How to Get Your Ads Out of the Facebook Learning Phase: Avoid Frustrations with ScaledOn’s LAL Audiences Strategy

Are you tired of your Facebook Ads getting stuck in the learning phase, limiting your campaign’s reach and performance? We, at ScaledOn, understand the frustration and challenges that come with these limitations. In fact, if your campaign fails to achieve at least 100 optimization events within the first 30 days, Facebook may limit your ads’ effectiveness. But fear not, as we’re here to share our expertise on how to bypass this issue and give your ads the boost they need.

Leveraging Lookalike (LAL) audiences is a key strategy we use to enhance ad performance and quickly break free from the learning phase constraints. By creating an LAL audience, you allow Facebook to match your target customers with similar individuals, expanding your reach and chances of conversion.

In this article, we’ll guide you through the steps to effectively use LAL audiences and share our proven techniques to get your ads out of the Facebook learning phase. By implementing these strategies, you’ll witness improved efficiency in your campaigns, enjoying increased conversions and sustained growth on the social media platform.

Understanding the Facebook Learning Phase

Significance of Learning Phase

The learning phase is an essential stage for Facebook Ads as it helps the platform’s algorithm to identify the best strategies for our campaigns. During this phase, our ads’ performance may be less stable and cost per result might be higher. However, it also ensures that we can ultimately optimize our campaigns for better results. By using techniques such as Lookalike (LAL) audiences, we can avoid getting stuck in the learning phase and push our Facebook Ads performance to the next level.

Machine Learning Behind Facebook Ads

Facebook Ads leverages machine learning to understand ad performance and optimize ads for maximum engagement and conversions. Each time one of our ads is shown, Facebook’s delivery system learns more about the ideal target audience, times of day, placements, and creative elements. Aiming for a minimum of 100 optimization events in 30 days can move our ads out of the learning phase, where they will perform at their most effective.

Challenges in Facebook Advertising

Ad Sets Limitation

In the world of Facebook advertising, one of the challenges we at ScaledOn faced was dealing with ad sets limitation. The learning phase may limit the performance of ad sets when they fail to achieve 100 optimization events within 30 days. This limitation can hinder the campaign’s success as it prevents the delivery system from optimizing the ad sets effectively. By using Lookalike Audiences (LAL), we were able to avoid the learning phase limitation and improve the performance of our campaigns.

Budget and CPA Hurdles

Another challenge we encountered was managing the balance between budget and Cost Per Action (CPA). Facebook’s advertising algorithm requires an adequate budget to learn and optimize the ads for better performance. However, setting a low budget might result in suboptimal performance. On the other hand, aiming for a higher CPA can increase the overall costs and affect the return on investment (ROI) negatively.

To tackle this issue, we carefully analyzed the performance data and made informed decisions to set an optimal budget level that would yield better results without sacrificing the efficiency of our ad spend. We also implemented monitoring and fine-tuning of our CPA goals to ensure we were not going overboard with our costs, yet still maintaining a competitive edge in the market.

By understanding and overcoming these challenges in Facebook advertising, we, at ScaledOn, succeeded in optimizing our ad set performance and balancing efficient budget allocation with realistic CPA goals, resulting in improved campaign outcomes.

Key Factors Impacting the Learning Phase

Bid Amount and Strategy

One of the key factors that can impact the Facebook Ads learning phase is the chosen bid amount and strategy. In our experience at ScaledOn, we have found that selecting the right bidding setting plays a crucial role in driving better results. When setting your bid amount, consider the value you want to attribute to your optimization event. This can help you be more competitive in the auction.

Audience size is another aspect closely related to bidding strategy. With too narrow an audience, your ad set might struggle to exit the learning phase, as it will have less potential for optimization events. On the other hand, too broad an audience might lead to inefficient spending. Striking the right balance between bid amount and audience size can help your ad set to learn faster.

Target Audience and Size

Understanding your target audience and their size is essential when it comes to the Facebook Ads learning phase. As mentioned earlier, using Lookalike Audiences (LAL) can be an effective way to avoid the learning limited status. LAL audiences enable you to extend your reach by targeting people who are likely to be interested in your product or service based on their similarity to your existing customers.

At ScaledOn, we recommend keeping a tailored audience size without making it too narrow or too broad. A smaller audience may lack enough optimization events to complete the learning phase, while a larger audience might result in poor performance. So, it’s vital to choose the appropriate audience size that allows your ad set to gather enough data and subsequently optimize for better performance.

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Importance of Optimization in Facebook Ads

At ScaledOn, we understand the significance of optimizing Facebook Ads to ensure maximum efficiency and effectiveness. Optimization plays a crucial role in Facebook Ads, not only for improving ad performance but also for avoiding the dreaded learning phase, which can limit ad reach. By using Lookalike (LAL) audiences, we can help you overcome the learning phase limitations and achieve better results.

Optimization Event

An optimization event is an essential aspect of Facebook Ads optimization. These events refer to specific user actions, such as clicking on a link, viewing a video, or making a purchase that show engagement and a higher likelihood of conversion. Facebook utilizes these optimization events to effectively target people more likely to take a desired action through your ad. Thus, ensuring your ads are achieving enough optimization events can help move your campaigns out of the learning phase quickly and enhance ad performance.

We, at ScaledOn, focus on leveraging optimization events to identify and target the most potentially valuable audiences for your campaigns. This approach significantly increases your ads’ chances of engaging with the relevant audience, leading to better campaign results and minimizing the chances of your ads being stuck in the learning phase.

Data Conversion Event

Another vital component of Facebook Ads optimization is the data conversion event. These events are specific actions taken by users, such as signing up for a newsletter, adding a product to the cart, or completing a purchase, that indicate progress towards achieving your campaign’s goal. Having a clear and well-defined conversion event helps Facebook’s algorithm to optimize and target users likely to complete these desired actions.

Our team of experts carefully assesses and selects the most appropriate data conversion events for your campaigns based on your business objectives. By doing so, we ensure that Facebook’s algorithm is effectively guided towards reaching users more likely to convert, leading to better campaign performance and reducing the likelihood of being stuck in the learning phase.

Strategies to Exit Facebook Learning Phase

Maintain High Ad Volumes

A key element to escaping the Facebook learning phase is to maintain high ad volumes. When we ensure that our ads have enough optimization events, it reduces the chances of our ads becoming learning limited. By running multiple ads simultaneously, we can collect sufficient data to optimize our campaigns and quickly exit the learning phase.

While working with high ad volumes, it’s essential to monitor performance and adjust the campaigns accordingly. By remaining vigilant, we can spot low-performing ads and make necessary changes to improve their success rate.

Establish Realistic Budgets

Setting a realistic budget for our campaign plays a crucial role in avoiding the learning limited status. With a balanced budget, we allocate our resources effectively, ensuring that our ads receive enough impressions to meet the required result actions. When determining our campaign budget, we should take into consideration factors such as the target audience, the objective, and the desired outcome.

A well-thought-out budget helps us to gather more data within a shorter period, which in turn allows our ads to complete the learning phase and achieve optimal performance.

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Effective Audience Targeting

One of the best strategies we employ to exit the learning phase of the Facebook ads is effective audience targeting. By utilizing Lookalike Audiences (LAL), we can find people who are similar to our existing customers. This method greatly increases the chances of attracting users who are most likely to convert.

When we establish targeted audience segments, we provide our ads with more relevant and actionable data. This enables us to avoid issues such as learning limited and ensures that our campaigns are optimized for success.

By following these strategic approaches, we at ScaledOn can expertly navigate the Facebook learning phase, optimizing our ad campaigns and maximizing results for our clients.

Avoiding Significant Edits to Maintain Ads Performance

At ScaledOn, we know how to tackle the issue of Facebook Ads being limited by the learning phase when a campaign doesn’t achieve 50 optimization events within the first 7 days. One of our strategies is to employ Lookalike Audiences (LAL). In this section, we’ll discuss some key approaches to avoid significant edits and maintain ad performance.

Place Stable Bids

Placing stable bids is crucial to maintaining optimal ad performance. Chopping and changing bid values can cause your ad set to re-enter the learning phase, which can hinder your campaign’s ability to optimize. We advise setting realistic bid values based on historical data and market trends. This allows our campaigns to stay on track and avoid constant bid adjustments, ensuring your ads reach their target audience more effectively.

Minimize Ad Set Edits

Avoiding unnecessary significant edits during the learning phase helps prevent disruptions to ad performance. We recommend limiting changes to ad sets, as it aids in quicker optimization. These significant edits may include adjustments to targeting, creative elements, optimization events, or adding new ads to your ad set. By minimizing edits, we allow the Facebook delivery system to stabilize and optimize ad performance, ultimately bringing your campaigns closer to achieving their objectives.

Best Practices for Utilizing Facebook Ads Manager

Optimal Ad Placement

When it comes to getting the most out of our Facebook ads, it’s crucial to ensure optimal ad placement. By selecting the right placements, we can make sure our ads get in front of the right audience and avoid getting stuck in the Facebook learning phase. We can leverage the placement optimization feature available within the Ads Manager to automatically find the best placements for our ads across various platforms such as Facebook, Instagram, and Messenger.

Keep in mind that it’s essential to test different placements to understand which ones work best for our business. Additionally, monitoring and refining our placements can help increase the ad performance, minimize the cost per conversion, and eventually get our ads out of the Facebook learning phase quicker.

Consolidating Multiple Ad Sets

Another strategy to improve our Facebook ad performance is consolidating multiple ad sets. When we have too many ad sets running, each ad set might struggle to gain enough conversions to exit the learning phase. By consolidating similar ad sets into a single one, we can accumulate more conversions and speed up the learning process.

For example, when targeting multiple Lookalike Audiences (LAL), we can combine them into one ad set instead of having separate ad sets for each audience. This not only streamlines our campaign but also reduces ad fatigue and helps us avoid the limitations of the learning phase.

To sum up, utilizing Facebook Ads Manager effectively and following best practices like optimal ad placement and consolidating multiple ad sets can significantly improve our ad performance. At ScaledOn, we leverage these strategies and more to help our clients achieve their advertising objectives on Facebook.


We at ScaledOn understand the importance of getting your Facebook Ads out of the learning phase. We know that campaigns can get stuck in the learning phase if they don’t achieve 100 optimization events within 30 days, which may limit your ad’s reach and performance. That’s why we advocate using Lookalike (LAL) audiences to help overcome these limitations and optimize your advertising efforts.

Utilizing LAL audiences can lead to better audience targeting, allowing your ads to reach a wider range of potential customers who share similar characteristics with your existing customers. This method can significantly improve the likelihood of achieving the necessary optimization events within the 30-day time frame, thus pushing your ads out of the learning phase.

In addition, we believe in constantly monitoring and analyzing ad performance, making adjustments as necessary to address any issues that may arise during the learning phase. By staying proactive, we can ensure that our campaigns continue to progress and generate meaningful results.

In short, our expertise in using LAL audiences, combined with our commitment to ongoing analysis and optimization, makes us confident in helping businesses improve their Facebook Ads performance and successfully exit the learning phase. Trust us to unlock the full potential of your advertising campaigns and maximize your return on investment.