The document discusses the development of an alternative to Google Analytics that focuses on security and transparency in analyzing web traffic, emphasizing fraud detection and optimizations for both marketers and publishers. It outlines various capabilities, including distinguishing between human and bot traffic, evaluating ad performance, and providing insights for improving conversion rates. The system allows users to identify and filter out bot traffic, ultimately aiming for better metrics and outcomes in digital advertising.

1. June 2020 / Page 0marketing.scienceconsulting group, inc.
fouanalytics.com
Site and Media Analytics
June 2020
Augustine Fou, PhD.
212. 203 .7239

2. June 2020 / Page 1marketing.scienceconsulting group, inc.
fouanalytics.com
Who am I?
• I am a digital marketer of 25+ years
• I look at analytics data every day
• I audit campaigns for fraud and other
anomalies that lower performance
• I teach clients how to spot fraud and
make optimizations to improve
outcomes themselves

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Why did I build it? How is it different?
https://www.linkedin.com/pulse/does-your-analytics-
platform-have-cybersecurity-dr-augustine-fou-/
https://www.linkedin.com/pulse/fouanalytics-
alternative-google-analytics-fraud/

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fouanalytics.com
Site
Analytics
Media
Analytics
“see fou yourself”
• alternative to Google Analytics
• secure, hardened against attack
• shows all details, no black box
• innovated w/ practitioners
• verify your own media/ads
• secure, hardened against attack
• shows details for decisioning
• recommended optimizations
for #publishers for #marketers

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Site Analytics

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Answers these questions…
1. Which visitors to my site are valuable
humans, vs bots (which bots)?
2. Where did they come from (paid media?
organic search? social?)
3. What did they do on the site? Did they
complete a goal/conversion?
4. How do I get more valuable visitors?

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Definitions
What each of the labels means
• Humans - 3 or more blue flags to confirm
• Some blue flags but not 3 or more
• Can’t label it either red or blue
• Tag was called, but no data was sent back (blocked)
• Tag was not called (not measurable)
• Bot – Search crawler
• Bot – Says its name honestly, (14,000 bot names)
• Some red flags, but not 3 or more
• Bots - 3 or more red flags to confirm

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Time Series – 1 quarter
Long term trends

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Time Series – 1 month
Monthly and daily pattern

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Time Series – 1 week
Weekly pattern

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Time Series – 1 day
Hourly volumes are visible

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Locations of human visitors
Where did human visitors come from?

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Analyze declared bots (orange)
Quickly isolate, analyze what bots are coming to the site

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Analysis of declared bots
Quickly isolate and analyze what bots are coming to site
• Bots declared
themselves
(lyticsbot,
moatbot)
• Bots came from
data centers

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Analysis of search bots
Quickly see which specific search bots arrived on the site
• Bots declared
themselves
(Googlebot,
Adsbot, Web
Preview)
• Bots came from
data centers

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Filtering bots and data centers
Once you know which visitors are bots, you can filter them
On-Site measurement,
bots are still coming
In-Ad measurement, bots
and data centers filtered
11% red
-9% (filtered GIVT
and data centers)
2% red

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Where do visitors click?
Click patterns on desktop and mobile screens, vs bots

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Media Analytics

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Answers these questions…
1. Where were my ads shown, actually
2. Were my ads shown to humans, plus
other characteristics like viewability,
duration, valid interaction, etc.
3. Which domains and apps were fraud or
sub-optimal (turn off in campaign)

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Full Funnel Measurement
In-Ad
(ad tag)
On-Site
(embed code)
Measure
Ads
Measure
Arrivals
Measure
Conversions
1743 156
A
more humans (blue)
good publishers
On-Site
(embed code)
social
native
video
display
affiliate

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Detect / reduce fraud in-flight
Launch Week 3 onwardWeeks 1-2
Initial baseline
measurement
Measurement after
first optimization
After eliminating several
“problematic” networks
Stacked percent chart
Blue (human)
Red (bots)

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Shift dollars to more humans (blue)
Marketer 1
• Blue means humans
• Red means bots
Marketer 2
“recommendation is to increase spend on
sources driving more humans (blue); reduce
spend on sources with more bots (red)”

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Shift budget to more human sources
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.

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Where is “red” coming from?
What domains and apps should I consider turning off?
domains mobile apps
Turn off domains and apps that have the most red

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Systematically reducing bots
• Bots can be programmed to send as much traffic and generate as many impresisons
and clicks as the advertiser wants
By systematically reducing ad
spend to ad networks and sites
that had the highest bots, and
increasing allocation to premium
publishers, the advertiser
increased ad impressions served
to humans, and lowered those
served to bots.

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Total cost vs unit cost (CPM)
Pay much higher CPM, spend less total, get more outcomes

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Media Analytics w/
Site & Conversion Tracking

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Better media = better outcomes
Measure
Ads
Measure
Arrivals
Measure
Conversions
346
1743
5
156
A
B
30X more human
conversion events
• More arrivals
• Better quality
more humans (blue)
good publishers
low-cost media,
ad exchanges

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Display 4
2,036 humans
human conversion rate
Humans click from ads to site
Site Traffic Conversions
8,482 818
4,216 humans
5%
human conversion rate
14,539 193
225 humans
9%
human conversion rate
2,248 23
168 humans
5%
human conversion rate
1,527 9
Display 3
Display 2
Display 1
Humans
40%

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Higher, more accurate conversion rates
378
411
357 361
512
495
525
409
595
536
552
596
437 452
380
425
532
489
592 584
403 416 415
587
570
490
463
516
400 389
418
Avg. 475 conversions /day
Avg. 3,526 sessions /day
Avg. 6,636 sessions /day
15% confirmed bots
Avg. 473 conversions /day
3% confirmed bots
0%
5%
10%
15%
20%
25%
Avg. 7.1% conversion rate
Avg. 13.5% conversion rate
“removing fake traffic,
doubles conversion rates”

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Measure conversions, correlate w/ humans
Organic sources
have more humans
(dark blue)
Conversion actions
(calls) show straight line
correlation to humans

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26,796
1,116
Comparing organic vs paid sources
Marketer’s Site
Traffic
Measure
Conversions
organic
335 humans
66%
paid media
6,698 humans
1,043
222
4X higher
conversion rate
16%
human conversion rate
human conversion rate

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+54%
Conversion Rate
4,248
1,812,906
= 0.23%
4,248
1,178,389
= 0.36%
Increase Conversion Rates
Make more accurate by subtracting bot traffic from denominator
Subtracted bots

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+92%
Conversion Rate
4,248
1,812,906
= 0.23%
4,248
942,711
= 0.45%
Increase Conversion Rates
Make more accurate by subtracting bot traffic from denominator
Subtracted bots (red),
and unmeasured (white)

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How to Install

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In-ad code for ad impressions
In-Ad
Example from DCM
(copy and paste ad tag)
In-ad tag
In-ad tag
In-ad tag
JS tag in ad creative

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Approved on Major Networks

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On-site code for your own website
Website / Landing Pages
(copy and paste JS embed
code into page footer, just
like analytics code)
Example
Google Analytics
BotAnalytics

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What it Collects

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Collects same data as Google Analytics
'time'
'window'
'appVersion'
'video'
'product'
'sessionStorage'
'localStorage'
'vendor'
'productSub'
'appCodeName'
'inner'
'cookieEnabled'
'appName'
'platform'
'timezone'
'cookies'
'language'
'doNotTrack'
'frame'
'hidden'
'outer'
'vendorSub'
'url'
'userAgent'
'fontrender'
'referrer'
'clock'
'sort'
'globals'
'TIMESTAMP'
'cookie'
'HTTP_USER_AGENT'
'HTTP_CONNECTION'
'HTTP_X_FORWARDED_FOR'
'HTTP_ACCEPT'
'HTTP_ACCEPT_LANGUAGE'
'GEO_LAT_0'
'GEO_LNG_0'
'GEO_CITY_0'
'GEO_REGION_0'
'GEO_COUNTRY_0'
'GEO_IP_0'
'GEO_AS_0'
'HTTP_ACCEPT_ENCODING'
'devicelight'
'document'
'framerate'
'plugins'
'HTTP_REFERER'
'orientation'
'HTTP_COOKIE'
'motion'
'mousemove'
'HTTP_DNT'
'scroll'
'custom_site'
'custom_campaign'
'custom_advertiser'
'custom_IO'
'custom_placement'
'custom_creative'
'custom_adunit'
'custom_section'
'HTTP_CACHE_CONTROL'
'mousedown'
'mouseup'
'click'
'touchstart'
'HTTP_UA_CPU'
'touchend'
'touchmove'
'HTTP_VIA'
'HTTP_X_WAP_PROFILE'
'HTTP_X_REQUESTED_WITH'
'HTTP_ACCEPT_CHARSET'
'HTTP_X_UIDH'
'keydown'
'keyup'
'touchcancel'
'GEO_LNG_1'
'GEO_IP_1'
'GEO_AS_1'
'GEO_COUNTRY_1'
'GEO_REGION_1'
'GEO_CITY_1'
'GEO_LAT_1'
'HTTP_X_ATT_DEVICEID'
'HTTP_X_BLUECOAT_VIA'
'keypress'
'HTTP_PRAGMA'
'HTTP_DEVICE_STOCK_UA’
'clicktype'
'HTTP_X_IMFORWARDS'
'HTTP_FORWARDED'
'HTTP_CLIENT_IP'
'HTTP_X_GETZIP'
'HTTP_X_OPERAMINI_PHONE_UA'
'HTTP_X_OPERAMINI_PHONE'
'HTTP_X_OPERAMINI_FEATURES'
'GEO_AS_2'
'HTTP_SCHEME'
'GEO_IP_2'
'GEO_COUNTRY_2'
'GEO_REGION_2'
'GEO_CITY_2'
'GEO_LNG_2'
'GEO_LAT_2'
'"frame'
'HTTP_X_P2P_PEERDIST'
'"clock'
'"document'
'HTTP_XROXY_CONNECTION'
'HTTP_PREFER'
'"devicelight'
'"scroll'
'HTTP_PROXY_CONNECTION'
'select'
'HTTP_CONTENT_SUPORT'
'HTTP_X_REAL_IP'
'HTTP_X_NETWORK_TYPE'
'"framerate'
'"mousemove'
'HTTP_X_BROWSER_TYPE'
'HTTP_X_NOKIA_DEVICE_TYPE'
'HTTP_X_MOBILE_GATEWAY'
'HTTP_X_COUNTRY_CODE'
'"globals'
'HTTP_CLIENTIP'
'HTTP_VERSION'
'"plugins'
'"touchend'
'noscript'

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Triangulating collected parameters …
• The javascript “tag” collects details
about the browser like the version,
the operating system, screen
resolution, plugins, etc.
• Uses standard javascript APIs which
are set and maintained by W3C
• No personally identifiable
information is collected
• Two or more parameters are
triangulated together to
determine human vs bot via flags
• Users don’t interact with it in any
way – does not log in, enter any
data
• Cookies are never used
• Not all parameters are collected
for every visit
If { useragent = iPhone } but { screen resolution ≠ known resolutions }
If { IP address = data center }
If { plugins = none } If { language = none } If { window = 1 x 1 }

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Performance Measures, Capacity on AWS
Average 30-40 million requests per hour
(peak 120 million /hr)
Average latency 4 - 5 milliseconds

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Network timings waterfall - async
• Our js tag is set to async so it is
called after the page finishes
loading (2.33s in red)
• And all write-backs are done
subsequently.