SOLUTION
Using Kochava Traffic Verifier, the team set filtering rules to block any campaign traffic originating from states where gambling is illegal. This real-time traffic filtering ensured that the wagering platform only paid cost-per-install (CPI) bounties for app install conversions within the target states where their ADW app was usable. With attribution proactively blocked, the team was also able to avoid time-consuming make-good negotiations with paid media partners.

IMPACT
The wagering platform’s team proactively eliminated CPI payouts for install conversions in the states where gambling is prohibited. With Kochava prevention tools blocking the payout incentive behind the fraud, the percentage of fraudulent clicks and installs in their campaigns was reduced dramatically.

IMPACT
The scalable fraud defense offered by the Global Fraud Blocklist, combined with the customization enabled through the Fraud Console, defended MobilityWare’s campaigns from billions of fraudulent clicks. Through blocking fraudulent traffic from stealing attribution credit, plus eliminating sources of repeated fraud from their overall media mix, MobilityWare achieved a 4X ROI from use of Kochava’s Fraud Prevention tools.

SOLUTION
The company hired Kochava to perform a fraud audit on their attribution data. The primary focus was to analyze click and install data attributed to all ad networks for two of the company’s apps on both iOS and Android platforms. The goal was to uncover any fraudulent activity impacting their campaigns. To determine fraudulent activity, Kochava leveraged their internally developed fraud detection algorithms to process the onboarded attribution data from the company. Kochava data science and client analytics teams performed a detailed analysis and consultation, delivering a comprehensive report of the findings.

IMPACT
The networks analyzed demonstrated varying levels of fraudulent activity ranging from fewer than 15% to over 85% identifiable fraud. Overall, 65% of all attributed installs for the time period were identified as fraudulent. The primary fraud tactic overwhelmingly utilized by the networks was click flooding. By flooding large numbers of fake clicks into the ecosystem, certain networks had an unfair advantage in gaining last-click attribution, not only stealing credit from other legitimate network partners, but also taking credit for truly organic installs. A significant volume of installs further exhibited behaviors that suggested they were manufactured and not real user installs. The company took swift action on the findings and their rates of fraud fell considerably as they stopped running with the networks with the highest rates of fraud.