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Alternative to ANA's end to end supply chain transparency study v final

The ANA RFP for an update to the supply chain transparency study from the ISBA in 2020 is not even asking the right questions; here's my view.

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Alternative to ANA's end to end supply chain transparency study v final

  1. 1. June 2021 / Page 0 marketing.science consulting group, inc. linkedin.com/in/augustinefou End-to-End Supply Chain Study Result Scenarios / Recommendations June 2021 Augustine Fou, PhD. acfou [at] mktsci.com
  2. 2. June 2021 / Page 1 marketing.science consulting group, inc. linkedin.com/in/augustinefou "The industry must now move to rectify the issues and restore the productivity of billions of dollars of marketing investments,“ said Bob Liodice, CEO of the ANA
  3. 3. June 2021 / Page 2 marketing.science consulting group, inc. linkedin.com/in/augustinefou What the ANA RFP asked for … The new study will add detail to prior studies, and separate “cost efficiency” from “ad effectiveness” “Cost Efficiency” “Ad Effectiveness” https://www.ana.net/content/show/id/pr-2021-programmatic-rfp
  4. 4. June 2021 / Page 3 marketing.science consulting group, inc. linkedin.com/in/augustinefou But … • The numerous forms of hidden arbitrage, waste, and fraud cannot be depicted in a simple waterfall chart (“if the definition of transparency is how much of my budget makes it into the publishers’ hands, that's a very flawed baseline.”) • Attempts to depict the losses and waste assumes you can see all of those problems and requires you to make assumptions about what average is “most representative” when in most cases “the extent is not known” • The reality of “productive portion of an ad dollar” is too scary for a trade association to publish truthfully; it is likely they will do what they have done before: position the result with PR, which masks the truth, is misleading, and therefore is useless • The recommendation “buy less programmatic” will not be tenable
  5. 5. June 2021 / Page 4 marketing.science consulting group, inc. linkedin.com/in/augustinefou Executive Summary OBJECTIVE OF THIS STUDY • Educate marketers on the inefficiencies, costs, hidden arbitrage, and risks associated with buying through programmatic channels and how these outweigh the alleged benefits of reach, targeting, and performance • Enable marketers to take immediate action on these new insights, without re- tooling, to optimize their digital ad spending, reduce waste and risk, and improve actual business outcomes All of the data compiled in this study come from 1) first-hand campaigns where all variables are known, 2) documented cases published by others publicly, and 3) data crowdsourced from practitioners anonymously
  6. 6. June 2021 / Page 5 marketing.science consulting group, inc. linkedin.com/in/augustinefou Historical Context
  7. 7. June 2021 / Page 6 marketing.science consulting group, inc. linkedin.com/in/augustinefou Publishers’ Studies of Supply Chain Wide variation in outcomes: 30c on the dollar went to pub; < 1% went to publisher buying own inventory Ad Budget Publisher 30% 100% 80% 60% 40% 20% https://mediatel.co.uk/newsline/2016/10/04/where-did-the- money-go-guardian-buys-its-own-ad-inventory 2016 The Guardian “for every pound an advertiser spends programmatically on the Guardian only 30 pence actually goes to the publisher.” Ad Budget Publisher 100% 80% 60% 40% 20% 2017 BusinessInsider “$40,000 worth of ad inventory through the open exchanges, the publication only saw $97.” http://adage.com/article/digital/business-insider-york-times- shed-details-ad-industry-s-biggest-problem/311081/ < 1%
  8. 8. June 2021 / Page 7 marketing.science consulting group, inc. linkedin.com/in/augustinefou Prior Transparency Studies Supply chain studies since 2014 corroborate that only 40 – 61% of dollar makes it to pubs for showing ads ISBA 2020: 51% “publishers received half of advertiser spend. 15% of advertiser spend – the unknown delta, representing around 1/3 of supply chain costs – could not be attributed.” Source: ISBA, May 2020 “went missing” Source: WFA, Sep 2014 Source: ANA, May 2017 WFA 2014: 40% “We have little or no clear understanding of what percentage (of digital spend) is being delivered to the media owner and what is being taken in fees from either the agency or middlemen. ” ANA 2017: 60% “Programmatic remains complex and often non-transparent. Our study revealed that this lack of transparency makes it difficult for advertisers to manage, measure and audit programmatic media investments.”
  9. 9. June 2021 / Page 8 marketing.science consulting group, inc. linkedin.com/in/augustinefou WARC/MAGNA 2018 Study 40% of the dollar reaches publishers; before any ad fraud is accounted for
  10. 10. June 2021 / Page 9 marketing.science consulting group, inc. linkedin.com/in/augustinefou Estimates of productivity over years 13% productive (2014), 10% productive (2017), 1% productive (2019) https://www.slideshare.net/augustinefou/display-ad- productivity-by-augustine-fou 13% (2014) 10% (2017) https://www.slideshare.net/augustinefou/effectiveness-of- digital-ads-q2-2017-update-by-augustine-fou/3 1% (2019) https://www.slideshare.net/augustinefou/ad- fraud-is-more-than-just-bots-jan-2019
  11. 11. June 2021 / Page 10 marketing.science consulting group, inc. linkedin.com/in/augustinefou Illustrative Scenarios
  12. 12. June 2021 / Page 11 marketing.science consulting group, inc. linkedin.com/in/augustinefou Advertisers thought they bought… Many loopholes and risks are not accounted for by simple waterfall chart; scenarios are needed Ad revenue diverted by domain spoofing Pay on bids won vs ads served Unspent media dollars go missing Random or replayed deviceID Faked valid traffic, viewable ads Analytics and measurement manipulation Attribution scamming Residential proxies to disguise traffic Media dollars to agency Set up campaign in DSP Buy white list domains Buy valid, viewable Buy real devices Buy “fraud free” Buy “verified” Did ad render? #marketers who think that having bot detection in place is enough clearly don’t know about these other “measurable issues” Catching the 1% IVT, missing rest Advertisers Publishers
  13. 13. June 2021 / Page 12 marketing.science consulting group, inc. linkedin.com/in/augustinefou 56% “Best Case” Scenario Shortest possible supply path, no agency, no targeting/verification, direct publisher Ad Budget Agency Fees DSP Ad Auction Funds SSP Publisher Ad Quality “discounts” Audience 100% 12% 10% 79% 23% 70% average viewability Tech, Data, Verification Fees Bid: $1.00 30% non-viewable Auction Funds: $0.88 Source: 10 first-hand campaigns, Jan – June 2021 100% 80% 60% 40% 20%
  14. 14. June 2021 / Page 13 marketing.science consulting group, inc. linkedin.com/in/augustinefou 35% “Ideal Conditions” Scenario No “unknown delta” (no arbitrage), minimized DSP, SSP fees, 10% data fee Ad Budget Agency Fees DSP Ad Auction Funds SSP Exchange Publisher Ad Quality “discounts” Audience 7% 7% 60% 85% Source: $TTD 10-Qs Tech, Data, Verification Fees 100% 80% 60% 40% 20% 15% 10% 20% 10% 55% 65% Source: ISBA 8% 10% Source: ISBA Advertiser to Publisher “Cost Efficiency” 40 cents on the dollar go to ‘non-working’ costs 75% 65% 69.8% viewability 1.5% ad fraud 5.8% brand risk Source: IAS H2 2020 MQR viewable no G-IVT brand safe “Ad Effectiveness” Publisher to Audience 25 more cents eaten up by ad quality issues “Using the most conservative estimates of ad quality issues” – IAS H2 2020 MQR
  15. 15. June 2021 / Page 14 marketing.science consulting group, inc. linkedin.com/in/augustinefou Ranges and Averages must be used Grouping together known costs, separating from “measurable issues,” ranges and averages cited 100% 80% 60% 40% 20% IVT / NHT 58% 42% Domain Spoofing 45% 55% Not Viewable 20% 80% Ad Blocking 85% 15% Off Target 34% 66% Ads Not Displayed 75% 25% Not Bot Ads.txt Matched Viewable Not Ad Blocked On Target Ads Displayed Agency 90% 10% Not Agency Exchange DSP / SSP 80% 20% Not Trading Data / Verification 80% 20% Not Fees Known Costs Measurable Issues
  16. 16. June 2021 / Page 15 marketing.science consulting group, inc. linkedin.com/in/augustinefou “Ignore-All-Issues” Scenario “Measurable issues” eat away at “working media” (productive = 99% · 99% · 99% · 99% ·62% ·50%) 100% 80% 60% 40% 20% IVT / NHT Not Bot Domain Spoofing 99% 1% Ads.txt Matched Not Viewable 62% 38% Viewable Ad Blocking Not Ad Blocked Off Target 50% 50% On Target Ads Not Displayed Ads Displayed Agency 90% 10% Not Agency Exchange DSP / SSP 80% 20% Not Trading Data / Verification 80% 20% Not Fees Known Costs Measurable Issues 10% 20% 20% 50% “working media” 35% 15% “likely productive ads” 99% 1% 99% 1% 99% 1% assume 1% domain spoofing assume 1% ad blocking assume 1% IVT/NHT assume 1% ad serving problems Source: IAS Q1 MQR Source: Nielsen
  17. 17. June 2021 / Page 16 marketing.science consulting group, inc. linkedin.com/in/augustinefou “More Realistic” Scenario “Measurable issues” eat away at “working media” (productive = 85% ·75% ·58% ·45% ·34% ·20%) 100% 80% 60% 40% 20% IVT / NHT 58% 42% Not Bot Domain Spoofing 45% 55% Ads.txt Matched Not Viewable 20% 80% Viewable Ad Blocking 85% 15% Not Ad Blocked Off Target 34% 66% On Target Ads Not Displayed 75% 25% Ads Displayed Agency 90% 10% Not Agency Exchange DSP / SSP 80% 20% Not Trading Data / Verification 80% 20% Not Fees Known Costs Measurable Issues 10% 20% 20% 50% “working media” 49% 1% “likely productive ads” Slide 18 Slide 19 Slide 20 Slide 21 Slide 22 Slide 23
  18. 18. June 2021 / Page 17 marketing.science consulting group, inc. linkedin.com/in/augustinefou Supporting Data
  19. 19. June 2021 / Page 18 marketing.science consulting group, inc. linkedin.com/in/augustinefou Domain Spoofing – ads.txt matching Match ads.txt sellerIDs to IDs in sellers.json Match type “direct” vs “reseller” correctly 1/3 matched 1/2 matched 45% matched https://deepsee.io/blog/evaluating-the-ecosystem
  20. 20. June 2021 / Page 19 marketing.science consulting group, inc. linkedin.com/in/augustinefou Directly Measured Ad Blocking Direct measurement is necessary, measurement done on-site • Ad blocking must be measured on-site. In-ad measurements are invalid • Desktop and mobile must be separated because ad blocking in mobile is very low (no plugins for mobile browsers, and most consumers don’t regularly use ad blocking browsers, they use built-in browsers) • Bots and “not measurable” must be excluded because bots don’t block ads (their job is to cause them to load) Business sites desktop mobile ad block rate 8 - 17% 0.6 – 0.8% Consumer sites desktop mobile ad block rate 11 - 12% 0.9 - 1%
  21. 21. June 2021 / Page 20 marketing.science consulting group, inc. linkedin.com/in/augustinefou IVT/NHT – Display Ads Only Not Measurable 0% No Client-Side Data 8% DESKTOP GIVT/SIVT Humans Other Disguised Traffic Fake Device App Fraud 67% 37% 2% 61% 0% 6% 3% MOBILE GIVT/SIVT Humans Other 33% 7% 5% 88% DEFINITIONS Not measurable – no tags sent (this should be zero, ads are called by JS) No Client-Side Data – no data sent back, ad blocker or browser block (e.g. Brave) Other – not enough blue or red labels to confirm Disguised Traffic – fake traffic, bounced through residential proxies Fake Device – multiple factors indicating fake device (software in datacenter) App Fraud – apps loading webpages and other non-bot fraud IAS: 1.4% (US)Source: IAS Media Quality Report H1 2020 IAS: 0.8% (US)Source: IAS Media Quality Report H1 2020 mobile app non-app
  22. 22. June 2021 / Page 21 marketing.science consulting group, inc. linkedin.com/in/augustinefou Bids won vs impressions served In most ideal conditions, 25% of ads purchased were never rendered “The more fraudulent the site, the larger the discrepancy, up to 100% (no ads served). 1. DSP: bids won by domain 2. Ad Server: ads served by domain 1 2 1 2 Low CPM campaign
  23. 23. June 2021 / Page 22 marketing.science consulting group, inc. linkedin.com/in/augustinefou Viewability – Display, Video DEFINITIONS measured viewable = intersection >50% + visibilityState=visible viewable-MRC = measured viewable + 1 second Display desktop only mobile web only app only measured viewable 25% 58% 18% viewable-MRC 20% 7% 9% Video desktop only mobile web only app only measured viewable 9% 7% 2% viewable-MRC 7% 4% 2% IAS: 71% (US) Source: IAS Media Quality Report H1 2020 IAS: 68% (US) IAS: 68% (US) Source: IAS Media Quality Report H1 2020 IAS: 66% (US) IAS: 79% (US)
  24. 24. June 2021 / Page 23 marketing.science consulting group, inc. linkedin.com/in/augustinefou Bad targeting, worse than “random” Ad tech targeting is based on segments inferred from website visitation patterns 1 parameter: gender 42% accuracy 2 params: gender+age 24% accuracy
  25. 25. June 2021 / Page 24 marketing.science consulting group, inc. linkedin.com/in/augustinefou Other Issues Not Depicted in Waterfall Chart Scenarios
  26. 26. June 2021 / Page 25 marketing.science consulting group, inc. linkedin.com/in/augustinefou All the fraud missed by IVT detection Other forms of fraud are severely under-reported by standard IVT detection tech vendors 1.3% fake traffic (bots) severely under-reported overall fraud bot detection sees this bot detection misses all these 58% 57% + = other fraud (sites/apps)
  27. 27. June 2021 / Page 26 marketing.science consulting group, inc. linkedin.com/in/augustinefou Incorrect measurements by IVT vendor Accredited IVT vendors are measuring incorrectly, no way to troubleshoot because “black box” false positives false negatives discrepancies Domain (spoofed) % SIVT esquire.com 77% travelchannel.com 76% foodnetwork.com 76% popularmechanics.com 74% latimes.com 72% reuters.com 71% Good domains are incorrectly marked
  28. 28. June 2021 / Page 27 marketing.science consulting group, inc. linkedin.com/in/augustinefou Vast botnets - primary use: ad fraud The most lucrative use for vast botnets is ad fraud 2019 2020 2017 2018 2015 2016 2013 2014 2011 2012 2010 IceBucket CTV fraud, 2B imps /day Coreflood DDoS, web scraping, credential stealing Grum 40B spam emails /day ZeroAccess Click fraud Bitcoin mining 488 TB traffic /day Avalanche Ransomeware, RATs, banking trojans Mirai IoT malware, 665 Gbps DDoS Methbot Video ad fraud, 300M /day Hyphbot Ad fraud, 34k websites 3ve Ad fraud 1.7M devices, 12B ad req /day Conficker DDoS,10M computers Judy Ad fraud, 41 apps, 37M devices Fireball Ad fraud browser hijack, 250M devices SndApps IMEI/personal info stealing, adware Chameleon Click fraud, fake mouse moves, ad fraud Rustock Email spam, 2,000 /second RottenSys Android adware, malware WannaCry Kelihos Necurs Zeus Dridex Emotet Spam, banking trojans Ransomware WePurchaseApps 127 apps, millions of downloads Cheetah 600 apps, 4+ billion downloads DO Global 6 apps, 50M downloads, ad fraud VidMate 500M installs, ad fraud iHandy 46 apps, 100M installs, ad fraud DiCaprio CTV fraud Grindr faking Roku streams CooTek 60 apps, 50M downloads, ad fraud ClearSkins Fake Sites, 80M video views /day Adap.tv Video ad fraud (unnamed) 5,000 mobile apps, display ad fraud Boris Network for sites for ad fraud FreeStreams Network of sites, audience overlap ad fraud DDoS Spam, banking, ransomware Ad fraud Google Digital Attack Map
  29. 29. June 2021 / Page 28 marketing.science consulting group, inc. linkedin.com/in/augustinefou Traffic available to purchase by credit card Thousands of sources to buy “traffic” widely and easily available Google “buy real human traffic” Select vendor and “traffic quality level” Host your own bots (cost $3.99/mo)
  30. 30. June 2021 / Page 29 marketing.science consulting group, inc. linkedin.com/in/augustinefou “Dark pooling” - ads.txt abuse Fraudsters spoof top domains with 40 – 42X more “unpermissioned” bid requests https://www.linkedin.com/pulse/unique-cookie-viewability-analysis-most-spoofed-domains-augustine-fou/
  31. 31. June 2021 / Page 30 marketing.science consulting group, inc. linkedin.com/in/augustinefou Oxylabs[.]io Smartproxy[.]com Method for disguising fake traffic Fraudsters disguise data center bots to appear to come from residential IP addresses, avoid detection 32 million IP addresses 40 million IP addresses 100 million IP addresses 72 million IP addresses
  32. 32. June 2021 / Page 31 marketing.science consulting group, inc. linkedin.com/in/augustinefou Missing Media Dollars Hidden arbitrage, principal trading, unspent media dollars Display Campaign 1 $3,300,000 (+10%) (agency invoice to advertiser) $3,000,000 DSP invoice to agency (paid by advertiser) $1,800,000 (-40%) DSP log total (bids won) On average, 24% of advertisers’ media spend was wasted/missing Mobile Campaign 2 $950,000 (campaign spend) $330,000 (35%) (invalid devices/fraud) Display Campaign 3 $7,900,000 (campaign spend) $1,500,000 (19%) (invalid devices/fraud) Display Campaign 3 $3,900,000 (campaign spend) $550,000 (14%) (invalid devices/fraud) Display Campaign 4 $7,000,000 (campaign spend) $1,700,000 (24%) (invalid devices/fraud) Source: Method Media Intelligence DSP logs show only $1.8 million of bids won, compared to the $3.0 million paid; $1.2 million went “missing” (not returned/credited to advertiser)
  33. 33. June 2021 / Page 32 marketing.science consulting group, inc. linkedin.com/in/augustinefou Malicious code to alter measurements Common practice to manipulate/alter measurements to trick fraud detection “the [malicious] code used by NMG is designed to interfere with the ability of third-party measurement systems to determine how much of a digital ad was viewable during a browsing session. This code manipulated data to ensure that otherwise unviewable ads showed up in measurement systems as valid impressions, which resulted in payment being made for the ad.” Buzzfeed, March 2018
  34. 34. June 2021 / Page 33 marketing.science consulting group, inc. linkedin.com/in/augustinefou tOLKnMEzcnARvLTvChnt tOLKnMEzcnARvLTvChnt tOLKnMEzcnARvLTvChnt tOLKnMEzcnARvLTvChnt tOLKnMEzcnARvLTvChnt tOLKnMEzcnARvLTvChnt tOLKnMEzcnARvLTvChnt tOLKnMEzcnARvLTvChnt Random vs Replayed Device IDs for fraud Collecting device IDs and replaying them to defeat fraud detection and frequency caps RANDOM deviceIDs lXvBEeRXPURtcKILYFYE IdUkQeWgqshMmfMdzlAx INIjBzHJHywhgRsMdQPe tiAnxwuKBNCjoMetZaPN UjtRbuUTvYUwmABhmPGH MDSUUgkENQkQDztavzfl iljoJEXUcLCEFwSdrwZn APbLSRUvlrIoofIchhLg NZXVVKCbymRYBSStNRYz UiSBmuDpYLkNvsHBKcri tOLKnMEzcnARvLTvChnt LZyhgblHtMIMaAliHWYB vKFknsnhGouIucYgxmdu • If fraud detection hasn’t seen a device before, the default action is to let the ad serve • Frequency caps based on deviceIDs are defeated, each device appears as new • Valid deviceIDs are harvested from real devices and sent to fake devices or apps to replay • Replayed deviceIDs are used by fraudulent apps to defeat fraud detection REPLAYED deviceIDs tOLKnMEzcnARvLTvChnt validated deviceID harvested
  35. 35. June 2021 / Page 34 marketing.science consulting group, inc. linkedin.com/in/augustinefou Fraud hidden in averages Waterfall charts require assumptions and average numbers to be plotted, hides fraud … lots of clicks but not from humans visible in line-item details 9.4% avg click rate
  36. 36. June 2021 / Page 35 marketing.science consulting group, inc. linkedin.com/in/augustinefou Suboptimal campaigns remain hidden Ad impressions used up by 4a, very low reach – not visible in totals/averages hourly details domain-level reach analysis
  37. 37. June 2021 / Page 36 marketing.science consulting group, inc. linkedin.com/in/augustinefou False data inserted into Google Analytics Faking mousemoves, clicks, etc; inserting false data into Google Analytics demo of faked mouse movement demo of faked Google Analytics traffic https://www.forbes.com/sites/augustinefou/2021/04/22/google-analytics-vulnerabilities-put-marketers-at-risk/
  38. 38. June 2021 / Page 37 marketing.science consulting group, inc. linkedin.com/in/augustinefou Supply path leakage, spoofing, arb Using more than 1 exchange to buy the same domain results in leakage/inefficiencies 1 domain, all exchanges 1 domain, one exchange “Even with an allow-list of 1 domain, 24% of impressions ‘went elsewhere’ if multiple exchanges were used for buying; find the primary/preferred exchange of the publisher and buy only through that one.” 24%
  39. 39. June 2021 / Page 38 marketing.science consulting group, inc. linkedin.com/in/augustinefou Duplicate bid requests for same ad slot Simultaneous bid requests increases risk of duplicate bid requests, buyers bidding against themselves https://deepsee.io/blog/header-bidding-activity-benchmarks-for-2021-so-far
  40. 40. June 2021 / Page 39 marketing.science consulting group, inc. linkedin.com/in/augustinefou Ad Dollars Funding Fake News/Disinformation Ad dollars flow away from real sites to fake news, hate speech, and disinformation sites Discovery of Fake Sites fake local news sites ads blocked on real sites
  41. 41. June 2021 / Page 40 marketing.science consulting group, inc. linkedin.com/in/augustinefou Brand safety issues continue Ad dollars flow to disinfo sites via programmatic; monitor exactly where your ads go Global Disinformation Index https://disinformationindex.org/wp- content/uploads/2021/06/GDI_AdD eck_ClimateChange_2June2021.pdf TAG (Trustworthy Accountability Group) BSI (Brand Safety Institute) giving framed “certified” plaques and scarves to members who paid the fee (doesn’t reduce brand safety issues)
  42. 42. June 2021 / Page 41 marketing.science consulting group, inc. linkedin.com/in/augustinefou Useless certifications, accreditations “content-level brand safety” for YouTube but porn,child abuse videos remain rampant https://www.adweek.com/programmatic/youtube-receives- content-level-brand-safety-accreditation-from-media-rating- council/ MRC (Media Ratings Council) “they measure what they said they would measure” TAG (Trustworthy Accountability Group) “self-attested, pay-to-play certification”
  43. 43. June 2021 / Page 42 marketing.science consulting group, inc. linkedin.com/in/augustinefou Fake audience segments by bots Bots can emulate any desirable audience segment to earn higher prices from marketers Journal of Clinical Oncology Bots pretend to be oncologists by collecting/replaying cookie bots pretend to be desirable audiences Adweek, Feb 2018 https://www.forbes.com/sites/augustinefou/2021/01/08/bots-emulate-any-audience-you-want-to-target-heres-how/
  44. 44. June 2021 / Page 43 marketing.science consulting group, inc. linkedin.com/in/augustinefou So What?
  45. 45. June 2021 / Page 44 marketing.science consulting group, inc. linkedin.com/in/augustinefou Industry Associations’ View Trade associations look at invalid traffic (IVT) by fraud detection; severely underestimates “The 11 percent decline in two years is particularly noteworthy” – ANA (2019) “IVT is between 1 – 3%” – GroupM (2019) “IVT is 1.05%” – TAG (2020) https://www.ana.net/miccontent/s how/id/rr-2019-bot-baseline https://www.tagtoday.net/pressrele ases/2020-us-fraud-study-release https://www.nexttv.com/news/groupm- puts-risk-of-ad-fraud-at-22-4b-globally
  46. 46. June 2021 / Page 45 marketing.science consulting group, inc. linkedin.com/in/augustinefou Site Analytics Ad Analytics “see fou yourself” • alternative to Google Analytics • secure, hardened against attack • shows all details, no black box • refined/updated w/ practitioners • verify your own media/ads • secure, hardened against attack • shows details for decisioning • recommended optimizations for #publishers for #marketers
  47. 47. June 2021 / Page 46 marketing.science consulting group, inc. linkedin.com/in/augustinefou FouAnalytics to continuously monitor Measuring for bots/fraud is not enough; measure for humans, remove bad domains/apps programmatic campaign large branding campaign good publisher 1 good publisher 2 good publisher 3
  48. 48. June 2021 / Page 47 marketing.science consulting group, inc. linkedin.com/in/augustinefou Turn off bad domains and apps All clicks came between 12a – noon; many suspicious mobile apps causing large numbers of clicks Clicks came from highly suspicious casino, gambling, crime, lotto, scratch off mobile apps All impressions used up before noon
  49. 49. June 2021 / Page 48 marketing.science consulting group, inc. linkedin.com/in/augustinefou Measure for Humans, not just for fraud Optimizing for low CPMs may increase risk of ad fraud; optimize for humans instead Lower quality paid sources mean higher cost per human – like 11X higher cost. Sources of different quality send widely different amounts of humans to landing pages. “showing ads to humans is the single most important factor for better digital.”
  50. 50. June 2021 / Page 49 marketing.science consulting group, inc. linkedin.com/in/augustinefou Improve outcomes by shifting spend Shifting dollars from programmatic channels [B] to good publishers [A], direct buys help Measure Ads Measure Arrivals Measure Conversions clean, good media low-cost media, ad exchanges 346 1743 5 156 30X better outcomes • More arrivals • Better quality A B
  51. 51. June 2021 / Page 50 marketing.science consulting group, inc. linkedin.com/in/augustinefou Recommendations • Previous supply chain studies showed that 50% of a marketer’s dollar goes to supply chain costs, leaving 50 cents for “working media” in ideal conditions • This study provides additional examples of “measurable issues” that further eat away at the “working media” leaving far less money for showing ads. • Many other issues are not depicted in the waterfall chart; but they remain sources of cost and risk for marketers buying ads through programmatic channels • Marketers should run their own “turn-off” experiments; also experiment with using fewer targeting parameters; business outcomes and ROI should go up • Marketers should shift dollars to buying direct from good publishers, to reduce and eliminate most of the supply chain costs and associated risks and waste

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  • jonholm1

    Jun. 3, 2021
  • soccernation

    Jun. 4, 2021
  • cwil

    Jun. 7, 2021

The ANA RFP for an update to the supply chain transparency study from the ISBA in 2020 is not even asking the right questions; here's my view.

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