mobile fraud prevention with adjust
TRANSCRIPT
We make data work for you 1
MOBILE ATTRIBUTION ANALYTICS 2016
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The three different types of fraud
Click SpamSimulated DevicesFake Installs
Some App
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+ Servers pretend to be apps and talk to analytics platforms
Fraud
+ Analytics SDK with SSL encryption + Shared secret
Prevention
Faking HTTP calls to trigger false installs
+ Publishers are paid for fake installs + Too many installs counted + Retention rates are underestimated
Effect
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Simulated installs & behaviour
+ Exclude all IPs from known data centers, proxies, Tor exit nodes or cloud providers from attribution.
Fraud
Solution
+ Devices are simulated with full OS stack or are triggered by "mechanical turks" to create legit install requests.
Effect+ Installs (and events) attributed to fraudulent publishers + Undercounted retention rates
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+ Limit number of clicks considered for fingerprinting from single IPs.
+ Detect extremely low yielding campaigns and deliver landing pages
+ with Javascript to create redirects and stop crawlers.
Fraud
Solution
Background clicks
+ Apps that, without the users interaction, crawl ads and click through any URL they find to spam fingerprinting and claim organic traffic.
Effect
+ Organic users are attributed to publishers
+ Ads show very strong in-app retention & engagement (organic)
+ Very low conversion rates
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Stolen ad budget is only one of the problems caused by fraud
Faked conversations!
Click spam- organic users
Fraud-free!
Some Users Removed!
Uncertainty
Higher investment
Lower investment
Uncertainty
same day
unlimited
0.01
1.500.000
5%
11%
6%
8%
12,00
0,01%
8.27%
63,250
5,000
29,000
7,590
5,000,000
3,400
Network C
Network B
Network A
Organic
Result? Problem? Day-7Retention
Purchase Conversion
CVR Installs Clicks
150,000
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Exploited ad budgets are only one of the problems caused by fraud
+ Organic user activity (like purchases) is poached by click spam campaigns
+ Click spam campaigns look like they perform very well (because the users are organic)
+ High-quality, premium campaigns usually perform less than organics
+ Campaigns with fake conversions have high initial conversion rates, low post-install conversion
The consequence: Your conclusions based on the data are uncertain, and sometimes wrong!
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Before
After
+ New networks contributed users. + Manually checked post-install data for suspicious IPs. + BI team contributed some insights, but marketing relied on + a few trusted partners to keep workload reasonable.
+ All new users automatically vetted. + Suspicious sources - users with datacenter IPs, using suspicious
VPNs, or Tor - were automatically isolated for separate analysis. + New partners rapidly evaluated.
+ Ad budget and time could now be used to try new marketing approaches.
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I now see that adjust’s fraud prevention is serving a
different purpose beyond just preventing a ‘bad’ install:
It’s my insurance policy when working with new partners.
We can try new things, and I’m free to experiment
because I trust the data. We can go big, and not hold back.
An Vu User Acquisition Lead at Rovio
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www.adjust.com
Stephanie Pilon [email protected]
Head of Product Marketing
adjust
Berlin
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All the details in our white paper
adjust.com/fraud-prevention