Multi-Touch Attribution: Bridging the Gap in Numerical Data Measurement Left by AI and LLMs

Artificial intelligence (AI) and large language models (LLMs) are revolutionizing the field of performance marketing, offering a wealth of capabilities to optimize campaigns and enhance customer engagement. However, despite their transformative potential, AI and LLMs need help in measuring numerical data effectively. Multi-touch attribution (MTA), such as what LeadsRx provides, addresses the gaps left by AI and LLMs and provides a holistic view of campaign performance.

AI and LLMs excel at processing and analyzing text-based data, but they struggle to effectively handle and interpret numerical data, which is crucial for performance marketing. This poses challenges in attributing conversions to specific touchpoints within the customer journey and accurately assessing campaign ROAS (return on ad spend).

This Nov. 28 article in AdExchanger addresses this very issue, adding “…according to the latest annual McKinsey Global Survey, marketers are mainly using generative AI for word-based tasks, such as crafting first drafts of text documents, personalizing messages and summarizing documents. But while LLMs are increasingly demonstrating their usefulness for brainstorming messaging, generating images and other creative applications in marketing, they are less well suited to solving fundamental challenges in programmatic advertising – especially performance advertising – which require learning from and responding to numerical data, not words.”

MTA: Addressing the Numerical Data Measurement Gap

MTA addresses these challenges by providing a granular view of how each touchpoint contributes to conversions. It utilizes various data sources, including website traffic, email interactions, and social media engagement, to track the customer journey and assign credit accordingly.

Key Advantages of MTA Over AI and LLMs in Numerical Data Measurement

You can’t beat raw numeric data synthesized into measured results that provide actionable insights to fuel strategies to improve marketing performance. With MTA, marketers receive:

  • Granularity and Context: MTA provides a more granular and context-aware understanding of numerical data, considering the nuances of customer interactions and the overall marketing ecosystem.
  • Attribution Accuracy: MTA employs sophisticated algorithms to accurately attribute conversions to specific touchpoints, reducing the risk of misattribution and providing a clearer picture of campaign effectiveness.
  • Actionable Insights: MTA generates actionable insights that inform future marketing strategies, enabling marketers to optimize campaigns and maximize ROAS.

The Promise of LLMs in Performance Marketing

LLMs offer a plethora of benefits for performance marketers, including:

  • Enhanced Content Creation: LLMs can generate high-quality, engaging content tailored to specific audiences, boosting campaign effectiveness.
  • Personalized Customer Interactions: LLMs can personalize interactions with customers, providing tailored recommendations and enhancing customer satisfaction.
  • Automated Ad Optimization: LLMs can analyze vast amounts of data to optimize ad campaigns in real-time, improving performance and reducing costs.

The Numerical Data Dilemma

Despite their transformative potential, LLMs pose challenges in measuring numerical data, a crucial aspect of performance marketing. These challenges stem from the inherent nature of LLMs and the complexities of numerical data, including”

  • Data Aggregation and Analysis: LLMs are adept at processing text-based data, but they struggle to aggregate and analyze numerical data effectively.
  • Contextual Understanding: LLMs may misconstrue the context of numerical data, leading to inaccurate interpretations and misleading insights.
  • Attribution and Causality: Establishing clear attribution and causality between LLM-driven actions and numerical outcomes is often challenging.

Overcoming the Hurdles

To effectively utilize LLMs in performance marketing while addressing the numerical data measurement challenges, consider these strategies:

  • Data Integration and Preparation: Integrate LLMs with existing data analytics tools to ensure seamless data flow and preprocessing.
  • Human-in-the-Loop Approach: Employ a human-in-the-loop approach, combining LLM-generated insights with human expertise to validate and refine numerical data analysis.
  • Explainable AI: Implement explainable AI techniques to understand the reasoning behind LLM-generated insights, enhancing trust and interpretability.
  • Continuous Monitoring and Refinement: Continuously monitor LLM performance and refine algorithms to improve their ability to handle numerical data effectively.

MTA and AI: Partners in Improved Marketing Performance

MTA more than complements AI and LLMs, providing a holistic approach to performance marketing measurement. On the flip side, AI and LLMs can enhance MTA by generating qualitative insights, identifying patterns and trends, and automating data analysis tasks. Essentially, all the amazing data gathered by MTA solutions can be processed using AI to enhance the final insights.

While AI and LLMs are revolutionizing performance marketing, they cannot fully address the challenges of measuring numerical data. MTA is an existing solution, providing a granular and context-aware view of numerical data, enhancing attribution accuracy, and generating actionable insights that inform marketing strategies.

By leveraging MTA in conjunction with AI and LLMs, marketers can comprehensively understand campaign performance and optimize their efforts for maximum impact.

To see how LeadsRx uses MTA to improve performance marketing, request a short demo.