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What is Scalarr?

Scalarr presents an innovative solution for detecting mobile ad fraud by leveraging state-of-the-art Machine Learning technology. To address the most pressing issues in mobile advertising, Scalarr implements a dual-layered strategy featuring next-generation algorithms that boast an outstanding accuracy rate of up to 97% in recognizing various types of in-app fraud. Users can take advantage of Scalarr’s offerings by delving into its capabilities to monitor, assess, and prevent mobile app install ad fraud through its unsupervised machine learning functionalities before any damage occurs. The platform effectively employs both unsupervised and semi-supervised machine learning methods to autonomously identify and comprehend fraud patterns within extensive datasets. By analyzing numerous clicks, installs, and post-install event variables, Scalarr greatly reduces the occurrence of both false positives and false negatives in its detection framework. With a sophisticated model designed to prioritize accuracy and thoroughness, Scalarr emerges as a powerful tool that delivers actionable insights at the individual conversion level, enabling advertisers to make well-informed decisions about their ad campaigns. This holistic methodology not only fortifies the integrity of mobile advertising efforts but also empowers marketers to optimize their strategies for better performance and outcomes. Ultimately, Scalarr’s commitment to innovation ensures a safer advertising environment for all stakeholders involved.

What is SILA?

SILA operates as an advanced growth catalyst driven by artificial intelligence, tailored for advertisers, agencies, and brands dedicated to digital marketing, with the primary goal of enhancing return on investment and ensuring that every dollar spent on advertising leads to meaningful conversions. Instead of acting solely as a fraud detection tool, SILA integrates real-time defenses against click fraud, comprehensive analytics, and campaign optimization features into one cohesive platform, providing marketers with an all-encompassing view of their campaign outcomes. By leveraging SILA, businesses have the opportunity to: Quickly detect and eliminate fraudulent or invalid clicks, protecting their ad budgets from unwarranted costs. Gain access to thorough evaluations of campaign performance through behavioral analytics, trends in clicks, and quality assessments of traffic. Automatically optimize campaigns by reallocating funds to the most productive segments and audiences. Precisely target their advertising to reach genuine users who are most inclined to interact with their products or services. Track return on investment and return on ad spend with clear metrics and customized dashboards for each campaign. Moreover, SILA is engineered for smooth integration with platforms such as Google Ads and Meta Ads, making it an adaptable option for contemporary digital marketers. This forward-thinking strategy not only enhances operational efficiency but also equips businesses with essential resources to excel in a competitive advertising environment, ensuring they remain ahead of the curve.

Media

Media

No images available

Integrations Supported

Adjust
AppsFlyer
Branch
Kochava
Singular
Tenjin
mParticle

Integrations Supported

API Availability

API Availability

Pricing Information

Pricing not provided
Free Trial Offered?

Pricing Information

Pricing not provided
Free Trial Offered?

Supported Platforms

SaaS

Supported Platforms

SaaS

Customer Service / Support

24 Hour Support
Web-Based Support

Customer Service / Support

24 Hour Support
Web-Based Support

Training Options

Online Training

Training Options

Not specified

Company Facts

Organization Name

Scalarr

Date Founded

2016

Company Location

United States

Company Website

scalarr.io

Company Facts

Organization Name

Gustafsson Cyber Group Ltd

Company Website

sila-ai.com

Categories and Features

Ad Fraud Detection

Not specified

Click Fraud

Not specified

Categories and Features

Ad Fraud Detection

Not specified

Click Fraud

Not specified

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