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Scalarr

Read 9 Scalarr reviews and testimonials from customers, explore 8 case studies and customer success stories, and watch customer videos to see why companies chose Scalarr as their Fraud Detection Software

Scalarr is an innovative, Machine Learning based anti-fraud solution that detects the mobile app install fraud with dramatically improved accuracy. There are a lot of different types of app install ad fraud, although the traditional rules-based analysis used by existing solutions helps to detect only few of them. By applying to both unsupervised and semi-supervised machine learning algorithms, Scalarr solves the problem by analyzing deeply hundreds of features and variables, thus reducing efficiently the number of false positive and false-negative errors. With its unique self-learning engine it helps mobile advertisers to stay ahead of fraudsters without any need to update constantly the rules, as they modify their attack techniques.

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Customer Rating Review Score based on 1415 reference ratings
4.8/5.0 (1415)

9Testimonials

  • “Scalarr is an integral part of our decision-making process when it comes to running user acquisition campaigns across all paid sources and helping us to make smart data-based decisions. The …

  • "Reaching success is a million dollar question. There can be probably dozens of good answers, and most of them might be correct, but in my opinion, data is the key. …

  • “Scalarr is the most accurate and sophisticated solution i,ve ever used to detect the fraud. Understanding data is the most important thing in mobile advertising and the Scalarr team does …

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8 Case Studies

  • How We Help ZiMAD Save Marketing Budget & Secure Revenue

  • How Huuuge Games double down on data analytics to boost growth and …

  • Mobile Company - Customer Case Study

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Additional Scalarr Information & Resources

Read Scalarr Reviews, Testimonials & Customer References from 9 real Scalarr customers.

Browse Scalarr Case Studies, Customer Success Stories, & Customer References from 8 businesses that use Scalarr.