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Zimad Case Study: Scalarr detected 3X more fraud compared to the previous anti-fraud solution.
Zimad
How ZiMAD boosts performance and saves marketing budgets with Scalarr’s anti-fraud solution.
Scalarr-Zimad partnership timeline
Market overview
$55.3B
Revenue
1.573B
Users
$2.4
CPI (Average cost per install)
$35.17
ARPU (Average revenue per user)
Company overview
Multimillion userbase worldwide.
Magic Jigsaw Puzzles - the most popular digital collection of jigsaw puzzles in the world.
>1M installs of paid traffic per month.
Important note
Before Scalarr’s audit, ZiMAD used a basic anti-fraud solution provided by an MMP.
Initial audit and trial phase
151K
fraudulent installs were detected over 3 months
95%
Attribution Fraud
3%
Bots & Device Farms
2%
Smart Bots
The prevalent fraud type in ZiMAD is Click Injection (89%), which occurs mainly in incentive CPA campaigns with a CPA rate range from $3 to $10.
Fraudsters mainly used incentivized traffic and Smart Bots to emulate post-install events.
As a result, 99% of payment events were faked by fraudsters.
Nature of fraud - Trial stage
8%
Fraudulent Installs
60%
Attribution Fraud
40%
Fraudulent Post-Install Activity
59%
Click Injection
35%
Bots & Device Farms
1%
Click Spam
5%
Smart Bots
Nature of fraud - Post-trial stage
0.5%
Fraudulent Installs
97%
Attribution Fraud
3%
Fraudulent Post-Install Activity
90%
Click Injection
1%
Bots & Device Farms
7%
Click Spam
2%
Smart Bots
Quote from our customer
Scalarr’s anti-fraud tool rapidly and accurately identified
all fraudulent activity regardless of how quickly fraudsters
changed their patterns. The main benefits of using
Scalarr are the certainty in directing marketing budgets
to quality sources and the confidence of acquiring real
users that we know are valuable.
- Aleksey Tishakov,
Head of User Acquisition at ZiMAD
Traffic Cleanup Results
3X more fraud has been detected compared to the previous anti-fraud solution
Fraud traffic share dropped from 8% to 0.5%
151K fraud installs were detected and refunded

Scalarr.io detects up to 40% more
complex fraudulent traffic.

This case study shows we found
3X more fraud.

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