2013 Merkle Inc. All Rights Reserved. Confidential. 1 1 Social
Analytics Andy Fisher Chief Analytics Officer Merkle Inc. November
15, 2013 2013 Merkle Inc. All Rights Reserved. Confidential Slide 2
2013 Merkle Inc. All Rights Reserved. Confidential. 2 Agenda About
Merkle Social Definitions Media and Analytics History Social
Integration Approaches Industry Examples Takeaways Slide 3 2013
Merkle Inc. All Rights Reserved. Confidential. 3 Company Overview
Bronze Stevie Award Business to Business Marketing Campaign of the
Year- Business Services 13 Ad Age A-List: Agency to Watch in 2012
Largest privately-held agency in U.S., Ad Age 12 6th Largest
CRM/Direct Marketing Agency, Ad Age 12 Recognized by SmartCEO
Magazine as a Future 50 Company 11 NCDM Awards 04, 05, 06, 09, 10,
11 Recognized by Forrester 03, 06, 07 &10 Multiple MAXI Award
Winner 10 11 13 Multiple DMA Innovation Award Winner 10 13
Distinctive experienceExtraordinary expertise Sustained 25% growth
since 1989Awarded and recognized Revenue in millions Privately held
by current management since 1988 150+ world class clients Manage
over 140 marketing databases Manage 1.6+ petabytes of customer data
Inform over $10 billion marketing decisions annually 2,000+
Employees 250+ advanced degreed statisticians 300+ dedicated
digital professionals 650+ marketing technology professionals 100+
creative professionals Slide 4 2013 Merkle Inc. All Rights
Reserved. Confidential. 4 Our clients represent many of the best
global brands Slide 5 2013 Merkle Inc. All Rights Reserved.
Confidential. 5 Definitions TypeDefinitionExample PaidPlacements
that and advertiser pays for on a social website Ads in FB
newsfeeds Sponsored content on Twitter OwnedExperiences where
advertiser controls the content Advertiser website Advertiser FB
Page EarnedContent about an advertiser where the advertiser does
not control the content Tweets from users Facebook likes Slide 6
2013 Merkle Inc. All Rights Reserved. Confidential. 6 A brief
history of Online Media 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
2005-20092009-20122013+ Slide 7 2013 Merkle Inc. All Rights
Reserved. Confidential. 7 Reality Check Sell side is far ahead of
the buy side Legal and operational infrastructure is problematic A
new combination of skills Agency Programmatic expertise Classical
planning expertise CRM First Party data management To do this well
you need Very new approach Legal Scale Business standards
(T&Cs, makegoods, etc) Integration of Creative and Analytics
Open Issues We believe this will become a major driver of value
Est. $8bn Totus spend by 2017 Slide 8 2013 Merkle Inc. All Rights
Reserved. Confidential. 8 History of Marketing Mix Optimization and
Attribution MMO begins as custom one-off projects
1940s-1970s1980s1990s2010s Mainly academic until 1970s First MMO
product in 1979 MMO is panel based, similar to attribution today
Low adoption, lack of data, lack of computing power Audiences
aggregated by content MMO (top down) and Attribution (bottom up)
unify Digital media disrupts MMO industry. Recovers by late 2000s
MMO scales outside CPG to 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. MMO
becomes the standard approach for CPG Models become more complex
First digital models in 1999 Bayesian, Markov, agent- based and
other models emerge First attribution models in 2005 (based on 1979
panel models) Computing problematic Regression Panel approaches
fade (will remain as forecasting tools) Focus on speed and
actionability Implementation becomes limiting factor Social become
the next frontier Slide 9 2013 Merkle Inc. All Rights Reserved.
Confidential. 9 9 Common Approach for Integrating Social 2013
Merkle Inc. All Rights Reserved. Confidential Slide 10 2013 Merkle
Inc. All Rights Reserved. Confidential. 10 Tie Engagement to
Revenue (Sample) 1.3x 1.5x 2.3x Segment Based Determine Critical
KPIs Constant Evolution Data drives what to measure Segment level
Individual level Activity level Compile Data Objective Function
Model the Data and Iterate Evaluate Online Offline Email Activity
(opens, clicks) Web behavior (weblog) Email subscription User
communities Online registration Coupon downloads Digital media
exposure Social Behavior POS Data Coupon Redemption Shopper Panel
FSP In store promo Brand X Brand Y Brand Z Step 1: Integrate Paid
and Owned Slide 11 2013 Merkle Inc. All Rights Reserved.
Confidential. 11 3%14%3%5% 15%5% 40% 0% Day 8-30Day 1-7Day 0-1
Transaction/ Conversion Actual experience Direct or Rules Based
(Basic) Probabilistic Model Based (Advanced) $ TV viewDirect mail
sentNewspaper viewDisplay viewSocial visitWebsite visitPaid search
click Mass and OfflineDigital 0% 100% $ $ Step 2: Build Attribution
Models Slide 12 2013 Merkle Inc. All Rights Reserved. Confidential.
12 Digital $83 calibration layer National media (TV & radio)
$140 Local media (TV & radio) $200 Direct mail $180 TOP-DOWN
MEDIA MIX MODEL (POS Data) Display/ Video $60 Email $80 Paid Search
$91 Social $113 Campaign $ BOTTOM-UP CONSUMER MODELING (Individual
Response Data) Direct mail $180 Placement $ Creative $ Engine $
Branded $ Keyword Segment $ Campaign $ Program $ Campaign $ Offer $
Network $Program $ Campaign $ Segment $ Program $ Campaign $
Segment $ @ SEGMENT BASED ATTRIBUTION Attribution needs to tie
engagement to sales and be customer centric by enabling
segment-based performance results of marketing Social $113 Step 3:
X-channel modeling with Paid, Owned and Earned Slide 13 2013 Merkle
Inc. All Rights Reserved. Confidential. 13 Reality Check MMO +
Attribution Media Mix Optimization together with Attribution One
model Integrates Offline and Online Most vendors doing some form of
this Historically the two are related Better MathX-Channel experts
Attribution gaps Incrementality Decay Factors Interplay of media
Math is understood Data Scientists All channel data Data Viz
Experts Hard to find To do this well you need Data Quality Cooke
Data has Issues Idea 1: Leverage CRM data Idea 2: Leverage
Panels/Good samples Idea 3: Statistical
Identification/Fingerprinting Idea 4: Model Cookie Deletion Idea 5:
First Party Data No Silver bullet Lack of Validation Rare in MMO
Very rare in Attribution No agreed upon methodology Open Issues
Social Challenges Predicitvity Models Sentiment Problems Slide 14
2013 Merkle Inc. All Rights Reserved. Confidential. 14 Agent
Digital Media Search Sales force automation / call planning
prioritized by agents segment and value mix Served Fixed Income
X-sell offer Increased bid amount on retirement planning Increased
Sales Force Prospecting Creative selection Site For display,
optimization output is directly fed to the Demand Side Platform Bid
Amounts Frequency Caps Cookie List Segment assignment code Next
Best Offer Decisioning Optimization Feed DMA & Daypart Weights
Plan & Buy Adjustments Keyword & creative weights Bid
Amounts & Creative For SEM, bidding engine weights are output
of analytics correlating keyword to high-value segments For site,
real-time integration of segments into offer management system Step
4: Deploy into optimization and personalization process Slide 15
2013 Merkle Inc. All Rights Reserved. Confidential. 15 Reality
Check Many people doing within channel personalization well Far
fewer are doing x-Channel personalization Cross Channel
Infrastructure Data source across channels Enterprise segmentation
Cross channel Technology Within-channel activation The technology
is good enough To do this well you need Organizational Silos
Different Goals Different notion of customer Lack of incentive
Different measurement approaches Open Issues Think this will become
a large practice moving forward Big opportunity for people in the
room Slide 16 2013 Merkle Inc. All Rights Reserved. Confidential.
16 Industry Successes (and Otherwise) 2013 Merkle Inc. All Rights
Reserved. Confidential Slide 17 2013 Merkle Inc. All Rights
Reserved. Confidential. 17 Tying social media initiatives to
business outcomes Model buzz as the response variable to media
Determine how buzz maps to box office sales Substantial historical
database for modeling Bass-diffusion models simulate buzz build
Incorporate HSX data Leverage buzz as campaign optimization tool
Real-time forecasts of box office Learnings Long dev process/custom
Treat as experimental Required deep vertical knowledge Success
driven by lack of feasible plan B CHALLENGE Company X needed a way
to optimize movie promotion dollars in an environment where every
dollar is spent prior to the first ticket sale. APPROACH: Slide 18
2013 Merkle Inc. All Rights Reserved. Confidential. 18
Incorporating buzz metrics into media mix models Incorporate buzz
into MMO (input and output) Results: Buzz showed no correlation
with sales Caveat 1: FB, Blogs, Twitter, YouTube only Caveat 2:
Sentiment analysis challenges Next steps refine approach and
continue Learnings Long dev process/custom Treat as experimental
Industry Backlash/PR issues CHALLENGEAPPROACH: Coca-Cola wanted to
leverage buzz to improve their MMO and potentially replace
expensive brand tracking surveys. Slide 19 2013 Merkle Inc. All
Rights Reserved. Confidential. 19 Getting past aggregate-level data
A technology retailer followed a four-step process to build out a
social data warehouse that will connect to the CRM database.
1.Identify available social data assets and sources 2.Evaluate
applicability of assets via three criteria: Identifiable - does the
data source help us connect to a customer/prospect record
Actionable - can this data source be used to power more targeted
marketing via email, social, or site content or analytics/insights
Scalable - how large is the population of identifiable and
actionable records now and what is its expected growth 3.Prioritize
social data assets based on score and review privacy implications
4.Define use cases to test CRM program impact CHALLENGE In order to
leverage social data for CRM, companies must get to segment-level
and even user- level data which isnt available from most social
media platforms. APPROACH Slide 20 2013 Merkle Inc. All Rights
Reserved. Confidential. 20 Forecasting Tune-in Challenge: Making
Social Measurement Predictive Example: Sharknado (SiFy) Sharknado
runs on SyFy Major social media sensation SyFy plans a rerun of
Sharknado SyFy green lights Sharknado 2 5000 tweets per MINUTE!
Twitter competition for tagline Sharknado rerun airs and 1.89 MM
Viewers Not too bad. But not great either. Sharktopus = 2.5 MM
Viewers Average = 1.5MM Viewers GoTH Red Wedding = 5.13MM Slide 21
2013 Merkle Inc. All Rights Reserved. Confidential. 21 Takeaways
Constantly Evolving Space Many problems and opportunities Trend
towards first party data Pockets of measurement success Long way to
go Social cannot be meaningfully measured in a silo Holistic
approaches are necessary Lots of hype and lots of content too!
