A Practical Perspective

IHS TECHNOLOGY Presentation

Brussels, 1 June 2016 The logic of data & the structural transformation of media: a practical perspective

ihs.com

Daniel Knapp, IHS

Source: Wellcome Library, London. Bills of Mortality form August 15 - 22, 1665. High death rate from plague. 1665. Under CC BY.40

SAMENESS DIFFERENCE

Not relevant for advertiser Website Buy Ad Network Buy Audience Buy CPM1 price = 1 price

€2.4 €0.6

€6.8 €1.2 €1.1 €0.4

Relevant for advertiser

Buying generic Website Audiences Traditional Ad Networks Audience buying puts individual price tags on produces a lot of ‘wastage’, i.e. provide inventory based on consumers / their unique data fingerprint, based on consumers that the advertiser does assumptions such the as demographic, behavioural, location and other data. not intend to reach. The quality of the bundling of sports lovers These price-tags are highly dynamic. Each advertiser website content, its brand reputation across different websites. allocates a different value to reaching a specific and basic visitor statistics are the only consumer at a given time. Audience buying frees the references for an advertising buy. task of reaching audiences from the editorial context, which used to be the best proxy to target otherwise largely unknown audiences. It also eliminates ‘wastage’.

Display

Video

Analytics Mobile

Branded Entertainment Predictive Predictive

Campaign Management Campaign Native

Plan platform measurement platform

- Social Execute

Emerging Channels Measurement/Attribution

Paid Search

Data Management Data Data Management Data Ideal type of crossof type Ideal Organic Search

Offline Media

Optimize

Probabilistic Matching Deterministic Matching Login-based data

Large-scale models look at signals Unique identifiers for each record Logged-on individuals at scale across from millions of devices (IP data are compared for a match, or an different devices. No cooperation with often main source). Clusters are exact comparison is used others required, no errors in matching used structure the data. As data is between fields. Often multisource because in contrast to conventional added, clusters are perpetually (e.g email plus Facebook plus deterministic matching, there are no reduced in size until a statistically store-login). For scale and obstacles through patchy, disparate significant association allows to pair accuracy, relies on cooperative and incompatible datasets. different devices to a single user. approach and sharing of IDs between market participants.

Example companies: Example companies: Example companies:

Online ad spend by transaction model 100%

90% 20%

80% 40% 36% 9% 52% 70%

60% 9% 50% 19%

40% 14% 71% 30% 55% 20% 41% 34% 10%

0% Europe 2015 Europe 2019 United States 2015 United States 2019 Traditional Non-RTB RTB

WPP: Revenue Structure by Segment WPP - Data vs Classic Ad Revenue

Datengetriebene Werbung Klassische Werbung Advertising, Media Investment Management Data Investment Management Public Relations & Public Affairs Branding, Identity, Healthcare and Specialist Communications

900 828 800 700 600 500 446 400

300 220 200 197 175 200 157 145 114 94 85 77 77 65 100 55 44 41 38 34 23 22 16 15 10 6 3

Other

Video

Mobile

Agency

Publisher

Optimizer

Ad Server Ad

Advertiser

Retargeter

Ad network Ad

Social Media Social

Tag Manager Tag

Ad Exchange Ad

Ad Verification Ad

Analytics Provider Analytics

Research Research Provider

Marketing Solutions Marketing

Supply Side Platform SupplySide

Business Intelligence Business

Website Optimization Website

Demand Side Platform Side Demand

Online Privacy Platform Online

Data Aggregator/Supplier Data

Content Management/Saas Content

Data Management Management Platform Data Creative/Ad Format Format Technology Creative/Ad

Online display advertising ecosystem

Source: Luma Partners

Flow of spend in digital ad ecosystem* 40% 100% $1.00 $0.10 90% $0.05 20% 80% $0.15 70% $0.12 60% $0.61 15% 50% $0.15

40% 15% $0.14 30% $0.29 20%

10%

0% Advertiser Agency Trading desk DMP/Data DSP Ad exchange SSP/Ad Publisher provider network *Illustrative only. Variances depending on deal negotiations, technology ownership. Refers to banner display advertising, assumes linear flow across all types of market participants listed. Data based on interviews with 43 ad tech companies, advertisers and publishers in Europe and the US (2014), and reviews of SEC filings by ad tech companies (e.g. Rubicon, Tremor, YuMe).

© 2015 IHS 8 Agencies develop new means of arbitrage, advertisers and marketers bring data tech in-house Flow of spend in digital ad ecosystem* 40% 100% $1.00 $0.10 90% $0.05 20% 80% $0.15 70% c $0.12 60% 15% 50% $0.15

40% 15% $0.14 30% $0.29 20%

10%

Global Ad Tech Funding (USDm)* 900

800

700

600 Other 500 DMP 400 Exchange SSP 300 DSP 200

100

0 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 *Source: Crunchbase, company reports, GP Bullhound, IHS interviews & calculations. Excludes China. Excluding M&A.

Date Company Acquisition* Value July 2015 ProSiebenSat.1 Smartstream.tv (majority) undisclosed June 2015 ProSiebenSat.1 Virtual Minds (majority) undisclosed May 2015 Verizon AOL (full) $4,400m February 2015 WPP Appnexus (minority) $25m November 2014 Publicis Sapient (full) $3,400m November 2014 Yahoo Brightroll (full) $640m October 2014 Telstra Ooyala/Videoplaza (full) undisclosed July 2014 RTL Group SpotXchange (majority) $144m July 2014 Yahoo Flurry (full) undisclosed May 2014 Google Adometry (full) undisclosed May 2014 AOL Convertro (full) $89m March 2014 Comcast FreeWheel (full) $320m February 2014 Facebook Liverail (full) $382m February 2014 Oracle BlueKai (full) $408m August 2013 AOL Adap.TV (full) $405m

Agency Online technology platform Broadcaster Cable provider *Represents full,majority & minority stakes. ND is non-disclosed. List non-exhaustive, focus on broadcasters & their competitive set.

Active 1st party consumer data assets (m) 1,600

1,400 +43% 1,200

1,000 +100% 800 +84% 600

400

200

0 Apple (Q3 2012-Q1 2015) Facebook (Q3 2012-Q1 Google (Q3 2012-Q1- 2015) 2015) ID with credit cards Monthly active users Cumulative Android activations

Facebook Amazon Yahoo Google Verizon WPP Ad server FBX and Liverail In-house Yahoo Ad DoubleClick Adap.tv Xaxis or ad tech (2014) demand-side Manager through AOL Videology platform platform (2013) Appnexus Content In-house news Amazon Yahoo original YouTube Huffington Branded production team; Prime series; MCNs Post (2011) content Partnerships with partnerships agencies such media companies with CNBC AOL.com as Group SJR (NYT, National and ABC news Geographic, etc.) and NBC Techcrunch Sports Group Gateway to Liverail Amazon Yahoo Screen YouTube AOL One Videology video Prime & BrightRoll (2006) Adap.tv (2014) Vidible (2014)

Audience Audience Network Inferred Flurry (2014) Google+ Verizon Kantar (2008) Data Amazon data profile data customer & comScore sets database Analytics Atlas Platform Amazon Yahoo Ad Adometry Convertro Kantar (2013) Analytics Manager & (2014) & (2014) Flurry Google Analytics Analytics

*Acquisitions are listed with the year in which they were acquired

“There is no doubt a current information asymmetry as these platforms know more than any advertiser or agency could know. The best we can do is learn within our own systems and build proxies for the information they have. We don’t know how big the gap is between what we deduce and the reality.”

Software companies with CRM products enter the They are already making strategic acquisitions to cut advertising value chain across old data silos. Example Oracle.

(CRM)

(cloud-based customer service)

(offline-online data conversion)

(online advertising data management platform)

Proxy Reach Viewability Engagement

Metrics GRP OCR vCE Click-through-rate, Likes understanding Purchase consumer Holistic Descriptive Correlated Causal Metrics Shopper Behaviour Buy-Through-Rate Incremental Sales Lift ROI

Campaign data Match data Purchase data Data pools (Impressions, Creative (Postal, Email, Cookie ID, (Auto, CPG, Retail, etc) Info, etc) Mobile ID, etc)

“Most strikingly, society will need to shed some of its obsession for causality in exchange for simple correlations: not knowing why but only what. This overturns centuries of established practices and challenges our most basic understanding of how to make decisions and comprehend reality.“ (Mayer-Schönberger & Cukier 2013: 18)

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