Effectiveness of Digital Ads Q2 2017 Update by augustine fou
1. Effectiveness of Digital Ads
Q2 2017 Update
July 2017
Augustine Fou, PhD.
acfou [at] mktsci.com
212. 203 .7239
2. July 2017 / Page 1marketing.scienceconsulting group, inc.
linkedin.com/in/augustinefou
“Before a single ad impression is shown,
a third of every dollar goes to ad tech
middlemen, in the very best scenario.”
“Before a single ad impression is shown,
a third of every dollar goes to ad tech
middlemen, in the very best scenario.”
3. July 2017 / Page 2marketing.scienceconsulting group, inc.
linkedin.com/in/augustinefou
Programmatic Digital Ad Productivity
Served ad impressions
Not NHT 74% 26% Avg NHT
“working digital media” fees, profit margin
(ad tech, middlemen)
1/3rd
2/3rd
“At the very best,
only 60 cents of
every dollar spent in
digital media
actually goes
towards ‘working
digital media’.”
“productive digital ads”
59% not viewable
35% ad blocked
Viewable 41%
Not Blocked
10%
confirmed humans 16%
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Good Publisher Digital Ad Productivity
3% Avg NHT
“working digital media” 100%
“productive digital ads”
87%
Served ad impressions
Not NHT 97%
confirmed humans 61%
Viewable 91%
Not Blocked
10% Not Viewable
5. July 2017 / Page 4marketing.scienceconsulting group, inc.
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Good Publishers vs Ad Exchanges
Ad Exchange Good Publisher Take-Away
Left after
Fees
60% 100% When buyers buy direct from publisher, 100%
of every dollar goes towards “working media”
Not Bots 74%
(avg NHT 26%)
97%
(avg NHT 3%)
Not bots, but doesn’t necessarily mean
humans. Buy direct from good publishers,
rather than use fraud detection tech to clean
up afterward.
Viewable 41% 91% Viewability is generally much higher in good
pubs than sites that belong to exchanges.
Not Ad
Blocked
80%
(avg 20% blocked)
100% Good publishers don’t call ads when ad is
active. This is confirmed when measuring in-ad.
Confirmed
Humans
16% 61% Good publishers have real content that real
humans want to read; so they have human
audiences. Also bots can’t make money going
there.
Productivity
of Ads 2% 54%
Buying from good publishers means your
dollar goes at least 27X further than buying
from programmatic sources. This is BEFORE
targeting and ad effectiveness.
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60¢ per $1 left for “working digital media”
“Supporting details confirm that for every $1 the advertiser
spends, only 57 – 63 cents goes towards digital media.”
“mark up”
“working media”
“working media”
“mark up”
7. July 2017 / Page 6marketing.scienceconsulting group, inc.
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NHT (bots) is night and day, so is humans
Good Publisher
Ad Exchange
8. July 2017 / Page 7marketing.scienceconsulting group, inc.
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Viewability by network type
“Taking viewability as 50% of the pixels in view or greater, we
can see statistically different rates of viewability by network.”
Good Publishers Ad Networks Open Exchange
91% viewable 66% viewable 41% viewable
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“Naked Ad Calls” are bad on exchanges
“Naked ad calls are ad impressions served without webpages”
Good Publishers
Exchange Media
Bottom of Barrel
47% avg
77% avg
11% avg
10. July 2017 / Page 9marketing.scienceconsulting group, inc.
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Ad blocking on good publishers’ sites
Marketing Science analyzed 1.5 billion pageviews and 3 billion ad impressions from 303 websites
of mainstream publishers in the U.S. and Canada in June 2017. The data was directly measured with
an embed code installed by publishers on their websites (on-page and in-ad). Mobile means mobile
web; no in-app.
The methodology includes the following:
• On-page measurement – measured the users arriving on the page that had ad blocking turned on.
• In-ad measurement – measured in the ad iframe – these would never be called if ad blocking
were active; our data confirms good publishers do not call ads when ad blockers are on.
• Portion of the data that could not be measured - on average 11% of the pageviews and 16% of the ad
impressions could not be directly measured for ad blocking, and thus excluded.
• Bots were scrubbed - on average, good publishers had 1 - 4% bots (NHT, IVT), which were scrubbed from the
data. Bots do not block ads, humans do. Therefore for accuracy of the ad blocking measurement, bot traffic
should not be counted, when reporting ad blocking rates.
(range was 6 – 14% blocking)
11. July 2017 / Page 10marketing.scienceconsulting group, inc.
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About the Author
July 2017
Augustine Fou, PhD.
acfou [@] mktsci.com
212. 203 .7239
12. July 2017 / Page 11marketing.scienceconsulting group, inc.
linkedin.com/in/augustinefou
Dr. Augustine Fou – Independent Ad Fraud Researcher
2013
2014
Follow me on LinkedIn (click) and on Twitter
@acfou (click)
Further reading:
http://www.slideshare.net/augustinefou/presentations
https://www.linkedin.com/today/author/augustinefou
2016
2015
13. July 2017 / Page 12marketing.scienceconsulting group, inc.
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Harvard Business Review
Excerpt:
Hunting the Bots
Fou, a prodigy who earned a Ph.D. from MIT at
23, belongs to the generation that witnessed
the rise of digital marketers, having crafted his
trade at American Express, one of the most
successful American consumer brands, and at
Omnicom, one of the largest global advertising
agencies. Eventually stepping away from
corporate life, Fou started his own practice,
focusing on digital marketing fraud
investigation.
Fou’s experiment proved that fake traffic is
unproductive traffic. The fake visitors inflated
the traffic statistics but contributed nothing to
conversions, which stayed steady even after the
traffic plummeted (bottom chart). Fake traffic is
generated by “bad-guy bots.” A bot is computer
code that runs automated tasks.