Post on 30-Nov-2014
description
Bots: How to Find Them &
Keep Them Out of Digital Advertising
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Agenda
Setting the Stage
Speakers
● Marvin Hidalgo Senior Product Manager, Integral Ad Science
● Vamshi Sriperumbudur VP of Platform Marketing, Yume
● Elizabeth Cowley VP Global Partnerships, Viewster
Open Discussion / Q&A
Marvin Hidalgo Senior Product Manager
What Are Bots?
Bots noun \ˈbäts\ a device or piece of software that
can execute commands, reply to messages, or
perform routine tasks, or problem routine tasks with
minimum human intervention
Illegal bots noun \(ˌ)i(l)-ˈlē-gəlˈbäts\ computers that
are compromised and whose security defenses have
been breached and control conceded to a third party
Botnet: noun \ˈbät net\ a collection of bots
communicating with command center in order to
perform tasks
Fraud: Why Does It Take Place?
• Supply and Demand
• Because of the way that we buy media:
• Eyeballs (CPM)
• Action Taken (CPC, CPA)
• Because it’s easy for hackers
Who Are the Participants?
Profile
Hacker:
Sex: Male
Age:18-35
Location: Eastern Europe, Asia
Background: good computer skills
Botnet Operator:
Sex: Male
Age: 34+
Location: Eastern Europe
Characteristics: Disregard to law, confident, driven by money
Typical Infected Computer Owner:
Technologically challenged
Owns a dated computer and software
Suburban, rural, household without kids
Unlikely to own a smart phone/tablet
How are Bots Detected ?
First we look at behavioral patterns
We flag the following un-human signals: Cookies that are deleted at the end of activity cycle
Intense activity
Reoccurring activities patterns/levels
At this point: some bots are detected, others are
able to go undetected
Next – we look at each impression • Signals that are untypical for human
• Density of page loads
• Density of page visits
• Untypical distribution of browsers
• Browser spoofing
• Conflicting measurement results
• Was the impression traded in a suspicious way
Cross validate all of the above and determine validity of signals and patterns
Bot
…or not
How do Publisher’s approach Fraud?
Proactive
Passive Pretend the problem
doesn’t exist
Knowingly or unknowingly sell bot traffic
Able to eliminate
some of the bot traffic
Eliminate most to
all bot traffic
Partially address the problem:
• Use a subpar list based solution
• Run the technology only on part of
the inventory
Are serious about fraud:
• Use cutting edge
technology to vet 100% of
inventory
Ad Industry | Market Opportunity
$517 $544 $568
$596
$117 $132 $146 $160
2013 2014 2015 2016
Global Ad Spend (in billions)
Total Ad Spend
Total Digital Spend
$171 $179 $188 $198
$42 $50 $57 $65
2013 2014 2015 2016
US Ad Spend (in billions)
Total Ad Spend
Total Digital Spend
Source: eMarketer, Dec. 2013 & Mar. 2014 reports
44% 42%
51%
61%
0%
10%
20%
30%
40%
50%
60%
70%
Q1 Q2 Q3 Q4
19% 22%
27% 25%
Q1 Q2 Q3 Q4
Ad Industry | Challenges
Non-human Mobile Activity
Non-human Web Activity
2013
Source: Solve Media, Mar. 2014
2013 US Non-human Traffic – Online & Mobile
Challenge: In 2013, non-human Online Activity in US grew 40% & non-human Mobile Activity grew 30%
Solution: Publishers and Advertisers should prioritize investments in anti-fraud technologies
CTV platform needs review
Traffic Quality Solutions
Viewability
Tracking and measuring ad positions on publisher
pages and properties
Fraud Detection
Monitoring of non-human traffic resulting from
traffic fraud designed to manipulate ad-serving
counts
Brand Safety
Consideration of the content quality and
context that appears next to the ad
Solutions | Viewability
At least 50% of the ad is visible on the users screen for at least 1 second
Above the fold
View Area Width x Height
Player Width x Height
3 Player Location
3
4 View Area
4
2 Player Size
2
5 No auto-play
5
1 Minimum Player Visibility
1
Solutions | Fraud Detection
1. Diligent evaluation of every publisher before adding to
the network to make sure they meet the standards
2. SDK / Process for Placement Quality – Algorithms to
grade value of each video ad impression and assign
quality score. Fraud detection capabilities to address:
• Click Fraud (bots browse/click on bogus & legit sites)
• Impression Scams (bogus ad requests)
• Ad Stacking (stacked muted auto-play ads behind in-
view video ad)
• Ad Clutter (multiple ads in close proximity)
3. SDK & Data – Machine learning for mimicking human
behavior to downgrade fraudulent inventory
4. SDK & Surveys – First-party audience survey
response rate to gauge inventory quality
5. Partnerships with right vendors who specialize in fraud
detection
Publisher Site
Domain blacklisted
Player not in view
Safe domain, player in-view
Additionally, review every site/app of a Publisher and their syndication partners:
Only click-to-play or user initiated video ad playback
No skip button for 15 or 30s ads
No muted audio
Post-rolls after video content not acceptable
Publisher Syndicated Sites
Solutions | Brand Safety
2 Context
2
1 Content
1
Solutions | YuMe in The Press
“YuMe wants to ensure that there's a human
behind every impression. These
improvements to our fraud detection system
and PQI, combined with our newly-launched
anti-spam best practices, will provide an
essential layer of comfort for those planning
media buys or monetizing ad inventory.”
- Jayant Kadambi, CEO & Co-Founder, YuMe
According to Radar Research, up to 50
percent of all ad impressions are never
seen by the intended audience. Inclusion of
unseen impressions in campaign reporting
results in inaccurate metrics.
This industry-first capability allows YuMe to
prevent ads from running in video players
that have been embedded on inappropriate
websites, and to work with publishers to
constantly monitor and improve the list of
sites where their syndicated and user-
embeddable players are appearing.
Oct 2013 Nov 2011 Jun 2010
Elizabeth Cowley VP Global Partnerships
What is Viewster?
Founded in 2007 & HQ: Zurich, Switzerland with offices in US, UK, & Australia
VOD Service with focus on TV and Movie content
Available globally in 10 languages on PC and 1M connected devices
# 18 US Video Content Provider by comScore reaching 18M unique visitors/month
Flavors of Bad Traffic
•Non-Human Traffic – bots
•Human but not Viewable Traffic – hidden iFrames “<iframe src=‘http://StealBrandMoney.com style=‘visibility:hidden;position:absolute;left0;top0;’></iframe>”
•Human & Viewable Traffic - Fraudulent Arbitrage
– Browser Plug Ins Spoof URL
– In-banner sold as onsite
– Multiple Players on same page
– Ads run in pop-under pages
– Ads auto-start BTF
Buyer is sold…
But gets this
Bad Traffic Ecosystem
Fraudsters sell bad Traffic Because they can – Buyer
metrics are fakable.
Bad traffic hits the market and real publishers with real video [and real associated costs] are forced to compete with fake traffic that has no content cost.
Real publishers look to buy affordable traffic to compete
Bad Traffic enters the marketplace DIRECTLY through exchanges & lower-end adnets. It can then be laundered and sold as premium. INDIRECTLY through legit publishers buying traffic to compete.
Removing Bad Traffic from the Marketplace
Stop Buying It!
Publishers, AdNets and AdExchanges need to stop buying bad traffic.
Brands Can Help Too
•Brands should expect to pay a decent price for decent inventory and a high price for premium inventory.
•Buyers should consider campaign metrics tied to human activity.
– Increased brand recognition
– Increased sales
– Hijack device camera to confirm eyeball on ad? [just kidding…]
How to Spot Bad Traffic
• Before Buying – Is the price unreasonably low?
– Check Alexa Rank – does the site have real traffic?
– Check referring URLs in Alexa – lets you know where the traffic’s traffic is coming from?
– Check against known offenders list
• Run Test Campaign – Manual checks to verify placement
– 3rd Party Traffic Verification such as IAS, Adometry or similar
– 3rd Party Viewability Tool such as OpenVV
• After Buying – Don’t fall for a bait & switch. Continue to monitor manually and via 3rd party
technologies.
Do not underestimate the value of a relationship between
buyer and seller.
Publisher Challenges with 3rd Party
Verification Tools
Publisher Traffic GOOD
BAD
Publishers need actionable data not just a verdict of good or bad. A good step would be if 3rd party verification technologies would work with publishers to determine where their algorithms are producing data useful to buyers and where it is not.
There is no standard for “good” or “viewable” traffic therefore technologies do not agree with one another.
IAB 3MS Initiative “Making Measurement Make Sense” – intended standardize digital media metrics and promote
shift from gross “served” impressions to audience-based “viewed” impressions (50% viewable for
a minimum of 1 second).
IAB Safe Frame 1.0 Initiative Open source iframe code that removes browser-type restrictions on the geometric approach and
enables the same level of verification coverage as browser optimization approach.
Open Video Viewability Consortium of video ad technology companies to develop a single video viewability standard.
TOGI Task Force “Traffic of Good Intent” task force created to address bad traffic.
Industry Best Practice Initiatives
Brendan Riordan-Butterworth
Director, Technical Standards
Marvin Hidalgo
Senior Product Manager
Elizabeth Cowley
VP Global Partnerships
Vamshi Sriperumbudur
VP of Platform Marketing