howtoturnbigdataintobetterdecisionspauwelsemac2016

How to turn better data into better decisions?

Prof. Dr. Koen PauwelsKeynote Speech

EMAC 2016

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)

But do they improve decisions ?

• “Our organization has more data than we could possibly use” (every survey since 2010)

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)

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

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)

From hype to scrutiny

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 andhoping it will include the information the examiner is looking for.”

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”

When

Big Data

Goes

Bad

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

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

Our take: human biases (3C’s) :

1) Confirmation bias

2) Communication misunderstandings

3) Control Illusions

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

Lean Start-up Model

1) Make hypotheses explicit & test them faste.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

Experiment tactics: the multi armed bandit

Experiment Strategy (Wiesel et al. 2011)

Google Adwords

High Base

Flyers

Base Group 1 Control

Low Group 2 Group 3

Field Experiment – Net Profit Changes | 19

Adwords

High Base

Flyers

Base € 81.39 € 10.84 Low € 153.71 € 135.45

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)

Integrate slow moving attitudes and fast online actions (Pauwels & van Ewijk 2013)

Web visits

KNOWCOGNITION

Aware

Consider Buy

LIKEClick

Visit

AFFECT

Prefer

Loyalty

Experience& Express

DOSearch

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

© Koen H. Pauwels 2015 / / /

Visualize & Simulate Simply: a slide bar

Heatmaps explore feasible profit liftsHeat 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 75TV

advertising in thousands of

$

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

© Koen H. Pauwels 2015 / / /

Visualize effectiveness over time

Compare profit from saved scenarios

| 26

How to turn Data into Decisions ?Big Data V’s Challenges C’s Lean Startup’s

AdviceVolume Confirmation Identify & Test

Hypotheses Fast

Variety Communication Visualize & Simulate the Right Metrics

Velocity Control Loop in Build-Measure-Learn

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)

It’s Not the Size of the Data – It’s How You

Use It:Smarter Marketing with Analytics and

DashboardsKoen Pauwels,

2014

Want to learn more ?

• 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?

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: