This article was co-authored by Matt Wakeman, Weicong Zhao and Joseph Enever, analysts in the Gartner Marketing Practice, covering marketing data and analytics.
Marketing leaders have long relied on various techniques to measure and communicate their impact, but traditional digital attribution methods often fall short. This limitation has fueled a growing interest in marketing mix modeling (MMM).
Nearly half of marketing leaders struggle to prove their value and gain recognition for their contributions, a recent Gartner survey found. MMM offers a compelling solution for marketing and enterprise functions like finance and supply chain, which use these models to articulate return on investment and optimize strategies.
As the demand for deeper, more frequent insights grows, MMM continues to expand its role, providing a robust framework and a crucial tool for organizations with substantial media budgets to maximize marketing efficacy.
The rise of marketing mix models
Marketing mix models have gained traction as legacy attribution methods struggle to quantify offline and brand efforts, often overemphasizing bottom-of-funnel tactics. Regulatory changes and uncertainties around third-party tracking and ad targeting have further accelerated the shift toward MMM. Reflecting its growing importance in marketing analytics, Gartner found that 64% of senior marketing leaders have adopted MMM solutions.
There are five primary use cases for MMM solutions, each catering to specific organizational needs:
- Basic mix modeling: Ideal for organizations new to MMM, this scenario emphasizes data management, model latency and adoption enablement for marketers.
- Enterprise mix modeling: Focuses on cross-functional adoption, business scenario planning and complex analytics, making it crucial for estimating all business factors impacting ROI.
- Big-budget advertising mix models: This scenario prioritizes media optimization and complex analytics and is tailored for advertisers with substantial media budgets.
- House of brands: Designed for organizations seeking to standardize and scale their MMM approach across multiple brands, emphasizing data management and media optimization.
- Self-service mix model: This model supports organizations desiring granular control over model specifications, focusing on data scientist adoption and complex analytics.
Dig deeper: Rethinking media mix modeling for today’s complex consumer journey
The role of genAI in MMM
The integration of genAI into MMM solutions is increasing, improving insight generation and simplifying the identification of optimal scenarios. AI-driven insights help uncover marketing performance drivers hidden across multiple data views, enabling more informed decision-making.
If you’re evaluating and selecting a marketing mix model solution, make sure to:
- Engage stakeholders across marketing, finance, data management, supply chain and executive partners to document data and output requirements, securing enterprise-wide buy-in before contract execution.
- Begin collecting and auditing two years of daily marketing and business conversion data before committing to a vendor, as data collection can significantly impact production timelines.
- Evaluate vendors based on their capabilities, industry experience and ability to address key organizational questions.
- Assess vendors’ current functionality and their integration of emerging trends and modeling techniques into future roadmaps.
As you navigate the complexities of proving marketing value, MMMs offer a powerful solution. By quantifying the overall impact of your efforts and optimizing business outcomes, MMM enables you to make data-driven decisions and improve marketing effectiveness.
With the integration of genAI and a focus on cross-functional collaboration, MMM is set to become an essential tool in the marketing arsenal, driving strategic growth and success in 2025 and beyond.
Dig deeper: Unlocking the full customer journey with advanced marketing measurement models
Contributing authors are invited to create content for MarTech and are chosen for their expertise and contribution to the martech community. Our contributors work under the oversight of the editorial staff and contributions are checked for quality and relevance to our readers. The opinions they express are their own.
