Evidnt’s Evidence-Based Measurement:

A New Standard in Marketing Attribution

1. Executive Summary

The demands on marketing organizations in today’s retail and consumer goods landscape are intensifying. Brands must prove return on investment (ROI), allocate spending more intelligently, and understand media effectiveness across increasingly fragmented channels. Traditional Media Mix Modeling (MMM) solutions—developed decades ago for broad national media—are no longer sufficient. They often fail to reflect the nuances of modern commerce, including SKU-level performance, regional variability, and retail-specific media dynamics.

Evidnt’s Evidence-Based Measurement redefines what MMM should be. Grounded in data science, scalable engineering, and real retail behavior, our platform is purpose-built to help marketers and analysts quantify the actual impact of every marketing dollar. This white paper introduces the foundational architecture, modeling techniques, and the practical outcomes delivered by our system, demonstrating why a new measurement standard is both necessary and possible. These practical outcomes will give you confidence in the effectiveness of our system.

2. Overview of Current Market Solutions

Current market offerings can be grouped into three main categories. First are open-source MMM tools such as Facebook Robyn or LightweightMMM. While these offer transparency and cost advantages, they require significant engineering effort, lack flexibility, and are challenging to scale for extensive CPG data. Their value is mainly academic or experimental.

Next are enterprise MMM platforms, such as Google Meridian or Nielsen Compass. These provide turnkey solutions with cloud infrastructure, but often operate as black boxes with limited transparency or adaptability. Finally, consulting-led custom builds from firms like Analytic Partners or McKinsey offer highly tailored solutions but at significant time and cost, making them impractical for ongoing experimentation or SKU-level modeling.

All of these frameworks are designed with broad-stroke assumptions. Few are equipped to ingest multi-store, multi-SKU datasets with precision, making them ill-suited for modern CPG brands and retail media strategies.

3. Limitations of Traditional MMM Approaches

Most legacy MMM approaches face three critical limitations. First is scalability. Running models at the store, SKU, or even sub-regional level pushes many platforms beyond their design limits. Second is temporal granularity. Without sophisticated trend modeling, these tools often misattribute lift to calendar effects or promotional spikes. Third is inflexibility. Many tools use hardcoded assumptions for lags, seasonality, and media saturation, which fail to reflect reality.

Furthermore, these systems generally do not incorporate retail-specific operational signals, such as stockouts, price shifts, or display activity—all of which significantly impact sales and, therefore, media attribution. The result is an incomplete and often misleading picture of media effectiveness.

4. Technical Foundation of Evidnt’s Solution

At the core of Evidnt’s Evidence-Based Measurement is a modern, scalable implementation of sparse Generalized Linear Models (GLMs). We support Poisson, LogNormal, and Tweedie likelihoods with built-in support for sparsity priors. This allows us to model long-tail, zero-inflated sales data commonly seen in retail.

We employ Maximum A Posteriori (MAP) estimation via L-BFGS optimization, rather than MCMC-based methods, which enables us to achieve stable convergence in hours, not days, without compromising accuracy. Trend regularization is handled through Random Walk priors that smooth over time, mitigating overfitting to promotional or seasonal noise.

Most notably, we’ve developed a domain-specific language (DSL) for feature encoding. This enables data scientists to express complex behaviors—such as lagged effects, saturation curves, decay functions, and time windows—as reusable building blocks. This not only increases modeling power but drastically reduces implementation time.

This methodology has been developed through the analysis of sales data dating back to 2013, encompassing over $60 billion in transactions across various payment types, including cash, credit, and other forms of payment. Our modeling framework has been refined through the integration of retail data from convenience stores, grocery chains, on-premise locations, and other retail environments, producing a robust, scalable, and adaptive solution that accelerates insight delivery and improves attribution accuracy.

Unlike many MMM tools that rely on Python and general-purpose scientific libraries, our platform is built in highly optimized Java. This gives us complete control over memory, thread management, and sparse data operations, enabling performance that far exceeds what is feasible with Python-based systems. Sparse computations and custom feature structures are executed efficiently on the CPU, which has proven to be a more effective approach than GPU acceleration for our use case.

Our algorithms are engineered to scale across massive datasets and models with millions of parameters. This scalability is critical for modern, multi-dimensional, data-rich MMM scenarios, allowing our platform to handle large volumes of data without compromising performance or accuracy.

Our system is built to support:

• Geographic hierarchies such as state → DMA → ZIP → store
• Product hierarchies from category → brand → UPC
• Daily or even hourly temporal resolution
• Our platform supports competitive modeling that includes not only the advertiser’s products but also competitors’ to establish accurate baselines and mitigate selection bias. This comprehensive approach ensures that our platform provides a complete picture of the market landscape.
• Combinations of all these layers simultaneously

This results in hyper-detailed models with extraordinary dimensionality and statistical power—something traditional tools are simply not designed to handle. And while stochastic methods, such as MCMC or variational inference, may fail to scale or converge, our approach reaches a global mathematical optimum deterministically and efficiently.

5. Platform Architecture

Our architecture is designed to operate at scale while retaining complete transparency and customization. The platform is implemented in Java and leverages multi-threaded processing and memory-efficient data structures optimized for sparse modeling.

Data is ingested from both internal and external systems, normalized, and passed through our feature engine, which applies both static and dynamic transformations. Modeling happens in parallel across grouped units—whether stores, SKUs, or brands—allowing for flexible training schedules and faster iteration. Outputs include contribution scores, lift attribution, and confidence intervals, all of which are accessible via dashboards or export APIs, ensuring complete transparency and security about the data you are working with.

The infrastructure is cloud-native and optimized for CPU-based execution. Unlike traditional machine learning (ML) workloads, sparse data structures and custom feature engineering processes are not well-suited for GPU acceleration. Our approach relies on a highly tuned CPU pipeline that delivers reliable performance at scale.

6. Feature Highlights and Capabilities

The platform is capable of modeling across multiple levels of granularity—from national roll-ups to region, store, or product groupings. Built-in transformations allow for saturation modeling, lag encoding, promotional window adjustments, and nonlinear carryover effects.

Evidnt also incorporates non-media signals natively, including pricing data, inventory constraints, competitive activity, and loyalty card behavior. Our outlier detection engine flags suspect model outputs using L1 residual penalization, improving trust in recommendations.

Media ROI can be computed at the level of tactic, channel, DMA, product, or customer segment, providing marketers with the insights needed for reallocation, planning, and activation.

7. Customization and Integration

One of the platform’s key advantages is its extensibility. Brands can plug in their datasets—such as promotional calendars, loyalty card analytics, and couponing data—to enhance attribution accuracy. Retailers can include store-level metadata such as stock levels, fulfillment modes, and geographic indicators.

Output integrations support direct delivery into business intelligence (BI) tools like Tableau, Looker, or Power BI. Campaign recommendations can also be piped into data management platforms (DMPs) or marketing automation platforms, enabling a closed-loop planning and activation environment.

8. Case Study: Milo’s Iced Tea DOOH Optimization

Milo’s, a growing beverage brand, launched a regional campaign to support its iced tea line and partnered with Evidnt to better understand the sales impact of its Digital Out-of-Home (DOOH) media investments. The challenge was to isolate the contribution of DOOH media in specific markets and store clusters where traditional attribution approaches had failed to deliver clear signals.

Using Evidnt’s Evidence-Based Measurement platform, sales data and DOOH delivery logs were integrated across targeted retail locations. The analysis revealed measurable improvements in performance in regions where media was active, identifying an 11.48% media-attributed lift in product sales.

More importantly, the brand was able to track performance in near real-time, enabling the optimization of media placements and creative rotations based on product velocity and store-level trends. This agile feedback loop led to smarter spending, improved return on investment, and closer alignment between media strategy and in-market performance.

9. Why This Approach is Better

Evidnt’s platform is purpose-built for today’s marketing and retail complexity. It supports granular modeling with complete transparency, converges quickly, and is flexible enough to reflect the realities of modern promotions and supply chains.

Most importantly, our platform empowers internal analytics teams to inspect, adjust, and extend models, bringing measurement closer to the speed and specificity of real-time decision-making.

10. Why Choose Evidnt

Evidnt combines proprietary data, technical innovation, and domain-specific design. We provide data coverage for over 28,000 retail locations, encompassing more than $12 billion in annual sales. However, the real power of our platform lies in its flexibility—retailers and brands can bring their data and apply our modeling methodology to gain deeper, more actionable measurements. This ensures ownership of insights while leveraging Evidnt’s proven technology.

Our licensing model supports both SaaS delivery and private cloud deployment. Whether you’re a CPG brand, a media agency, or a multi-location retailer, our Evidence-Based Measurement can be tailored to your needs.

To learn more, visit evidnt.co, contact info@evidnt.co, or schedule a workshop with our analytics team.

11. Conclusion

The future of measurement is granular, transparent, and actionable. Evidnt’s Evidence-Based Measurement platform brings a new standard to MMM—grounded in science, engineered for speed, and built for the realities of retail.

In an industry where precision is paramount, the best decisions are evidence-based. Let’s make them together.