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State of Ad Blocking Jan 2019

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Ad blocking must be measured on-site; in-ad measurements are not valid. Mobile needs to be broken out from desktop. The portion of data that cannot be measured also needs to be disclosed. And finally, bots must be excluded, otherwise the numbers are artificially low.

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State of Ad Blocking Jan 2019

  1. 1. January 2019 / Page 0marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou State of Ad Blocking Jan 2019 January 2019 Augustine Fou, PhD. acfou [at] mktsci.com 212. 203 .7239
  2. 2. January 2019 / Page 1marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou Measurement Notes Direct measurement is necessary, measurement done on-site • Ad blocking must be measured on-site. In-ad measurements are invalid because the ad should not have been called if ad blocking were on. • Ad blocking is measured on-site by deliberately calling an asset called ad.gif and seeing what percent fails (blocked) and what percent loads • 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 must be excluded because bots don’t block ads (their job is to cause them to load); if not excluded, bots skew blocking numbers lower • The percent of data that cannot be measured also needs to be disclosed because ad blockers block tracking and measurement tags • And yeah, things should add up to be 100% (internal data consistency)
  3. 3. January 2019 / Page 2marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou Overall ad blocking – B2C, B2B Higher ad blocking in b2b compared to b2s, more mobile in b2c B2C (Consumer) Jan 2019 EXCLUDE BOTS RAW (percent of data) percent NOT Blocked Blocked mobile 61.8% 0.4% 0.7% blocking rate desktop 27.6% 3.1% 10.2% blocking rate not measured 4.8% 97.8% 2.2% bots B2B Jan 2019 EXCLUDE BOTS RAW (percent of data) percent NOT Blocked Blocked mobile 34.5% 0.5% 1.3% blocking rate desktop 39.9% 9.0% 18.5% blocking rate not measured 7.9% 91.8% 8.2% bots
  4. 4. January 2019 / Page 3marketing.scienceconsulting group, inc. linkedin.com/in/augustinefou Excluding bots is necessary Bots don’t block ads, so they skew blocking numbers lower Publisher A (Jan 2019) RAW (percent of data) percent NOT Blocked Blocked mobile 15.2% 0.3% 1.6% blocking rate desktop 69.5% 4.8% 6.5% blocking rate not measured 10.2% 100.0% bots not excluded • Bots skew ad blocking lower, should be excluded • Bots are mostly non-mobile, so the “desktop” number was most affected by bots. Publisher A (Jan 2019) EXCLUDE BOTS RAW (percent of data) percent Adblock:0 Adblock:1 mobile 15.0% 0.3% 1.6% blocking rate desktop 32.0% 4.6% 12.5% blocking rate not measured 7.2% 59.1% 40.9% bots excluded

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