe Online research is becoming a major channel for shopping related research, especially via mobile phones 21% +75% 2012 2010 12% Online research via a mobile phone
  11. 11. McKinsey & Company | 10 Social networks are becoming gateways to other activities … First place to log in on the computer First place to get access to content Video discovery tools Percent of respondents who selected social networks +73% 2011201020092008 19 21 17 11 +162% 2011201020092008 35 31 20 13 +130% 2011201020092008 14 109 6
  12. 12. McKinsey & Company | 11 Number of days before and after the launch of Free mobile -4-6 0-2 2 64 0 2,000 1,000 … which makes online communities a highly cost effective marketing channel Online buzz for Free mobile was much higher than any competitor… Buzz volume; Index, as part of total normalized researches
  13. 13. McKinsey & Company | 12 The end of average – online consumer is diverse and sophisticated Online consumer segments and attributes relative to average respondent Size of segment, indexed 16 "Digital media junkies" 30 "Traditionalists" 13 "Digital communicators" 9 "On-the-go workers" 21 "Video digerati" 9 "Professionals"11 "Gamers"
  14. 14. McKinsey & Company | 13 2.9 2.7 2.5 2.3 2.1 Offline buyers Online buyers Acceptance of targeted online ads Index Financial situation Declared response In great difficulty In difficulty Making ends meet Well-off Very well-off % of the population: 10% The “de-averaged” online customer requires highly targeted marketing in this data rich world
  15. 15. McKinsey & Company | 14 What if we get it wrong and get lost in zettabytes?
  16. 16. McKinsey & Company | 15 5 areas where you need to act A wealth of data out there: use it It applies to your organisation Data based decision making Managing through Big data Real business impact
  17. 17. McKinsey & Company | 16 0 10 20 30 40 50 60 70 80 90 100 2006 2007 20082005 2009 2010 2011 2012 Economic recession Euro crisis Greek debt crisis Intensity Free data holds insights +516% +223%
  18. 18. McKinsey & Company | 17 People can predict flu epidemics – and you can watch in real time Stomach Flu Flu 07/11/11 13/02/1230/01/1216/01/1202/01/1219/12/1105/12/1121/11/11 0 10 20 30 40 50 60 70 80 90 100 Number of searches on ‘flu’ doubling implies you have more than 1/3 chance of catching it in the next 2 weeks
  19. 19. McKinsey & Company | 18 Big data permits “nowcasting,” eg. consumer product launch and search Index; normalized 100 at launch date Launch Sales Online searches Intensity Percent Week 5Week 4Week 3Week 2Week 1One week before launch 0 20 40 60 80 100 120 140
  20. 20. McKinsey & Company | 19 Big data permits “nowcasting,” e.g. movie box office and social media Box office index vs. social mentions; 2 weeks in advance, worldwide Tweet rate correlation outperformed existing market-based predictors correlation coefficient between a movie tweet-rate and its box office performance
  21. 21. McKinsey & Company | 20 A whole „flora‟ of start-ups using Big data is emerging… Crunch geolocalized data MAP MY MOBILES
  22. 22. McKinsey & Company | 21 …with diverse approaches and objectives Understanding time usage Crunch Twitter Marketing Investing
  23. 23. McKinsey & Company | 22 Kaggle is generating very unusual and insightful content through large data analyses Unusual insights Innovative solution for statistical/ analytics outsourcing In the Eurovision contest, Israel will vote disproportionately for Belarus If you watch a movie that ends in a number, you will probably think less of it than if it had a different title Quality of online photos predicted by captions: ▪ Higher-rated captioned Peru, Cambodia, Michigan, tombs, trails and boats ▪ Poorly-rated captioned San Jose, mommy, graduation and C.E.O.
  24. 24. McKinsey & Company | 23 Obama campaign and Big Data Full team of non political tech innovators hired to develop highly specific profiles of potential voters This allows strong tailoring of messages Constant enrichment of the database with results to tested approaches Obama’s 2012 campaign organized as a real digital operation
  25. 25. McKinsey & Company | 24 Amazon continually runs tests and analyzes vast amounts of data to optimize web site design – EVERY MINUTE different versions of website for same user in sessions 1 minute apart  25+ PhD’s  7+ parallel different versions  Statistical and empirical  Real time
  26. 26. McKinsey & Company | 25 Example of best practice Average for key competitors EBITDA 2000–10 Compound annual growth rate (%) Granular customer insight has become a crucial competitive differentiator 6.0 10.8 5.0 11.7 -2.2 26.6 -4.9 29.5 1.7 56.5 Media Bank Retailer Insurance
  27. 27. McKinsey & Company | 26 Big Data can generate significant financial value across sectors US health care US retail Europe public sector administration Manufacturing Global personal location data
  28. 28. McKinsey & Company | 27 eric_hazan@mckinsey.com (@Eric5555) philipp_nattermann@mckinsey.com http://cmsoforum.mckinsey.com @McK_CMSOForum Thank you!

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