case studies of reducing bots fraud by augustine fou

Case Studies of Reducing Bots/Fraud

April 2017Augustine Fou, PhD.acfou [at] mktsci.com 212. 203 .7239

For Advertisers

April 2017 / marketing.scienceconsulting group, inc.

linkedin.com/in/augustinefou

Advertiser myths about ad fraud1. Fraud is 2% and ‘on the wane’

No. It’s because you’re catching less of it, because bots are better at hiding (think Methbot)

2. Fraud is ‘priced in’No. You may be paying 1/10th the CPM, but you’re buying 10x more impressions; an ad shown to a bot is useless.

3. We’ve got fraud-free guaranteesNice. But this assumes the detection tech can detect it; what if it can’t detect it, or it gets tricked? Is it fraud free?



Reduce bots/NHT in display campaignsPeriod 1 Period 3Period 2

Initial baseline measurement

Measurement after first optimization

After eliminating several “problematic” networks



“Humanness” of different sources

Organic sources have more humans (dark blue)

Conversion actions (calls) are well correlated to humans; bots don’t call



Higher quality means lower cost per human

Lower quality paid sources mean higher cost per human acquired – like 11X the cost.

Sources of different quality send widely different amounts of humans to landing pages.



Way better outcomes from better mediaMeasure

AdsMeasure Arrivals

Measure Conversions

good publishers, clean media, low bots

low-cost media, ad exchanges

346

1743

5

156

30X better outcomes

• More arrivals• Better quality

A

B



Make analytics more accurate and clean

7% conversion rate 13% conversion rateartificially low actually correct

For Publishers



Publisher myths about ad fraud1. Fraud doesn’t affect us, there’s low bots on our site

Bots don’t come in large quantities to your sites; they just collect a cookie and go elsewhere to create ad impressions

2. We have bot protection on our siteNice. But what if bad guys pretend to be your site by passing fake data, and put your brand reputation at risk?

3. We have high quality trafficGreat. We believe you. But what if bot detection tech accuses you of high bots (falsely)?



Good publishers take action to reduce bots

Publisher 1 – stopped buying traffic

Publisher 2 – filtered data center traffic



Filter bots to protect advertisers

On-Site measurement, bots are still coming

In-Ad measurement, bots and data centers filtered

10% red

-7% (filter data center traffic)

3% red



Defend against incorrect measurementsNHT counts higher

than ad impressions

1. In-ad measurement can’t look outside iframe

ad tag / pixel(in-ad measurement)

Challenges to measurement accuracy

2. Extrapolations from small sample or short time-based sample

Sources 1 and 2 corroborate

Source 3 completely off



Reduce javascript trackers to protect users

42 trackers24.3s load time

8 trackers1.3s load time



Reduce cookie matching, re-identification

specialized audience:oncologists

Journal of Clinical Oncology

specialized audience can be targeted elsewhere

“cookie matching”(by placing javascript on your site)

FOR EXAMPLE ONLY

ad revenue diverted away

Fraud comes in large numbers



$83B digital spend (2017)CPM and CPC fraud in context of digital spend

Source: eMarketer March 2017

Search Spend$40 $40

Display Spend Other

$16$30

$3

Google Search FB Display

$4E $11E

What’s left for good publishers

CPC Fraud

$5 Google Display

CPM Fraud

(75% of search) (40% of display)

$8$6

60% fraud

$29(outside Google/Facebook)

(display opportunity)

Source: eMarketer March 2017

• $7.2B out of $12B (2016)• $11B out of $19B (2017)40%

https://www.emarketer.com/Article/Google-Facebook-Increase-Their-Grip-on-Digital-Ad-Market/1015417






“sites that carry ads”

Top good domains vs “sites that carry ads”

Source: Verisign, Q4 2016329M

domains

$80B search + display

Google Search

FB+GOOG Display

$29billion

“mainstream sites you’ve heard of”

WSJESPN

NYTimes

EconomistReuters

Elle

top 1 million + next 10 million

159 million unknown sites

100% botpageviews on

“fraud sites”

99% human pageviews are on

“sites you’ve heard of”

3%

carry adsno ads

http://www.verisign.com/en_US/domain-names/dnib/index.xhtml



Little ad spend left for good publishersAd

verti

sers

Publishers are left with 30%

Bad Guyssiphon dollars OUT of the ecosystem

30% ($6B)

60% ($11B)

Ad Blockingusers use ad blocking to

protect themselves

10% ($2B)

Ad Tech“plumbing” and verification

Source: The Guardian, Oct 2016

$5B to Google Display

$16B to Facebook Display

Display Spend $

40B

Disp

lay

Spen

d

http://mediatel.co.uk/newsline/2016/10/04/where-did-the-money-go-guardian-buys-its-own-ad-inventory/



Example – 92% impressions cleaned up

Increased CPM prices by 800%

Decreased impression volume by 92%

Source: http://adexchanger.com/ad-exchange-news/6-months-after-fraud-cleanup-appnexus-shares-effect-on-its-exchange/

260 billion

20 billion

> $1.60

< 20 cents



Single botnet steals 15% of video ad spend

Source: Dec 2016 WhiteOps Discloses Methbot Research

“Methbot, steals $2 billion annualized; and it avoided detection for years.”

1. Targeted video ad inventory$13 average CPM, 10X higher than display ads

2. Disguised as good publishersPretending to be good publishers to cover tracks

3. Simulated human actionsActively faked clicks, mouse movements, page scrolling

4. Obfuscated data center originsData center bots pretended to be from residential IP addresses

http://www.whiteops.com/METHBOT







About the AuthorApril 2017Augustine Fou, PhD.acfou [at] mktsci.com 212. 203 .7239



Dr. Augustine Fou – Independent Ad Fraud Researcher2013

2014

Follow me on LinkedIn (click) and on Twitter @acfou (click)

Further reading:http://www.slideshare.net/augustinefou/presentationshttps://www.linkedin.com/today/author/augustinefou

2016

2015

http://www.linkedin.com/today/author/augustinefou

http://twitter.com/acfou

http://twitter.com/acfou

http://www.slideshare.net/augustinefou/presentations

https://www.linkedin.com/today/author/augustinefou



Harvard Business Review – October 2015

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.

case studies of reducing bots fraud by augustine fou

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