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Predictive Behavioral Targeting at ekstrabladet dkat ekstrabladet.dk

Introduction

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Maria VoigtProject Manager – ekstrabladet.dkD

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ETSALG Predictive Targeting Evaluation Project

Marketing Research Department

Advertising Method Expert

Stephan NollerManaging Director – nugg.ad

Profound experience in data analysis

Founded nugg.ad in 2006

at TNS Group (Emnid, Infratest)

at industry association AGOF

Introduction to ekstrabladet.dk

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Part of the media concern JP/Politikens HusD

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JP/Politikens HusLargest news site in Denmark

The site:The site:Breaking news User profile

In comparison Ekstra Bladet is like:Aftonbladet, SwedenVG NVG, NorwayBild, GermanyThe Sun, UK

The Danish Market

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ETN

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have internet access.

Danish internet populationDanish internet population4 million real users3 billion page impressions

Challenges

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LAD Demands for:D

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Better results (more clicks, more sales)Documented pay-off

Is targeting the solution?Is targeting the solution?

Ekstra Bladet tested two campaigns using Predictive Behavioral Targeting (PBT) from nugg.ad

Predictive Behavioral Targeting (1/3)

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LAD 1ekstrabladet.dk/news 1 2 1

Jim | Jane | Tom Tim

DET

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LG

3

3

2

Content Channels (Measured)

ekstrabladet.dk/people 3 1 3

ekstrabladet.dk/sport 0 2 3

ekstrabladet.dk/tech 2 1 2

Step 1: Click Measurement (all Users)

Predictive Behavioral Targeting (2/3)

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LAD ekstrabladet.dk/news 1 2 1

Jim | Jane | Tom Tim

1DET

NETSA

LG

Content Channels (Measured)

ekstrabladet.dk/people 3 1 3

ekstrabladet.dk/sport 0 2 3

ekstrabladet.dk/tech 2 1 2

3

3

2

Demography(Survey)

Gender 0 1 0

Age 3 3

Product Interests(Survey)

Body Care 2 4 2

Food 0 1 2

Psychography (Survey) Trendsetter 0 2 2

Step 2: Online Survey (Sample)

Predictive Behavioral Targeting (3/3)

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LAD ekstrabladet.dk/news 1 2 1

Jim | Jane | Tom

1

Tim

DET

NETSA

LG

Content Channels (Measured)

ekstrabladet.dk/people 3 1 3

ekstrabladet.dk/sport 0 2 3

ekstrabladet.dk/tech 2 1 2

3

3

2

Demography(Survey)

Gender 0 1 0

Age 3 3

0

3

Product Interests(Survey)

Body Care 2 4 2

Food 0 1 2

2

2

Psychography (Survey) Trendsetter 0 2 2 2

Step 3a: Search for Tims nearest statistical twinpStep 3b: Tim inherits missing values from his “twin” Tom

Case Study – FDM Travels

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LAD Campaign: weekend holidaysD

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Ca pa g ee e d o days

Aim: sales/purchases

Creatives:

Case Study – FDM Travels

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LAD

300

DET

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LG 200

250

Rat

e

136150

Clic

k Th

roug

h R

no PBT

100

50

100Inde

x C

PBT

0

50

HolidaysHolidays

Day 1‐2

Case Study – FDM Travels

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LAD

300

DET

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LG 200

250

Rat

e

136150

Clic

k Th

roug

h R

no PBT

100 10093

50

100Inde

x C

PBT

0

50

Holidays Plane or TrainHolidays Plane or Train Tickets

Day 1‐2 Day 3‐4

Case Study – FDM Travels

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LAD

300

DET

NETSA

LG 200

250

Rat

e

136155

150

Clic

k Th

roug

h R

no PBT

100 100 10093

50

100Inde

x C

PBT

0

50

Holidays Plane or Train Household incomeHolidays Plane or Train Tickets

Household income >54.000€ AND

(Holidays OR Plane or Train Tickets)

Day 1‐2 Day 3‐4 Day 5‐6

Case Study – FDM Travels

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LAD

300

DET

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LG 200

250

Rat

e

136155

150

Clic

k Th

roug

h R

no PBT

100 100 100 10093

106

50

100Inde

x C

PBT

0

50

Holidays Plane or Train Household income Household incomeHolidays Plane or Train Tickets

Household income >54.000€ AND

(Holidays OR Plane or Train Tickets)

Household income >54.000€

Day 1‐2 Day 3‐4 Day 5‐6 Day 7‐8

Case Study – FDM Travels

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LAD

300

DET

NETSA

LG

183200

250

Rat

e

136155

183

150

Clic

k Th

roug

h R

no PBT

100 100 100 100 10093

106

50

100Inde

x C

PBT

0

50

Holidays Plane or Train Household income Household income WomenHolidays Plane or Train Tickets

Household income >54.000€ AND

(Holidays OR Plane or Train Tickets)

Household income >54.000€

Women

Day 1‐2 Day 3‐4 Day 5‐6 Day 7‐8 Day 9‐10

Case Study – FDM Travels

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LAD

280300

DET

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LG

183200

250

Rat

e

136155

183

150

Clic

k Th

roug

h R

no PBT

100 100 100 100 100 10093

106

50

100Inde

x C

PBT

0

50

Holidays Plane or Train Household income Household income Women Holiday ANDHolidays Plane or Train Tickets

Household income >54.000€ AND

(Holidays OR Plane or Train Tickets)

Household income >54.000€

Women Holiday AND Household Income

>54.000€ AND Women

Day 1‐2 Day 3‐4 Day 5‐6 Day 7‐8 Day 9‐10 Day 11‐12

FDM Travels: Conversion Rates/Online Sales

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Not only click through rates increased. Also sales, which was the aim of the campaign, did.

DET

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LG

275

250

300

200

250

e

150

Con

vers

ion

Rat

e

no PBT

PBT

100100

Inde

x C

50

0

Case Study – DFDS Seaways

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LAD Campaign: Rockcruise competitionD

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Aim: Click Through Rate

Creatives:

Case Study – DFDS Seaways

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LAD

300

DET

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239247

200

250

118

145

125

150no PBT

100 100 100 100 100 100

118

96100

PBT

0

50

Top Article Top Article Top Article

Couples 30-49 20-29

Overall Findings

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LAD Predictive Behavioral Targeting achieves much better

results than ordinary campaignsDET

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LG

results than ordinary campaigns

Creatives and target group must be harmonizedTargeting criteria m st be combined smartlTargeting criteria must be combined smartly

Targeting helps the advertiser redefine/optimize the target grouptarget group

Publisher can offer the advertisers a better product with optimized inventorywith optimized inventory

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Questions?