The concept of Return on Ad Spend (ROAS) continues to be a major challenge in the era of iOS 14.5 and increased privacy concerns. Advertisers are finding it increasingly difficult to accurately measure and match the cost and attribution data required for ROAS calculation.
This blog will explore the challenges advertisers face in quantifying ROAS in a privacy-centric world and discuss various techniques to address these challenges.
ROAS, or Return On Advertising Spend, is a critical marketing metric that helps evaluate the effectiveness of digital advertising campaigns. However, the fragmentation of data across different ad networks has historically made it difficult to align cost and attribution data.
Each ad network operates on its own data scheme, business rules, and models, making it challenging to standardize cost data and match it with attribution. The advent of privacy-centric attribution models like SKAdNetwork (SKAN) and web-to-app attribution with Apple iOS 14.5 and the ATT framework has further compounded the data fragmentation and complexity.
Most ad networks provide varying levels of data granularity for cost and attribution. For example, Network A provides campaign, adset, ad, and country-level cost data, but offers different attribution granularity for SKAN and non-SKAN attribution. This inconsistency in reporting structure and data granularity poses a challenge when trying to combine and analyze cost and attribution data accurately.
To address this challenge, marketers often need to store and analyze the same data multiple times, leading to increased complexity and potential errors in data analysis.
Ensuring accurate cost per install (eCPI) and ROAS measurements becomes challenging in the new privacy-centric landscape. The overlapping installs between SKAN and non-SKAN attribution methods result in different metrics and discrepancies in reported data. This lack of consistency in reporting can make it difficult to make informed decisions based on accurate data.
The anonymized nature of SKAN data adds to the complexity, as it becomes impossible to deduplicate installs or attribute them to specific attribution methods accurately.
There are several ways to address the challenges of measuring ROAS and eCPI accurately in a privacy-centric world.
One approach is to manually collect cost and attribution data from the ad network's dashboard and match it in a spreadsheet.
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Alternatively, marketers can develop their own programmatic solution by creating connections with partner APIs and integrating cost and attribution data.
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Another option is to utilize a third-party cost aggregation tool that automates the collection and correlation of cost and attribution data into the advertiser's own business intelligence (BI) system.
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It is important to note that none of the above solutions can independently deduplicate the SKAN and non-SKAN install data to provide accurate eCPI and ROAS metrics. The accuracy of these metrics relies on the attribution partner's ability to handle deduplication and provide accurate data reporting.
Xpend from AppsFlyer offers a Single Source of Truth (SSOT) solution that can deduplicate overlapping installs and provide accurate eCPI and ROAS metrics. By leveraging Xpend's data aggregation capabilities, marketers can accurately measure and analyze their cost and attribution data to make informed decisions and optimize their advertising performance.
Measuring ROAS and eCPI in a privacy-centric world presents several challenges due to data fragmentation and accuracy issues. However, using a comprehensive solution like Xpend can help marketers overcome these challenges by providing accurate and deduplicated cost and attribution data. With precise measurement and analysis, marketers can optimize their advertising efforts and drive better overall performance.
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