Building A Unified Marketing Measurement Capability For Retail Media Network

Executive Summary

This article details a crucial strategic initiative for a major retailer: the development of a unified marketing measurement (UMM) framework. The UMM framework aims to resolve inconsistencies between Marketing Mix Modeling (MMM) and Multi-Touch Attribution (MTA) outcomes by delivering consistent, causal incrementality measurements. Therefore, this project seeks to overcome the challenges of fragmented marketing measurement through the integration of advanced MMM with current MTA capabilities.

This synergistic approach will ensure consistent, accurate channel performance measurement, enable precise forecasting, and optimize marketing spend for its advertising partners. By providing a single, trustworthy view of marketing effectiveness across all channels, this capability will strengthen advertiser relationships, providing better transparency for media spend’s impact and an ability to optimize spend allocation across channels.

1. Unified Measurement in Retail Media

A sophisticated, unified marketing measurement framework is essential for the modern retail environment. Given the challenges posed by the current fragmented landscape, a unified marketing measurement (UMM) capability is critical for growth and a distinct competitive advantage. A successful UMM framework can combine insights from MTA, MMM and real-world experiments, providing a harmonized and consistent measurement landscape.

1.1. The Evolving Retail Media Landscape: Challenges and Opportunities

The retailer sector operates within a complex omnichannel media environment, characterized by intricate customer journeys that span online, in-store, and hybrid interactions. Retail media networks are tasked with navigating these diverse touchpoints to provide advertisers with a comprehensive understanding of their advertising impact.

Sainsbury's, through its Nectar360 Pollen platform, is actively pursuing a strategy to unify audience insights, media planning, activation, optimization, and measurement across physical stores, online, and offsite channels. This initiative aims to connect these disparate environments into a cohesive omnichannel approach, reflecting a broader industry recognition of the imperative for integrated measurement.1

Retail media itself is experiencing a period of rapid expansion. According to this study, some UK retailers are reporting YoY ad spend growth exceeding 50%. This acceleration presents a substantial opportunity for revenue generation but also introduces "growing pains" related to measurement standardization and transparency (source). Retailers are actively seeking to replicate the success of global leaders like Walmart in the retail media space by integrating digital in-store, online, and data assets to drive incremental sales for their clients.

A significant advantage for supermarkets lies in their access to rich first-party customer data, primarily through extensive loyalty programs such as Sainsbury's Nectar. This proprietary data is invaluable for precise retail media targeting and measurement, particularly as evolving privacy regulations increasingly restrict reliance on third-party cookies.

A critical observation in this evolving landscape is the presence of a "Measurement Credibility Gap." While the retail media sector is growing rapidly, it remains complicated and challenging to navigate, partly due to a lack of measurement standardization and transparency.

Advertisers frequently express difficulty in assessing the true effectiveness of their campaigns within their broader media plans without independent, third-party measurement (found this Nielsen study). This situation suggests that despite the significant revenue potential retail media offers to retailers, a lack of trusted, unified measurement could impede advertiser confidence and long-term investment.

1.2. Limitations of Siloed MMM and MTA Approaches

Marketing Mix Modeling (MMM) and Multi-Touch Attribution (MTA) are distinct methodologies, each with unique strengths and weaknesses.

MMM offers a holistic, aggregate perspective, relying on historical data and external factors such as weather and economic data. It is ideally suited for long-term strategic planning and comprehensive budget allocation across all channels, encompassing both online and offline media (read this Blog post for more info).

Conversely, MTA focuses on granular, user-level digital interactions, providing near real-time insights for tactical optimization within specific digital channels.

When these methodologies are employed in isolation, they frequently lead to contradictory conclusions regarding channel performance. For instance, an MMM might indicate that a particular channel is underperforming, while MTA data suggests it is a top performer (see more examples here). This inherent inconsistency creates confusion and impedes effective decision-making (see this Google whitepaper).

Furthermore, isolated MTA approaches typically overlook the impact of offline activities and broader external factors such as seasonality, weather patterns, or economic conditions, resulting in an incomplete view of overall marketing impact. While traditional MMM provides a holistic view, it can be slow, resource-intensive, and retrospective in nature, often lacking the speed and precision required for dynamic, real-time tactical adjustments in a fast-paced retail environment.

The utility of MTA is also increasingly challenged by evolving data privacy regulations, including GDPR and iOS tracking restrictions, and the ongoing deprecation of third-party cookies. This "signal loss" diminishes MTA's accuracy and overall usefulness. In contrast, MMMrelies on aggregated data and is not affected by privacy-driven changes (our Blog post for more info).

This creates a "Decision Paralysis" effect. When different measurement methodologies operate within organizational silos. This fragmentation leads to biased recommendations and budget decisions, with little to no consistency. The inevitable contradictions that arise can result in marketers allocating significant spend to channels that, in reality, do not yield effective results.

1.3. The Solution: Unified Marketing Measurement

The vision for Sainsbury’s is to establish Unified Marketing Measurement (UMM), not as a singular model, but as a comprehensive methodology. This approach strategically combines multiple techniques, including MMM, MTA, and experiments, to establish a single source of truth for actionable strategic direction.

The core objective is to unify insights across diverse models, mitigate conflicting data, and accurately measure both short-term campaign performance and long-term growth.

The benefits of such a unified framework are extensive:

• Accurate Channel Performance: By integrating MMM's holistic perspective with MTA's granular detail, the supermarket will gain a comprehensive understanding of how online and offline channels contribute to overall performance. This enables precise attribution of incremental sales across all retail media channels.
• Reliable Forecasting: A unified approach facilitates robust scenario planning and provides predictive insights into the business impact of future marketing initiatives. This shifts the focus from merely reporting backward-looking ROI to enabling forward-looking and forecasting.
• Optimized Marketing Mix: The framework supports dynamic budget allocation based on marginal return on investment (mROI), allowing for the identification of optimal investment levels while taking into account for diminishing returns in different channels. Ensuring efficient resource allocation, and preventing oversaturation of high-performing channels.

For advertising partners, providing transparent, consistent, and actionable performance insights will build significant trust and clearly demonstrate the true value proposition of the Sainsbury’s RMN. This, in turn, is expected to drive increased investment and foster stronger partnerships. Furthermore, a unified, privacy-safe framework is essential for navigating the evolving data landscape in a cookieless future.

In the "walled garden" era of retail media, advertisers seek standardized measurement and self-service tools. Independent, third-party measurement is crucial for trust and cross-channel comparison. Developing a unified, transparent MMM capability that aligns with existing MTA and accurately measures channel performance will position the Sainsbury’s RMN as a preferred platform. This transforms the "walled garden" weakness into a competitive strength, offering a "single source of truth" for advertisers' cross-channel media planning.

2. ELIYA’s Proposed Unified Marketing Measurement Framework

This section details the architectural and methodological approach for building the unified MMM capability, emphasizing how it integrates and enhances existing MTA insights to provide a holistic and accurate view of marketing effectiveness.

2.1. Foundational Principles: Incrementality, Causality, and Holistic View

A modern marketing measurement framework must fundamentally shift its focus from mere correlation to true causation. Traditional MMM, while capable of identifying trends, often struggles to differentiate between two events happening together and one event directly causing another. The objective is to move beyond simply understanding "what happened" to definitively determining "what caused it to happen". The most accurate and reliable method for measuring causal impact is through incrementality testing and GeoLift experiments.

Furthermore, a comprehensive framework must adopt a holistic view that encompasses all touchpoints. This means including both digital and offline channels to achieve a complete picture of marketing performance. It is essential to account for offline touchpoints such as in-store promotions, print flyers, and the nuanced impact of loyalty programs, which MTA fails to capture.

This emphasis on causality and incrementality addresses "The 'Why' Behind the 'What'." The transition from only observing correlation to establishing causation is fundamental for truly optimizing spend allocation. While MMM can generate hypotheses about marketing effectiveness, controlled experiments are considered essential to validate these hypotheses and confirm the true causal impact.

2.2. Synergistic Integration of MMM and MTA

The most effective approach to marketing measurement involves using MMM and MTA in a complementary fashion, rather than treating them as competing methodologies. Each tool serves distinct, yet interconnected, purposes within the marketing ecosystem.

MMM is ideally suited for strategic allocation. It excels at providing a high-level, comprehensive view necessary for long-term strategic planning, overall budget allocation across diverse channels, and understanding the macro-level impact of external factors on sales.

Conversely, MTA is valuable for tactical and short-term optimization. It provides granular, real-time insights within specific digital channels, enabling the fine-tuning of campaign performance, and a detailed understanding of customer journeys. MTA can answer specific, immediate questions such as "Is campaign one performing better than campaign two?".

A critical aspect of this synergistic integration is bridging discrepancies and ensuring consistency between the outputs of these models. This is achieved through "triangulation," a practice that combines insights from multiple methodologies (MMM, MTA, and incrementality testing) to form a more detailed and reliable perspective. Each method possesses unique strengths and inherent limitations, and triangulation leverages the strengths of one approach to compensate for the weaknesses of another, resulting in a more dependable blended view.

"Calibration" is another crucial technique employed to reconcile discrepancies. If MMM suggests a channel is underperforming while MTA indicates it is a top performer, this signals a need for incrementality testing to ascertain the true impact. Subsequently, calibration factors (multipliers) can be applied to align the data from both models. Calibration ensures the accuracy and reliability of the models by aligning their predicted results with actual observed data, often derived from controlled experiments.

Establishing a unified interpretation framework is also vital. This involves clearly delineating the appropriate use cases for MMM (e.g., long-term strategy, channel mix decisions) versus MTA (e.g., short-term campaign optimizations). Furthermore, it requires educating internal teams on how to effectively interpret and apply insights from both models, thereby ensuring alignment in decision-making processes.

Reconciling conflicting model interpretations is key. "Triangulation" and "calibration" unify insights. Adobe's Mix Modeler unifies MMM and MTA for consistent measurement in a cookieless environment. Sainsbury's needs a sophisticated framework to validate MMM and MTA outputs, providing advertisers a single, trustworthy view of campaign performance and optimal investment.

2.3. ELIYA leverages Advanced Bayesian Techniques

Bayesian statistical methods are increasingly prevalent and represent a significant advancement in modern MMM. These models provide a robust framework for handling inherent uncertainties in marketing data, incorporating prior knowledge (e.g., from experiments or attribution data) directly into the model, and producing more reliable ROI estimates even with limited historical data.

The adoption of Bayesian methods facilitates a shift from “Black Box” to a transparent and explainable model. Historically, traditional MMMs were often perceived as opaque "black boxes" provided by third-party vendors, lacking transparency in their underlying methodologies. ELIYA’s fully Bayesian approach promotes transparency by allowing data scientists and marketing teams to understand precisely how the models operate. Furthermore, they enable the incorporation of "prior knowledge" or expert beliefs, which can include valuable insights derived from MTA or incrementality tests. This integration makes the models more interpretable and adaptable to the Sainsbury's specific business context.

3. Phased Implementation Plan for MMM Capability

Building a unified MMM capability is a complex undertaking that requires a structured, phased approach. This plan emphasizes data readiness, iterative model development, and seamless operational integration.

3.1. Phase 1: Data Foundation and Harmonization

The cornerstone of Unified Marketing Measurement (UMM) is the establishment of a robust, unified data pipeline. This involves creating a centralized data repository that normalizes both online and offline data, enabling seamless integration into both MMM and MTA models. This pipeline must be designed to collect and integrate data from a diverse array of sources:

• Aggregated Sales Data: Historical sales figures are fundamental for MMM to understand overall business outcomes, long-term trends, and the base sales volume.
• Impression Logs & Clickstream Data: Detailed digital ad impressions, clicks, video views, and comprehensive user interaction data are vital for MTA's granular analysis of customer journeys and digital touchpoint attribution.
• First-Party Customer Data (Loyalty Card Data): Data derived from loyalty programs, such as Sainsbury's Nectar, offers exceptionally rich insights into customer preferences, purchasing behaviors, and overall patterns. This represents a unique and powerful asset for UK supermarkets, providing privacy-compliant, user-level data that can effectively bridge online and offline customer behavior.
• In-Store POS Data: Point-of-Sale (POS) data provides critical insights into in-store sales, promotional effectiveness, and product performance. Integrating this with digital data is essential for achieving a truly omnichannel view of the customer journey and marketing impact.
• Print Media Exposure Data: Data pertaining to print flyers, newspaper advertisements, and other traditional media channels are often overlooked by digital-first MTA models. MMM can effectively incorporate these, and specific tracking mechanisms (e.g., QR codes, personalized URLs) can establish a link between print exposure and digital actions.
• External Factors: Data on seasonality, prevailing economic conditions, competitor activity, public holidays, and even local weather patterns are crucial as they significantly influence sales and are critical for the accuracy and robustness of MMM.

Data quality management and standardization are paramount in this phase, as high-quality data forms the foundation of MMM. This requires implementing rigorous protocols for data collection, cleansing, and standardization. We assume this is done by Sainsbury’s internal data engineering team. But here are the key aspects:

• Defining Measurable Metrics: Clearly establishing what constitutes data quality and setting specific, measurable objectives for data accuracy and completeness.
• Standardizing Data Formats: Harmonizing disparate data formats and sources to ensure consistency across the entire dataset. This is particularly vital for enabling accurate cross-channel comparisons.
• Automated Data Cleansing & Validation: Implementing processes to automatically identify and correct errors, remove duplicate entries, and ensure data completeness and accuracy. AI/ML algorithms can significantly streamline these time-consuming processes.
• Data Governance Framework: Establishing clear data ownership, defining roles and responsibilities across the data lifecycle, and ensuring robust security and privacy compliance measures are in place.

3.2. Phase 2: Model Development and Calibration

This phase focuses on the technical construction and refinement of the measurement models. It begins with building robust MMM models, leveraging advanced Hierarchical Bayesian techniques. Bayesian methods are particularly recommended for their ability to handle inherent uncertainties, incorporate prior knowledge (e.g., from experiments or attribution data) into the model, and produce more reliable ROI estimates even with limited data.

Hierarchical models can further enhance this by allowing for the analysis of performance across different regions or product categories, facilitating shared learning while accounting for local market nuances. These models must also incorporate non-media drivers such as seasonality, economic trends, public holidays, and competitor actions to accurately attribute sales impact and isolate the true effect of marketing. Moreover, concepts like adstock (the delayed effect of media) and saturation or diminishing returns are crucial for accurately modeling media effectiveness and identifying optimal investment levels.

Simultaneously, existing MTA models will be reviewed and enhanced to align with the objectives of unified measurement. This may involve transitioning from simpler algorithmic approaches to more sophisticated data-driven attribution models that utilize machine learning to analyze real user interactions and attribute credit more accurately.

A critical component of this phase is the implementation of rigorous calibration techniques. This process is essential for aligning the outputs of MMM and MTA and ensuring consistency across measurement perspectives.

Incrementality testing, through controlled experiments such as geo-experiments will be conducted to determine the true incremental impact of specific campaigns. The results from these experiments will serve as the ground truth to calibrate and validate both the MMM and MTA models. Calibration factors derived from these tests will be applied to adjust attribution rules in MTA and to multiply MMM outputs, bringing the models into closer alignment and resolving discrepancies.

From snapshot to dynamic learning, this phase transforms traditional MMM. Calibration, through frequent updates and continuous learning, ensures models remain current. This iterative approach, combined with AI/ML and Bayesian methods, allows dynamic adaptation to changing market dynamics, consumer behavior, and competitive shifts. This agility provides timely, relevant, and accurate understanding in the fast-paced retail environment.

3.3. Phase 3: Operationalization, Forecasting, and Optimization

The final phase focuses on operationalizing the newly developed MMM capability, making its insights accessible and actionable for both internal teams and external advertisers. ELIYA will work with the Sainsbury’s product team for deploying the MMM model in production and integration with the existing dashboard.

A key deliverable in this phase is enabling robust scenario planning and dynamic budget allocation. The MMM capability must support WHAT-IF scenario testing, allowing users to predict the impact of different budget allocations and marketing strategies on business outcomes. This functionality moves beyond static reporting to prescriptive analytics, empowering dynamic optimization of marketing spend to maximize return on ad spend (ROAS) and achieve specific business objectives.

Establishing a continuous learning and refinement loop is also paramount. This involves implementing a feedback mechanism where model performance is continuously monitored, validated against real-world outcomes, and refined based on new data and changing market dynamics. This iterative process ensures that the models remain relevant, accurate, and reliable.

This phase represents a crucial transition "From Insights to Actionable Intelligence. It is not sufficient to merely generate understanding; these must be actionable and accessible to advertisers. This necessitates translating complex model outputs into clear, concise recommendations for budget allocation and campaign adjustments. By providing clear, prescriptive recommendations and user-friendly tools for scenario planning, the supermarket can empower its advertising partners to effectively optimize their retail media investments, driving tangible business outcomes and solidifying the value proposition of the retail media network.

4. Expected Business Outcomes and Value Creation

Implementing the unified MMM capability is projected to yield significant business benefits, quantifiable across enhanced channel performance, improved financial outcomes, and strengthened advertiser relationships.

4.1. Enhanced Channel Performance and ROI Measurement

The unified framework will provide a precise understanding of the incremental sales generated by each retail media channel, including online platforms, in-store promotions, print media, and loyalty program initiatives. This moves beyond simple correlation to establish causal impact. This capability enables accurate ROI measurement across the entire media mix.

This capability translates to Maximizing Every Advertising Dollar. With a clear understanding of incremental sales and the points of diminishing returns, advertisers can avoid wasted spend. This directly results in improved marketing efficiency and higher return on ad spend (ROAS). The ability to "maximize every advertising dollar" becomes a tangible and measurable outcome. For advertisers, this signifies a substantial improvement in the efficiency and effectiveness of their retail media spend, leading to higher campaign ROAS and superior overall business outcomes.

4.2. Accurate Forecasting and Dynamic Budget Allocation

The advanced MMM models, rigorously calibrated with incrementality tests, will offer robust forecasting capabilities for future marketing performance and sales outcomes. This shifts the supermarket and its advertisers from a reactive posture to proactive, data-driven decision-making.

The framework will provide data-driven recommendations for optimal budget allocation across all channels and campaigns. This ensures that investments are aligned with specific business objectives and expected returns. The focus will be on directing marketing spend to channels that drive the maximum incremental sales. The ability to run WHAT-IF scenarios will empower advertisers to explore different budget plans and compare their potential impact, enabling more confident and strategic investment decisions.

4.3. Strengthening Advertiser Partnerships and Retail Media Growth

By providing a unified, calibrated view of marketing effectiveness, the retailer will offer advertisers transparent and consistent data. This will significantly reduce conflicts arising from disparate data sources and build stronger trust. This directly addresses the "lack of transparency" and "inconsistency" issues prevalent in the current retail media landscape.

The demonstrable, accurate ROI and clear optimization pathways provided by the new capability will incentivize advertisers to increase their spend within the retail media network (RMN). This directly contributes to the growth and profitability of the retailer’s RMN. Furthermore, the privacy-safe nature of MMM and the framework's inherent ability to adapt to a cookieless world, providing more robustness over MTA-only approach.

By offering advanced, unified measurement capabilities, retailer can elevate the role of measurement and position itself as a strategic partner that actively helps advertisers achieve their broader business objectives. This transforms the relationship beyond simple ad placement to a collaborative, data-driven partnership.

This strategic shift will not only secure existing advertiser relationships but also attract new, sophisticated advertisers who seek robust measurement and optimization capabilities. Ultimately, it transforms the RMN into a high-value, indispensable component of brands' marketing ecosystems, driving sustained revenue growth for retailer.