The Rise of Retail Media Networks: Why Traditional Measurement Methods Struggle to Capture RMN Campaign ROAS Fully

The retail media network (RMN) landscape is booming. Brands are captivated by the power of RMNs to target consumers at the precise moment of purchase, driving significant revenue growth for these platforms. However, a critical gap exists in accurately measuring the return on ad spend (ROAS) for RMN campaigns.

This article delves into the limitations of traditional measurement methods and how Data POEM's AI-powered solution using Neural Networks can enhance Advertiser's current models to optimize ROAS for RMNs.

The Challenge: Untangling the Complexities of RMNs

Established, traditional market mix modeling (MMM) services are challenged by the intricacies of the modern retail environment. These challenges prevent brands from properly assessing the true impact of their RMN campaigns and limit their ability to optimize for higher ROAS.

Let's explore the roadblocks Advertisers face:

• Omnichannel Labyrinth: The modern customer journey is a complex web of online and offline touchpoints. Traditional MMMs struggle to untangle this web and accurately attribute sales to specific RMN campaigns.
• Data Silos: Impeded visibility Retailers often keep sales data separate from ad campaign data, creating data silos. This fragmented view makes it difficult for traditional MMMs to create a holistic picture of the customer journey.
• Short-sighted Attribution Windows: Traditional models employ fixed attribution windows, often missing sales that occur outside this timeframe. Consumers might see an RMN ad but make a purchase later, leading to an underestimation of the campaign's effectiveness.

These shortcomings leave brands in the dark, unable to fully grasp the effectiveness of their RMN investments.

Enter the AI Advantage: How Data POEM's Solution Empowers Brands

Data POEM offers a complementary solution: AI Causal Learning Engines powered by Neural Networks augment Advertisers' current measurement approach. This combined technology overcomes the limitations, providing brands with the tools they need to measure ROAS and optimize their RMN campaigns accurately.

Here's how Data POEM's AI addresses the key challenges:

• Advanced Attribution Models: Neural networks can analyze vast datasets, identifying complex relationships between RMN campaigns and sales across various touchpoints. This allows for a more precise attribution model, capturing the true impact of RMN campaigns across the entire customer journey.
• Unified Data Analysis: Breaking Down Silos Data POEM's AI engine seamlessly integrates data from disparate sources, including sales and ad campaign data. This unified view empowers brands to understand how RMN campaigns interact with other touchpoints, creating a holistic picture of the customer journey.
• Dynamic Attribution Windows: Capturing the Full Picture Traditional models often miss sales due to fixed attribution windows. Data POEM's AI utilizes advanced algorithms that account for delayed purchases, ensuring all sales influenced by the RMN campaign are properly attributed regardless of the timeframe.

By leveraging these capabilities, Data POEM's AI engine empowers brands to:

• Measure True ROAS: Gain a crystal-clear understanding of the revenue generated by RMN investments. This data-driven approach enables brands to develop informed optimization strategies for maximizing the return on their ad spend.
• Maximize Campaign Effectiveness: Data POEM's AI can pinpoint areas for improvement within RMN campaigns, such as ad targeting and creative elements. By identifying these areas, brands can optimize their campaigns for higher ROAS.
• Boost Overall Advertising ROI: Optimizing RMN campaigns alongside other marketing efforts through AI-powered insights allows for a synergistic effect. This means the combined efforts of all channels can amplify brands' overall return on investment.

The Future of Retail Media Measurement: Powered by AI

The future of retail media is undeniably bright, but accurate measurement is essential to unlock its full potential. Data POEM's AI Causal Learning Engines with Neural Networks have the power to complement advertisers' current measurement approach, providing clarity on ROAS measurement for RMNs. By harnessing the power of AI, brands can finally measure the true impact of their campaigns, optimize their spending for maximum return, and maximize the value they extract from this rapidly growing advertising channel.