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Attribution case study | Ad:tech NY 2012 | Encore Media Metrics
 

Attribution case study | Ad:tech NY 2012 | Encore Media Metrics

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Attribution case study presented at Ad:tech NYC in 2012 by Steve Latham, ceo of Encore Media Metrics

Attribution case study presented at Ad:tech NYC in 2012 by Steve Latham, ceo of Encore Media Metrics

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    Attribution case study | Ad:tech NY 2012 | Encore Media Metrics Attribution case study | Ad:tech NY 2012 | Encore Media Metrics Presentation Transcript

    • Attribution Case StudyImpactful InsightsNovember 2012Steve Latham Bradley MayEncore Media Metrics KSL Media@encoremetrics @kslmedia
    • Background•  Agency: KSL Media•  Client: Retail Gasoline Brand•  Display Media (20+ vendors) –  Platforms: PC-Based + Mobile –  Placements: Branding + Direct response –  Formats: Video, Rich Media, Flash•  Paid and Natural Search
    • Objectives•  Campaign Goals –  In-Ad Engagement •  Brand metrics (awareness) •  Take action: Download mobile app, view videos –  On-site Engagement •  Download content and mobile apps •  View video spots •  Find a station •  Register•  Holistic View of Media Performance –  Conversion paths –  Role of each Channel and Platform –  Performance by Publisher and Placement
    • Conversion Path Insights •  Converters utilized numerous channels •  Among Converters who were exposed to ads: –  Served 6.7 ads –  Visited 3.2 times before converting –  Paid + Natural Search accounted for 28% of Visits Sources of Visits in Conversion Paths =====> Display Direct Natural PaidConversion Paths IMPs Visits Referring (Web) Nav Search SearchAll Visitors 6.7 3.2 1.0 1.0 0.4 0.4 0.3Relative Contribution 62.9% 37.1% 31.3% 31.3% 14.0% 13.8% 9.6%Includes Converters who were exposed to ads, grouped in natural clusters via machine-learning algorithm.Based on Modeled data (after excluding outliers and excess impressions served):
    • Frequency Analysis •  Optimal frequency was 6.7 Impressions •  Actual frequency was 35 –  Publishers: 12 –  DSP: 67 ImpressionsServedtoVisitorsbyFrequencyTier NumberofVisitorsperFrequencyTier350,000"" 9,000"" All"Others" DSP" All"Others" DSP"300,000"" 8,000"" 7,000""250,000"" 6,000""200,000"" 5,000""150,000"" 4,000""100,000"" 3,000"" 50,000"" 2,000"" 1,000"" !"""" !"""" " " " 5" " 10 o"9" 20 "19" 30 "29" 40 "39" 50 "49" 0" 9" 0" 9" 0" 9" 0" 9" 0" 9" >1 9" " 1" 2" 3" 4" 0" 10 "to"5 20 o"19 30 o"29 40 o"39 50 o"49 9 00 "9 "to "to "to "to t to " " " 5" " 10 "9" 20 19" 30 29" 40 39" 50 49" 0" 9" 0" 9" 0" 9" 0" 9" 0" 9" >1 9" " t t t t 1" 2" 3" 4" 0" 10 "5 20 "19 30 "29 40 "39 50 "49 9 to 00 " " " " "9 "to "to "to "to "to to to to to to
    • Channel Performance •  After attributing credit for Assist Imps and Clicks: –  PC Display Ads comprised 18% of Actions (excl. mobile) •  Exceeded Paid + Natural Search –  Display CPA fell by 83% vs. last click Ac#onsByChannel120,000 Last Click Attributed100,000 80,000 60,000 Cost%Per%Ac+on%By%Channel% $50 Last Click Attributed 40,000 $45 $40 20,000 $35 $30.54& 0 $30 Direct Nav Org Search Referrals Email Paid Search Display Mobile Display $25 $20 $15 $10 $3.05& $5 $2.54& $2.77&$2.94& $0.87&$0.86& $0 Email Paid Search Display Mobile Display
    • Vendor Performance•  After attributing credit for Assist Imps and Clicks: –  5 Winners (vs. only 2 using Last Click) –  4 Challengers –  4 Laggards Without Attribution, 7 of 9 vendors would have been cut from the plan Converters Assist Actions CPA:.Last CPA: Efficiency Vendor Assist.Clicks Rating (Last.Click) Impressions (Attrib.) Click Attributed Oppty Ad#Network#C 6 4 589 ############ 150 $3766.30 $150.42 56% Laggard DSP 2,593 4,248 23,095 #########9,237 $35.55 $9.98 44% Winner Publisher#G 241 73 4,460 #########1,002 $69.50 $16.71 n/a Winner Publisher#H 26 10 2,081 ############ 538 $4715.19 $227.89 55% Laggard Publisher#J 1 0 614 ############## 44 $9266.41 $211.51 42% Laggard Publisher#MA 10 2 895 ############ 314 $804.44 $25.58 52% Winner Ad#Network#ME 19 1 5,117 #########1,922 $2403.05 $23.75 44% Winner Publisher#MO 16 3 694 ############ 268 $529.79 $31.60 44% Challenger Publisher#P 139 211 8,225 #########2,318 $343.60 $20.61 73% Winner Ad#Network#R 32 3 2,217 ############ 594 $802.91 $43.26 30% Challenger Publisher#TE 63 19 2,495 ############ 542 $387.27 $45.04 36% Challenger Publisher#TA 12 37 112 ############## 72 $2021.62 $335.54 34% Laggard Publisher#Y 482 8 2,979 #########1,788 $142.23 $38.33 31% Challenger Totals 3,646 4,624 53,858 #######18,859 $141.59 $27.37 45%
    • Vendor (Publisher) Performance•  Ranked Vendors based on 3 criteria•  Recommended Actions and Freq. Cap for Each Cost$Per On$Site$CPA Cost$Per Action$toWeb$Display Passive (Attrib) Active$Int. take Recommended(Frequency( Audio/VideoAd$Network$C Laggard Challenger Winner Pause Publisher$Y$ 4.6$$DSP Winner Winner Laggard Increase Publisher$TA$ 9.6$$Publisher$G Winner Winner Increase Publisher$TE$Publisher$H Laggard Challenger Pause Ad$Network$R$ 7.9$$ Publisher$P$ 8.4$$Publisher$J Laggard Pause Publisher$MO$ 5.6$$Publisher$MA Winner Winner Increase Ad$Network$ME$ 5.4$$Ad$Network$ME Winner Winner Increase Publisher$MA$ 5.9$$Publisher$MT Challenger Laggard Optimize Publisher$J$Publisher$P Winner Winner Winner Optimize Publisher$H$ 8.3$$Ad$Network$R Challenger Laggard Pause Publisher$G$Publisher$TE Challenger Laggard Pause DSP$ Ad$Network$C$Publisher$TA Laggard Pause 8.4$$Publisher$Y Challenger Challenger Optimize 0$ 2$ 4$ 6$ 8$ 10$
    • Key Takeaways•  Start with a Holistic view –  Not all Channels and Placements serve same purpose –  Groups of publishers may have different objectives•  Display Ads are effective in creating awareness –  Accounted for 18% of Attributed Actions (excluding mobile) –  Performance varied dramatically by vendor –  Found significant over-serving to same users•  Search played a Supporting role•  Mobile ads were very efficient for Mobile actions –  App downloads and video views at very low CPA•  Frequency must be monitored closely
    • Additional Insights•  There is no “silver bullet” –  Must look at numerous metrics and factors•  It’s still Art and Science –  Requires oversight and subjective reasoning•  Frequency: too much can hurt performance –  Builds case for fewer, higher quality placements•  Offline promotion can skew on-site results –  Excluded specific Actions when modeling results•  Fragmentation = Obfuscation –  e.g. hard to see how Mobile gets lift from PC display
    • How Insights are Applied•  Evaluate and Segment Media Vendors –  Performance by Platform and Format –  In-ad (branding) vs. On-site (response)•  Optimize Media Plan –  Allocate budget to top performers –  Manage frequency more effectively –  Improve efficiency and buying power•  Improve partnership with Clients and Vendors –  Better accountability and transparency
    • Contact Info bmay@kslmedia.com Steve@EncoreMetrics.com @SteveLatham @kslmedia @EncoreMetrics http://KSLmedia.com http://EncoreMetrics.com http://Attribution101.com