© 2011 MIT Center for Digital Business. All rights Reserved.

Ad Morphing May 19th, 2011

Proprietary & Confidential

Glen Urban, George Pappachen, Gui Liberali MIT, WPP/Kantar, Erasmus University

Google and WPP Supported Research

Purpose

• Test web site morphing methodology on advertising

• Serve ad to match cognitive style

• Increase CTR, purchase propensity?

Morphing Site Designs

Click descriptor Website

Bayesian update

Priors on distribution of decision styles

Updated user decision-style descriptor On-line

Optimization

Optimal morph for current user

Pgm: probability of purchase for individual in group g given morph m

Source: Hauser et al., 2009

Side by Side Comparison Morph 1 Morph 8

Visual

Technical

Content

More

Content

Audio

Less

Content

General

Content

Source: Hauser et al., 2009

Data Collection

Consumer Profiling

Targeting by Cognitive

Style

Analysis of Results

• Track clickstreams on CNET.com • Survey small segment to determine cognitive style clicking patterns

• Using MIT engine, determine cognitive style of cookies from clickstream

• Remessage cookies on CBS ad network

• Morph AT&T banner messaging to match cognitive style

• Learn optimal matching dynamically

• Measure lift in clickthrough rate behavior for morphed vs. non- morphed AT&T ads

Project Plan

Research Design • Priming Study – clicks to cognitive

style – Bayesian model

• 8 AT&T banners, 4 styles

• 15 CNET pages monitored, up to 5 links monitored per page

• Once ad shown, stays with same ad

• 30% of respondents in control condition (random ad)

• 70% of respondents in test condition (morphing ad)

• 250 thousand + impressions

• 20 thousand impressions to reach convergence – “burn in”

• Each monitored click updates priors on cognitive style

• After 5 updates serves optimal ad

Morph Design – 8 Alternatives

• Same product, vary amount of content – features vs. deal

• “Learn more” versus “Buy it now”

• Font

• Color

• Web-like banners and traditional banners

• Different format

8 Experimental Ads

All ads had the word ‘refurbished’

1 2

3

4

5

6

7

8

MIT-CNET/CBS Collaboration System coded into www.CNET.com across multiple divisions

1. Content engineering team

• Infer cognitive style from clicks

• Compute optimal morph using current assignment rules

2. Ad serving team

• attempted ads, served ads (depends on inventory, sales)

3. BI/reporting team

• daily report of successes and failures to MIT update assignment rules

MIT-CNET/CBS Flow

CNET Site

Generate G matrix:

Bayesian Engine:

Ad Server:

Check for ad

priority, serve ad if

appropriate

Ad Selection:

30% (control): Show random ad

from 8 options

70% (test):

Site Visitor

Update r

Update G (8x4) on

CNET side

Make table of day’s

activity, send to MIT

On 5th click,

select ad to

show.

G

Make

attempt

Sho

w Ad

Impression

Tracker:

Record ad

impression,

timestamp,

click-

through,

current r

CNET

MIT

Update data accumulator

matrices α and β.

Summary

Table

α, β

Load data conversion

matrix D.

D

• Improves knowledge of optimal assignments as more and more users go through the system

• Burn-in phase

Dynamics of Morphing

Traffic

Reviews: 48.6% News: 49.8% Downloads: 0.94% Video: 0.06%

Experimental Ad

impressions on MIT-

monitored CNET

pages between April

12th, 2011 and May

3rd, 2011.

Home: 0.5%

Current Results Click-Through Rates

Overall: 182k impressions, 60.8k cookies

TEST (Morphing Ads) CONTROL (Random) DIFFERENCES

CTR test group

n test clicks test

CTR control group

n control clicks

control diff p

Lift (%) Impression Level

Impression 0.211% 12,801 27 0.118% 6,783 8 0.093% 0.066 79%

Cookie 0.248% 8,873 22 0.116% 4,296 5 0.132% 0.054 113%

Contextual Morphing: 19.5k impressions, 13.2k cookies Limited to all visited CNET webpages with "phone” or "htc” in its URL between April

12th and May 5th. Most are reviews and news.

TEST (Morphing Ads) CONTROL (Random) DIFFERENCES

CTR test group

n test clicks test

CTR control group

n control clicks

control diff p

Lift (%) Impression Level

Impression 0.168% 170,107 285 0.151% 97,038 147 0.016% 0.284 11%

Cookie 0.183% 52,014 95 0.163% 23,280 38 0.019% 0.533 12%

Cognitive Style of Users (divided by Ad seen)

Test (Morphing) Average 1 2 3 4 5 6 7 8

Deliberative-Holistic 0.09 0.05 0.07 0.03 0.14 0.20 0.03 0.04 0.15

Deliberative-Analytical 0.42 0.74 0.27 0.59 0.28 0.24 0.45 0.34 0.41

Impulsive-Holistic 0.23 0.12 0.11 0.16 0.40 0.34 0.20 0.30 0.22

Impulsive-Analytical 0.27 0.09 0.55 0.23 0.18 0.22 0.32 0.33 0.22

Control (Random) Average 1 2 3 4 5 6 7 8

Deliberative-Holistic 0.08 0.07 0.08 0.08 0.08 0.09 0.06 0.08 0.06

Deliberative-Analytical 0.45 0.46 0.45 0.48 0.47 0.43 0.44 0.44 0.46

Impulsive-Holistic 0.22 0.23 0.19 0.22 0.22 0.18 0.25 0.23 0.24

Impulsive-Analytical 0.25 0.25 0.29 0.21 0.23 0.30 0.25 0.25 0.24

Ads Assigned by Cognitive Style

Test (Morphing) 1 2 3 4 5 6 7 8

Deliberative-Holistic 0.07 0.10 0.04 0.20 0.28 0.04 0.06 0.21 Deliberative-Analytical 0.22 0.08 0.18 0.08 0.07 0.14 0.10 0.12

Impulsive-Holistic 0.06 0.06 0.09 0.22 0.18 0.11 0.16 0.12 Impulsive-Analytical 0.04 0.26 0.11 0.08 0.10 0.15 0.15 0.10

Control (Random) 1 2 3 4 5 6 7 8

Deliberative-Holistic 0.12 0.13 0.13 0.13 0.15 0.10 0.13 0.10 Deliberative-Analytical 0.13 0.12 0.13 0.13 0.12 0.12 0.12 0.13

Impulsive-Holistic 0.13 0.11 0.13 0.13 0.10 0.14 0.13 0.14 Impulsive-Analytical 0.12 0.14 0.10 0.11 0.15 0.12 0.12 0.12

Some Implications

– Adaptation of on-line ads to match cognitive styles has a clear impact on CTR

– Cognitive styles can provide insight for on-line strategy

– Morphing works best for contextual advertising

Relevant Challenges, Opportunities

– Adapt banners and entire browsing experience

– Adaptation based on Geo-targeting?

– More live experiments:

• Large-scale, multiple-platform experiment, test of campaigns across different (competing?) sites

• Controlled experiment to explore budget allocation among overall awareness and different stages along the funnel