For campaigns focused on awareness or traffic, this answer is often clear. When it comes to conversion goals, things get more complex.
The challenge is understanding which campaigns and channels actually drive revenue when multiple activities are running at the same time. That includes separating conversions that would have happened anyway from those influenced by marketing, and identifying where the budget delivers the highest conversion value.
Most marketing channels claim a share of the revenue they contributed. Add those claims together, and the total often exceeds the revenue actually generated. This is why evaluation needs an independent way to allocate value across channels in a consistent, reliable way.
There are two main approaches to this problem: Attribution and Media Mix Modelling (MMM).
What is Marketing Attribution?
Marketing Attribution (Attribution, for short) is the process of linking marketing campaigns to conversions on a website or app.
When a user clicks on an email, display ad, or paid search result and later makes a purchase, that conversion can be attributed to the campaign that drove the interaction.
Attribution relies on campaign identifiers being correctly added to URLs so user interactions can be tracked across channels.
For many marketers, Attribution remains the primary way of evaluating performance because it provides a direct, measurable link between marketing activity and outcomes.
What is Mediamix modelling?
Media Mix Modelling (MMM) measures the impact of marketing activities at an aggregated, statistical level. While the exact wording of the three “M’s” varies, the underlying concept remains the same.
MMM combines data such as marketing spend, impressions, traffic, and total revenue into a statistical or machine-learning model. The model looks for patterns between these variables and explains changes in revenue based on changes in marketing activity.
The more complete the input data, including market conditions and external factors, the more accurately the model can explain revenue variation.
From explanation, the model moves to evaluation. Channels that account for a larger share of revenue change are assigned a higher value, resulting in an overview of channel contribution, often supported by additional insights or recommendations.
Marketing Attribution vs. Media Mix Modelling
Marketing attribution creates direct links between individual user interactions and conversions. Media Mix Modelling does not operate at the user level. Instead, it identifies patterns in aggregated data over time.
Attribution focuses on specific journeys, for example, a user converting after clicking a particular ad. Media Mix Modelling looks at broader trends, such as how changes in spend on a channel relate to changes in revenue on a given day.
Both approaches aim to assess marketing effectiveness, but they do so from very different perspectives.
Combining Marketing Attribution and Media Mix Modelling
In practice, Marketing Attribution and Media Mix Modelling are often used side by side because they address different measurement needs.
Media Mix Modelling is particularly strong at:
Marketing Attribution is strongest when it comes to:
Used together, Attribution and MMM dovetail to provide both tactical and strategic insights. The combination gives marketers a measure of short-term performance with a longer-term, holistic view of marketing impact.
Six CommonTypes of Marketing Attribution
Attribution becomes complex when users interact with multiple campaigns and channels before converting. A user might first arrive via paid social, return later through paid search, and finally convert after visiting the site directly.
How conversion value is assigned depends on the attribution logic used. Some models credit the first interaction, others the last, while more advanced approaches distribute value across multiple touchpoints.
Below are the most common attribution models, ranging from simple to more advanced:
First click:
All conversion values are assigned to the first campaign or channel a user interacted with in their journey. This model emphasizes awareness and acquisition but ignores later influences.
Last click:
All conversion values are assigned to the final interaction before conversion. While simple and intuitive, this model tends to overvalue bottom -of- funnel channels and undervalue earlier touchpoints.
Non-direct last click:
Similar to last click, but direct traffic is ignored unless it is theonly interaction. If a user arrives via a campaign and later returns directly, the value is assigned to the last non-direct campaign they interacted with.
Linear:
Conversion value is evenly distributed across all touchpoints in theuser journey. This model assumes that every interaction contributes equally.
Markov:
A probabilistic model that calculates the contribution of each channelby measuring how conversion likelihood changes when a channel is removed fromthe journey. It accounts for order and interaction effects between channels.
Data-driven:
The Google Analytics 4 attribution model is based on machine learning.The exact logic is not fully transparent, but in practice it often yields outcomes similar to those of Markov-based models.
Building orBuying Marketing Attribution Tools
Buying
Most teams use attribution through existinganalytics or attribution tools like Google Analytics or Adobe Analytics. These platforms apply predefined attribution models to tracked campaign data, making them a practical starting point for evaluating performance across channels.
In addition, there are specialized attributiontools such as:
Building
Creating an attribution model in-house isusually driven by the need for greater control. This tends to apply when standard models cannot reflect how customers move between channels, or when attribution logic needs to be adapted to a specific business model.
Typical steps to build your own attributionmodel:
In practice, building your own attribution model involves working with behavioural data, defining how user journeys should be interpreted, and applying custom attribution logic. This approach offers flexibility, but also adds complexity and ongoing maintenance. For most organisations, the decision comes down to whether that added control justifies the effort compared to using an off-the-shelf solution.
Once an attribution model is in place, marketing activity can be analysed across the funnel. At the upper funnel, campaign costs can be compared with the attributed value. Closer to conversion, attribution data can be combined with order, CRM, or margin data to understand which channels drive revenue, longer-term value, or profitability.
Limitationsof Attribution Marketing
The main limitation of Marketing Attribution is that it rarely reflects the full picture of how marketing influences conversions. Even with the right attribution model, important signals are often missing from the data. The challenge is twofold:
Six CommonApproaches to Media Mix Modelling
Media Mix Modelling is not a single fixed method. Most MMMs share the same goal of estimating the incremental impact of marketing efforts, but differ in how they model uncertainty, handle complexity, and support decision-making. Here are a few common types of Media Mix Modell to consider:
Econometric:
A traditional approach that models how changes in marketing activity and external factors relate to changes in outcomes over time. This is the long-established “classic MMM” foundation used across many organisations and vendors.
Bayesian:
A probabilistic approach that estimates marketing impact while explicitly representing uncertainty in the results. Many modern open-source MMM frameworks use Bayesian methods to make modelling more robust when data are noisy or incomplete.
Machine Learning Assisted:
An approach that uses machine learning techniques, such as regularised regression, to manage many variables and reduce overfitting. Meta’s Robyn is a well-known example of this direction, combining regression with automation and optimisation routines.
Hierarchical:
A model structure that estimates marketingimpact across multiple levels at once, for example, by region, product line, or market, while keeping results consistent across the whole business. This is useful when performance varies meaningfully by geography or segment.
Geo-level:
An approach that leans on geographic variation over time, comparing how changes in spend align with changes in outcomes across regions. This can be helpful when national-level data hides important regional differences.
Hybrid:
An approach that combines MMM with incrementally tests (such as geo experiments) to improve confidence in causal impact and reduce reliance on purely historical correlation. Many modern MMM guides position experimentation as a way to strengthen MMM results.
Building orBuying Media Mix Modelling
As with attribution, there are multiple tools available for Media Mix Modelling analysis, including:
The main advantage of hosted tools is automated data integration. The downside is cost: MMM tools often come with high subscription fees, while many organizations only review MMM results quarterly.
Building a custom solution yourself is often driven by specific organisational needs or cost considerations.
A typical Media Mix Modelling build flow looks like this:
How to Acton Marketing and Media Mix Modelling Results
Attribution and Media Mix Modelling are only useful if they inform decisions. Before analysing results, teams should be clear about which actions the data is meant to support, particularly around budget allocation and channel strategy.
Budget changes should be made carefully. Strong current ROI does not guarantee proportional returns at higher spend levels, so insights are best used to guide small, controlled adjustments rather than large shifts.
In practice, this means using measurement to support an iterative approach: adjust budgets incrementally, monitor performance, and feed results back into the model to understand where additional investment is likely to deliver incremental value.
Five KeyTakeaways
Conclusion
Marketing Attribution and Media Mix Modelling answer different questions about performance. Attribution explains how individual campaigns and touchpoints contribute to conversions, while MMM shows how marketing activity influences revenue at a broader, aggregated level, including offline and awareness channels.
Combining both allows teams to allocate budgetmore effectively and improve performance with greater confidence over time.
FAQs
What’s the difference between marketing attribution and Media mixmodelling?
Marketing Attribution links individual conversions to specific campaigns or touchpoints. Media Mix Modelling uses aggregated data to estimate how marketing activity influences overall revenue over time.
Which channels can Media Mix Modelling measure that Attribution cannot?
Media Mix Modelling can measure non-clickable and offline channels such as TV, radio, print, and awareness campaigns that attribution models typically miss.
How much data is needed for Media Mix Modelling to work well?
Media Mix Modelling usually requires at least one to two years of consistent historical data to reliably separate marketing impact from seasonality and external factors.
Can Marketing Attribution and M be used together?
Yes. Attribution supports tactical optimisation at the campaign level, while MMM informs strategic budget and channel decisions. Used together, they provide a more complete view.
Can Media Mix Modelling be used to predict future performance?
Media Mix Modelling can be used for forecasting by simulating how changes in marketing spend may affect future revenue, supporting planning and budget decisions.