Overcoming the 5 Challenges of MTA Marketing with Unified Marketing Measurement and Optimization

In the dynamic and evolving world of digital marketing, the way we measure its effectiveness is constantly changing. The advent of new technologies, the enforcement of global privacy regulations, and shifts in online media consumption patterns all influence our approach to evaluating the impact of digital marketing.

Amidst these ongoing changes, there is a significant emphasis on understanding and implementing multi-touch attribution (MTA), with the latest concern being the challenge of maintaining reliable signals to connect the various stages of the customer journey.

Understanding MTA as a method used to assign value to each touchpoint along the path to conversion is crucial in digital marketing. This approach aims to credit as many touchpoints as possible before the point of sale. For MTA marketing to be effective, it must consider all the marketing channels a customer may engage with during their journey to purchasing within a time window representing the decision cycle.

Multi-Touch Attribution Model Examples

Most digital platforms offer prebuilt MTA solutions to assess the credit of each touchpoint, each with its advantages and disadvantages. Here are some examples of attribution models.

1. Last-touch attribution, also known as “last-click,” is a marketing attribution model that assigns 100% of a conversion to the final touchpoint just before the conversion. While straightforward, this model tends to overemphasize lower-funnel channels and underestimate the impact of upper-funnel efforts.

2. The linear attribution model is an approach that assigns equal credit to each touchpoint in a customer’s journey to a conversion. In the context of multi-touch attribution, credit is distributed equally across all touchpoints that contribute to conversion. For example, if a customer engages with a social media ad, clicks on a search ad, and then visits a website before converting, linear attribution will allocate equally, 33% credit to each touchpoint.

3. Time decay attribution is a marketing model that credits customer interactions with marketing materials closer to a purchase. It assumes that the most recent interactions are the most influential in a customer’s purchase decision.

4. Algorithmic or data-driven attribution models utilize machine learning to analyze customer journeys and allocate credit to marketing touchpoints. This enables marketers to understand the influence of various marketing touchpoints and make better-informed decisions to enhance campaigns.

Reassessing MTA Marketing Strategies

Throughout the history of marketing, achieving the flawless attribution model has been an elusive goal. Marketers have been striving to develop a measurement framework that can demonstrate their value. However, this has proven to be challenging when using MTA as a stand-alone capability.

While there have been significant advancements in MTA marketing over the past decade with the proliferation of machine learning (ML) and artificial intelligence (AI), the future of attribution still holds many uncertainties, prompting the industry to innovate and reconsider its approaches. This is further confounded by the evolving marketing data landscape, with platforms like Meta, Google, and Amazon becoming more restrictive in sharing data.

Despite the varied approaches to MTA, there are common underlying themes compelling the industry to reassess its strategies.

• Data Fragmentation: The digital landscape is vast, fragmented, and rapidly expanding. Brands often struggle to consolidate and make sense of data from different sources, leading to incomplete or inaccurate attribution insights due to unaddressed omitted variable bias.
• Cross-Device Tracking: In today’s multi-device world, customers interact with ads across many screens. Tracking user behavior across devices is almost impossible due to missing data caused by privacy regulations (GDPR, ITP, CCPA) and Walled Gardens. Nowadays, brands often use multiple MTAs across publishers, resulting in unrealistic return on advertising spend (ROAS) estimates, as Walled Gardens do not share ad interactions with each other.
• Data Science Talent: Building robust MTA models requires advanced statistical techniques and machine learning algorithms. Many brands lack the in-house expertise to tackle this independently, often leading to oversimplified and misguided attribution approaches and outputs. You could easily make the argument, particularly given that MTA is seemingly in a state of perpetual evolution, that brands should outsource MTA and similar analytics and focus on doing the things that make them great brands.

• Budget Constraints: Implementing an effective multi touch attribution solution requires a significant investment of time and budget. Brands could benefit from working with external marketing analytics experts to complement their in-house expertise. In a rapidly changing data and analytics environment, not having the proper analytics roadmap and strategy could be costly regarding budget and financial decision-making.

• Standardization Challenges: Establishing the appropriate set of standards is crucial. The current MTA landscape lacks standardization, as different vendors and platforms employ different methodologies and definitions. There is also a shortage of validation and proof points, which are becoming increasingly important in establishing the trust necessary to drive usage and adoption.

The Next Phase in the Evolution of MTA Marketing

Where the data is reliable, multi touch attribution remains a powerful tool, but its methodology needs to evolve for the modern marketing era. It’s time for a forward-thinking solution that integrates multiple tools, leveraging advanced statistical models and ML/AI.

The shift from using MTA alone to a Unified Marketing Measurement Approach aims to change how marketers allocate and optimize their budgets. This involves moving away from solely assigning credit to individual channels to optimizing every marketing channel based on the incremental sales and profit they generate. This concept is the foundation of Unified Marketing Measurement.

Unified Marketing Measurement integrates all marketing, financial, operational, external, and consumer data into a comprehensive solution through advanced Marketing Mix Modeling, Agile Attribution, and Incrementality Testing. This powerful analytics approach equips marketers to assess the impact and efficiency of their marketing and other critical brand investments effectively, driving improved performance through ongoing optimization, targeted strategies, and experimentation.

Agile Attribution represents the next generation of MTA. It incorporates advanced machine learning and AI techniques in an always-connected, always-on approach. Since deterministic data is not always reliable or accurate, it’s time to adopt a more advanced approach that combines a mixture of event level and placement level. This newly evolved MTA approach solves critical modeling issues, such as missing channels or platforms, data privacy limitations, and declining third-party data signals, ensuring that marketers and media planners can optimize all elements of their buys.

Where Do Marketers Go From Here?

While the challenges of MTA are significant, they are not impossible to overcome. The key is to begin with consistent, reliable data and adopt a Unified Marketing Measurement approach incorporating testing and adaptation. Be prepared to pivot, especially when the data and models contradict conventional wisdom. We are entering a paradigm shift where we optimize based on incremental sales and not just on credit assignment.

It is crucial to thoroughly assess all claims around multi touch attribution, as there is no perfect solution in isolation. Implementing the ideal approach involves continuous refinement, optimization, testing, and calibration.