Multi-Touch Attribution vs. Marketing Mix Modeling
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In the complex world of marketing analytics, two prominent methodologies help businesses understand the effectiveness of their channel efforts: Multi-Touch Attribution (MTA) and Marketing Mix Modeling (MMM). While both aim to shed light on what's driving their outcomes (revenue, conversions, etc.), they differ significantly in their approach, granularity, and ideal use cases. Understanding these distinctions is crucial for marketers seeking to analyze their performance.
Quick overview
Multi-Touch Attribution
The goal of Multi-Touch Attribution is to assign a credit (a fraction of conversion) to touchpoints along the user journey. There are many ways to approach this problem. For example, with a user who converted after x visits from distinct channels, we could focus on all history or just a specific window of time before the conversion. We may also decide to put more weight on recent interactions (last-click & time decay), focus on first-touch, divide the interactions equally (linear), put more weight on first and last interaction (U-shaped or position-based), or allow machine learning to determine which touchpoints tend to be most influential (Data-Driven).
By the end of the analysis, at a granular level, each historical touchpoint (sessions, impressions, etc.) in the user's journey will have an assigned credit, and at a higher level, each channel will be able to claim a portion of the total revenue.
Marketing Mix Modeling
Unlike with MTA, Marketing Mix Modeling takes a top-down approach. Instead of tracking individual user journeys and touchpoints, MMM uses aggregated historical data, typically on a weekly or monthly basis. This data includes marketing spend and performance metrics for various channels (both online, like paid search and social, and offline, like TV, radio, and print), alongside sales/conversion data. Meridian specifically also encourages you to provide this data per desired geographical region. The model input also incorporates other factors that can influence outcomes, such as seasonality, economic conditions, competitor activities, and promotional events.
The primary goal of MMM is to determine the contribution of each marketing input to the overall outcome on top of the expected baseline (sales without marketing). Modern MMM solutions (Meridian) also provide additional features such as response curves (diminishing returns), decay curves (lag effects), budget optimizations, and model fit analysis.
A Comparative Look
| Feature | MTA | MMM |
| Data Granularity | Highly granular, individual user-level data with specific touchpoints/interactions. A connected user journey is highly important | Aggregate, high-level data of spend per channel, total revenue, business, and outside factors grouped by date range |
| Data Sources | Analytics data (web, app), CRM data, email marketing, advertising data, offline interactions... | Sales data, marketing spend across all channels, promotional data, competitor activity, external factors... |
| Data Issues | Difficult to connect a single user journey across a single let alone multiple platforms. Often reliance on cookies and limited by privacy restrictions |
It may be difficult to aggregate data across multiple teams responsible for spend Usually, at least 2-3 years of data is desired. Data across these channels should be formalized per geo, time aggregations... |
| Time Horizon | Can be executed for a shorter timeframe and provide real-time insights into campaign performance | At least a couple of years of data is desired, with model refresh usually happening monthly or quarterly |
| Output | Assigned credit to each touchpoint of the user journey | ROI estimates for each marketing channel, baseline impact, saturation and decay curves, budget optimization |
| Complexity & Cost | This can vary from using a built-in MTA model in your analytics platform, to connecting touchpoints from clean rooms, offline interactions, online data to execute advanced attribution using ML | Can be complex and resource-intensive to develop and update models, requiring specialized analytical skills. Data Engineering of bringing all the data together may play a big factor in complexity as well |
| Typical Use Case | Analyzing and optimizing digital campaigns and channels. Going beyond last-click attribution. | Strategic budget allocation across all marketing channels, understanding impact of outside factors as well as the behavior of individual channels beyond ROI |
| Channels | Optimized for digital channels where anonymous and identified users can be tracked across multiple touchpoints | A more holistic approach by incorporating online and offline channels as well as non-marketing drivers like seasonality, economic conditions... |
As you can see, there are many differences between the two approaches to understanding the effectiveness of your marketing channels. MMM operates on a macro-level, offering more strategic understanding of holistic marketing efforts and external factors, while MTA excels at micro-level insights, providing credit to each user touchpoint.
While they stand out at different things, these two approaches are often complementary and can work together very well.
*MTA and MMM aren't the only ways to analyze marketing performance. Incremental lift studies offer another option. These focus on the impact of specific channels or campaigns. They use aggregated data (similar to MMM) but can be run for shorter timeframes (like MTA). They can also be used to help confirm the insights you get from MTA and MMM.
In conclusion, both MMM and MTA are powerful but distinct methods for analyzing marketing performance. A good understanding of both methodologies allows you to select, apply, and activate the results of the modeling and improve the efficiency of your marketing efforts.