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Maximize Your ROAS: Cutting-Edge Attribution Strategies for Mobile Gaming
CASE STUDY

Major QSR Proves Incremental Lift from Connected TV Ad Campaign

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CHALLENGE
A national quick service restaurant (QSR) chain in the US needed to prove the efficacy of their connected TV ad campaigns with the nation’s leading OTT and connected TV (CTV) platform. Observing incremental lift in customer engagement with the QSR’s mobile app would be the key to unlocking further ad spend and optimizing future campaigns.

SOLUTION
Advanced Measurement solutions provided by Kochava enabled an unbiased and data-driven assessment of the campaign’s incremental lift. To begin, the OTT/CTV platform provided over 2 million hashed primary IP addresses – 1.7M of which belonged to households in the ad exposure group, with the remaining 300K in holdout as a control group. A primary IP/household identity graph provided a 66.1% match rate against both groups.

To confirm there was no bias between the exposure and control groups, Kochava data science analyzed household devices within each group using proprietary device scoring, which measures attributable qualities like activity recency & frequency, geo visitation, etc. The device scores were nearly identical, confirming no bias.

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With the Kochava SDK integrated into the QSR mobile app, full visibility was provided into app installs and in-app engagement down-funnel of the OTT/CTV ad campaign. Mobile devices tied to the households with campaign exposure were observed in comparison to those within the control group.

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IMPACT
With a 90% confidence interval, Kochava showed the campaign generated a significant incremental lift, resulting in over 4,800 purchases. Campaign exposure was also clearly shown to influence the timing of app installs with the greatest likelihood occurring within 4 hours of exposure to the ad. Ideal impression frequency for the household was 3-10, with less than three under-performing and more than 10 showing no benefit.

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panel for accurate and scalable incremental lift analysis.

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This use case is one example of the impact of Kochava solutions for advertisers. Kochava makes no guarantee of individual results.