Beyond Demographics:Targeting Likely Consumers through Psychographic Traits

Steven Millman

Chief Scientist

@stevenmillman

Although the depth and breadth of data have exploded in recent years, most advertisers

still target based on simple targets based on known customers and demographics

Analysis of the psychographic traits of consumers offers a powerful new way to

understand predictors of consumer choice and to activate more sophisticated targets

with higher ROI

The Challenge

Simmons National Consumer Study (NCS)

◦ Syndicated, commercial research service used by media companies, advertising agencies and marketers

◦ National study of U.S. consumers

◦ Surveys approximately 25,000 adults 18+ annually

◦ Universe is non-Hispanic and Hispanic

◦ Year-round recruitment and measurement

◦ Large-scale self-administered mail questionnaire

• Survey available in both English and Spanish

• Collects media consumption, product/brand usage, attitudes and lifestyle information

About the Simmons National Consumer Study

Making Simmons data available at scale to distribution channels

49

NHCS Respondent

s

25K respondents

Granular Consumer attributes

PII available on these respondentsfrom Experian

& directly collected

Distribution channels such as

Experian

90% US HHs

Connected to all media execution channels

PII available on all these HHs

Seed set of 25K using direct PII

Matches

Developed 10 variables that are common across both universes

Using these 10 variables as

predictors, created a link between our 25K respondents and 90% US HHs

with varying levels of accuracy

Modeled all NCS attributes on 90% US HHs in Experian

Consumer View

Available across all major activation channels

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4INFO Aerserv MediaMathAcquireWeb AOL (AOL, Adap.TV) Millennial Media

AOL AppNexus MiraAT&T Audience Science MobileFuse LLC

Bridge Marketing BDEX (Big Data Exchange) Ninth DecimalCablevision BrightRoll Oracle (BlueKai)

Comcast Celtra PandoraDIRECTV Collective RocketFuel (RocketFuel, X+1)

Dish CrispMedia Rubicon Project (Seller Cloud)Facebook DataXu Simpli.fiInstagram DemandBase SpotXchange

Millennial Media Dstillery TapAdMdotM DXPMedia The Trade DeskMSN engage:BDR ThinkNear

Ninth Decimal eXelate Tomorrow NetworksPlaceIQ EyeView TremorVideoTake 5 Fiksu TubeMogul

TRA (Tivo Research) Google (Adometry, DBM, DFP, DFA, AdWords) TurnRoku HipCricket Undertone

Simulmedia Innovid VerveViant Media (Specific Media, Vindico,

MySpace) Jivox Viant (Specific Media)

Yahoo Krux VideologyZeta Interactive Lotame Xaxis

AdChina Manage.com Yahoo! (Genome)Adelphic Mbuy (Mediaocean) YuMeAdobe

Sample out of box segmentation solutions

41 psychographic scales across 10 categories

Examine brand and TV show choice via data mining on nearly 600

Simmons psychographic attributes and 265

DriverTags™

Focus on variables that are predictive of brand or media choice at the 95%

confidence level

Score all respondents on their likelihood to use the

brand or watch the programming

The new approach

Predictive Consumer Insights (PCI) targets likely brand users more efficiently than demos alone

The predictive power of PCI is more than 250% greater than targeting on demos alone

The methodology (simplified)

FINALMODELDemos+ 6Environmental+ 5Health+ 9Lifestyle+ 4Economic

Demos+9Environmental+ 6Health+ 12Lifestyle+ 8Economic

Significant improvement in predicting brand usage

56

5.6% 5.6%

9.2%5.6%

14.8%

All Categories (2723 Brands)

Demos Only Predictive Consumer Insights Total

Overall Gain vs Demos Alone

264%

Consistent performance across brand categories

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5.6% 4.1% 6.0% 7.4% 6.1%

9.2% 6.8%

13.8% 10.8%

26.7% 14.8% 10.9%

19.8% 18.2%

32.8%

All Brand Types (2,723)Food CPG (900) Alcohol (40)Department/Clothing Stores (45)TV Shows (205)

Demos Only Predictive Consumer Insights

+266% +246%

+538%+264% +330%

All Brand Types2,723

Food CPG900

Alcohol40

Department/ClothingStores 45

TV Shows205

• Once a final predictive model has been generated that

estimates the likelihood for any individual to be a brand

consumer or TV show viewer, it becomes possible to

compare the distribution of those likelihoods among

consumers and non-consumers.

• The natural assumption is that the average likelihood of

being a consumer is higher than the average likelihood of

a non-user, an assumption that is uniformly been reflected

in the data.

Targeting the most likely prospects: buyers & non-buyers

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Targeting prospects by likelihood of brand use

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Non-brand users most likely to be brand users

Brand UsersNon-Brand UsersLi

kelih

ood

of

Use

We currently can run Predictive Consumer insights on over 2,700 brands and shows, which will increase to over 4,000 by the end of 2018. These currently include:

• 1,300 Food, Drinks, & Snacks

• 301 Household Products

• 238 Entertainment, Electronics & Shopping

• 213 TV, Movies, & Radio

• 145 Newspapers, Internet, & Magazines

• 170 Personal Care Products

• 77 Health & Medicine

• 100’s more in smaller categories

Brand prospect segments for thousands of brands & shows

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Activating prospects while suppressing non-buyers

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Seamless activation of Simmons data to various activation channels including

DSPs, Exchanges, DMPs, Addressable TV, etc

Thank you for listening!