Digital Media Analytics January 30, 2014 Agenda Introduction - - PowerPoint PPT Presentation
Digital Media Analytics January 30, 2014 Agenda Introduction Module 1: Brief Overview of Digital Media Ecosystem Module 2: Competencies and Technology Needed to Succeed Module 3: Discussion of Data Created within Ecosystem
January 30, 2014
2 A Glossary has been provided as a separate handout
“Should we increase or decrease the spend on Video Advertising?” “We will like to get results of digital attribution every quarter” “Can we understand which banners ads should be served to different customers?”
“How will the results match with my Marketing Mix model?”
“If the digital media is not meeting the benchmarks, should we change the creative?” “What is the Rx impact of my Digital campaign in first Half of the year?”
“Do I have all the data I need for digital attribution?“
“Can you assess the performance
Campaigns?”
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“Are my paid media campaigns properly set up for tracking, reporting, and measurement?”
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There Is A Power Shift Happening in Customer and Health Branding Today
1950s+ AGE OF BRAND
creation of national brands via TV
Brands at Scale BRAND
in control
1980s+ AGE OF BIG BOX
physically get closer to customer
Big Box Format RETAILER
in control
2010+ AGE OF PATIENT- CENTRICITY
low cost 1:1 personalized engagement
Digitization CUSTOMER
in control
1995+ AGE OF INTERNET
direct-to- consumer business model
eCommerce CHANNEL
in control
Macro-trends are changing the landscape…
Digitization of everything Social networks at scale Consumer mobility
…and Customers are responding
Shift in media consumption patterns for Patients, Caregivers, HCPs and Payers Changing Customer Behaviors Mass consumer to consumer engagement
Know Me
then
Amaze Me
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The Always Addressable Customer
And marketers are responding by And consumers are changing
Mass consumer to consumer engagement Changing consumer purchase behaviors Shift in media consumption patterns Focusing on big data and its ability to drive value Embracing digital media and channels to enhance customer experience Putting the customer at the center of business strategy Social networks at scale
2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 7.0—— 6.5 — 6.0—— 5.5 — 5.0—— 4.5 — 4.0—— 3.5 — 3.0—— 2.5 — 2.0—— 1.5 — 1.0 —— 0.5 —
2008-2013
Social media 0.1 to 1.1 Digital content 1.7 to 2.4 Consumer hours spent per day on non-digital channels are decreasing, while use of digital channels are steadily increasing
Hours
2008-2013
TV 3.8 to 3.1 Radio 1.6 to 1.4 Print 0.7 to 0.4
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Mobile Advertising Increase Stay about the same Decrease Don’t use
69% 20% 70% 29% 64% 24% 23% 50% 19% 48% 13% 20%
Social Media Advertising Video Advertising Rich Media Advertising Standard Display Advertising Connected TV / IPTV
Survey on the amount marketers will increase/decrease their budgeting in these forms of advertisement
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The Rapidly Growing Digital Advertising Market
Current and projected market share from 2011-2015
Internet Outdoor Cinema Radio Television Magazines Newspapers
7.3%
2011 2015 20.3% 15.9% 9.4% 7.3% 39.9% 40.0% 7.1% 6.6% 6.7% 6.3% 16.1% 23.4% 0.5 % 0.6% 10
Digitization is Creating Massive Amounts of Data For Digital Marketing
“There were 5 exabytes (5 million terabytes) of information created between the dawn of civilization through 2003 but that much information is now created every 2 days, and the pace is increasing.”
182 billion
e-mail messages are sent each day
29.8 billion
ads served by Google each day
70 billion
pieces of content shared on Facebook every month
400,000
bid requests per second processed
ad platform
Merkle manages over 3 peta-bytes of marketing data, increasing by approximately 10TB/month” Storage for one client includes 8,824,526,619 page views (to be exact) and over 24 terabytes in a single database
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Programmatic Media Has Now 50%+ Share in US Digital Media Market with RTB the Fastest Growing Area
76% 62% 47% 36% 27% 21% 17% 13% 18% 25% 29% 32% 32% 31% 11% 19% 28% 34% 41% 47% 52% 100% 80% 60% 40% 20% 0% 2011 2012 2013 2014 2015 2016 2017
US: Programmatic Share (% of Digital media transactions)
RTB Non-RTB Non-Programmatic
Source: Magna Global
Overall Digital Media Marketplace - $61B by 2017 $8B in RTB media by 2017 growing at 59% CAGR $8B in “Custom Audience” by 2017 Over half of all digital media today is bought programmatically
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Targeting & Personalization
next best product, customer value Measurement
“partial credit” cross-media and channels Channels & Media
Personas Known Anonymous Individual
Data generated by digitization is driving addressability at scale across audience platforms at the individual level
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The Addressability Spectrum Anonymous Partially Identified Identified
Identification:
Unknown Some Knowledge Well Known
Knowledge:
Addressability is the degree to which customer data (anonymous or identified) can be used to increase the target-ability and personalization of marketing impressions and experiences
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The consumer clicks on an online ad, which conveys their city through the IP address.
Definition: The degree to which you can use customer (anonymous or identified) data to increase the targetability and relevance of marketing impressions and experiences Level of identification
Anonymous Partial ID Full ID Low Medium High
Level of knowledge
The Addressability Spectrum
Interest in product Location
Definition: The degree to which you can use customer (anonymous or identified) data to increase the targetability and relevance of marketing impressions and experiences Level of identification
Anonymous Partial ID Full ID Low Medium High
Level of knowledge
The Addressability Spectrum
The consumer submits their email address, which helps point the way to data about them that is elsewhere online.
Interest in product Location Email Public online activity
Definition: The degree to which you can use customer (anonymous or identified) data to increase the targetability and relevance of marketing impressions and experiences Level of identification
Anonymous Partial ID Full ID Low Medium High
Level of knowledge
The Addressability Spectrum
Highest value The consumer logs into Facebook, providing an exact name and identity.
Interest in product Location Email Public online activity Real name
Definition: The degree to which you can use customer (anonymous or identified) data to increase the targetability and relevance of marketing impressions and experiences Level of identification
Anonymous Partial ID Full ID Low Medium High
Level of knowledge
The Addressability Spectrum
The consumer reads a story
their IP address.
Interest in topic Region
Definition: The degree to which you can use customer (anonymous or identified) data to increase the targetability and relevance of marketing impressions and experiences Level of identification
Anonymous Partial ID Full ID Low Medium High
Level of knowledge
The Addressability Spectrum
The consumer’s IP is cross- referenced with publicly available data, showing the general location where they live.
Interest in topic Region Specific location
Level of identification
Anonymous Partial ID Full ID Low Medium High
Level of knowledge
The Addressability Spectrum
High value Later, the same IP is logged at a shopping site. Linking the news story, location, and retailer allows a targeted ad to be served to the consumer.
Interest in topic Region Specific location Interest in specific product
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Automation
Addressability Phenomenon Is Reaching New Levels of Sophistication Due to Rise of Audience Platform An Audience Platform is a technology that enables automated, real- time delivery of targeted, personalized experiences to individuals (known and anonymous) at scale utilizing first and/or third party data
Customer Data
Marketer
1st Party Data 3rd Party Data Automation Individual Level Delivery
Audience Platforms Audience Owners
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The evolution of digital media in recent years is a good example
But Addressability Is Not Just About Display Media – Search is Evolving Very Quickly As Well
Universal Platform Differentiated by Device Integrated Media and Site Targeting
How many people are searching and for what terminology Increased options controlled by the search engines for delivery Use of Remarketing programs in search and display to customize
Targeting
search behavior with not personalization
and carrier
time functions
prior site visitation
user profiles
Optimization
costs Performance by customer
Keyword Audience Driven Keyword Location/Device/Time Keyword
Level of insight
Search Evolution 2010 - 2013
Formats
Location/Device/Time 18
Some Search Marketing Ads still Struggle in Medical Legal Review
– Many PharmaCo’s still fail to approve SEM submissions beyond Exact Match
– Cannot use co‐morbid or off‐label indications to target keywords
– Approvable but still must be qualified and on label
– Approvable but the url cannot include any product representation
– No MLR barrier but requires an alignment with Commercial Team
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Industry Example
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Clicking Here Lands Here
Landing pages should contain:
Landing pages should not:
Industry Example
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the user’s Search Query
– 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
campaign based on keyword sets to determine highest producing click- through and conversion rates.
The Big Trends – Where This Is All Heading
Digital media convergence – digital media has taken on forms in custom content, video, social, and mobile , search Known individual level targeting reaches massive scale as the core of the strategy Mass adoption of social log-in will reach huge scale and open massive addressability
Cross device targeting maturity will accelerate – Google and Twitter to lead the way (Apple, smart TV) 1st party audience expansion and extension creates massive addressable scale opportunity Commerce - Data driven inventory will expand dramtically as large commerce expand business model to monetize first party data assets – eBay, Amazon Publisher - Google and Facebook off platform extension - stack integration will create massive addressable audience scale (Atlas and DART acquisitions used to drive ubiquity and integration of advertiser, social, and paid media targeting and reach
Advertisers will have to deal with complexity of the closed media platforms as large players such as Google and Facebook create “walled data gardens” through their stack acquisitions Search will be the next big addressable platform Programmatic media buying explodes Unique content will drive growth
demand which
big addressable platform at scale (Netflix – Orange is the New Black) The info-mediary starts to take shape
into their own experience – the value exchange 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.
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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
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June 2012 October 2012 May 3 2013 July 11 2013 August 9 2013 August 22 2013 July 1941
July 1941
The first ever television ad, a ten-second Bulova watch spot, airs prior to a Brooklyn Dodgers and Philadelphia Phillies game.
Sept 1998 1994 March 2012 August 22 2013 July 1941 March 2012 June 2012 October 2012 May 3 2013 July 11 2013 August 9 2013 Sept 1998
1994
The first display ad from AT&T
1994 July 1941 March 2012 June 2012 October 2012 May 3 2013 July 11 2013 August 9 2013 August 22 2013 1994
September 1998
Google launches service
Sept 1998 July 1941 June 2012 October 2012 May 3 2013 July 11 2013 August 9 2013 August 22 2013 Sept 1998 1994
March 2012
March 2012 July 1941 October 2012 May 3 2013 July 11 2013 August 9 2013 August 22 2013 Sept 1998 1994 March 2012
June 2012
June 2012 July 1941 June 2012 May 3 2013 July 11 2013 August 9 2013 August 22 2013
October 2012
October 2012 Sept 1998 1994 March 2012 July 1941 June 2012 October 2012 July 11 2013 August 9 2013 August 22 2013
May 3, 2013
May 3 2013 Sept 1998 1994 March 2012 July 1941 June 2012 October 2012 May 3 2013 August 9 2013 August 22 2013
July 11, 2013
July 11 2013 Sept 1998 1994 March 2012 July 1941 June 2012 October 2012 May 3 2013 July 11 2013 August 22 2013
August 9, 2013
August 9 2013 Sept 1998 1994 March 2012 July 1941 June 2012 October 2012 May 3 2013 July 11 2013 August 9 2013
August 22, 2013
August 22 2013 Sept 1998 1994 March 2012
Innovation in the Platforms is Picking up Significant Speed and Volume
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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
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to CRM data for better targeting, measurement and segmentation
Site and Email
$3 MM $8.6MM $14.6MM $16.4MM
We Believe This Has Value To Our Brands in Tens of Millions
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$7.8MM $20.8MM $34MM $36.4MM
Acquisition of new customers Improvement in Patient Adherence
Improvement in Intent to Prescribe/ Intent to Ask My Doctor over Baseline*
NPV of Revenue Impact
$4.8MM $12.1MM $19.4MM $20MM
to Visit and Ask my doctor
+5 +10 +5 +5
YEAR 1 YEAR 2 YEAR 3 YEARS 4+5
Introducing…The Health Platform Marketer
expert
buyer
attribution expert
Payer Economist
Health Platform Marketer wears many hats & embodies the competencies needed to successfully operate in today’s digital world
champion
designer
preference advocate
strategist
manager
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Health Platform Marketer Represents a Dramatic Shift From Traditional Marketing Skills and Competencies
The Traditional Marketer The Health Platform Marketer
Big Data informs Big Ideas in CRM Channels Media buying driven through the tech stack and audience platforms Integrates consumer experience across media and channels at segment and individual level Big Idea informs DTC or HCP Campaign Programs disconnected from the lived customer experience Marketing moves in internet speed through programmatic approach to decisioning and execution Media buys reliant on buying clout and scale Marketing moves at the speed of human decision
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Twitter handle Cookie 3rd party ID address Digital set top ID Mailing address IB ID Pinterest ID GooglePlus ID Device ID
70’s 80’s 90’s Today
12 Main Street Philadelphia, PA
Email Phone number
00’s
617-555-0728
12 Main Street Philadelphia, PA 617-555-0728 12 Main Street Philadelphia, PA
John@doe.com
617-555-0728 12 Main Street Philadelphia, PA John@doe.com
#JohnnyDoe 01100100010 0110001 //asdohs.hhd.net
617-555-0728 12 Main Street Philadelphia, PA John@doe.com
#JohnnyDoe 01100100010 0110001 //asdohs.hhd.net JD@gmail.com Pinterest: jdoe JD’s iphone
011001000 100110001
01100100010 0110001
//asdohsd.asiudhscns/html
The Platform Marketer knows he/she must maximize his addressable market through high coverage of consumer identifiers and knowledge in the database This requires mastery
addressability in the database and constant collaboration and leadership with technology It also requires deep consumer insight and experience skills to design and implement the experiential “value exchange” that incents consumers to provide data (e.g. why should one identify on a site with one’s facebook log-in?)
Platform marketers bring addressable data skills that facilitate the exchange of identity and data for personalization and relevance.
Health Platform Marketer – The Addressability Expert
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The Platform Marketer knows customer segmentation intimately and uses it as a core strategic tool to better understand opportunities and risks in the market
Health Platform Marketer – The Segment Portfolio Manager
Young Trendse er Enlightened Consumer Time Savvy Mom Thri er
$1.5 $10M $5 $30M $15 $10 $4.5 $12M $3
Shopper
Tradi onal Media Promo onal Spend
$8.5 $3.5 $6
Social/Digital Media
Total Marke ng Dollars
$4 $70M $4 $3.5 $2 $3
$14.5 $22.5 $29
Store Foot
Prices for Up Sell Customer
Mass Marke ng
ROI
$8M $14M $38M $48M
$100M Customer value analysis highlights the right investments that should be made to each segment and the return that can be expected Segmentation needs to be fully
and tracked over time 32
Segmentation Gives us the Ability to Feed Individualized Moments to Change Health Behavior
PERSONAL EXPERIENCE
CONNECTED CUSTOMER PROFILE SEGMENTATION
message treatment IP context
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Convert Purchase Activate Triggered Direct Mail
CONNECTED CUSTOMER TM PROFILE DRIVES MESSAGING
Social Incentive Review Incentive Recommend Pairing Re-Purchase Re-Activate
Over Time, Pharma Customers Receive Smart Messaging Based on Customer Preference & Segment Behavior
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Concept In-Action
Web Mobile SMS Printable Integrated Experience Delivery
Step One
Industry Example
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Concept In-Action
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Step Two
Mobile Coupon Printed Coupon Redeemed at pharmacy
Customer Segment identified
Tailored email Tailored SMS
Industry Example
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Industry Example
Case Example: BrandX Adherence Program
program provides personalized education, emotional support and smoking cessation tips.
content is continuously updated based on input received from patients in real-time.
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User Texts “URGE” To Receive Tips RedShop Rx Sends Coping Tips Via SMS
CONTENT & CONTEXT INTENT & BEHAVIOR ANONYMOUS INDIVIDUAL IDENTIFIED INDIVIDUAL
context custom content intent geo-location device ID anonymous cookie 1st party cookie name & email behavior 3rd party segments probabilistic ID
The Platform Marketer is a master of the ever evolving Audience Platform targeting and
The Health Platform Marketer – The Audience Platform Expert
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A Short History Of Digital Media Buying
Audiences aggregates by scaling niche content Audience aggregated by third party data Audiences aggregated by content
Differentiation created by Media Skills Differentiation Created by Optimization Differentiation Created by Technology
Audience aggregated using known relationships
Differentiation Created by Data Integration and Analytics
1995-2005 2005-2009 2009-2012 2013+
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Digital Media 10 Years Ago
Buying is relationship based with targeting and
very coarse level. “Transparency line” ends at the network and publisher level – what falls below the line is “black box” . Buying is done across numerous platforms without the ability to manage frequency and cost resulting in significant waste . Just as bad (or worse), targeting capability does not allow for targeting the right individuals.
“Black box” ad networks “Black box” ad networks “Black box” ad networks Direct sales force “Remnant” inventory “Remnant” inventory “Remnant” inventory “Premium” inventory Publisher Publisher Publisher Publisher Publisher Publisher Publisher Publisher
Agency Approved Campaigns
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Digital Media Today – Challenges Remain For Life Sciences Adoption
Buying is done using a data-driven targeting skill- set and mind-set. Consolidated buying platforms allow for complete transparency and granular targeting – no more black box. Real-time-bid environment allows for access to premium and remnant inventory that gets bid on auction-style based on the value to the advertiser. Direct buys and paid social leverage data and technology to cross multiple channels while remaining customer- centric.
Publisher Publisher Publisher Publisher Publisher Publisher Publisher Publisher
Integrated Media Management Platform
Data & enabling technology
Real-time bidding auction
Paid Social Direct Buys Programmatic (DSP)
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Targeting Framework
Consolidated Buying Platform (DSP) Trading Desk
Lookalike Modeling Match converted consumers to anonymous ID and create look- alike predictive model to identify “like” cookies/ placement
through RTB Online Audience Segments Identify high performing
segments (“auto intenders”) and target these anonymous users through the DSP Re-Targeting Identify users visiting site (anonymous or authenticated) and target customized impressions after they leave the site Online-Offline Direct Match Match offline “top deciles” to cookies through third party match providers and target known consumers on a 1-1 level
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Health Platform Marketer – Programmatic Media Buyer
The Platform Marketer brings programmatic buying skills to the enterprise
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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
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In the last 18 months, we see advanced marketers rationalize this technology into a unified stack
Audience Platforms Platform Marketer Stack Name & address 3rd party cookie 3rd party segment Context 1st party cookie Device ID Geo-location Social ID / handle Execution Currencies Campaign Management DMP Attribution & Insights On-boarding Ad Serving & Tag Management Identity Management Marketing Database 45
Market Forces Require A Different Type of Analyst
with their customers and react to changes in customer behavior instantly” Forrester
than ever before through, display, social, video, and mobile.
addressable through digital media
Forrester
widely adopted, most emails are batch, and organizations are not unlocking the value of user-level ad and site targeting.
Analytic methods and tools need a big data reboot Analytics is not matching up to real- time marketing
Data scientists are critical to drive digital and offline data integration
Analytics is a constraint as media becomes more targetable
and more technically sophisticated than the BI toolkit” Green plum
for organizations to take advantage of big data.” McKinsey
and technologists figure out what data is valuable and how it should be integrated
from a variety of sources is the top challenge preventing
customer analytics. Forrester
Customer Analytics as a Marketing Competitive Advantage
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SUMMARY The Health Platform Marketer
Addressability at scale has and will create competitive advantage The new addressable platforms will require new analytical competencies! Massive budgets are already being shifted to take advantage of this
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Optimized Channel Experience (Targeting and Personalization)
Value is Unlocked Within The Digital Marketing Value Chain
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First Party Data Second Party Data Third Party Data
Integration
Measurement and Budget Allocation (Attribution) Connected Customer Insight (Data Integration) The Digital Marketing Value-Chain
Anonymous Behavior Tracking
Anonymous User Identifiers
User Data Collection Methods
34x43292jk2395kls9ef876 50
How A Website Works
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HTTP
requests for web pages (hypertext)
– Methods (GET, POST, PUT, DELETE,...) – Response codes
– Host, User Agent, Referrer, Cookies
HTTP Request (from client)
GET / HTTP/1.1 Host: www.linkedin.com User-Agent: Mozilla/5.0 (Windows NT 5.1; rv:21.0) Gecko/20100101 Firefox/21.0 Accept: text/html,application/xhtml+xml,application/xml; Accept-Language: en-US,en;q=0.5 Accept-Encoding: gzip, deflate Cookie: leo_auth_token=... Connection: keep-alive [optional request body, e.g. when posting data from a form]
HTTP Response (from server)
HTTP/1.1 200 OK Server: Apache-Coyote/1.1 Content-Encoding: gzip Vary: Accept-Encoding Content-Type: text/html;charset=UTF-8 Content-Language: en-US Date: Fri, 07 Jun 2013 01:49:26 GMT Connection: keep-alive Set-Cookie: _lipt=deleteMe... [response body; e.g. html content goes here]
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How Web Data Capture Generally Works
Typical site visitor
1
192.168.2.49
Looking for ways to donate food in her
banks and clicks on paid search ad for Feeding America 2 Lands on food bank search page 1 GA sees that browser coming Google.com
paid seach has no cookie, drops 1st party cookie on browser, and counts browser as a new site visitor Google Analytics is web analytics tool for Feeding America (Javascript on all pages)
2
GA records all actions taken by user on site in Google collection server. Java script instructs what data to send.
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
Web Browser Feeding America Web Server
Firefox
Google Analytics Collection Server
1
Web server notifies Google analytics collection server of request
2 3 Collection server looks to see if user
has a cookie and drops cookie if no cookie exists Collection server captures behavior
rules
4
Web browser requests content from Feeding America
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?
Source: http://www.whatsmyuseragent.com
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Cookie Background
What is a cookie?
browser, that are used to maintain state throughout your visit to a website (HTTP is a stateless protocol)
cross-domain cookie access is not allowed by your web browser) There are two flavors of cookies important to this discussion
(Example: NIKE assigning a cookie to browser of NIKE.com)
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 3rd party content. (Example: Google assigning a cookie to a browser on NIKE.com )
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IP Addresses
the Internet.
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
– 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)
– 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.
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Death of The Cookie?
cookies
(including mobile devices)
antivirus software
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What About Cookie Tracking on 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
Users can opt out of tracking.
– 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
TRUSTe)
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What About IP Addresses?
providers and demand side platform vendors (DSPs)
about 45% to the zip level
month’s time
addresses”
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Device Fingerprinting
deletion issue
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Our Observations About Digital Data Landscape
– 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
– 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)
– 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
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Digital Data Sources (Digital media and Channels)
Site Display Social*
Party Identifiers First Party Data Capture (Example)
Primary First Party Data Systems
Third Party Data Providers (Example)
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Digital Data Sources (Digital media and Channels)
Site Display Social*
Party Identifiers First Party Data Capture (Example) Third Party Data Providers (Example)
browser type, etc)
Banner clicks, email clicks)
views, downloads, etc)
signup, purchases, quotes, information requests, etc)
browser type, etc)
view or click (quote, purchase, etc)
likes, interests, geo, etc
(site, apps, etc)
specific social network (Twitter, Linkedin, etc)
Primary First Party Data Systems
Coremetrics, etc)
(Media Math, Turn, [X+1])
Twitter) and social platforms
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Digital Data Sources (Cont.)
Party Identifiers First Party Data Capture (Example) Third Party Data Providers (Example)
Banner clicks, email clicks)
(keywords, bid amount, cost, creative, etc)
click (quote, purchase, etc)
Primary First Party Data Systems
analytics, apps
platforms (Kenshoo, Marin)
and social platforms
Mobile Search Email
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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 Delivered 2/1/2012
Patient Home Address Email Address Mobile # Cookie ID Ad ID
Shown Display Ad 2/2/12 3:05pm Visits branded site and signs up for free voucher. Provides Email 2/2/12 3:06 pm Sent Email 2/2/12 5:05pm Opens Email 2/2/12 9:30 pm Visits clinic and receives brochure for compliance program 2/6/12 9:00 pm Signs up for patient program via mobile 2/6/12 9:15 pm
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User ID Date Time Event ID Event Description 1234 2/1/2012 DM437 DM Delivered 1234 2/2/2012 3:05 pm DI9076 Display Impression 1234 2/2/2012 3:06 pm CC068 Signed up on site for free voucher 1234 2/2/2012 5:05 pm EM087 Sent Email 1234 2/2/2012 9:30 pm EM088 Opened Email 1234 2/2/2012 9:30 pm EM089 Clicked Email 1234 2/6/2012 9:00 pm PS674 Clicks Paid Search 1234 2/6/2012 9:15 pm Q8740 Mobile Enrollment
User Event Table Event Meta Data
Event ID EM087 Creative A2346 Fight depression Offer OI92365 30 day trial Product P978 Rx Description
Customer Event Stream Activates Cross-Channel and Media Interaction data
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DM Delivered 2/1/2012 Shown Display Ad 2/2/12 3:05pm Visits branded site and signs up for free voucher. Provides Email 2/2/12 3:06 pm Visits clinic and is given brochure for compliance program 2/6/12 9:00 pm Sent Email 2/2/12 5:05pm Opens Email 2/2/12 9:30 pm Signs up for patient program via mobile 2/6/12 9:15 pm
Patient Connected Recognition Enables the customer Event Stream
Granular attribution allow us to fractionally assign credit to each touch point into event stream prior to conversion
68 15% 20% 20% 40% 5%
Event Date Cost Attributed Credi t Value DM Delivered 2/1/2012 $.35 .05 $100 Display Impression 2/2/2012 $.001 .20 $400 Microsite engagement 2/2/2012 $13.20 .30 $600 Sent Email 2/2/2012 $.02 .10 $200 Clinic Brochure 2/2/2012 $12.50 .15 $300 Mobile Enrollment 2/2/2012 $.03 .20 $400
Predicted Customer LTD Value: $2,000
Customer Level Attribution Program Level Attribution This scenario represents success in that the predicted customer value is realized/confirmed and there is a strong program ROI.
Campaign Display-DSP Spend $10,000 Impressions 1,000,000 Inc TRx 1,320 Inc NRx 102 Value per Rx $30 Total Value $42,660 ROI 327%
Patient
Measure Assess Tune Fire trigger email based
User receives email with important information about their disease state with link to web page with discussion points for their visit with physician Patient program brochure picked up in physicians office
Contact Management Manages user interaction strategy and rules
Value is Unlocked as We Can Influence the Customer’s Future Behavior
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DM Delivered Shown Display Ad Visits branded site and signs up for free voucher. Provides Email 2/2/12 3:06 pm 2/1/2012 2/2/12 3:05pm 2/2/12 3:06 pm
Personalization Dynamically assembles personalized communication package
Intervention strategy and rules are used to aid customer to next step in conversion process
Mobile call to action enables customer to easily sign up for patient program while leaving physician’s office. User immediately receives mobile coupon.
Patient
Opportunity to Drive Smarter Planning and Messaging at the Segment and Customer level
70 Which individuals and segments should we target? What channel should this individual be communicated through? Given the potential value of this customer how much should I spend to impact behavior? How often and in what sequence should I communicate with this prospect? Given their history what offer, service, or communication should be delivered? What product would this individual most likely be interested in? What is the best way to engage with this customer? How frequent should contacts be? Targeting Best Media/ Channel Allowable Spend Contact Optimization Offer Optimization Product / Disease State Messaging
Value is Unlocked Within The Digital Marketing Value Chain
72
First Party Data Second Party Data Third Party Data
Integration
Optimized Channel Experience (Targeting and Personalization) Measurement and Budget Allocation (Attribution) Connected Customer Insight (Data Integration) The Digital Marketing Value-Chain
Engaged in an ever expanding number
is challenging business leaders to broaden channel reach & execution capabilities Barraged by an increasing number
communications Expecting personalized and relevant interactions; they are self-selecting to engage with brands that provide relevance and timeliness Assuming brands are aware of their past interactions and expect brands to use this data to manage a worthwhile relationship dialogue
73
Source: Forrester Research “Use Customer Analytics to Get Personal”, by Srividya Sridharan February 17, 2012
74
2000 2013 2006 2003 2009
The Evolution of Market Leaders in Personalization
SOLUTION-FOCUSED CHANNEL-FOCUSED CUSTOMER-FOCUSED
Capability
−Collaborative Filtering −Content-based Filtering −Ensemble Learning
−Segmentation −1:1 Predictive Modeling Data - Limited to a small set of relevant customer interactions Experience - Isolated personalization interaction Capability - Personalization execution silo'd in channel specific tools (web, email, display advertising, search) Data - Channel specific customer interaction and profile data Experience
channels Capability
delivery
to optimize decision logic Data - Integrated customer interaction data across online and offline channels Experience
channels Isolated techniques limited to one
Disparate approaches to personalization primarily achieved in channels separate from each
Coordinated solution across multiple interactions and channels that leverages a complete view of the customer
75
Level 5 Leading Edge
Optimal Personalization [Contextual Relevance]
journey stage
customer decision behavior
Values to deliver the necessary response from the customer
Level 4 Consistent Best Practice
Moderate Personalization [General Relevance]
attributes
Level 3 Industry Competitive
Limited Personalization
Level 2 Developing, Inconsistent
Sporadic Personalization
measurement; lack of data-driven content
Level 1 Limited to No Capability
No Personalization
performance; static content, no versioning
CAPABILITY
High Low
76
Personalization Is A Process, Not An Outcome
Source for Image and Quotes: Forrester, February 17, 2012, “Use Customer Analytics To Get Personal”
Where do we personalize next? Do we understand all
place and by who? Do we have the right data? Is that system integrated yet? Is it fast enough and does it scale? Is the content written? How do we manage changes? Will this rule conflict with existing rules? How do we manage so many different yet related rules? Is the experience consistent across all channels? How do we know this is working? Is it working because of what we are doing or someone else? How can we continuously improve? How do we react quickly and confidently?
77
Analytical Marketing Database
Decision Services Interactive Conductor
Web Service API
Data Insights
Decision Management Business Rules Testing Optimization
Decision Management Components
Underlying technology architecture supporting channel-specific technologies enabling consistent, personalized customer experience across touch points.
CC DM EM Display Search Social Mobile Site Agent
Campaign Feed
Batch Lists Channel-specific interface File delivery - latent
Insights Data Benefits:
govern customer interactions
for insight driven communications
Omni-channel connectivity
for timely communications
learning for continuous learning and tuning
78
Industry Example
potential, receives a rep call to discuss ease of use...
79
Industry Example
Sarah is a CV patient at risk of a serious health event. She searches Google for treatment options after her PCP visit…..
80
Industry Example Customer Journey
Driving Awareness For New Hospital Facility
Sally ….
81
Integration
Consumer
Discovery
Development
Optimization
Priority
Development
Requirements
Configuration
Monitoring
Integrated Personalization Solution Overview
Platform
Marketing Data Warehouse
Tools (e.g. SAS, R)
Management System Integration
Personalization Tools/Plug-ins
Management Tool
Dashboards
TECHNOLOGY
DATA ANALYTICS EXPERIENCE EXECUTION
PROCESSES
82
Personalization Engine Analytic Methods
easiest to automate
Method Description Examples
Information Filtering (Highly automated & self learning)
Machine learning based techniques
an item in order to recommend additional items with similar properties [More limited-easier to get started]
has preferences such as this) look like another user who likes/ purchases xyz” [More robust-cold start problem]
approaches [Best but most complex]
Decision Rules-Tree (Very custom, sequencing)
sequence of actions taken by user
Propensity Models (Custom models for few important decisions)
Statistical modeling based techniques
fewer offers, products, or options but rich consumer history
83
History of Marketing Mix Modeling and Attribution
MMO begins as custom one-off projects 1940s-1970s 1980s 1990s 2010s
until 1970s
product in 1979
based, similar to attribution today
Low adoption, lack of data, lack
power
Audiences aggregated by content Audiences aggregated by content Audiences aggregated by content
MMO (top down) and Attribution (bottom up) unify Digital media disrupts MMO industry. Recovers by late 2000s MMO scales
include Auto, Finance and Pharma Modern MMO emerges in CPG Industry 2000s
Syndicated scanner data revolutionizes industry Mathematics of paid digital fixed, computation cost falls to $0 Computer power increase (still mainframes though) Mathematics of digital need to be created.
the standard approach for CPG
more complex
models in 1999
Markov, agent- based and other models emerge
models in 2005 (based on 1979 panel models)
problematic
approaches fade (will remain as forecasting tools)
and actionability
becomes implementation
the next frontier
85
3% 14% 3% 5% 5% 5% 15% 5% 5% 40% 0% 100%
Merkle Recommends a Modeled Attribution Approach Across All Media
Day 8-30 Day 1-7 Day 0-1 New Customer
Actual experience
Credit over applied to bottom
touches often ‘invisible’ Creates flawed financial view of performance
Direct or Rules Based Modeled
Model-adjusted interaction Most accurate and actionable
TV view Direct mail sent Print view Display view Social visit Website visit Paid search click
Mass and Offline Digital
Assess media performance by measuring the incremental impact
86
Granular attribution allow us to fractionally assign credit to each touch point into event stream prior to conversion
87 15% 20% 20% 40% 5%
Event Date Cost Attributed Credi t Value DM Delivered 2/1/2012 $.35 .05 $100 Display Impression 2/2/2012 $.001 .20 $400 Microsite engagement 2/2/2012 $13.20 .30 $600 Sent Email 2/2/2012 $.02 .10 $200 Clinic Brochure 2/2/2012 $12.50 .15 $300 Mobile Enrollment 2/2/2012 $.03 .20 $400
Predicted Customer LTD Value: $2,000
Customer Level Attribution Program Level Attribution This scenario represents success in that the predicted customer value is realized/confirmed and there is a strong program ROI.
Campaign Display-DSP Spend $10,000 Impressions 1,000,000 Inc TRx 1,320 Inc NRx 102 Value per Rx $30 Total Value $42,660 ROI 327%
Patient
87
Promotion Mix Solution (Top Down Approach)
PROMOTION RESPONSE ANALYSIS
Share Change/Volume
Direct Mail eMail Mobile Display / Search
Rep Details Samples Tele-Detailing Managed Care Physician & Patient Demographics
Promotion Mix Modeling
Insights:
Personal Promotion
Personal and Other Promotion
Response curves
Marginal ROIs
Channel Inputs:
Brand Managed Care Status Competitor Share Competitor Managed Care Status Physician Attributes
Market Factor Controls
NRx Volume/ Market Share
= Carryover
Effects Personal Promotion Efforts Non-Personal Promotion Efforts TREND & Others
+ + +
Speaker Programs/ Seminars/ Journal Advtg.
+
spend on sales. It uses historical time-series data to measure the promotion impact
88
Output Data
10 20 30 40 50 60 1 3 5 7 9 11 13 15 17 19 21 23
Revenue Time
Sales Decomposition
TV Direct Mail Radio Print Base
Base 51% Print 14% Radio 8% Direct Mail 11% TV 16%Segment 1
p i t p it i t
i
x y
1
Statistical Models
Brand Sales = “B”*Units of Touchpoint Example: For every direct mail piece sent via iConnect, sales increase by 2.5 Rx and for every email sent, sales increase 0.3 Sales = 2.5 * Direct Mail Units + 0.3 * Emails
50 100 150 200 250 300 350 5 10 15 20 25 30 TimeTV
200 400 600 800 1000 1200 5 10 15 20 25 30 TimeDirect Mail
50 100 150 200 250 5 10 15 20 25 30 TimeRadio
20 40 60 80 100 120 140 160 180 5 10 15 20 25 30 TimeInput Data
Details Mobile Email
Segment 1: Sales = 3.1* Direct mail + 0.8 * Email units + 0.5 * (PDEs)2 Segment 2: Sales = 2 * Direct mail + 0.1 * Email units + + 0.4 * (PDEs)2 Segment 3: Sales = 0.5 * Direct mail + 1.5 * Email units + + 0.2 * (PDEs)2
HYPOTHETICAL DATA Segment view enabled through use of random effects in mixed modeling approach
Model Selection: Output Benefits Based on Data Structure
89
Example: Channel Contributions
Details 30% Samples/Detail 3% Spot TV 1% National TV 19% Print (News) 3% Digital Display 3% Paid Search 2% Speaker Programs 2% Direct Mail 1% iConnect DM 1% Email 1% Carryover 34%
Top Down Model provides a High Level Contribution Allowing Us to Allocate Total Spend Budget and Assess Historical Performance
90
We Need Both Top Down And Bottom Up Measurement Methods
Bottom-up (Customer Level Data) Top-down (Aggregated Data)
Display
$60
Video
$80
Search
$91
Direct mail
$75
Social
$113
Branded - $87 Video 1 - $121 Video 2 - $35 Video 3 - $213 Video 4 - $23 Not branded - $99 Social 1 - $50 Social 2 - $163 Social 3 - $456 Remarketing - $12 Programmatic - $80 Guaranteed - $130
National media (TV & radio)
$140
Local media (TV & radio)
$200
Direct mail
$180
Digital
$83
DM 1 - $11 DM 2 - $93 DM 3 - $210 DM 4 - $235
Integrated measurement
All measurement levels (Media, platform, campaign, placement) All measurement dimensions (Customer segment, product, geography)
91
Integrated Attribution Provides Output Within Measurement Levels and Dimensions
performance
budgeting and planning processes
Media-level results
Monthly
driving new customers by segment
customer experience to drive better personalization and targeting by tactic and segment
Segment-level results
Monthly
programs were and were not performing
adjust existing programs
Campaign diagnostics
Daily
$0 $50 $100 $150 $200 $250 $300 $350 $400January February March April May
CPA by Channel
DM Brand TV Display Email Organic Search Paid Search Segment Penetration (Index) Campaign Segment 1 Segment 2 Segment 3 Segment 4 Segment 5 Display 1
120 90 100 120 85
DM 1
95 75 55 95 105
Alt Media 3
130 50 90 130 114
Display 1
120 95 50 120 87
DM 4
55 150 140 55 79
Social 1
95 200 143 95 100
Display 1
85 75 22 85 75
Search 1
200 98 100 200 97
Social 1
75 101 75 75 120
Display 2
30 130 120 30 115
Overall
100 100 100 100 100 Performance Drivers Campaign Effective CPM Contact Frequency (Within Media) Contact Frequency (Across Media) Unique Response Rate % Remarketing % Exclusive DM 1 5.28 $ 3.0 12.0 0.1565 10% 10% DM 2 1.69 $ 3.9 15.0 0.0552 20% 20% Alt Media 3 18.87 $ 3.1 7.0 0.6122 15% 15% Alt Media 7 0.34 $ 39.4 45.0 0.1096 10% 10% Search 1 0.39 $ 5.3 6.0 0.0130 20% 20% Social 1 1.71 $ 66.0 78.0 0.5884 15% 15% Email 1 1.44 $ 2.1 5.0 0.0162 10% 10% Search 1 44.99 $ 5.3 10.0 1.2132 20% 20% Display 4 96.41 $ 2.5 30.0 1.3916 15% 15% 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
93
Insights Portal
Centralized location to access understand historical performance, plan for future, set up targeting, and analyze customers
Centralized Insights Portal
* Note: all data changed to protect confidentiality
Media Targeting and Personalization Customer Insights Measurement and Attribution Planning and Forecasting Integrated Dashboards
14 * Note: all data changed to protect confidentiality94
Enterprise Measurement Platform Requirements
Solution is Focused on An Enterprise Measurement Platform, Not Just a Digital Attribution Tool
Support all media Best KPI accuracy at any level Ability to report out KPIs based on key, customized business dimensions Integrated and customizable performance reporting Robust decision support Tight integration with marketing database Scalable and flexible solution architecture Action support, not just product support Digital, mass and offline direct Media, campaign, placement/keyword Segment, Product, Geography, and Time One place to go for program performance, insights, and analysis Ability to run what if scenarios and machine
best plan at any level Ability to push data from marketing database to attribution platform and back into database for action Big data platform with robust customer identity management and ability to absorb frequent changes to data and requirements Help lead change management and ongoing insights extraction and action processes 95
Output Module Attribution + Media Planning/Optimization Engines
Inbound Channels
Insights Portal Store Display Sms Social DM Analytic Modeling Data Collection Transform Event Stream Reports/ Dashboards Interface Recommendations / Targeting Optimization
300+ variables
recommendations
platforms, etc.
Merkle Attribution Solution –Reference Architecture
Planning Tools
Outbound Media
Call Center Site Search TV Radio Email
Input Intelligence Action
96
Input
Merkle Attribution Platform Physical View - Modules & Process
CR
Intelligence Action
97
Measurement Output Must be Easily Integrated Into Targeting Algorithms
Cookie Keyword/ Cookie
User ID Conversion ID Event ID Attribution Weight 1234 C76532 DM437 .05 1234 C76532 DI9076 .32 1234 C76532 PS674 .11 1234 C76532 Q8740 .25
Model-based attribution weights Digital platforms Targeted ads Real-time bidding
Publisher Publisher Engine Engine
Attribution data 1000101110101 0100111001110 Demand Side Platform (DSP) Search Bid Platform Anonymous targeting Anonymous Data 98
SUMMARY
Financial management must evolve to an enterprise-wide initiative to be most effective
Enterprise scale is necessary
Analytics and technology must be tightly integrated to create these solutions
Analytics alone is not enough
Significant value can be created by taking even a few steps forward in the evolution of the four Financial Measurement capabilities
Value potential is enormous
99
Customer level modeling used to understand relative contribution of each marketing touch to the ultimate conversion activity.
Attribution Modeling Approach
Probability
sequences across direct and digital media
interactions, calculate the probability of conversion for that sequence
conversion probabilities for interaction sequences, isolate the individual impact of each
a weight to it
Conversion for the sequence with display 1 interaction
Conversion for the sequence without display 1 interaction Response Probability
Display 1
Weight for D1 = [ Probability(conversion for the sequence) - Probability (conversion for the sequence without D1)
Display 2 Search 1 Display 2 Search 1 Response Probability
101
Industry Example
Example: Data Flow
Doubleclick
(Display, mobile, video, email, mobile video)
Client M site log
(Conversion events, landings from natural search)
Search providers
(Paid search)
Cookie ID with all touchpoints, conversion events User Shorthand TP stream Conversion 293832704 DDVSMPVC Converted 99920125 DSVPNV Didn’t convert
220M TPs per week 130M TPs per week, 13K conversions per week 180K TPs per week 102
Industry Example
Example: Model Details
Primary Conversion event
Design Yours
Secondary Conversion event
Online purchase
Channels
Display Paid Search Video Social Mobile Mobile Video Email
Measurement
Modeled attribution (attributes each conversion event among that user’s touchpoints)
Model Inputs Exposure Recency and Decay Interactions between media
Control for Sequence
Frequency Media Type Funnel Stage
Model remarketing pool expansion Model search
Conversion Type
103
Industry Example
Example: Model Insights – Significant Predictors
======================== Predictor Sig Non ======================== creativeid 183 22 pageid 693 128 buyid 3 0 siteid 67 1 countryid 49 95 state 57 9 browserID 11 1 browserVersion 41 26
adid 111 135 creativetype 8 0 creativesize 12 0 DayofWeek 7 0 timeofday 4 0 mediaName 7 1 ======================== Total 1262 420 Gini: 45% +/- 1% Week of 10/13/2013
104
Industry Example
Model Insights - Transference Maps
Purchase
105
Industry Example
Example: Reports
Reports tend to look like typical media optimization reports
106
Industry Example
Example: Overall Results
Based on initial results, by optimizing digital media spend, Company M is able to:
using advanced attribution.
107
Industry Example
Example: Recommendations
– Out performs last click optimization. – Estimates more accurate CPA’s over time.
– In addition to the media spend costs savings, there is ample opportunity to improve within channel optimization (10-15%).
– Non-branded paid search drives intent but very few online purchases. – Mobile advertising drives intent but no online purchases. – Expand paid search keywords: Select Script Fill keywords drive Intent at decent CPAs (<$130):
– 10% of total impressions goes to a small sample that received 30+ impressions. Capping frequency can provide significant cost savings.
with other not overlapping
– The xyz Network in particular has a significant overlap with other publishers
108
Industry Example
Example: Insights
– Chrome is the most common browser driving intent. – Safari users are less likely to be interested caregiver content. – Although only 4% of all impressions were served on an iPad, iPad represented 10% of attributed impressions associated with script fills.
– In general, sites that drive intent also drive conversion. – Site 1 and Network A are an exceptionally strong performers for intent and conversion.
– Remarketing is not as effective for driving intent as it is for driving purchases. – Remarking CPA’s are better than overall CPAs. The purchase CPA is 9 times lower than average
– Display is higher in the funnel and drives all other channels. – Video is an important part of the user journey. The relative value of video changes from week to week
109
Realities of Working With Digital Data
– 80% to 90 % of work may be data related – 10% to 20% of work may be modeling and insight development
– Agencies won’t share data (or don’t know how to share) – Data will be missing in certain channels (especially social) – Costing data is incorrect – Data won’t mean what you think it means
– Mapping digital behaviors to Rx
– Do beta engagements now
– Set expectation correctly on timing (at least a Quarter) – Level set with agencies – you (the client) owns the data
111
Working With Your Agency
– Although this requires work, good agencies like proof of ROI – If you agencies are uncomfortable, this may be the sign of bigger problems
– What is your approach to attribution and ROI? – Is this socialized through your organization?
– Agencies work best with clearly defined roles and Direct and clear clients – Agencies prefer strong leadership
– Get access to raw data (test that agencies can deliver it or you have access) – Try to analyze it yourself – Review attribution marketplace – Do a one-off pilot project
112
Working With Your Agency
– Ex: How do caregivers behave on your website vs. script holders? – Ex: How do you drill your media plan to different segments? – Challenge yourself to understand the techniques for instrumenting segmentation
– Not huge in Pharma today – But it will be huge soon – Get Ready!
113
Three Exercises
114