Matt Esslinger Presentation

Confidential

Data-Mining Social Audiences to Impact a Brand’s Marketing Mix Matt Esslinger, COO of XA.net ([email protected]) October 21, 2011

Confidential

Confidential

Social Media and Data: Biggest Problems for CMOs

Source: IBM Global Chief Marketing Officer Study (n=1,734)

Brands see shifting user demographics and need to use social data - and take user feedback into account earlier - in the market planning and product processes

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Presentation Notes

Most CMOs are struggling in one vital respect — return on investment (ROI). Our research shows the measures used to evaluate marketing are changing. Nearly two-thirds of CMOs think return on marketing investment will be the primary measure of their effectiveness by 2015. But proving that value is difficult. Even among the most successful enterprises, half of all CMOs feel insufficiently prepared to provide hard numbers.

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Social Media + Analytics: CMOs to Increase Spend!

Source: IBM Global Chief Marketing Officer Study (n=1,734), Oct 2011

% of CMOs reporting under preparedness Plans to increase the use of technology

Chief Marketing Officers of the world’s largest companies globally have been taken by surprise by the explosion in consumer data and the transformative potential of social media – and need technology to address it

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The vast majority of CMOs believe there are three key areas for improvement. They must understand and deliver value to empowered customers; create lasting relationships with those customers; and measure marketing’s contribution to the business in relevant, quantifiable terms.

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Facebook Targeting: The Big Picture

US Facebook audience addressable by:

Gender 99% Age 95% Status 63% Education 36% Workplace 6%

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Shows the overlap between different types of targeting capabilities. Some are powerful but have a low overall penetration (like workplace targeting at 6%). Facebook advertising is about using smart tools like optim.al to understand and make sense of the various ways you can triangulate to the right audience for your offer!

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Facebook Targeting: Some Highlights • Younger people are more addressable than older people:

• A 14 year-old is twice as likely as a 45 year-old to like something on Facebook

• Women are more addressable than men:

• 19% more likely to share interests; 13% more likely to be married; 6% more likely to be engaged • Exception: men are 12% more likely to list as single

• Addressable data is sticky, and sticky across types:

• Users who like any single keyword overindex for 94% of all other keywords • Users who list a workplace overindex for 92 % of all keywords • Users who list a workplace are 24% more likely to share a relationship status

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Some anomalies in the data, plus explaining that people for whom there is a little bit of data, tend to have more data. Thus beware you don't just target users where there is data, always use demographic targeting too to round out and get access to a bigger sample where it makes sense!

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Likes & Interests: Age Distribution

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50

100

150

200

13 18 23 28 33 38 43 48 53 58 63

Age

Interest Addressability Index by Age

Young people are much easier to address with keywords than older people

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Older users don't share as much data about themselves on Facebook; they start underindexing at age 30 and continues from there. Any keyword targeting should be modified with age to make sure you're not just hitting kids.

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Likes & Interests: Reach Distribution

1

10

100

1,000

10,000

100,000

1,000 10,000 100,000 1,000,000 10,000,000

Audience Size

Keyword Count by Audience Size (log-log)

Popular

Mid-Tail

Few keywords have very large audiences, and many keywords have small audiences

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A small number of interest targets are BIG. A lot are small, so beware.

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Data = The Currency of Advertising and Attention

25-34

Female

Portland

Proprietary Audience Response Database

Hyperlocal data

3rd party Interest/demographics Advertiser targeting

Ad targets + category intent

25-34 Female Portland Strollers

We are the first company to bring together rich data from both social media (ads, apps etc.) + real-time display sources and give marketers a way to take immediate action to reach those users with the optimal message

Social Predict TM

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Here’s the one shameless plug for XA’s data capabilities…

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SocialPredict

300,000 Networks

650,000+ Targetable Interests

Content-Specific Metadata …creates structure among…

…of users organized into…

…providing a view into…

SocialPredict Provides a Unique Data Foundation XA.net uses 3+ years of historical social graph data to make audience predictions

…which has 370 million unique edges and is growing every day…

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Gardening Gardeninggarden olive gardenplants olive garden bread stickslandscaping plantsantiques breadsewing bird watching home improvement gardener Swimming Swimminglifeguards swimming poolwater polo olympic swimmingtanning lifeguardingdiving hot poolstubing water polohot tubs triathalon Chess Chessscrabble nilbackgammonmonopolyping pongmathematicscard games

Musician Musicianrecording Anthony Hamiltonperforming Saul Williamsguitarist Neo-Souldrummerproducersongwriter

Ayn Rand Ayn RandAtlas Shrugged Atlas ShruggedThe FountainheadAnthemLibertarianObjectivismGeorge Orwell

Optim.al: Proprietary Audience Segmentation

Data from millions of audience profiles

Proprietary Facebook datasets combined with external data: Company Lists Likes & Interests Schools & Majors Semantic Scrapes

Automated Suggestions optim.al vs. Facebook

More. Relevant.

Targeting.

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So, our data-driven approach is much more robust than what you can get direct from Facebook via their automated suggestions. And we’ve been scraping data from our friends in Palo Alto since 2007…

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Facebook: Keyword Selection Process 1. Translate an AdWords list from purchase intent to people

a. NLP: tokenization and stemming to facilitate Facebook keyword discovery “realtor” “realtor marketer”, “national association realtors”… b. Categorical Expansion: amplify with vertical keywords “Bloomberg” “CNBC”, “Motley Fool”, “Yahoo Finance”… c. Audience Suggestion: add highest indexing keywords among a core user base “Barack Obama” “The Daily Show”, “NPR”, “Coffee”

2. Filter the keyword list, based on stated reach and CPC objectives 3. Slice into minimally overlapping segments and create a controlled environment

for testing and optimization

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We provide custom services for search marketers to take their keyword lists and use them to find audiences. You CANNOT use the same words for both purposes, but they can be one of many inputs into the process.

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Facebook: Keyword Selection Process – Proof Targeting Method CTR Conversion Rate CPA

Facebook Broad Category: Small Business Owners .015% 3.9% $28.65

Search Keyword-Driven (Intent+Behavior) Precise Interests

.033% 11.6% $10.71

1. Broad Category Targeting (BCT) does not pass the smell test • BCT identifies165,000 Small Business Owners under the age of 17

2. Only Precise Interest Targeting allows for deep relevance and segmentation

3. Search keywords (properly translated) can identify highly-qualified,

precisely target-able audience segments Confidential

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A customized optim.al-created target list for a B2B advertiser in this case study SIGNIFICANTLY outperformed the "broad category" targeting provided by Facebook.

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Our Proprietary Data: A Visual Social Graph

http://optim.al/blog/

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Presenter

Presentation Notes

The interest graph is dominated by a dense, central cluster of Republican politicians (‘mike huckabee’, ‘tim pawlenty’), media personalities (‘bill o’reilly’, ‘sean hannity’), and Republican boosters (‘federalist society’, ‘positively republican’, ‘tea party patriots’). Mitt Romney, Michele Bachmann and Rick Perry are all solidly in this cluster, expressing the sort of ‘race-to-the-base’ into which presidential primary contests tend to evolve. Sarah Palin, on the other hand, is just barely connected to the core cluster; suggesting that, despite years of rumor regarding a presidential candidacy, Palin is not a viable Republican contender. In fact, the actual Sarah Palin who emerges from this interest graph is less a politician and more a suburban strip mall celebrity, hovering between teenage heartthrobs, reality TV stars and fast food chains.

http://optim.al/blog/�

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What Does All This Mean to a Brand? Data drives their Strategy and Execution, both Online and Offline

A luxury auto brand learned that most category fans are young + aspirational & not likely buyers, but they also found the loyal owner segment within their base

Analyzed millions of US fans to discover interest-based clusters of underserved

consumers by ZIP Code. These deep and surprising insights were revolutionary

Online Strategy

Offline Strategy

Confidential

Confidential

Data-Mining Social Audiences to Impact a Brand’s Marketing Mix Matt Esslinger, COO of XA.net ([email protected]) October 21, 2011

Confidential