Ana Carreira Vidal’s Post

In a business environment where 83% of CEOs across various industries acknowledge brand marketing as a catalyst for growth, it is concerning that nearly half of CFOs hinder marketing proposals lacking a discernible connection to value. This phenomenon is particularly salient in the current challenging market conditions. Within the context of Google's Modern Measurement framework (https://goo.gle/3PDKdqa), we introduced the Modern Brand Measurement playbook (https://lnkd.in/dWSfeAzB) as a resource to empower advertisers to work strategically with their brand investments. This playbook encompasses three key steps: ✅ Within the context of assessing brand investments' impact on measurable business outcomes, identify a key performance indicator (KPI) that serves as a leading indicator, thereby providing valuable insights into the time-lagged effects of such investments. ✅ Implement a reporting system that continuously monitors the KPI and its alignment with business objectives. ✅ Take appropriate actions based on the results, such as planning, optimizing and allocating expenditures towards initiatives that yield the highest returns, as determined by the KPI. In my professional experience, many organizations have yet to fully harness the potential of data-driven decision-making. While organizations often have access to valuable data, they may not be effectively leveraging it to guide their actions. How does your organization incorporate brand effectiveness data into decision-making processes?

This week, we were delighted to welcome Ana Carreira Vidal, Media Effectiveness Measurement Specialist at Google and author of the Google Measurement Playbook. After sharing our initial thoughts on the Playbook, we were thrilled to dive deeper with its creator! In this episode, Ana discusses her motivations for creating the Playbook, the importance of educating the market, and her insights on why incrementality testing still isn’t universally adopted. Tune in to hear about the Playbook's public reception and much more. Catch up now! 🚀 ⏯ YouTube: https://lnkd.in/dZiZsYia 🎧 Spotify: https://lnkd.in/dXMDcU5E

The "Modern Measurement Playbook" published by Google caused quite a stir a few weeks ago. Rightly so, because the playbook is the first of its kind from a major player in advertising. It describes very extensively and practically standard best practices on how to implement #marketingmeasurement and #triangulation. That’s why we are very excited to announce that one of the authors, Ana Carreira Vidal (Media Effectiveness Measurement Specialist at Google), is going to be on the Marketing Measurement Matters live podcast here on Linkedin this Thursday. Tim Kreienkamp, Dr. Tim Wiegels and I are looking forward to discussing the playbook and the following topics with Ana: - What was the motivation for Google to publish the playbook? - How was the playbook received by the industry? - What’s the adoption rate for the practices described in the playbook? - How to motivate brands to invest in incrementality tests? Looking forward to an intriguing and inspiring discussion!

You might’ve heard the old adage; “I know part of my spend is wasted on marketing. The trouble is, I don’t know which part”. When it comes to validating that a media strategy’s contribution is more than its cost; look no further than the recently published Modern Measurement playbook (https://lnkd.in/gHk7Zy2T), built to equip marketers to measure media contribution more precisely.  Today’s marketers know that not all attributed conversions (conversions attributed to ads) are incremental conversions (conversions that wouldn’t have otherwise occurred without ads). So how can we optimize our Media Mix for higher incrementality? Attribution models are an immediate way to gut-check your customers typical journey through your ad touchpoints, as highlighted in Brandon’s post (https://lnkd.in/gRMsGvgh). But don’t stop there -  if attribution-based decisions are level one for budgeting decisions, incrementality experiments are level two. Incrementality experiments capture the purest assessment of media contribution, and the results can serve to validate attributed performance and even inform your future optimizations through a front-end multiplier. Validate:  Let’s say you see a large difference in performance between Google Ads attribution and another measurement tool. This is often the case with view-based strategies like YouTube for Action, Demand Gen, and Performance Max. Use an incrementality experiment like Conversion Lift Studies to explore the true performance of the strategy in question, validating the incremental contribution with the rigor of causality.  Calibrate:  Now that you’ve learned the relationship between attribution and incremental performance for a particular strategy, you may choose to use a multiplier to help inform future views of performance. Note that Conversion Lift measures a specific campaign grouping in a specific time period, so it’s important not to assume multipliers apply to other campaign types outside of tested ones, and equally important to refresh results when making big budget decisions. I prefer to refresh results at least twice a year. Test and Learn, then Test Again:  Getting a sense of a strategy's current contribution is an invitation to improve. Optimize and test again at a cadence that works for you!