The keynote speech discusses how organizations can better utilize large amounts of data to improve marketing decisions. While data analytics provide opportunities, many big data projects fail to meet expectations or improve decisions due to human biases. The speech recommends adopting lean startup principles of making hypotheses explicit, visualizing metrics, and implementing a build-measure-learn process to test ideas quickly. Combining both traditional analytical skills and big data approaches is important to effectively leverage data for smarter marketing decisions.

1. How to turn better data into
better decisions?
Prof. Dr. Koen Pauwels
Keynote Speech
EMAC 2016

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. But do they improve decisions ?
• “Our organization has more data than we
could possibly use” (every survey since 2010)

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. 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. 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)

8. From hype to scrutiny

9. 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.”

10. 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”

11. When
Big Data
Goes
Bad

12. 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

13. 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

14. Our take: human biases (3C’s) :
1) Confirmation bias
2) Communication misunderstandings
3) Control Illusions

15. 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

16. 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

17. Experiment tactics: the multi armed bandit

18. Experiment Strategy (Wiesel et al. 2011)
Google Adwords
High Base
Flyers
Base Group 1 Control
Low Group 2 Group 3

19. Field Experiment – Net Profit Changes
| 19
Adwords
High Base
Flyers
Base € 81.39 € 10.84
Low € 153.71 € 135.45

20. 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)

21. 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

22. 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

23. Visualize & Simulate Simply: a slide bar
23 © Koen H. Pauwels 2015 / / /

24. 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

25. Visualize effectiveness over time
25 © Koen H. Pauwels 2015 / / /

26. Compare profit from saved scenarios
| 26

27. 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

28. 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)

29. 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?

30. • 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?

31. 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: