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© 2013 Merkle Inc. All Rights Reserved. Confidential. 1 1 Social Analytics Andy Fisher Chief Analytics Officer Merkle Inc. November 15, 2013 © 2013 Merkle.

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Presentation on theme: "© 2013 Merkle Inc. All Rights Reserved. Confidential. 1 1 Social Analytics Andy Fisher Chief Analytics Officer Merkle Inc. November 15, 2013 © 2013 Merkle."— Presentation transcript:

1 © 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

2 © 2013 Merkle Inc. All Rights Reserved. Confidential. 2 Agenda About Merkle Social Definitions Media and Analytics History Social Integration Approaches Industry Examples Takeaways

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 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

4 © 2013 Merkle Inc. All Rights Reserved. Confidential. 4 Our clients represent many of the best global brands

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

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

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&C’s, 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

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

9 © 2013 Merkle Inc. All Rights Reserved. Confidential. 9 9 Common Approach for Integrating Social © 2013 Merkle Inc. All Rights Reserved. Confidential

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 Activity (opens, clicks) Web behavior (weblog) 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

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

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 $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

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

14 © 2013 Merkle Inc. All Rights Reserved. Confidential. 14 Agent Digital Media Search Sales force automation / call planning prioritized by agent’s 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

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

16 © 2013 Merkle Inc. All Rights Reserved. Confidential. 16 Industry Successes (and Otherwise…) © 2013 Merkle Inc. All Rights Reserved. Confidential

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:

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.

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 , 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 isn’t available from most social media platforms. APPROACH

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 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

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!


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