How Dutch Beauty Brands Use Marketing Mix Modeling to Scale Past 150K per Month

Dutch beauty brands use marketing mix modeling to understand the true causal impact of their marketing spend, allowing them to move beyond flawed attribution models and achieve scalable growth. By analyzing aggregated data, they can identify which channels drive incremental sales and refine their budget allocation for maximum return on investment, breaking through the common revenue plateaus.

The 150K Plateau: Why Your Beauty Brand's Growth Has Stalled

The 150K Plateau is a common growth barrier for ecommerce brands where increased ad spend no longer generates a proportional increase in revenue, leading to declining ROAS. Unlike simple budget exhaustion, this plateau signals a systemic failure in marketing attribution. Causality Engine solves this by replacing flawed correlation-based models with causal inference to reveal true campaign impact.

Your Dutch beauty brand is stuck. You hit €100,000 per month, then €120,000, and now you are hovering around €150,000. Every euro you pour into Meta or TikTok ads feels less effective. Your ROAS is declining, and you have no clear path to scaling further. This is not a sign of failure. It is a symptom of a broken system. The marketing attribution models you rely on are lying to you, and it is costing you growth.

For years, the industry has pushed last-touch and multi-touch attribution as the gold standard. These models are simple to understand but fundamentally flawed. They assign credit based on correlation, not causation. They cannot tell you if a channel is genuinely driving new customers or just taking credit for sales that would have happened anyway. This is the world of cannibalistic channels, where your marketing efforts are actively working against each other.

Before: Trapped by Flawed Attribution

Flawed attribution refers to marketing models that incorrectly assign credit to touchpoints, leading to misinformed budget decisions. Unlike causal analysis, which identifies true drivers of sales, flawed attribution relies on simple correlations, like last-click, often overvaluing channels that are not genuinely creating new customers. For ecommerce brands, this means wasted ad spend and stalled growth.

Before embracing a new approach, Dutch beauty brands operate in a fog of uncertainty. They are data-rich but insight-poor. Their dashboards show a flurry of activity: clicks, impressions, and conversions attributed to various channels. Yet, the overall business growth remains stagnant. The marketing team is refining for platform-reported ROAS, a metric that is increasingly detached from actual profitability. They are trapped in a cycle of reallocating budget based on data that is, at best, misleading and, at worst, completely wrong.

This is the reality for many brands in the Dutch ecommerce landscape. They are making decisions based on a flawed understanding of their marketing performance. They see that a particular TikTok campaign generated a high number of conversions and decide to double down on that channel. What they do not see is that many of those customers were already on a path to purchase, influenced by a combination of PR, influencer marketing, and brand awareness efforts. The TikTok ad was merely the final touchpoint, not the primary driver of the sale. This reliance on flawed data leads to wasted ad spend, a problem our waste calculator can help quantify.

After: Clarity and Scalable Growth with Marketing Mix Modeling

Marketing Mix Modeling (MMM) is a statistical analysis technique that quantifies the impact of marketing and non-marketing activities on sales. Unlike attribution models that track individual user journeys, MMM uses aggregated historical data to provide a top-down, causal view of performance. This allows ecommerce brands to understand the true ROI of each channel and refine their budget for incremental sales, not just correlations.

Now, imagine a different reality. A reality where you can see the true impact of every marketing euro you spend. This is the power of Marketing Mix Modeling (MMM). Unlike traditional attribution, MMM does not rely on user-level tracking. Instead, it uses aggregated data to model the relationship between your marketing inputs and your business outcomes. It is a top-down approach that provides a holistic view of your marketing performance, as detailed in seminal research on the topic [1].

By analyzing historical data on sales, ad spend, pricing, and promotions, MMM can isolate the causal impact of each marketing channel. It can tell you how much each channel contributes to your overall sales, how your channels interact with each other, and what the point of diminishing returns is for your ad spend. This is the world of behavioral intelligence, where you can finally understand the why behind the what. For Dutch beauty brands, this means a fundamental shift in how they approach growth. Instead of being beholden to the black-box algorithms of ad platforms, they can make data-driven decisions based on a causal understanding of their marketing ecosystem. They can identify and eliminate cannibalistic channels, reallocate budget to the most effective channels, and confidently scale their ad spend without seeing a corresponding drop in ROAS. To see how different attribution models compare, check out our attribution modeling tool.

The Bridge: From Correlation to Causation

Causal inference is the process of determining cause and effect, moving beyond simple correlation to understand the true impact of a variable. Unlike traditional marketing attribution, which often mistakes correlation for causation, causal inference uses scientific methods to identify which marketing activities genuinely drive sales. For ecommerce, this means making budget decisions based on what actually works, not just what appears to be working.

The bridge from the 'before' to the 'after' is causal inference. This is the scientific process of determining cause and effect. While traditional marketing attribution is based on correlation, MMM is a form of causal inference. It allows you to move beyond simply observing that two things are related and start to understand if one is actually causing the other.

One of the key techniques used in modern MMM is Bayesian inference, a topic we explore in depth in our guide to Bayesian Marketing Mix Models. This approach allows you to incorporate prior knowledge and beliefs into your model, resulting in more accurate and stable results. For example, if you have a strong belief that your brand's PR efforts have a significant impact on sales, you can incorporate that belief into your model. This is a far more sophisticated approach than simply letting an algorithm decide what is important, a concept further explored in Google's research on the challenges of media mix modeling [2].

Another powerful tool in the causal inference toolbox is the analysis of causality chains. These are the complex sequences of events that lead to a conversion. A customer might see a TikTok ad, then a week later see an influencer's post, and then finally make a purchase after seeing a retargeting ad on Meta. Traditional attribution models would give all the credit to the Meta ad. A causal approach, however, would recognize the role that each touchpoint played in the customer's journey. Causal inference is not just a theoretical concept. It is a practical tool that can be used to make better marketing decisions. By understanding the causal relationships between your marketing activities and your business outcomes, you can unlock new levels of growth and profitability. To get started with our causal inference tools, check out our developer quickstart guide.

Practical Steps to Implement Marketing Mix Modeling

Implementing Marketing Mix Modeling involves a structured process of data collection, model building, validation, and insight generation to sharpen marketing spend. Unlike simply tracking platform metrics, this approach requires aggregating historical sales and marketing data to build a causal model. For ecommerce brands, this provides a clear roadmap to reallocate budgets, improve ROAS, and drive scalable growth.

So, how can you get started with marketing mix modeling? Here are a few practical steps:

1. Data Collection & Aggregation: The first and most critical step is to gather and aggregate all the necessary data. This includes historical data on your sales (revenue and volume), ad spend per channel (e.g., Meta, Google, TikTok), impressions, clicks, pricing changes, promotions, and any other external factors that might influence your sales, such as seasonality or competitor activities. The data should be aggregated at a consistent frequency, typically weekly or daily. The more granular and comprehensive your data, the more accurate and insightful your model will be.

2. Model Building & Specification: Once you have your data, you can start building your model. This is typically done using statistical software packages like R or Python, with libraries specifically designed for MMM, such as Robyn or LightweightMMM. The model specification involves defining the relationship between the dependent variable (sales) and the independent variables (marketing activities and other factors). This includes specifying the functional form of the relationship (e.g., linear, non-linear), the lag effects of advertising, and the carryover or ad-stock effects.

3. Model Validation & Calibration: After you have built your initial model, it is crucial to validate it to ensure that it is statistically sound and provides accurate results. This involves checking the model's goodness-of-fit, the statistical significance of the variables, and the plausibility of the estimated coefficients. You should also perform out-of-sample validation to test the model's predictive accuracy on new data. Calibration involves fine-tuning the model parameters to improve its performance.

4. Insight Generation & Decomposition: Once you are confident in your model, you can start using it to generate actionable insights. The primary output of an MMM is the decomposition of sales into its various drivers. This allows you to quantify the contribution of each marketing channel to your overall sales, as well as the contribution of non-marketing factors such as seasonality and price. You can also use the model to calculate the ROI of each marketing channel and to understand the diminishing returns of your ad spend. Our ROAS calculator can provide a starting point for this analysis.

5. Action, Refinement & Iteration: The final step is to take action on your insights to sharpen your marketing mix. This might involve reallocating your budget from less effective channels to more effective ones, adjusting your ad spend levels to avoid diminishing returns, or changing your promotional strategy. It is important to treat MMM as an iterative process. You should continuously monitor your results, update your model with new data, and refine your marketing strategy based on the latest insights. Causality Engine is a behavioral intelligence platform that uses causal inference to replace broken marketing attribution for ecommerce brands.

Your Competitors Are Already Doing This

Competitive advantage in marketing is now being defined by the adoption of causal inference and marketing mix modeling over traditional attribution. While laggards remain stuck on vanity metrics, leading brands are using MMM to gain a true understanding of marketing ROI and scalability. For Dutch beauty brands, this means the choice is between data-driven growth and inevitable stagnation.

The most successful Dutch beauty brands are no longer relying on outdated attribution models. They are embracing the power of marketing mix modeling and causal inference to gain a competitive edge. They understand that in today's complex marketing landscape, the only way to achieve scalable growth is to have a deep, causal understanding of your marketing performance. While you are struggling to make sense of your platform-reported ROAS, they are busy building a more efficient and effective marketing machine. Causality Engine provides the tools to do just that.

This is not a trend. It is a fundamental shift in how marketing is measured and refined. The brands that embrace this shift will be the ones that thrive in the years to come. The ones that do not will be left behind, trapped in a cycle of declining returns and stagnant growth. The choice is yours. Causality Engine is a behavioral intelligence platform that uses causal inference to replace broken marketing attribution for ecommerce brands.

FAQ

What is the main difference between marketing mix modeling and attribution?

Marketing mix modeling is a top-down, strategic analysis using aggregated data to measure the causal impact of marketing channels on sales. In contrast, attribution is a bottom-up, tactical approach that assigns credit to individual touchpoints along the customer journey. MMM focuses on the why of sales, while attribution focuses on the what.

Is marketing mix modeling only for large companies?

No. While MMM was traditionally a complex and expensive undertaking, modern tools and techniques have made it accessible to businesses of all sizes. Platforms like Causality Engine now offer MMM as a service, making it possible for small and medium-sized businesses to use the power of this technique without needing a dedicated data science team.

How can I get started with marketing mix modeling?

The first step is to start collecting and consolidating the right data. This includes historical data on your sales, ad spend, pricing, and promotions. Once you have this data, you can either build your own model using open-source libraries or use a platform like Causality Engine to automate the process and provide actionable insights.

What is the ROI of marketing mix modeling?

The ROI of marketing mix modeling is significant, often leading to a 15-30% improvement in marketing efficiency. By identifying and reallocating budget from underperforming channels to those that drive genuine incremental sales, brands can unlock substantial growth without increasing their overall marketing spend. It shifts the focus from spending more to spending smarter.

How does marketing mix modeling handle online and offline channels?

Marketing mix modeling is uniquely capable of measuring the impact of both online and offline channels in a single, unified model. Because it does not rely on cookies or user-level tracking, it can incorporate data from TV, radio, print, and out-of-home advertising alongside digital channels to provide a truly holistic view of marketing performance. Discover your true ROAS.

References

[1] Marketing Mix Modeling (MMM) -Concepts and Model Interpretation [2] Challenges And Opportunities In Media Mix Modeling [3] Market Data - Nederlandse Cosmetica Vereniging [4] The Netherlands Cosmetics Industry Outlook 2022 - 2026