Introduction: Understanding the Language of Modern Commerce
For any aspiring marketer, mastering the language of retail media is no longer optional—it’s essential. This dynamic field has become a dominant force, with a recent Skai report noting that 92% of consumer goods marketers consider it their most important channel.
However, this rapid growth brings challenges, including concerns about proving return on investment (ROI) and navigating gaps in measurement. The first step to mastering this landscape is learning its language. This glossary provides simple, foundational definitions for the essential vocabulary you’ll need to build your expertise and confidently navigate the future of commerce.
——————————————————————————–
1. Foundational Concepts: The Building Blocks of Retail Media
This section defines the core entities and overarching strategies that shape the retail media landscape.
1.1. Retail Media Network (RMN)
A Retail Media Network (RMN) is an advertising ecosystem owned and operated by a retailer, which leverages its valuable first-party shopper data—such as purchase history and on-site browsing behavior—to offer advertising opportunities to brands.
What this means for a marketer: With over 200 RMNs competing globally, the industry is headed for a “significant shakeout” where only those that innovate will thrive. For marketers, this means scrutinizing potential RMN partners not just on their audience size, but on the sophistication of their measurement tools and their ability to provide unique insights. Choosing the right partners is a critical strategic decision that directly impacts your ability to prove the value of your ad spend.
1.2. Commerce Media
Commerce Media is the evolution of retail media, representing a broader strategy that uses commerce data—signals like what people purchase, browse, and search for—to inform and execute media buying across all channels, both on- and offline. According to the Skai 2024 State of Retail Media, “This new designation refers to any media strategy/buying that ties back to some form of commerce data to facilitate a transaction.”
What this means for a marketer: Commerce media is no longer a prediction; it’s the reality of modern marketing. This approach allows you to break down the traditional silos between channels. By using purchase data as the connective tissue, you can create a single, holistic customer journey that seamlessly guides a consumer from initial discovery on social media or CTV to the final sale on a retailer’s website.
1.3. Full-Funnel Strategy
A full-funnel retail media approach is a strategy designed to engage and influence consumers at every stage of their purchasing journey, from initial brand awareness and consideration to, ultimately, conversion.
What this means for a marketer: A full-funnel strategy offers two key advantages, according to marketing leaders surveyed in the Skai report:
- Consistent Messaging (61% of leaders): It ensures your brand’s story and value proposition remain coherent across every touchpoint a consumer encounters.
- Influence at Every Stage (59% of leaders): It allows you to engage with potential customers whether they are just learning about a product or are ready to make a purchase decision.
However, it’s important to recognize that this is still an area of growth for the industry. With only 25% of organizations reporting that they have achieved full-funnel maturity, mastering this approach presents a significant competitive advantage.
——————————————————————————–
With these foundational concepts in place, we can now turn to the critical question every marketer must answer: “How do we know if it’s working?” Let’s explore the metrics that measure performance.
——————————————————————————–
2. Core Measurement & Performance Metrics
This section covers the essential metrics used to evaluate the effectiveness and financial return of retail media campaigns.
2.1. Incrementality
Incrementality is the measure of the lift a campaign generates beyond what would have happened anyway. It isolates the sales and conversions that occurred specifically because of advertising efforts, answering the critical question: “What would have happened without this campaign?”
What this means for a marketer: In retail media’s fragmented ecosystem, traditional attribution can be misleading. Incrementality is crucial because it helps you prove the true causal impact of your advertising. As ad spend reaches record highs, demonstrating this causal link is no longer a nice-to-have; it’s essential for validating your investments and securing future budgets.
2.2. Incrementality Testing
Incrementality testing is the practical, scientific method used to measure incrementality. It is typically based on a test versus control methodology, where one group is exposed to advertising while a similar control group is not, allowing for a direct comparison of outcomes.
What this means for a marketer: There are three primary methods you can use to structure these tests:
- Geo-based holdout testing: Dividing markets geographically to create test and control regions.
- Audience-based control groups: Creating matched cohorts of users and showing ads to one group while suppressing them for the other.
- Time-based experiments: Comparing campaign performance during an active period against a baseline period with similar characteristics.
2.3. Attribution Modeling
Attribution modeling is the set of methods used to estimate how different media exposures (like seeing a display ad or clicking a sponsored product) contribute to a final outcome, such as a sale.
What this means for a marketer: While useful, you must understand the limitations of attribution. The IAB/MRC guidelines warn that attribution models can be biased. For example, a model that only considers digital data might wrongly attribute a sale to a digital ad when other factors—like a television commercial, a price drop, or a store promotion—were the actual drivers of the purchase. Always consider the full context when analyzing attribution reports.
2.4. Key Financial Metrics
This table breaks down the core financial metrics used to assess campaign performance.
| Metric | What it Tells a Marketer |
| Return on Ad Spend (ROAS) | Measures the gross revenue generated for every dollar spent on advertising. Proving a positive ROAS is a primary goal, but retailers report that proving ROI is a significant challenge, with 38% considering it highly challenging. |
| Advertising Cost of Sale (ACOS) | A metric commonly used on Amazon, ACOS shows advertising spend as a percentage of sales generated directly from those ads. It is calculated as: Ad Spend ÷ Ad Sales. |
| Total Advertising Cost of Sale (TACOS) | This metric measures advertising spend relative to total sales (both ad-driven and organic). It helps you understand advertising’s broader impact on overall brand growth and its ability to lift organic sales. It is calculated as: Ad Spend ÷ Total Sales. |
——————————————————————————–
Understanding what to measure is half the battle. Now, let’s explore the how—the specific data and technologies that make these advanced calculations possible.
——————————————————————————–
3. Data & Technology Enablers
This section explains the key technologies and data types that are fundamental to how retail media targeting and measurement function.
3.1. First-Party Data
First-party data is information a company collects directly from its own customers with their consent. In retail media, this includes a shopper’s purchase history, loyalty card information, and browsing behavior on a retailer’s website or app.
What this means for a marketer: This data is the “treasure trove” of Retail Media Networks, allowing you to create highly personalized campaigns that reach the most relevant audiences. As third-party cookies disappear, access to this data is a massive competitive advantage. However, leveraging it isn’t seamless; the top challenge for retailers (cited by 48%) is the interaction with other systems (e.g., CRM, DSP), a technical hurdle that can impact campaign execution.
3.2. Data Clean Rooms
A data clean room in retail media is a privacy-safe collaboration environment where brands and retailers analyze combined first-party datasets under strict access controls, producing aggregated insights without exposing personal data or raw records between parties.
What this means for a marketer: Data clean rooms are becoming essential infrastructure. They solve the critical problem of how you and your retail partners can securely share and analyze data to enable more accurate attribution and measurement. This technology allows for deeper collaboration while complying with privacy rules and protecting sensitive customer information.
3.3. Device Matching Approaches
To understand the full customer journey, device matching is the process of identifying the same user across their multiple devices (e.g., smartphone, laptop, tablet). The IAB/MRC guidelines outline two main approaches.
What this means for a marketer: You must understand the trade-offs between accuracy and scale when evaluating data from partners.
- Deterministic Approach: This method relies on matching personally identifiable information (PII), such as a logged-in email address or phone number, across different devices. It is highly accurate because it is based on a confirmed identity.
- Probabilistic Approach: This method uses statistical modeling of non-PII data points—like IP address, device type, and browsing patterns—to calculate the likelihood that multiple devices belong to the same person. This approach offers greater scale but is less precise.
——————————————————————————–
Armed with this knowledge of the underlying data and technology, you’re ready to explore the specific ad buying tactics that bring a retail media strategy to life.
——————————————————————————–
4. Ad Buying & Tactical Terms
This section covers the mechanics of ad buying and specific tactics marketers use within retail media platforms.
4.1. Auction Models
In retail media, ad placements are typically sold through an automated auction model. The type of auction a platform uses directly impacts how much an advertiser pays and what their bidding strategy should be.
| Auction Model | How it Works | Bidding Strategy for Marketers |
| First-Price Auction (Used by Walmart) | The winning bidder pays the exact price they bid for the ad placement. | Bid more conservatively and test incrementally. Since you will pay your full bid amount, it’s important not to overbid. |
| Second-Price Auction (Used by Amazon) | The winning bidder pays just $0.01 more than the second-highest bidder’s bid. | Bid more aggressively, up to the true maximum value of a click. You know you will rarely pay your full maximum bid. |
4.2. Connected TV (CTV)
Connected TV (CTV) refers to television content that is streamed over the internet on devices such as smart TVs, gaming consoles, or through streaming sticks.
What this means for a marketer: CTV represents a major growth opportunity. By using retail media data, such as a household’s past purchases, you can personalize TV advertising to bridge the gap between upper-funnel brand awareness and measurable sales. However, its adoption currently faces several obstacles:
- Budget limitations (cited by 58% of marketers)
- Challenges in measurement (cited by 51% of marketers)
- Technical and data limitations, including integration complexity (37%) and a lack of actionable data (39%).
——————————————————————————–
Bringing It All Together: From Vocabulary to Value
Mastering these terms is the first step toward building a successful retail media practice. The concepts are interconnected: valuable first-party data is what allows Retail Media Networks to offer powerful full-funnel strategies. The true, causal impact of these strategies is best measured through incrementality testing, a process that is increasingly facilitated by secure data clean rooms. By understanding how these pieces fit together, you can move beyond simply knowing the vocabulary and begin using it to create real business value.