Why Is MTA (Multi-Touch Attribution) Not Enough?

By Mickey Singh (LinkedIn)

As digital marketing continues to evolve, understanding the effectiveness of your campaigns has become more crucial than ever before. Multi-Touch Attribution (MTA) has long been used to measure campaign effectiveness; however, as the industry becomes more complicated, MTA may no longer suffice as a measurement approach.

MTA (Multi-Touch Attribution) is an attribution model which assigns credit to various marketing touchpoints along a customer journey, helping marketers optimize campaigns and allocate budgets more effectively. This approach has become particularly vital in digital marketing, where customers often utilize multiple channels before purchasing. MTA does have some shortcomings, most notably its emphasis on click-through data. As such, MTA may fail to capture the full value of marketing activities that don’t result in direct clicks, such as display ads or social media posts; these so-called view-through interactions play an essential part in driving brand recognition, and consideration are frequently misrepresented by MTA models. Given these limitations, it would be prudent to explore other approaches for measuring marketing effectiveness, including,

• Marketing Mix Modeling (MMM): MMM, fundamentally a regression model, provides a holistic view of marketing efforts by assessing how various channels contribute to overall sales or revenue, including indirect effects that don’t immediately result in clicks or conversions. Although MMM provides insights into indirect effects, its implementation often relies on aggregated data that needs more granularity to optimize specific campaigns or tactics – although emerging technology firms like OptiMine¹ may help address this limitation. Furthermore, historical performance data cannot accurately predict future performance in rapidly evolving marketing landscapes.

• Data-Driven Attribution (DDA), provided by ad measurement platforms like Google Campaign Manager 360 (DCM): DDA uses sophisticated algorithms to assess view-through and click-through data, providing an overall picture of marketing performance across channels. View-through data can provide marketers with a deeper insight into how different touchpoints contribute to their campaigns, even if they don’t result in direct clicks. DDA may seem like a “black box,” as its algorithms and processing methods are usually proprietary and opaque to marketers. As a result, marketers may need help to comprehend and trust its results. Adopting DDA may also require considerable time and resources to integrate and analyze large volumes of data. 

As marketing campaigns expand across channels and evolve, marketers need to recognize the limitations of traditional attribution models like MTA. By exploring alternative solutions such as MMM or Data-Driven Attribution offered by platforms like DCM, marketers can gain a more holistic view of their campaigns while making data-driven decisions to produce more remarkable results. As the industry evolves, marketers should adapt and explore all available avenues to optimize their campaigns effectively.

Reference: 

1. The Pros and Cons of Marketing Mix Modeling (MMM): https://optimine.com/pros-and-cons-of-marketing-mix-modeling/