Over the last months, I’ve spoken with +20 performance marketing leaders at large scale-ups and enterprises to understand what they are currently going through and how the best teams are adapting and staying ahead of the curve.

We all know these are difficult times for most marketing teams. Three main observations stood out:

1) More pressure and scrutiny, with a focus on profitable growth

2) Signal loss - measurement is top of mind for everyone

3) High pace of change - the digital marketing landscape is evolving faster than ever

Let's dive into each trend and learn how the best performance marketing teams are adapting.

1) More pressure, scrutiny and focus on profitable growth

Growth at all costs is dead. With the new economic downturn, marketing budgets are significantly tighter and there’s more scrutiny than ever. Unfortunately, many teams were affected by layoffs. Profitable growth is the new reality.

In this context, marketers need to do more with less and show the impact of their work. This requires aligning marketing with real business outcomes (beyond ROAS or CPA).

🟢 The best performance marketing teams are focused on delivering profitable, sustainable growth. They link marketing with business outcomes by defining the right north star metrics, such as POAS (profit on ad spend), Contribution Margin and CLV

⚠️ However, most teams struggle to activate backend data (COGS/GM, CLV, Internal Attribution) for campaign optimization and budget allocation. Teams tend to optimize in-platform for ROAS (or even worse CPAs), leading to misaligned goals

⏭️ Focus on accurate decisions aligned with business results:

• Go beyond platform ROAS or CPA by activating backend data for campaign optimization and budget allocation

• Focus on incrementality to understand the true impact of your marketing efforts

• Activate internal attribution to ensure an accurate view of performance

2) Signal loss

Attribution is flawed due to tracking limitations. Thus, attribution models are becoming less reliable. iOS 14, SKAN, potential cookie-loss, etc – all of these challenges make measurement harder.

In this context, marketing and data teams need to find ways to navigate this privacy-centric era.

🟢 The best marketing and data teams combine Multi-Touch Attribution (MTA) with Media Mix Modeling (MMM) and Incrementality 

⚠️ Most marketing and data teams are lagging behind on this journey:

• Data teams get stuck with reactive ad-hoc work (i.e., Jira tickets and maintenance), taking time away from measurement initiatives

• Many teams over-index on Attribution, overlooking Incrementality and MMM

• Turning measurement models into better decisions is hard. Many teams ignore the activation challenge, ending up with unused MMM and incrementality tests
  
  

⏭️ How to turn marketing measurement initiatives into business outcomes:

• Automate marketing data engineering work (API updates, broken pipelines) and adopt a marketing analytics platform for effective self-service, freeing up the data team to focus on measurement

• Combine MTA, MMM and Incrementality

• Consider how to operationalize measurement initiatives from the start. Collaborate closely with your data team and operationalize measurement in reporting

3) High pace of change

The digital marketing landscape is evolving faster than ever. Every week, there are new updates from the main platforms (Google, Meta, Tiktok, etc), that are doubling down on AI and automation (e.g., Google PMax and Meta ASC+).

In addition, new platform updates, trends, products and success stories pop up every week. The pace of change is incredibly high.

🟢 The best teams test a lot and iterate quickly. They constantly test new concepts and tweaks — creative, keywords, budgets, etc. — with short feedback cycles. They also dive deep into cross-channel and cross-market insights to come up with new test ideas.

⚠️ Most marketing teams get stuck in turning data into insights:

• No self-service / Dependance on data teams - any new dashboard or analysis requires data support, often taking weeks or months, leading to many untested hypotheses

• Significant time wasted on manual data preparation - blending backend data with channel data, pulling numbers together for WBRs, etc - all this takes time away for running tests

• Testing something new takes too much manual data preparation work

  
  

⏭️ How to create an agile testing culture and environment:

• Democratize access to data and insights for quick insights and easy data exploration

  • Have a single source of truth (marketing analytics platform) with all data and metrics (channel, attribution, backend), down to the most granular level

  • Enable self-service - marketers should be able to easily build dashboards, add/edit metrics, manipulate data etc, without SQL or data support - to run lots of tests and iterate fast

• Try new tactics and run new tests without overlooking the fundamentals - avoid getting blinded by FOMO

The road ahead

Looking ahead, these trends are likely to accelerate in 2025. The economic backdrop is likely to prevail for some time, putting pressure on profitable and incremental growth. The privacy-centric era is also here to stay. AI is likely to contribute to an even greater pace of change.

While the paradigm is rapidly changing, the fundamentals remain the same. Performance marketing teams need to focus on driving profitable growth, be agile, and test a lot. Building strong partnerships and effective collaboration with data teams is essential to navigating signal loss. These organizations will be able to stay ahead of the curve and build an advantage over everyone else.