In today’s ever-evolving marketing landscape, understanding the performance of your marketing efforts is crucial. Marketing attribution assigns credit to different marketing channels and touchpoints for conversion, helping businesses optimize their marketing efforts and allocate budgets effectively.
Multi-touch attribution (MTA) and marketing mix modeling (MMM) are two widely used methods for optimization. MTA is useful for understanding the customer journey and optimizing individual channels, while MMM offers a comprehensive view of marketing performance and helps businesses make more informed decisions.
In this guide, we’ll walk you through these approaches and explain the differences between multi-touch attribution vs marketing mix modeling.
Understanding Marketing Mix Modeling
Marketing mix modeling (MMM) is uniquely positioned to measure both the direct and indirect effects of marketing activities, making it a robust tool for marketers aiming to optimize their marketing spend and strategy. Here’s everything you need to know about MMM.
What is marketing mix modeling?
Marketing mix modeling (MMM) is a data-driven marketing approach used to analyze the effectiveness of various marketing activities and their impact on sales. By leveraging statistical analysis, MMM helps businesses understand the contribution of different marketing channels, such as TV, radio, print, and digital, to their overall performance. It provides a comprehensive view of how marketing efforts influence consumer behavior and drive revenue.
How Does MMM Work?
MMM attribution collects data from multiple sources, including sales data, marketing expenditures, and external factors like economic conditions and competitive actions. The data is then processed using advanced modeling techniques, such as machine learning and causal inference, to identify relationships between marketing activities and business outcomes.
These models take into account complex interactions, such as the synergy between channels and diminishing returns on marketing spend with marginal ROI (mROI). The insights generated enable marketers to:
- Validate their gut instincts with data
- Allocate marketing budgets more effectively
- Forecast future performance
- Optimize their marketing channel mix
By understanding these complex interactions, businesses can make informed decisions to maximize their return on investment.
MMM is particularly advantageous because it allows for the incorporation of a wide range of data sources, both online and offline. This inclusivity ensures that businesses are not just optimizing digital efforts but are also making the most of traditional marketing channels.
By including external factors in the analysis, the MMM model provides a more accurate and holistic view of the effectiveness of marketing campaigns, taking into consideration the broader market dynamics that influence consumer behavior.
Keep reading: Why marketing mix modeling is trending
Understanding multi-touch attribution
Multi-touch attribution (MTA) breaks down the customer journey. While MTA provides valuable insights for optimizing digital marketing efforts, its limitations—like focusing solely on digital interactions—highlight the need for broader tools like marketing mix modeling (MMM).
What is multi-touch attribution (MTA) in marketing?
Multi-touch attribution (MTA) is a method used to assign credit to various touchpoints along the customer journey that lead to a conversion. The MTA approach focuses on digital channels, tracking user interactions across different platforms and devices. MTA aims to provide a granular understanding of how each touchpoint contributes to the final sale, allowing marketers to optimize their digital strategies and improve conversion rates.
How does MTA work?
MTA typically relies on digital tracking technologies, such as third-party cookies and pixel tags, to gather data on user behavior. It uses simpler algorithms to assign credit to each touchpoint, often focusing on short-term metrics, such as clicks, impressions, and engagements.
Limitations of MTA
MTA has a narrow scope that overlooks offline activities and external factors. Despite this, MTA is valuable for understanding the digital user journey and optimizing digital marketing efforts. It provides immediate insights into the effectiveness of different channels and campaigns, enabling marketers to make quick adjustments to improve performance.
One of the challenges with MTA is its reliance on last-click or first-click attribution models, which can oversimplify the customer journey by giving disproportionate credit to specific interactions. This can lead to a skewed understanding of which channels are truly driving conversions.
Moreover, with increasing privacy concerns and the upcoming cookieless world, the effectiveness of MTA is becoming more limited. Businesses must adapt to these changes and consider how to integrate broader, more comprehensive approaches like MMM into their strategy to ensure a full understanding of their marketing efforts.
How does marketing mix modeling differ from multi-touch attribution
While both MTA and MMM aim to measure marketing performance, they take vastly different approaches. Understanding the following differences will help you choose the right method for your strategy.
1. View of marketing performance
MTA focuses on digital channels and often misses the impact of offline touchpoints. Conversely, MMM evaluates the full spectrum of marketing activities, providing a comprehensive view. This allows businesses to understand how both online and offline channels contribute to overall performance.
MMM’s holistic approach ensures that marketing strategies are optimized across all channels, not just digital, leading to more balanced and effective campaigns.
2. Use of first-party data
MTA tracks user interactions within a specific attribution window, focusing on short-term metrics. MMM uses first-party data from various sources over a longer period, offering insights into long-term performance and strategic decision-making. This broader data utilization helps in understanding sustained marketing impact and allows for better long-term planning, like creating an annual marketing plan and resource allocation.
3. Impact of external factors
MTA often overlooks external factors like market trends and economic conditions. MMM incorporates these factors, providing a more accurate understanding of marketing impact. By considering these external influences, MMM offers a more realistic evaluation of marketing effectiveness, allowing businesses to adjust their strategies based on real-world dynamics.
4. Application of advanced modeling techniques
MTA uses simpler algorithms to assign credit within a lookback window. Marketing mix modeling employs advanced machine learning and causal inference techniques, leading to more actionable insights. These sophisticated methods allow MMM to uncover deeper relationships between marketing activities and business outcomes.
The chart below summarizes the key differences between MMM and MTA.
| Aspect | Multi-touch attribution (MTA) | Marketing mix modeling (MMM) |
| Focus | Optimizes user journey and touchpoints | Evaluates incrementality in marketing and contribution to overall revenue |
| Scope | Answers “How did this conversion come to be?” | Answers “How effective is the overall marketing mix in driving growth?” |
| Timeframe | Short-term metrics within a specific attribution window | Long-term impact on revenue and strategic decision-making |
| External factors | Limited consideration of external factors | Incorporates market trends, economic conditions, and competitive actions |
| Modeling techniques | Simpler algorithms for assigning credit | Advanced techniques like machine learning and causal inference |
Benefits of MMM over MTA
In the battle between MMM vs MTA, MMM goes beyond the capabilities of MTA, offering a more comprehensive, reliable, and scalable solution to optimize your marketing strategy. Here’s how it sets itself apart.
1. Comprehensive view of marketing performance
MMM includes both digital and non-digital channels for a complete view of marketing performance. MMM platforms enhance this by offering real-time data and historical insights, helping marketers make better decisions quickly.
2. Long-term strategic planning
MMM supports long-term planning with historical data analysis. Keen’s AI-powered platform allows marketers to plan for the future, ensuring resilient and adaptable strategies. This strategic approach helps businesses stay competitive in a changing market.
3. Data reliability and accuracy
MTA relies heavily on digital data between MTA and MMM, vulnerable to ad-blockers and privacy issues. MMM typically draws from a broader dataset. For example, Keen is committed to providing trustworthy AI and ensures accurate data integration for comprehensive analysis, making the insights more robust and reliable.
4. Scalability for different business sizes
MMM is adaptable to businesses of all sizes and industries—another win for MMM in the MTA vs MMM debate. Keen facilitates this with low data requirements and easy integrations, allowing for efficient model creation. This flexibility ensures that MMM can meet diverse business needs.
Multi-touch attribution vs marketing mix modeling: Which one are you choosing?
In the comparison between MMM and MTA, MMM stands out for its comprehensive view and support for long-term strategy. The goal is to make informed, data-driven decisions and optimize marketing efforts.
With Keen as your partner, you can achieve these objectives and drive marketing success. Start your free trial today!
