Attribution is any action that impacts any KPIs of your various marketing activities. Attribution determines which marketing interactions influence a customer's decision to convert, and helps you understand which tactics are the most effective and should get most of your marketing budget. There are different types of attribution models, like first-touch attribution, last-touch attribution, multi-touch attribution (MTA), and so on. Traditionally, marketers have used single-touch models, such as last-click attribution, where full credit is assigned to one interaction in the customer journey. While this approach is straightforward, it overlooks the multifaceted nature of modern consumer behavior, where multiple touchpoints across various channels contribute to a conversion.
Limitations of single-touch models
Single-touch models fail to capture the complexity of contemporary customer journeys. Customers often engage with multiple channels, such as social media, email, search engines, and display ads, across different devices and over extended periods. By attributing conversion credit to only the first or last touchpoint, these models tend to over-emphasize certain channels, like paid search, and undervalue others such as upper-funnel awareness campaigns, leading to skewed insights and suboptimal budget allocation.
Multi-touch attribution
Multi-touch attribution (MTA) is a marketing measurement method that assigns conversion credit across multiple marketing touchpoints within a customer's journey. Rather than attributing success to a single interaction, MTA evaluates the cumulative influence of various events, such as ad impressions, email clicks, and website visits, and distributes credit proportionally. This approach provides a more comprehensive and realistic view of marketing effectiveness.
Funnel's MTA leverages advanced machine learning techniques, specifically Long Short-Term Memory (LSTM) neural networks, to analyze sequential user behavior. This allows for a nuanced understanding of how different touchpoints contribute to conversions over time.
Advantages of MTA in marketing measurement
Granular insights: MTA operates on user-level data, enabling you to assess the performance of individual campaigns, channels, and even specific creatives.
Data-driven decision-making: By accurately evaluating the contribution of each touchpoint, you can make informed decisions about budget allocation and campaign optimization.
Cross-channel transparency: MTA helps identify the interactions that are undervalued by traditional attribution models, providing a clearer picture of the customer journey.
Integration with advanced modeling: MTA serves as a foundation for sophisticated modeling techniques, such as LSTM, which can learn from sequential user journeys and predict conversion behavior with greater precision.
Limitations of MTA
While MTA offers significant advantages, it also presents certain challenges:
Data limitations: The effectiveness of MTA depends on the availability of user-level data. With increasing privacy regulations and the decline of third-party cookies, collecting comprehensive data can be difficult.
Upper-funnel attribution: MTA may struggle to accurately measure the impact of upper-funnel activities, such as brand awareness campaigns, which don't always result in immediate clicks or conversions.
Cross-device tracking: Tracking users across multiple devices remains a challenge, potentially leading to incomplete attribution data.
Short-term focus: MTA tends to emphasize immediate performance, potentially overlooking the long-term effects of marketing efforts.
Integrating MTA with other models
To address these challenges, Funnel uses a triangulated approach that combines MTA with marketing mix modeling (MMM) and incrementality testing. This integrated strategy leverages the strengths of each methodology:
MTA provides detailed insights into digital, user-level interactions.
MMM offers a macro-level perspective, accounting for external factors and long-term trends.
Incrementality testing validates the causal impact of marketing activities through controlled experiments.
By combining these approaches, you can achieve a more accurate and comprehensive understanding of their marketing performance.