How AI Can Solve Retail Media's Growing Pains

Retail media's growth is slowing, despite outpacing overall advertising growth. This maturation phase presents challenges, including ecosystem fragmentation, rising costs, measurement issues, and integration complexities. However, artificial intelligence (AI) offers a promising solution.

Why Retail Media Growth Is Slowing

The industry is facing a perfect storm:

1. Fragmentation: Numerous retail media networks complicate brand management.
2. Rising Costs: Competition drives up CPCs and CPMs, questioning ROI, especially with Walmart's push for 25% YoY spending increases despite stagnant sales growth for some brands.
3. Measurement Challenges: Lack of standardization hinders performance comparison.
4. Integration Complexity: Seamless on-site/off-site integration remains elusive.

Brands are becoming more selective in their retail media spending.

How AI Addresses These Challenges

AI is being applied across the campaign lifecycle:

1. Smarter Planning & Audience Development: AI-driven scenario planning optimizes budget allocation across networks, predicting outcomes and maximizing performance. Buy-side platforms like Skai and Xnurta leverage AI for omnichannel insights and automated media plan development.

2. Automated Campaign Optimization: AI dynamically adjusts bids, pacing, and creative rotation in real time across multiple networks. This allows for "AI-Driven Campaign Orchestration & Content Optimization," coordinating campaigns across various channels and automatically adjusting tactics based on real-time performance. AI algorithms analyze real-time sales data to shift budget towards higher-converting elements.

3. Unified Measurement & Attribution: AI integrates multi-touch attribution (MTA) and market mix modeling (MMM) for a holistic performance view. This includes leveraging synthetic or proxy data to fill measurement gaps while maintaining privacy.

4. AI-driven Creative: AI generates written, visual, and video creative, and even AI-powered landing pages, offering self-service creative options and reducing production costs. This integrates well with automated campaign optimization.

Challenges to AI Implementation in Retail Media

Despite AI's potential, significant hurdles exist:

• Technical Fragmentation: Unique data ecosystems and measurement standards across retailers hinder cross-network AI implementation.
• Data Quality: Clean, organized datasets are crucial, but retailers often struggle with data silos and inconsistent quality. Concerns around consumer privacy also complicate matters.
• Data Privacy & Competitive Concerns: Balancing data privacy with AI's data needs is critical. Collaboration is essential for effective data sharing while maintaining consumer trust.

Realistically Achievable Steps

To maintain growth, retail media networks should prioritize:

• Standardization: Invest in baseline measurement standards and technical capabilities. API availability is key, as noted by PepsiCo’s Ram Krishnan.
• Core Data Infrastructure: Build a solid data infrastructure before implementing advanced AI features. A trusted data collaboration partner can help establish a cohesive data framework.
• Real-Time Bidding (RTB): Explore RTB for automated bidding across networks, providing standardized data flow and improving efficiency.
• Improving Ad Relevancy: Focus on serving highly relevant ads, as CTRs for sponsored products are significantly lower than organic results. Improving this is crucial for scaling retail media.

The Future: A Balanced Approach

AI is not a silver bullet, but a strategic tool. Successful implementation requires a pragmatic approach that addresses the sector's unique challenges. The future of retail media lies in balancing technological innovation with advertiser needs, creating more relevant, measurable, and efficient advertising experiences.

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