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howtoturnbigdataintobetterdecisionspauwelsemac2016

  1. 1. How to turn better data into better decisions? Prof. Dr. Koen Pauwels Keynote Speech EMAC 2016
  2. 2. Wonderful marketing analytics for today’s data-rich environments • In Academic settings: Wedel & Kannan (2016) • And in practice: “Data is the new oil” (Intl. Meeting of Marketing & Data Scientists” GfK)
  3. 3. But do they improve decisions ? • “Our organization has more data than we could possibly use” (every survey since 2010)
  4. 4. But do they improve decisions ? • “Our organization has more data than we could possibly use” (every survey since 2010) • 70% of CEOs have lost trust in their marketing teams, stating marketers “live too much in the brand, creative, and social bubble” (Fournaise 2012 Global Marketing Effectiveness Program)
  5. 5. But do they improve decisions ? • “Our organization has more data than we could possibly use” (every survey since 2010) • 70% of CEOs have lost trust in their marketing teams, stating marketers “live too much in the brand, creative, and social bubble” (Fournaise 2012 Global Marketing Effectiveness Program) • “I have more data than ever, less staff than ever, and more pressure to demonstrate marketing impact than ever”—A CMO
  6. 6. Big data project issues • > 55% of big data projects not completed even more fail to meet expectations (Iyer 2014) • Big data passed Hype Cycle, moves through Trough of Disillusionment (Gartner 2014)
  7. 7. From hype to scrutiny
  8. 8. Big Data is often (Marr 2014) “like sitting an exam and not bothering to read the question, simply writing out everything you know on the subject and hoping it will include the information the examiner is looking for.”
  9. 9. Big Data should be (Marr 2014) “about the interface between the analytical, experimental science that goes on in data labs, and the profit and target chasing sales force and boardroom”
  10. 10. When Big Data Goes Bad
  11. 11. Examples of big data gone bad • “Keep Calm and Rape a Lot” T-shirt (Solid Gold Bomb code combines popular memes) • Google Flu trends predicts winter more than the flu: residual autocorrelation + seasonality
  12. 12. Why does this happen? Lazer et al. 2015 • Big data hubris: big data assumed to substitute for traditional data collection & analysis “It’s not the Size of the Data, it’s what you do” e.g. GFT underperforms other flu models but can be combined as it provides complementary info • Measurement dynamics (Peters et al. 2013): Google updates its algorithm often for profits & ‘popular’ terms makes search endogenous
  13. 13. Our take: human biases (3C’s) : 1) Confirmation bias 2) Communication misunderstandings 3) Control Illusions
  14. 14. Which match the 3Vs of Big Data: • Volume: with more data, you have more opportunity to find confirmation for your idea • Variety: text used as quotes by one manager, volume or valence metrics by others,… • Velocity: fast changing, real-time metrics give illusion of control, but are not leading KPIs
  15. 15. Lean Start-up Model 1) Make hypotheses explicit & test them fast e.g. Zappos: will consumers buy shoes online? 2) Visualize and Simulate with the Right Metrics: Consider or Love Brand? Social media or Survey? 3) Build-Measure-Learn loop (Reis 2011): create Minimum Viable Product and adjust to feedback
  16. 16. Experiment tactics: the multi armed bandit
  17. 17. Experiment Strategy (Wiesel et al. 2011) Google Adwords High Base Flyers Base Group 1 Control Low Group 2 Group 3
  18. 18. Field Experiment – Net Profit Changes | 19 Adwords High Base Flyers Base € 81.39 € 10.84 Low € 153.71 € 135.45
  19. 19. 2) Variety challenges • ‘My colleague in charge of social listening brings great insights, but he can’t tell me why they said it and in what context”, Barry Jennings, Global Marketing Insights Director, Dell (2013) • ‘A limitation of analytics which only make use of customer records is that intangible but important variables such as brand awareness, image and attitudinal data, are absent’ – Kevin Gray (2013)
  20. 20. Integrate slow moving attitudes and fast online actions (Pauwels & van Ewijk 2013) Web visits KNOW COGNITION Aware Consider Buy LIKE Click Visit AFFECT Prefer Loyalty Experience & Express DO Search
  21. 21. Right Metrics: Love Marks or Safe Bets ? Low sales conversion High sales conversion Low response to marketing Liking Emerging Awareness Mature Consideration Emg Cost More Mat (-) High response to marketing Consideration Mat Awareness Emg Liking Mature Cost More Emerging
  22. 22. Visualize & Simulate Simply: a slide bar 23 © Koen H. Pauwels 2015 / / /
  23. 23. Heatmaps explore feasible profit lifts Heat Map of the Interaction of Two Marketing Variables on Profits Price in $ #REF! 10 15 20 25 30 35 40 45 50 55 60 65 70 75 TVadvertisinginthousandsof$ 0 0.02 1.04 1.92 2.64 3.22 3.65 3.93 4.06 4.04 3.87 3.56 3.09 2.47 1.71 250 0.65 1.68 2.56 3.28 3.86 4.29 4.57 4.70 4.68 4.51 4.19 3.73 3.11 2.35 500 1.25 2.27 3.15 3.87 4.45 4.88 5.16 5.29 5.27 5.10 4.79 4.32 3.70 2.94 750 1.79 2.81 3.69 4.41 4.99 5.42 5.70 5.83 5.81 5.64 5.33 4.86 4.24 3.48 1000 2.28 3.30 4.18 4.91 5.48 5.91 6.19 6.32 6.30 6.13 5.82 5.35 4.73 3.97 1250 2.72 3.74 4.62 5.35 5.92 6.35 6.63 6.76 6.74 6.58 6.26 5.79 5.18 4.41 1500 3.11 4.13 5.01 5.74 6.32 6.74 7.02 7.15 7.13 6.97 6.65 6.18 5.57 4.80 1750 3.45 4.48 5.35 6.08 6.66 7.09 7.37 7.50 7.48 7.31 6.99 6.52 5.91 5.14 2000 3.74 4.77 5.65 6.37 6.95 7.38 7.66 7.79 7.77 7.60 7.28 6.82 6.20 5.44 2250 3.99 5.01 5.89 6.62 7.19 7.62 7.90 8.03 8.01 7.84 7.53 7.06 6.44 5.68 2500 4.18 5.21 6.08 6.81 7.39 7.81 8.09 8.22 8.21 8.04 7.72 7.25 6.64 5.87 2750 4.32 5.35 6.23 6.95 7.53 7.96 8.24 8.37 8.35 8.18 7.86 7.40 6.78 6.02 3000 4.42 5.44 6.32 7.05 7.62 8.05 8.33 8.46 8.44 8.27 7.96 7.49 6.88 6.11 3250 4.46 5.49 6.36 7.09 7.67 8.10 8.38 8.51 8.49 8.32 8.00 7.54 6.92 6.15 3500 4.46 5.48 6.36 7.09 7.66 8.09 8.37 8.50 8.48 8.31 8.00 7.53 6.91 6.15 3750 4.40 5.43 6.30 7.03 7.61 8.04 8.32 8.45 8.43 8.26 7.94 7.48 6.86 6.09 4000 4.30 5.32 6.20 6.93 7.50 7.93 8.21 8.34 8.32 8.15 7.84 7.37 6.75 5.99 4250 4.14 5.17 6.04 6.77 7.35 7.78 8.06 8.19 8.17 8.00 7.68 7.22 6.60 5.84 4500 3.94 4.97 5.84 6.57 7.15 7.57 7.85 7.98 7.97 7.80 7.48 7.01 6.40 5.63 4750 3.69 4.71 5.59 6.32 6.89 7.32 7.60 7.73 7.71 7.54 7.23 6.76 6.14 5.38 5000 3.38 4.41 5.29 6.01 6.59 7.02 7.30 7.43 7.41 7.24 6.92 6.46 5.84 5.08
  24. 24. Visualize effectiveness over time 25 © Koen H. Pauwels 2015 / / /
  25. 25. Compare profit from saved scenarios | 26
  26. 26. How to turn Data into Decisions ? Big Data V’s Challenges C’s Lean Startup’s Advice Volume Confirmation Identify & Test Hypotheses Fast Variety Communication Visualize & Simulate the Right Metrics Velocity Control Loop in Build- Measure-Learn
  27. 27. Why ‘traditional’ skills are key • The biggest reason that investments in big data fail to pay off, though, is that most companies don’t do a good job with the information they already have. They don’t know how to manage it, analyze it in ways that enhance their understanding, and then make changes in response to new insights. (Leek et al. 2015)
  28. 28. It’s Not the Size of the Data – It’s How You Use It: Smarter Marketing with Analytics and Dashboards Koen Pauwels, 2014 Want to learn more ? Questions?
  29. 29. • Contact me at koen.h.pauwels@gmail.com • LinkedIn/Twitter handle: koenhpauwels • My blog: https://analyticdashboards.wordpress.com • Professional Facebook page: https://www.facebook.com/pages/Smarter-Marketing- with-Analytics-Dashboards/586717581359393 • And check out my practical book: It’s not the Size of the Data, it is How You Use it: Smarter Marketing with Analytics & Dashboards Want to learn more?
  30. 30. It’s not the Size of the Data, it is How You Use it: Smarter Marketing with Analytics & Dashboards • Available at: http://www.amazon.com/Its-Not-Size-Data- How/dp/0814433952 • LinkedIn/Twitter: koenhpauwels • Facebook: https://www.facebook.com/pages/Smarter- Marketing-with-Analytics-Dashboards/586717581359393 • Blog: https://analyticdashboards.wordpress.com Want to learn more? My book:

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