Retail media is the fastest-growing sector of digital advertising, with spend rising over 20% year over year. However, two myths hinder RMNs: 'too many RMNs' causing fragmentation, and 'RMNs need external demand.' A common misguided solution is building a retail media SSP, which manages multiple demand sources. However, SSPs fail to scale because they don't improve existing inventory performance, lack ML-driven relevance for 1:1 personalization, and shift performance risk to advertisers.
In programmatic RTB, RMNs risk commoditizing inventory as advertisers optimize across competitors. This leads to low budget utilization and fill rates. Moreover, most revenue comes from endemic advertisers, shaping strategies around a smaller subset of demand.
SSPs were designed for a different scale than retail media, where advertisers typically focus on 10-20 key RMNs. RMNs are mini walled gardens with unique advantages: transaction-close inventory, best first-party data, and built-in brand relationships. To scale, they should follow the playbook of Amazon, Meta, and Google—using top tech and ML for relevance, simplifying demand scaling while de-risking media investment, and expanding inventory without harming organic metrics.
A purpose-built, ML-driven partner like Moloco can maximize first-party data predictive power, enabling RMNs to control their ecosystem and unlock sustainable growth.
First-party data, collected directly from users with consent, is crucial for marketers due to privacy regulations limiting third-party data. It enables accurate personalization, compliance, and cost savings. Key steps include ethical collection, maintaining clean data, and using it internally for product/marketing optimization and externally via commerce media networks.
Onsite retail media ads remain the most critical driver of RMN growth, accounting for over 80% of ad spending. They offer higher ROAS, better margins, and brand safety. Leading RMNs like Amazon and Walmart generate most media revenue from onsite. Growth can be unlocked through ML optimization, self-serve platforms, and outcomes-based campaigns, even without massive traffic increases.
Earned media is unpaid, third-party brand exposure from reviews, shares, or media mentions. It builds trust, expands reach, and boosts SEO. Marketers can leverage it via customer reviews, influencer endorsements, and UGC.
Cross-platform measurement resolves the common problem of fragmented, device-level reporting that inflates ROAS and misallocates budgets. By unifying customer identity across web, mobile, CTV, and other surfaces, marketers gain a single view of LTV and attribution. AppsFlyer provides this via CUID stitching and Product Line grouping, enabling real-time, deduplicated insights without manual BI work. Key benefits include accurate cross-platform ROAS, elimination of duplicate attribution, and reliable data for AI-driven optimization.
This guide helps app marketers select a Mobile Measurement Partner (MMP) by covering essential features like privacy-first measurement, unified attribution, fraud protection, and advanced analytics. It emphasizes choosing an MMP that integrates easily, scales with business growth, and provides reliable data for optimizing marketing ROI across teams.
Digital retail maturity shifts focus from downloads to omnichannel experiences, engagement, and ecosystems. Key data: 8.7B app downloads, 400B web visits, mobile 59% of web visits in Q1 2026. Competitive advantage comes from quick commerce, loyalty, content-led discovery, and connected in-store. For ad ops, prioritize engagement and frequency over acquisition; mobile is dominant; ecosystem expansion is critical.
Marketing attribution is critical for connecting spend to revenue, but platform self-reporting and last-click bias distort budget decisions. Single-touch models (first/last-click) are simple but miss the full journey; multi-touch models (position-based, data-driven) are more accurate but require robust data. Mobile attribution is particularly challenging due to ATT, SKAdNetwork, and cross-platform gaps, necessitating a mobile measurement partner (MMP) for independent, deduplicated measurement. Clean attribution data is essential for AI-driven optimization—bad signals lead to bad decisions. Starting with position-based attribution and incrementality testing provides a practical foundation.
In-app bidding is increasingly preferred over waterfall due to efficiency, with around 80% of publishers now using it. It reduces latency, manual work, and improves ARPDAU by enabling simultaneous bids from all buyers. ML models in platforms like Moloco optimize bids in real-time, while waterfalls allow manual pricing control but risk inefficiency and reduced advertiser interest.
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