Measuring true marketing ROI while navigating the cookieless world

In the ever-evolving landscape of digital marketing, measuring return on investment (ROI) has become increasingly challenging, particularly when combined with the sunsetting of the third-party cookie. This article explores the difficulties marketers face in determining the optimal allocation of advertising budgets and highlights the routes that can help measure true marketing ROI in the absence of third-party cookies.

As costs rise and new marketing channels emerge, marketers find themselves grappling with the need to spend more across multiple platforms. However, acquiring accurate data to distinguish the performance of each channel becomes more elusive. This problem affects three levels of estimation: granular user ID data, aggregated channel-level models, and the complex space in between.

“70% of CMOs and marketing leaders say theythet still can’t measure the true ROI of all of their digital marketing campaigns.” (Source: Deloitte CMO Survey)

To address the challenges posed by limited data access, marketers can choose between two models: journey-based or trend-based. Journey-based models offer granular insights and precise understanding of user behaviour within integrated platforms like Amazon. On the other hand, trend-based models work with aggregated data, sacrificing some precision but offering a more automated and ongoing measurement approach.

Due to the inherent drawbacks of both the journey and trend based models, the AI-powered “MROI” measurement model was built to tackle the rising pressure and challenges of the media landscape. This leverages probabilistic Bayesian methods to estimate ROI based on historical data and channel performance, helping marketers make data-driven decisions on budget allocation and optimisation. By implementing the MROI modell, businesses can achieve increased efficiency, incremental revenue, and better cost distribution.

Launching new initiatives requires a testing framework that consists of defining the business problem, determining the regions for experimentation, budgeting accordingly, and investigating results. This approach allows marketers to avoid skewing results and ensures a productive and agile marketing team.

With the limitations on third-party data, marketers can turn to first-party data and AI-driven solutions. By combining the knowledge and capabilities of platforms like Google and AWS, businesses can enhance audience targeting and creative development. AI enables marketers to understand user interests and preferences, leading to more effective campaigns and improved marketing performance.

Collaborating with complementary businesses and sharing second-party data has become increasingly valuable in the absence of third-party cookies. Algorithms and data exchanges facilitate partnerships that allow companies to leverage each other’s audiences, leading to more precise targeting and enhanced campaign outcomes.

While measurement differs from activation, marketers can integrate the insights gained from measurement into their activation strategies. By aligning messaging, creative content, and targeting based on customer profiles, businesses can maximise their marketing impact and prove the effectiveness of their campaigns.

Measuring true marketing ROI without third-party cookies is a complex task, but it is not insurmountable. By leveraging strategic and tactical models, embracing AI-driven solutions, implementing testing frameworks, and fostering collaborations, marketers can navigate the evolving digital landscape with confidence. Despite the challenges, the potential for improved marketing performance and increased revenue makes this journey worthwhile.