Why It’s Important
To outperform most Meta advertisers, do these 3 things:
- Optimize for high-value goals like purchases.
- Ignore individual ad metrics.
- Study top-performers.
Identify the winners and iterate. Create similar variations to keep content fresh.
Here’s what that looks like.
In this Article
Data, users & automation.
To break down this advice and explain each step in detail, let’s dive into the strategy behind each piece.
This approach leverages data-driven decision-making, user empathy, and the automation capabilities of Meta’s ad platform to create a highly effective and optimized campaign structure.
Here’s how each step builds on Meta’s strengths and insights, and why digital marketers should pay close attention to each aspect.
1. Optimize for the most valuable goal
...that gets enough conversion data (typically purchase)
In digital advertising, setting your campaign’s objective is the first strategic decision you make—and it’s critical.
On Meta’s platform, the choice of optimization goal signals to the algorithm what type of user behavior you want it to prioritize.
If you’re running an eCommerce or direct-to-consumer brand, optimizing for “Purchase” (or the highest-value conversion available in your funnel) gives Meta the best data to find and target users most likely to convert.
Why it’s important
Data Density
Meta’s machine learning algorithm, like any artificial intelligence, relies heavily on data volume and frequency to make accurate predictions.
When you optimize for an event that generates sufficient conversion data, it basically guarantees that the algorithm will have a steady stream of signals to refine and enhance targeting accuracy.
Higher Value Leads to Higher MER
Optimizing for a lower-funnel action like Purchase (as opposed to something higher up like “Add to Cart”) focuses Meta on users more likely to complete the full conversion process.
Meta wants to deliver the audience you ask for! If you don’t let them, you miss the chance to improve your ROI over time.
Key Takeaway for Digital Marketers
By aligning your objective with the most valuable and frequent conversion event, you allow Meta’s algorithm to leverage its full predictive power.
This leads to more effective campaign performance and a more efficient ad spend.
Source
Facebook’s Conversion Optimization – Meta Business Help Center explains how focusing on specific objectives influences campaign results by enhancing the machine learning model’s efficiency and targeting.
2. Don’t make decisions based on ad-level metrics
Including CPA, CPM, CPC, CTR, ROAS, etc.
This one is huge. No matter what, avoiding micromanaging individual ad metrics.
Meta’s algorithm uses a holistic view of performance across all metrics to optimize ad delivery.
Disrupting this process by turning off ads based solely on one metric (e.g., a high CPM or low CTR) can reduce the effectiveness of the algorithm’s optimization.
Why it’s important
Multi-Metric Optimization
Meta’s machine learning algorithms look at a broad set of factors, including user behavior, historical performance, and real-time auction dynamics.
Turning off ads based on isolated metrics can remove ads that are still important in the broader context.
For example, an ad with a high CPM might still be part of a sequence that leads to conversions— killing the ad breaks the sequence.
Learning Phase Stability
If you constantly adjust or turn off ads based on isolated metrics, it can reset Meta’s learning phase— which means losing at least 1-2 weeks of conversion potential.
Frequent changes interfere with the algorithm’s ability to learn over time. That would block achieving stable, optimized performance.
Key Takeaway for Digital Marketers
Trust Meta’s machine learning system to manage performance based on overall campaign goals.
Allow ads to accumulate sufficient data before making decisions, focusing on aggregated results rather than individual metrics.
Source
Meta Learning Phase Guide – Meta emphasizes the importance of stable learning phases and discourages frequent changes to campaigns as this disrupts the system’s ability to optimize efficiently.
3. Study the ones with the highest spend
Learn about the users who saw, clicked, and acted. Understand WHY these ads are working.
Understand why certain ads perform well by analyzing the content that Meta prioritizes— which do they spend more on? Why?
More spend = better ad. Meta’s algo prioritizes ads that drive campaign goal desired outcomes.
Ads with higher spend have been ID’d by Metas as more likely to meet campaign objectives effectively.
Why it’s important
Look for patterns.
Ads that attract clicks and conversions are reflective of messaging, visuals, or offers that appeal to your target audience.
By examining these ads closely, marketers can identify patterns, such as specific messaging angles, formats, or creative styles that drive engagement.
Strategic Empathy
Not to be confused with Chris Voss’ Tactical Empathy, putting yourself in the consumer’s shoes to understand why they engaged with certain ads can help refine future ad creative.
WHY are they clicking on THIS ad? What makes this one resonate?
This empathetic approach to analysis can also reveal psychological and emotional triggers that you might not have anticipated.
Key Takeaway for Digital Marketers
High-performing ads are your guideposts.
Rather than reinventing the wheel, study what is already working and explore ways to replicate and enhance these elements in new ads.
Source
Meta Blueprint: Consumer Insights – Meta highlights the value of consumer behavior analysis to improve ad relevance, which in turn enhances campaign performance.
4. Make more ads like those
...but different
Once you ID high-performing ads, create similar ads that retain the winning elements with slight variations.
This allows you to broaden your testing without abandoning what already works.
Why it’s important
Incremental improvement via iteration aliteration
Small adjustments allow you to test variations in messaging, visuals, or calls-to-action, refining your approach without risking a drastic change that could hurt performance.
This process, often called “micro-testing,” allows for incremental improvement over time— just make sure you keep track of your experiments, variants, and winners!
Audience Fatigue
Creating similar but slightly different ads helps avoid “audience fatigue,” where users tire of seeing the same creative repeatedly.
By regularly refreshing your ad content with variations, you maintain audience interest and engagement, keeping your campaigns effective over the long term.
5-10 new assets per month should be plenty for most smaller brands.
Key Takeaway for Digital Marketers
Iterative testing is a best practice in digital marketing.
It allows you to capitalize on high-performing themes while introducing enough variation to continue learning and improving without disrupting the core elements that drive conversions.
Source
Facebook Creative Hub – Meta advises using iterative creative testing as a best practice to optimize and scale ad performance while keeping content fresh and engaging for audiences.
5. Combined impact of these steps
The whole is always greater than the sum of its parts.
By following these four steps, you’re essentially setting up a robust, data-informed system that leverages the full capabilities of Meta’s machine learning and ad delivery algorithms.
Here’s why the entire approach works so effectively.
Data-Driven Optimization
By focusing on high-value goals and leveraging big-picture data (rather than individual metrics), you create empower Meta’s machine learning to operate at its best.
Empathetic Consumer Insights
Studying consumer behavior and optimizing around what resonates allows you to tailor your campaigns to real user preferences, leading to better ad performance.
Continuous Improvement via Iterative Testing
Reproducing successful ad elements with slight adjustments ensures that you’re always improving your approach without losing what makes the ads effective.
Why it’s important
This strategy doesn’t just help with optimizing campaigns but also builds a framework for sustained growth and learning.
You’re not just creating ads; you’re generating insights that compound over time, allowing you to stay ahead of the competition by continuously refining your ad strategy based on actual user engagement and conversion data.
Key Takeaway for Digital Marketers
Mastering Meta’s ad platform isn’t just about understanding the metrics—it’s about creating a balance of data, empathy, and strategic creativity.
By following these steps, marketers can ensure they’re making the most of Meta’s automated systems, engaging audiences authentically, and keeping ad content relevant and effective.
Sources
Credit where it’s due: Barry Hott started an excellent discussion on LinkedIn in Nov 2024, which became the inspiration for this article.
- Meta Business Help Center – Conversion Optimization Guidelines
- Meta Blueprint – Consumer Insights Analysis Best Practices
- Meta Learning Phase Guide – Best Practices for Stable Learning Phases
