Digital Media Analytics January 30, 2014 Agenda Introduction - PowerPoint PPT Presentation

But Addressability Is Not Just About Display Media – Search is Evolving Very Quickly As Well Search Evolution 2010 - 2013 Audience Driven Location/Device/Time Location/Device/Time Level of insight Keyword Keyword Keyword Differentiated by Integrated Media and Universal Platform Device Site Targeting How many people are searching Use of Remarketing programs in Increased options controlled by the and for what terminology search and display to customize search engines for delivery • Audience profile data from • Based on exact keyword • Anonymous by device type prior site visitation Targeting search behavior with not and carrier • Unique experiences based on • Geography, day of week and personalization user profiles time functions • Match type and keyword • Extended match types Performance by customer • Device Targeting for Mobile • Segment Optimization • Location specific ads and • Value • Intent costs • Pure text only • Site Links • Video • Video Ads Formats • Form Extensions • Image/Logo ads • Product Price Ads • Click to Call • Maps/Location Extensions 18

Some Search Marketing Ads still Struggle in Medical Legal Review • Broad Match (Branded) – Many PharmaCo’s still fail to approve SEM submissions beyond Exact Match • Unbranded Ads – Cannot use co‐morbid or off‐label indications to target keywords • Patient Common Terms for Symptom Key Words – Approvable but still must be qualified and on label • Unbranded URL’s within unbranded ads – Approvable but the url cannot include any product representation • Branded Ads on Competitive Brand Name – No MLR barrier but requires an alignment with Commercial Team 19

Landing Page Best Practices Clicking Here Lands Here Landing pages should contain: • Strong CTAs • Content Highlighting Special Offers Landing pages should not: • Differ within an ad group 20 Industry Example

Ad Copy Best Practices • Ad text should include copy related to the user’s Search Query • Utilize multiple variations of ad copy – Brand awareness (with full generic name) – Highlight Special Offers – Pay no more than $25 – Non brand description with non brand destination URL – Call to action to learn more • Rotate ad copy throughout campaign based on keyword sets to determine highest producing click- through and conversion rates. 21 Industry Example

The Big Trends – Where This Is All Heading 1 st party audience expansion and extension Known individual creates massive addressable scale opportunity level targeting Cross device Digital media reaches massive targeting Commerce - Data driven inventory will expand dramtically convergence – scale as the core of as large commerce expand business model to monetize first maturity will digital media has the strategy accelerate – party data assets – eBay, Amazon taken on forms in Google and custom content, Mass adoption of Publisher - Google and Facebook off platform extension - Twitter to lead social log-in will reach video, social, and stack integration will create massive addressable audience the way (Apple, mobile , search huge scale and open scale (Atlas and DART acquisitions used to drive ubiquity smart TV) massive addressability and integration of advertiser, social, and paid media opportunities targeting and reach Unique content Advertisers will have to deal will drive growth The info-mediary with complexity of the closed of video-on- Search will be starts to take shape media platforms as large Programmatic demand which the next big - Consumer influence players such as Google and media buying opens yet another addressable into their own Facebook create “walled data explodes big addressable experience – the platform gardens” through their stack platform at scale value exchange (Netflix – Orange is acquisitions the New Black) 22

Some of these platforms are creating addressability beyond the domain of their own native platforms This has not happened yet, but the connections can be made at scale As endemic health buys fall out of acceptable ROI targets, updating media plans to reflect more efficient, targeted media through audience buying is essential. 23

Some of these platforms are creating addressability beyond the domain of their own native platforms …and like clockwork, a week later, Facebook makes this announcement 24

Innovation in the Platforms is Picking up Significant Speed and Volume August 22 August 22 August 22 August 22 August 22 August 22 August 22 August 22 August 22 August 22 August 9 August 9 August 9 August 9 August 9 August 9 August 9 August 9 August 9 August 9 2013 2013 2013 2013 2013 2013 2013 2013 2013 2013 July 11 July 11 July 11 July 11 July 11 July 11 July 11 July 11 July 11 July 11 2013 2013 2013 2013 2013 2013 2013 2013 2013 2013 May 3 May 3 May 3 May 3 May 3 May 3 May 3 May 3 May 3 May 3 2013 2013 2013 2013 2013 2013 2013 2013 2013 2013 October October October October October October October October October October 2013 2013 2013 2013 2013 2013 2013 2013 2013 2013 June June June June June June June June June June 2012 2012 2012 2012 2012 2012 2012 2012 2012 2012 March March March March March March March March March March Sept Sept Sept Sept Sept Sept Sept Sept Sept Sept 2012 2012 2012 2012 2012 2012 2012 2012 2012 2012 July July July July July July July July July July 2012 2012 2012 2012 2012 2012 2012 2012 2012 2012 1998 1998 1998 1998 1998 1998 1998 1998 1998 1998 1994 1994 1994 1994 1994 1994 1994 1994 1994 1994 1941 1941 1941 1941 1941 1941 1941 1941 1941 1941 July 11, 2013 August 22, 2013 June 2012 August 9, 2013 May 3, 2013 October 2012 March 2012 September 1998 1994 July 1941 Google launches service The first display ad from The first ever television ad, AT&T a ten-second Bulova watch spot, airs prior to a Brooklyn Dodgers and Philadelphia Phillies game. 25

BIG Digital Trends in Health – Where Is It All Heading? The audience platform has become highly ADDRESSABLE and is reaching MASSIVE SCALE Marketers seeking growth and competitive advantage will now be “leaning in” hard on these platforms MOVING HUGE AMOUNTS OF BUDGETS from mass media and traditional direct marketing We are already seeing marketers moving that budget at scale and seeing 20-40% LIFT IN MEDIA PERFORMANCE … and we are just getting started But the challenge is that YESTERDAY’S MARKETER DOES NOT HAVE THE SKILLS AND TOOLS to really go beyond the haphazard “bag of tactics” and gimmicks to really leverage the opportunity here We need to evolve … introducing THE HEALTH PLATFORM MARKETER 26

We Believe This Has Value To Our Brands in Tens of Millions YEAR 1 YEAR 2 YEAR 3 YEARS 4+5 NPV of Revenue $7.8MM $20.8MM $34MM $36.4MM Impact • 1%-3% increase in customer acquisition over 5 years Acquisition of new • Increased response/conversion from digital media efficiency by connecting Anonymous Data customers to CRM data for better targeting, measurement and segmentation • Increased effectiveness of remarketing and personalization (offer/package) in Search, Display, Site and Email $3 MM $8.6MM $14.6MM $16.4MM Improvement in • Roughly 1bp (~31k) improvement in Adherence YOY over next 5 years Patient Adherence • Combine Customer service, and contact data to improve $4.8MM $12.1MM $19.4MM $20MM Improvement in Intent to • Use Connected data to predict/model Improvement in Intent to Prescribe for HCPs and Intent Prescribe/ Intent to Ask to Visit and Ask my doctor My Doctor over Baseline* +5 +10 +5 +5 27

Introducing The Health Platform Marketer What’s Changing and Why You Should Care?

Introducing …The Health Platform Marketer Health Platform Marketer wears many hats & embodies the competencies needed to successfully operate in today’s digital world • Decision science PhD • Change advocate and champion • Audience platform expert • Consumer experience designer • Marketing technologist • User experience expert • Programmatic media buyer • Creative advocate • Endemic Media Expert • Consumer privacy & preference advocate • Addressability expert • Multi-channel program • Measurement and strategist attribution expert • Direct Marketer • Chief Patient/ HCP/ Payer Economist • Segment portfolio manager 29

Health Platform Marketer Represents a Dramatic Shift From Traditional Marketing Skills and Competencies The Traditional Marketer The Health Platform Marketer Big Idea informs DTC or HCP Campaign Big Data informs Big Ideas in CRM Channels Media buying driven through the tech stack and Media buys reliant on buying clout and scale audience platforms Programs disconnected from the lived customer Integrates consumer experience across media and experience channels at segment and individual level Marketing moves at the speed of human Marketing moves in internet speed through decision programmatic approach to decisioning and execution 30

Health Platform Marketer – The Addressability Expert Platform marketers bring addressable data skills that 12 Main Street Philadelphia, PA facilitate the exchange of identity and data for 01100100010 //asdohsd.asiudhscns/html 0110001 personalization and relevance. 617-555-0728 John@doe.com Pinterest: jdoe John@doe.com 01100100010 JD@gmail.com 12 Main Street 617-555-0728 0110001 //asdohs.hhd.net Philadelphia, PA #JohnnyDoe JD’s iphone 617-555-0728 12 Main Street John@doe.com 01100100010 12 Main Street Philadelphia, PA 12 Main Street 011001000 0110001 //asdohs.hhd.net Philadelphia, PA Philadelphia, PA 100110001 #JohnnyDoe 617-555-0728 70’s 80’s 90’s 00’s Today Email Mailing Phone Twitter handle IB ID Cookie 3 rd party address number Pinterest ID ID address GooglePlus ID Digital set top ID Device ID The Platform Marketer It also requires deep consumer This requires mastery knows he/she must of consumer insight and experience skills to design and implement the experiential “value maximize his addressable addressability in the exchange” that incents consumers to market through high database and coverage of consumer constant collaboration provide data (e.g. why should one identify on a site with one’s facebook identifiers and knowledge in and leadership with the database technology log-in?) 31

Health Platform Marketer – The Segment Portfolio Manager The Platform Marketer knows customer segmentation intimately and uses it as a core strategic tool to better understand opportunities and risks in the market Time� Savvy� Thri er� Young� Enlightened� Mom� Trendse er� Consumer� Shopper� � $1.5� $3� $10M� $2� $3.5� Marke ng � Promo onal� $15� $10� $5� $30M� --� Spend � Tradi onal� $3� <$1� $12M� $4� $4.5� Media � Social/Digital� $6� $3.5� $18M� $8.5� --� Media � Total� Marke ng� $29� $22.5� $14.5� $4� $70M� Dollars� Store� Foot� � Prices� for� Customer� � Mass� Traffic � Up� Sell � Loyalty � Marke ng � ROI� $100M� $38M� $14M� $8M� $48M� Segmentation needs to be fully operationalized, reported on Customer value analysis highlights the right investments that and tracked over time should be made to each segment and the return that can be expected 32

Segmentation Gives us the Ability to Feed Individualized Moments to Change Health Behavior SEGMENTATION treatment context message IP offer CONNECTED CUSTOMER PERSONAL PROFILE EXPERIENCE 33

Over Time, Pharma Customers Receive Smart Messaging Based on Customer Preference & Segment Behavior Re-Purchase Convert Re-Activate Purchase Activate Recommend Pairing Review Incentive Social Incentive Triggered Direct Mail CONNECTED CUSTOMER TM PROFILE DRIVES MESSAGING 34

Concept In-Action Step One Web Mobile SMS Integrated Experience Delivery • Personalized Email and SMS CRM Program • Printable & Mobile Coupons Printable 35 Industry Example

Concept In-Action Step Two Mobile Coupon Tailored email Customer Segment identified Redeemed at pharmacy 36 Printed Coupon Tailored SMS 36 Industry Example

Case Example: BrandX Adherence Program • BrandX mobile adherence program provides personalized education, emotional support and smoking cessation tips. • Message frequency, mix and content is continuously updated based on input received from patients in real-time. User Texts “URGE” To RedShop Rx Sends Receive Tips Coping Tips Via SMS 37 Industry Example

The Health Platform Marketer – The Audience Platform Expert The Platform Marketer is a master of the ever evolving Audience Platform targeting and optimization capabilities CONTENT & INTENT & ANONYMOUS IDENTIFIED CONTEXT BEHAVIOR INDIVIDUAL INDIVIDUAL device ID 1 st party cookie intent behavior context probabilistic ID 3 rd party geo-location custom name & anonymous segments content email cookie 38

A Short History Of Digital Media Buying Audience Audiences Audience Audiences aggregated aggregates by aggregated by aggregated by using known scaling niche third party data content relationships content 1995-2005 2005-2009 2009-2012 2013+ Differentiation created Differentiation Created Differentiation Created Differentiation Created by Media Skills by Optimization by Technology by Data Integration and Analytics 39

Digital Media 10 Years Ago Buying is relationship based with targeting and Agency optimization done at a very coarse level. Approved Campaigns “Transparency line” ends at the network and publisher level – what falls below the line is “black “Black box” ad networks “Black box” ad networks “Black box” ad networks Direct sales force box” . “Remnant” “Remnant” “Remnant” “Premium” Buying is done across inventory inventory inventory inventory numerous platforms without the ability to manage frequency and cost resulting in significant waste . Publisher Publisher Publisher Publisher Publisher Publisher Publisher Publisher Just as bad (or worse), targeting capability does not allow for targeting the right individuals. 40

Digital Media Today – Challenges Remain For Life Sciences Adoption Buying is done using a data-driven targeting skill- Data & enabling set and mind-set. technology Consolidated buying platforms allow for complete transparency Integrated Media Management Platform and granular targeting – no more black box. Direct Buys Programmatic (DSP) Paid Social Real-time-bid environment allows for access to premium and remnant inventory that Real-time bidding auction gets bid on auction-style based on the value to the advertiser. Publisher Publisher Publisher Publisher Publisher Publisher Publisher Publisher Direct buys and paid social leverage data and technology to cross multiple channels while remaining customer- centric. 41

Targeting Framework Lookalike Online Audience Online-Offline Re-Targeting Modeling Segments Direct Match Consolidated Buying Platform (DSP) Match converted Identify users consumers to Identify high Match offline Trading Desk visiting site anonymous ID performing “top deciles” to (anonymous or and create look- online audience cookies through authenticated) alike predictive segments (“auto third party match and target model to identify intenders”) and providers and customized “like” cookies/ target these target known impressions after placement anonymous users consumers on a they leave the opportunities through the DSP 1-1 level site through RTB 42

Health Platform Marketer – Programmatic Media Buyer The Platform Marketer brings programmatic buying skills to the enterprise 43

Platform Marketer – The Stack Expert Platform marketers has strong expertise in state of the art and emerging marketing technology and how it drives business value 44

In the last 18 months, we see advanced marketers rationalize this technology into a unified stack Audience Platforms 3 rd party cookie 3 rd party segment Name & address Context Execution Currencies 1 st party cookie Device ID Geo-location Social ID / handle Campaign Management DMP Attribution & Insights Platform Marketer On-boarding Stack Ad Serving & Tag Management Identity Management Marketing Database 45

Market Forces Require A Different Type of Analyst • “The data science toolkit is more varied • Analysts must help marketers and more technically sophisticated than Analytic methods and and technologists figure out what the BI toolkit” Green plum data is valuable and how it tools need a big data • “There is a shortage of talent necessary should be integrated reboot for organizations to take advantage of • Managing and integrating data big data.” McKinsey from a variety of sources is the top challenge preventing organizations from making use of customer analytics. Forrester Customer Analytics as a Marketing Competitive Advantage Analytics is a constraint as Data scientists are critical to media becomes more drive digital and offline data targetable integration • More ads are targetable at a user level than ever before through, display, social, video, and mobile. • 1/3 of US online adults are always addressable through digital media • “Marketers must be able to keep pace Forrester Analytics is not with their customers and react to • Yet, advanced user-level attribution is not changes in customer behavior instantly” widely adopted, most emails are batch, matching up to real- Forrester and organizations are not unlocking the time marketing value of user-level ad and site targeting. • Batch analytics is no longer sufficient 46

SUMMARY The Health Platform Marketer The new Massive budgets addressable Addressability at are already being platforms will scale has and will shifted to take require new create competitive advantage of this analytical advantage opportunity competencies! 47

Digital Data and Data Integration What data is created and how it can be connected to create value?

Value is Unlocked Within The Digital Marketing Value Chain The Digital Marketing Value-Chain Optimized Channel Measurement and Connected Customer Experience Budget Allocation Insight (Targeting and (Attribution) (Data Integration) Personalization) First Party Data Integration Second Party Data Third Party 49 Data

Anonymous Behavior Tracking Anonymous User Identifiers Data Collection Methods • Cookies • JavaScript • IP address • Pixels/Beacons • Device fingerprints (Probabilistic Ids) • Packet Sniffing • Mobile Device ID • Web Server • Social handle User • Cookie ID: 43Jx41LKs980s • IP address: 192.168.2.49 • Device fingerprint : 34x43292jk2395kls9ef876 50

How A Website Works 51

HTTP HTTP Request (from client) HTTP Response (from server) GET / HTTP/1.1 HTTP/1.1 200 OK Host: www.linkedin.com Server: Apache-Coyote/1.1 User-Agent: Mozilla/5.0 (Windows NT 5.1; rv:21.0) Content-Encoding: gzip Gecko/20100101 Firefox/21.0 Vary: Accept-Encoding Accept: text/html,application/xhtml+xml,application/xml; Content-Type: text/html;charset=UTF-8 Accept-Language: en-US,en;q=0.5 Content-Language: en-US Accept-Encoding: gzip, deflate Date: Fri, 07 Jun 2013 01:49:26 GMT Cookie: leo_auth_token=... Connection: keep-alive Connection: keep-alive Set-Cookie: _lipt=deleteMe... [optional request body, e.g. when posting data from a form] [response body; e.g. html content goes here] • HTTP (HyperText Transfer Protocol) – Protocol for requesting and responding to requests for web pages (hypertext) • Request/Response – Methods (GET, POST, PUT, DELETE,...) – Response codes • Stateless protocol • Request line, Response status line, Header, Body info – Host, User Agent, Referrer, Cookies 52

How Web Data Capture Generally Works Google Analytics is web analytics tool for Looking for ways to donate food in her Feeding America (Javascript on all pages) 1 Typical site community. Does search on local food banks and clicks on paid search ad for visitor 1 GA sees that browser coming Google.com Feeding America paid seach has no cookie, drops 1 st party cookie on browser, and counts browser as a new site visitor • Cookie ID: 43Jx41LKs980s • IP address: 192.168.2.49 2 GA records all actions taken by user on site in Google collection server. Java script instructs 2 Lands on food bank search page what data to send. • IP address: 192.168.2.49 3 When she leaves the site the session is marked as complete and session metrics such as time on site, etc. are calculated 3 Next time she comes to the site GA recognizes the browser based on cookie ID 53

A Data Flow View of Data Capture Feeding America Web Server 2 Web server notifies Google analytics collection server of request Google Analytics Collection Server 4 1 Web browser requests content from Collection server captures behavior Feeding America on site per pre-configured collection rules Web Browser 3 Collection server looks to see if user has a cookie and drops cookie if no Firefox cookie exists Note: Pages load for user regardless if collection server can complete their actions. If user leaves page before collection script completely loads then no data capture will happen. 54

What Data Is Passed To The Collection Server? • Cookie ID (Assuming browser accepts cookies) • IP + user agent data Source: http://www.whatsmyuseragent.com • Contextual information (Where you are) • http://espn.go.com/mens-college-basketball/ • Note: this data is sometimes masked on third party sites • Referrer (where did you come from) • Behavioral (What you did) • Basic — clicked on ad (Beacons) • Extensive – watched 1/3 of video (Javascript) 55

Cookie Background What is a cookie? • Small snippets of plain text containing a key, value pair, and saved within the browser, that are used to maintain state throughout your visit to a website (HTTP is a stateless protocol) • Cookies can only be read and written by the domain to which they belong (i.e. cross-domain cookie access is not allowed by your web browser) There are two flavors of cookies important to this discussion • First-party cookies – Belong to the same domain as the requested web page (Example: NIKE assigning a cookie to browser of NIKE.com) • Third-party cookies – Belong to domains other than the domain of the requested web page. These are read and written by separate third-party HTTP requests on the web page, commonly for advertising and tracking purposes, but also for providing 3 rd party content. (Example: Google assigning a cookie to a browser on NIKE.com ) 56

IP Addresses • IP Address (Internet Protocol Address) – A unique address for finding any machine connected to the Internet. This is how client requests and server responses are sent by routers to the correct location over the Internet. • IPv4 address – 32 bits => 2 32 = 4,294,967,296 unique addresses • IPv6 address – 128 bits => 2 128 = 340,282,366,920,938,463,463,374,607,431,768,211,456 unique addresses – Went live 6/6/2012, there will be several years of transition – Every machine will be able to have a unique public IP address in the future – http://www.pcworld.com/article/257037/ipv6_five_things_you_should_know.html • Static vs. Dynamic IP addresses – There are a limited number of IPv4 addresses which can be assigned by ISPs to machines that connect to the Internet – Most home IP addresses are dynamic and are periodically reassigned (usually assigned at the home router level, and the router tracks your machines on the internal home network using separate private IP addresses) • Composition of IP addresses – Generally, the part on the left corresponds to the network, and the part on the right corresponds to the specific machine – Allocated in hierarchies of blocks that read from general to specific, left to right – There is no set of rules or patterns to read these blocks (like there is with a zip code for example), instead there are databases maintained for looking up IP allocations – GeoIP lookup databases are maintained by various services for identifying geo location by IP address. 57

Death of The Cookie? • This is really a conversation about 3 rd party cookies, not first party • In general, third party cookies have a shorter shelf life than first party cookies • Recent studies suggest that about 40% of devices don’t accept third party cookies. Upwards of 60% of cookies may be deleted within 30 days (including mobile devices) • Third party cookies are most often not deleted by user, but by spyware or antivirus software 58

What About Cookie Tracking on Mobile Devices? • Third party cookies have limited tracking usage for mobile devices – Most mobile devices don’t accept cookies by default – Concern as well that long term viability of these cookies may be in question for PCs • In April 2013 Apple exposed a new device ID for tracking at user level (IDFA) within IOS6. Users can opt out of tracking. • Vendors are emerging that are creating persistent device IDs for targeting and attribution • Vendors are emerging that are creating persistent device IDs for targeting and attribution – Vendors include Ad Truth, BlueCava, Tapad and others – ID persistence length varies by device – Vendors use combination of deterministic and probabilistic ids – 80%+ mobile device coverage/accuracy is possible today • Many ID tracking can be used in conjunction with ad privacy compliance solutions (ex. TRUSTe) 59

What About IP Addresses? • It is harder to find published data IP uniqueness. • Most of what I have learned has come from confidential communications with IP data providers and demand side platform vendors (DSPs) • About • 85-90% of US IP addresses can be accurately tracked back to a DMA • 60% of devices with an IP address can be traced back to a known SCF and about 45% to the zip level • 25 to 35% of IPs can be reliably tracked back to a residence over at least one month’s time • This is likely to get worse before it gets better as we are “running out of IPv4 addresses” 60

Device Fingerprinting • Device fingerprinting is emerging as one way to resolve third party cookie deletion issue • Originated out of fraud detection and has migrated to marketing • We estimate that many fingerprint technologies are more than 90% accurate. Click here https://panopticlick.eff.org/ • Biggest issue is privacy and adoption to date is still relatively low • Companies such as Bluecava, and Iovation specialize in this area 61

Our Observations About Digital Data Landscape • First-party customer data generally has the highest marketing value – There are many opportunities for companies to collect first-party digital data across digital medias and channels – Most companies do not a cohesive plan for utilizing first party digital data • Third-party digital data is still in its infancy resulting in opportunity and risk – Shirting legal environment has huge implications for using third party data (Ex. Internet Explorer Do not Track). Legal should be involved in strategy development – Difficult to determine the quality and integrity of digital data providers – Audience scaling is still a big challenge – Quantitative approach is necessary to locate and extract value from third-party sources (Example Merkle Digital Data Optimization Lab) • Three capabilities are critical to companies creating competitive advantage within digital data – Ability to effectively identify and extract digital data with business value – Ability to integrate across digital and offline data sources – Ability to utilize both online and offline customer data in real time interactive environments 62

Digital Data Sources (Digital media and Channels) Site Display Social* Party DATA “DEPTH” Identifiers Primary First Party DATA GENERATORS Data Systems First Party Data DATA “BREADTH” Capture (Example) Third Party Data DATA MARKETPLACE Providers (Example) 63

Digital Data Sources (Digital media and Channels) Site Display Social* • Cookie ID (Primary) • Cookie ID (Primary) • Social Handle (Primary) Party • IP Address • IP Address • Email (Facebook) Identifiers • Order ID, Cust#, Profile ID • Order ID, Cust#, Profile ID Primary First Party • Web Analytics tools (Omniture, • Ad servers (DFA, Atlas) and DSP • Social Networks (Facebook*, Coremetrics, etc) (Media Math, Turn, [X+1]) Twitter) and social platforms Data Systems • Browser User agent (IP geo, OS, • Browser User agent (IP geo, OS, • FB user profile data including browser type, etc) browser type, etc) likes, interests, geo, etc • Referral site • Ad impressions • FB friends email/profile data First Party Data • Campaign data (SEO, SEM, • Ad campaign meta data • FB own site wall posts Capture Banner clicks, email clicks) • Ad clicks • FB custom social engagement (Example) • Internal site search (site, apps, etc) • Ad site conversions — post ad • Engagement on site (clicks, • Other engagement based on view or click (quote, purchase, views, downloads, etc) etc) specific social network (Twitter, Linkedin, etc) • Conversion on site (email signup, purchases, quotes, information requests, etc) Third Party Data Providers (Example) 64

Digital Data Sources (Cont.) Email Search Mobile • Device ID (Primary) • Cookie ID (Primary) • Email (Primary) Party • Cookie ID (Primary) • IP Address Identifiers • Order ID, Cust#, Profile ID • Order ID, Cust#, Profile ID Primary First Party • SMS platforms (iloop),Web • Web analytics platforms, search ad • Social Networks (Facebook, Twitter) analytics, apps platforms (Kenshoo, Marin) and social platforms Data Systems • Email Send • Search Ad clicks • SMS send and click • Email open and click • Mobile site browsing • Search campaign meta data (keywords, bid amount, cost, First Party Data • Email campaign metadata • Campaign data (SEO, SEM, creative, etc) Banner clicks, email clicks) Capture • Ad site conversions- post ad • Geo location (Example) click (quote, purchase, etc) • Custom App engagement data Third Party Data Providers (Example) 65

The Customer Event Stream Connects Cross-channel and Media Interaction Data The Customer Event Stream is enabled as the customer engages with the brand DM Shown Display Ad Visits branded site Sent Email Visits clinic and Signs up for Patient Delivered 2/2/12 3:05pm and signs up for 2/2/12 5:05pm receives patient program 2/1/2012 free voucher. Opens Email brochure for via mobile Provides Email 2/2/12 9:30 pm compliance 2/6/12 9:15 pm 2/2/12 3:06 pm program 2/6/12 9:00 pm Home Address Email Address Mobile # Cookie ID Ad ID 66

Customer Event Stream Activates Cross-Channel and Media Interaction data Connected Recognition Enables the customer Event Stream DM Shown Display Ad Visits branded site Sent Email Visits clinic and is Signs up for Patient Delivered 2/2/12 3:05pm and signs up for 2/2/12 5:05pm given brochure patient program 2/1/2012 free voucher. Opens Email for compliance via mobile Provides Email 2/2/12 9:30 pm program 2/6/12 2/6/12 9:15 pm 2/2/12 3:06 pm 9:00 pm User Event Table User ID Date Time Event ID Event Description Event Meta Data 1234 2/1/2012 DM437 DM Delivered 1234 2/2/2012 3:05 pm DI9076 Display Impression Event ID EM087 1234 2/2/2012 3:06 pm CC068 Signed up on site for free voucher Creative A2346 Fight depression 1234 2/2/2012 5:05 pm EM087 Sent Email 1234 2/2/2012 9:30 pm EM088 Opened Email Offer OI92365 30 day trial 67 1234 2/2/2012 9:30 pm EM089 Clicked Email Product P978 Rx Description 1234 2/6/2012 9:00 pm PS674 Clicks Paid Search 1234 2/6/2012 9:15 pm Q8740 Mobile Enrollment

Granular attribution allow us to fractionally assign credit to each touch point into event stream prior to conversion 15% 20% 40% 20% 5% Patient This scenario represents success in that the predicted customer value is realized/confirmed and there is a strong program ROI. Customer Level Attribution Program Level Attribution Predicted Customer LTD Value: $2,000 Attributed Campaign Display-DSP Spend $10,000 Credi Event Date Cost t Value Impressions 1,000,000 Inc TRx 1,320 DM Delivered 2/1/2012 $.35 .05 $100 Inc NRx 102 Display Impression 2/2/2012 $.001 .20 $400 Value per Rx $30 Microsite engagement 2/2/2012 $13.20 .30 $600 68 Total Value $42,660 Sent Email 2/2/2012 $.02 .10 $200 ROI 327% Clinic Brochure 2/2/2012 $12.50 .15 $300 Mobile Enrollment 2/2/2012 $.03 .20 $400

Value is Unlocked as We Can Influence the Customer’s Future Behavior Intervention strategy and rules are used to aid customer to next step in conversion process Contact Management Manages user interaction strategy and rules Patient program Fire trigger email based brochure picked up in physicians office on website interactions 2/2/12 2/2/12 2/1/2012 3:05pm 3:06 pm Measure Assess Tune Mobile call to action User receives email with DM Shown Visits branded Patient Delivered Display Ad important information enables customer to site and signs up for free about their disease state easily sign up for patient voucher. program while leaving with link to web page Provides Email physician’s office. User with discussion points for 2/2/12 3:06 pm immediately receives their visit with physician mobile coupon. 69 Personalization Dynamically assembles personalized communication package

Opportunity to Drive Smarter Planning and Messaging at the Segment and Customer level Targeting Which individuals and segments should we target? What channel should this individual be communicated Best Media/ Channel through? Given the potential value of this customer how much Allowable Spend should I spend to impact behavior? Contact How often and in what sequence should I Optimization communicate with this prospect? Given their history what offer, service, or Offer Optimization communication should be delivered? Product / Disease What product would this individual most likely be 70 State interested in? What is the best way to engage with this customer? Messaging How frequent should contacts be?

Digital Targeting and Personalization How data can be used to drive more targeted communications

Value is Unlocked Within The Digital Marketing Value Chain The Digital Marketing Value-Chain Optimized Channel Connected Customer Measurement and Experience Insight Budget Allocation (Targeting and (Data Integration) (Attribution) Personalization) First Party Data Integration Second Party Data Third Party 72 Data

Today, Consumers are… Engaged in an ever Barraged by an Expecting Assuming brands expanding number increasing number personalized and are aware of their of channels , which of messages and relevant past interactions is challenging communications interactions; they and expect brands business leaders to are self-selecting to to use this data to broaden channel engage with brands manage a reach & execution that provide worthwhile capabilities relevance and relationship timeliness dialogue 73

But, Most Business Leaders Approach To Personalization Is Patchy… • Emphasize digital channels only • Focus on superficial customer attributes • Fail to determine causal impact of personalization • Project aggregate group behavior to individuals • Rely on asynchronous customer data • Encourage channel myopia • Missing the real-time dimension in their approach, thinking and capabilities Source: Forrester Research “Use Customer Analytics to Get Personal”, by Srividya Sridharan February 17, 2012 74

The Evolution of Market Leaders in Personalization 2000 2003 2006 2009 2013 SOLUTION-FOCUSED CHANNEL-FOCUSED CUSTOMER-FOCUSED Isolated techniques limited to one Disparate approaches to Coordinated solution across or two personalized interactions personalization primarily achieved multiple interactions and channels in channels separate from each that leverages a complete view of other the customer Capability Capability - Personalization Capability • Recommendation systems • Integrated mobile and social execution silo'd in channel specific • Ability to optimize timing and − Collaborative Filtering tools (web, email, display − Content-based Filtering advertising, search) delivery • More control for companies − Ensemble Learning • Content/offer optimization Data - Channel specific customer to optimize decision logic − Segmentation interaction and profile data − 1:1 Predictive Modeling Data - Integrated customer Experience interaction data across online • Relevant communications Data - Limited to a small set of and offline channels • Inconsistent experience across relevant customer interactions channels Experience • Improved relevance Experience - Isolated • Consistent experience across personalization interaction channels 75

Levels of Personalization Maturity Optimal Personalization [Contextual Relevance] Level 5 High • Combines multiple personalization enablers to give a multi-dimensional understanding at a Leading Edge journey stage • Not only informs but also influences the customer’s mindset • Delivers a unique and competitive customer interaction • Addresses customer values: Contains the prevailing emotional criteria that best informs customer decision behavior • Considers what will motivate: Has behavior stimulus that best connects with the Customer Values to deliver the necessary response from the customer Moderate Personalization [General Relevance] Level 4 • Provides data-driven, relevant content and product offers based on general customer Consistent Best attributes CAPABILITY Practice • Timely; applicable content or offer; addresses customer preference(s) • Aligned with Brand/Promise drivers Level 3 Limited Personalization • Focuses primarily on segment’s channel preferences Industry • Delivery is appropriate and optimized for the media; channel, platform; addresses heuristics, Competitive Level 2 Sporadic Personalization • Mass-only attributes considered for content, limited to general versioning (region, language) Developing, • Inconsistently optimized for media or channel; only occasional personalization; limited Inconsistent measurement; lack of data-driven content Level 1 No Personalization • Not optimized for media, channel or platform; no personalization; not measured for Limited to No Low performance; static content, no versioning Capability 76

Personalization Is A Process, Not An Outcome Do we have the right data? Is that Where do we personalize system integrated yet? Is it fast next? Do we understand all enough and does it scale? of the decisions that are in place and by who? Is the content written? How do we manage changes? Is the experience consistent Will this rule conflict with across all channels? existing rules? How do we manage so many different yet related rules? How do we know this is working? Is it working How can we continuously because of what we are improve? How do we react doing or someone else? quickly and confidently? Source for Image and Quotes: Forrester, February 17, 2012, “Use Customer Analytics To Get Personal” 77

Decision Management Components Underlying technology architecture supporting channel-specific technologies enabling consistent, personalized customer experience across touch points. Benefits : • Rules engine to DM EM Agent Display Search Social Mobile Site CC govern customer Channel-specific interface File delivery - latent interactions • Integration with AMD for insight driven Campaign Feed Interactive Conductor communications Web API Batch Lists • Service Open environment for Data Insights Data Omni-channel Insights connectivity Decision Services • Real-time capability for timely Business Decision communications Rules Testing Optimization Management • Testing and machine learning for continuous learning Analytical Marketing and tuning Database 78

Dr. Jones, a sub-optimized HCP with growth potential, receives a rep call to discuss ease of use... Dr. Jones Industry Example 79

Sarah is a CV patient at risk of a serious health event. She searches Google for treatment options after her PCP visit….. Industry Example 80

Customer Journey Driving Awareness For New Hospital Facility Sally …. Industry Example 81

Integrated Personalization Solution Overview DATA ANALYTICS EXPERIENCE EXECUTION • Customer Data • Opportunity • Personalization • Planning Priority Integration Discovery PROCESSES • Setup and Decision • Interaction Design • Single View of the • Decisioning Configuration • Media Planning Consumer Development • Reporting and • Channel Integration • Data Insights • Testing and Monitoring • Asset/Media Optimization Development • Behavioral Impact • Delivery Requirements • Data Management • Predictive Analytics • Content • Campaign Platform Tools (e.g. SAS, R) Management System Management Tool TECHNOLOGY Integration • Integrated • Decision Engine • Reporting and • Channel Marketing Data Dashboards • Testing Module Warehouse Personalization Tools/Plug-ins 82

Personalization Engine Analytic Methods • Cross channel personalization engines should support a variety of analytical methods • Most single channel recommendation products just rely on information filtering since it is easiest to automate Method Description Examples Information Filtering Machine learning based techniques • Content based filtering – utilize discrete characteristics of (Highly automated & self an item in order to recommend additional items with learning) similar properties [More limited-easier to get started] • Collaborative based filtering- “User behaves like this (or has preferences such as this) look like another user who likes/ purchases xyz” [More robust -cold start problem] • Hybrid filtering- Combination of content and collaborative approaches [Best but most complex] • Trigger actions- If user does this then do that Decision Rules-Tree • Adaptive rules- Next action or content varies based on (Very custom, sequencing) sequence of actions taken by user Propensity Models Statistical modeling based techniques • Next best product/offer/action modeling - Used in cases where (Custom models for few fewer offers, products, or options but rich consumer history important decisions) 83

Cross Channel Measurement How data can be used to drive more targeted communications

History of Marketing Mix Modeling and Attribution Modern MMO MMO scales Digital media MMO (top down) MMO begins as emerges in CPG Audiences outside CPG to Audiences disrupts MMO Audiences and Attribution custom one-off Industry include Auto, industry. (bottom up) projects aggregated aggregated aggregated Finance and Recovers by late unify by content by content by content Pharma 2000s 1940s-1970s 1980s 1990s 2000s 2010s Low adoption, Syndicated Computer power Mathematics of Mathematics of lack of data, lack scanner data increase (still digital need to be paid digital fixed, of computing revolutionizes mainframes created. computation cost power industry though) falls to $0 • Bayesian, • Mainly academic • Computing • MMO becomes • Focus on speed Markov, agent- until 1970s problematic the standard and actionability based and other approach for CPG • First MMO • Regression models emerge • Implementation • Models become product in 1979 becomes • First attribution • Panel more complex implementation • MMO is panel models in 2005 approaches fade • First digital (based on 1979 • Social become based, similar to (will remain as panel models) attribution today forecasting tools) models in 1999 the next frontier 85

Merkle Recommends a Modeled Attribution Approach Across All Media Assess media performance by measuring the incremental impact of each marketing activity Day 8-30 Day 1-7 Day 0-1 Actual $ New experience Customer Credit over applied to bottom of funnel touches. Other touches often ‘invisible’ Direct or Rules Based Creates flawed financial 0% 100% view of performance Model-adjusted interaction Modeled Most accurate and 3% 14% 3% 5% 5% 5% 15% 5% 5% 40% actionable Mass and Offline Digital Direct mail sent TV view Print view Display view Website visit Social visit Paid search click 86

Granular attribution allow us to fractionally assign credit to each touch point into event stream prior to conversion 15% 20% 40% 20% 5% Patient This scenario represents success in that the predicted customer value is realized/confirmed and there is a strong program ROI. Customer Level Attribution Program Level Attribution Predicted Customer LTD Value: $2,000 Attributed Campaign Display-DSP Spend $10,000 Credi Event Date Cost t Value Impressions 1,000,000 Inc TRx 1,320 DM Delivered 2/1/2012 $.35 .05 $100 Inc NRx 102 Display Impression 2/2/2012 $.001 .20 $400 Value per Rx $30 Microsite engagement 2/2/2012 $13.20 .30 $600 87 Total Value $42,660 Sent Email 2/2/2012 $.02 .10 $200 ROI 327% Clinic Brochure 2/2/2012 $12.50 .15 $300 Mobile Enrollment 2/2/2012 $.03 .20 $400 87

Promotion Mix Solution (Top Down Approach) • Promotion Mix Modeling is an econometric technique used to quantify the impact of promotion spend on sales. It uses historical time-series data to measure the promotion impact Personal Non-Personal Speaker NRx Volume/ = Carryover TREND & + Promotion + Promotion Efforts + + Programs/ Market Effects Others Efforts Seminars/ Share Journal Advtg. Rep Details Samples Channel Inputs: Tele-Detailing Insights: PROMOTION RESPONSE Share Change/Volume Direct Mail • Impact of Personal eMail ANALYSIS Promotion Promotion Mix • Impact of Non- Mobile Modeling Personal and Other Promotion Display / Search • Promotion Response curves • Total and Managed Care Marginal ROIs Market Factor Controls Physician & Patient Brand Managed Care Status Demographics Competitor Share Competitor Managed Care Status Physician Attributes 88

Model Selection: Output Benefits Based on Data Structure Statistical Models Input Data Output Data TV p Details        350 p y x i 300 t i it t 250  Segment 1: 200 i 1 150 100 60 50 Sales Decomposition 0 0 5 10 15 20 25 30 50 Sales = 3.1* Direct mail + 0.8 Time Direct Mail 40 * Email units + 0.5 * (PDEs) 2 1200 TV Revenue 1000 30 Direct Mail 800 600 Radio 400 20 Segment 2: Print 200 0 Base 10 0 5 10 15 20 25 30 Time 0 Sales = 2 * Direct mail + 0.1 * Radio Mobile 1 3 5 7 9 11 13 15 17 19 21 23 250 Time Email units + + 0.4 * (PDEs) 2 200 150 100 Brand Sales = “ B ”*Units of Touchpoint 50 600 0 87 71 Segment 1 500 0 5 10 15 20 25 30 Segment 3: 49 TV Example: For every direct mail piece sent via 62 46 Time 42 400 Direct Mail 73 Revenue 73 Radio 300 Direct Mail Print TV Print iConnect, sales increase by 2.5 Rx and for every Email 200 11% 298 16% 280 Base 180 100 Base 160 Sales = 0.5 * Direct mail + 1.5 * 140 51% email sent, sales increase 0.3 - 120 Segment 1 Segment 2 100 Print 80 Radio 14% Email units + + 0.2 * (PDEs) 2 60 8% 40 Sales = 2.5 * Direct Mail Units + 0.3 * Emails 20 0 0 5 10 15 20 25 30 Time Segment view enabled through HYPOTHETICAL DATA use of random effects in mixed modeling approach 89

Example: Channel Contributions Top Down Model provides a High Level Contribution Allowing Us to Allocate Total Spend Budget and Assess Historical Performance Details 30% Carryover 34% Email 1% Samples/Detail iConnect DM 3% 1% Spot TV Direct Mail National TV 1% 1% 19% Paid Search 2% Speaker Programs Digital Display Print (News) 2% 3% 3% 90

We Need Both Top Down And Bottom Up Measurement Methods Top-down (Aggregated Data) National media (TV & radio) Local media (TV & radio) Direct mail Digital $140 $200 $180 $83 Integrated All measurement dimensions All measurement levels (Customer segment, product, (Media, platform, measurement geography) campaign, placement) Display Video Search Direct mail Social $60 $80 $91 $75 $113 Remarketing - $12 Video 1 - $121 Branded - $87 DM 1 - $11 Social 1 - $50 Video 2 - $35 DM 2 - $93 Programmatic - $80 Social 2 - $163 Not branded - $99 Video 3 - $213 DM 3 - $210 Guaranteed - $130 Social 3 - $456 Video 4 - $23 DM 4 - $235 Bottom-up (Customer Level Data) 91

Integrated Attribution Provides Output Within Measurement Levels and Dimensions Media-level results Segment-level results Campaign diagnostics Monthly Monthly Daily • More accurate view into media • Visibility into how each tactic was • Visibility into “why” different performance driving new customers by segment programs were and were not performing • Important input into ongoing • Important data to feed into • Diagnostic data markers can use to budgeting and planning processes customer experience to drive better personalization and targeting by adjust existing programs tactic and segment Segment Penetration (Index) Performance Drivers CPA by Channel Contact Contact Unique $400 Campaign Segment 1 Segment 2 Segment 3 Segment 4 Segment 5 Effective Frequency % Campaign Frequency Response % Exclusive $350 CPM (Within Remarketing Display 1 120 90 100 120 85 (Across Media) Rate $300 Media) DM 1 95 75 55 95 105 $250 DM 1 $ 5.28 3.0 12.0 0.1565 10% 10% Alt Media 3 130 50 90 130 114 $200 DM 2 $ 1.69 3.9 15.0 0.0552 20% 20% $150 Display 1 120 95 50 120 87 Alt Media 3 $ 18.87 3.1 7.0 0.6122 15% 15% $100 DM 4 55 150 140 55 79 Alt Media 7 $ 0.34 39.4 45.0 0.1096 10% 10% $50 Social 1 95 200 143 95 100 Search 1 $ 0.39 5.3 6.0 0.0130 20% 20% $0 Display 1 85 75 22 85 75 Social 1 $ 1.71 66.0 78.0 0.5884 15% 15% January February March April May Search 1 200 98 100 200 97 Email 1 $ 1.44 2.1 5.0 0.0162 10% 10% Social 1 75 101 75 75 120 Search 1 $ 44.99 5.3 10.0 1.2132 20% 20% DM Brand TV Display Display 2 30 130 120 30 115 Display 4 $ 96.41 2.5 30.0 1.3916 15% 15% Email Organic Search Paid Search Overall 100 100 100 100 100 Display 2 $ 0.63 1.5 18.0 0.0049 10% 10% Total $ 0.66 4.0 22.6 1.812% 10% 10% 92

Analytics and Modeling - Things to consider • • Estimation of touch point effects Retargeting • Sequencing and assists • Estimation of paid search clicks • Validation • Time effects • Attribution Formula • Repeated touch points 93

Centralized Insights Portal Measurement and Insights Portal Media Targeting and Attribution Personalization Centralized location to access understand historical performance, plan for future, set up targeting, and analyze customers Planning and Customer Insights Forecasting Integrated Dashboards 14 * Note: all data changed to protect confidentiality * Note: all data changed to protect confidentiality 94

Solution is Focused on An Enterprise Measurement Platform, Not Just a Digital Attribution Tool Enterprise Measurement Platform Requirements Ability to report out KPIs Integrated and customizable Support all media Best KPI accuracy at any level based on key, customized performance reporting business dimensions One place to go for program Media, campaign, Segment, Product, Geography, Digital, mass and offline direct performance, insights, and placement/keyword and Time analysis Tight integration with Scalable and flexible solution Action support, not just Robust decision support marketing database architecture product support Big data platform with robust Ability to run what if Ability to push data from Help lead change customer identity scenarios and machine marketing database to management and ongoing management and ability to optimization to create the attribution platform and back insights extraction and action absorb frequent changes to best plan at any level into database for action processes data and requirements 95

Merkle Attribution Solution – Reference Architecture Input Intelligence Action Insights Portal Attribution + Media Planning/Optimization Engines Inbound Channels • Portal Interface Wrapper • Fraction allocation model with comparative techniques • Tableau Reporting Solution • Multi-stage statistical modeling approach (logistic regression) • Use of all available information; principal components on Site Store Call 300+ variables Center Reports/ Planning Interface • Forward looking scenario planning capability Dashboards Tools Outbound Media Output Module • Target individuals and apply recommendations Display Search Sms Transform Analytic Event • Integration with DSPs, search Modeling Stream platforms, etc. DM TV Social Optimization Radio Email Recommendations / Targeting Data Collection 96

Merkle Attribution Platform Physical View - Modules & Process Input Intelligence Action CR 97

Measurement Output Must be Easily Integrated Into Targeting Algorithms Model-based Digital Real-time Targeted attribution weights platforms bidding ads Cookie Conversion Event Attribution User ID ID ID Weight 1234 C76532 DM437 .05 Demand Side Publisher Platform 1234 C76532 DI9076 .32 (DSP) 1234 C76532 PS674 .11 Anonymous targeting 1234 C76532 Q8740 .25 Publisher Keyword/ Cookie 1000101110101 0100111001110 Attribution data Search Bid Engine Anonymous Data Platform Engine 98

SUMMARY Financial management must evolve to Enterprise scale an enterprise-wide initiative to be most is necessary effective Analytics and technology must be Analytics alone tightly integrated to create these is not enough solutions Significant value can be created by Value potential taking even a few steps forward in the evolution of the four Financial is enormous Measurement capabilities 99

CASE STUDY Attribution