With advancements in technology and the increasing number of digital media users, digital advertising models have surpassed traditional advertising methods, leading to a major transformation. The widespread use of smartphones, tablets, and computers, coupled with the growing influence of social media, continues to position digital advertising as a dominant force in the industry.

This new digital landscape has enabled the development of new target audiences, behaviors, and marketing strategies. Digital algorithms leverage machine learning to connect businesses with potential customers effectively.

The primary advantages of digital advertising over traditional methods are its ability to target specific audiences and measure metrics with precision. Leading platforms like Google and Facebook (Meta) have provided the necessary infrastructure for digital advertising, introducing various ad types to reach potential customers efficiently.

Popular Digital Advertising Models

1. Banner (Display) Ads
2. Social Media Ads
3. Influencer Marketing
4. Native Advertising
5. Video Ads
6. Email Advertising
7. Mobile Ads
8. PPC (Pay-Per-Click) Ads
9. Remarketing
10. Pop-Up Ads
11. Search Engine Advertising (SEM & SEO)

Frequently Used Ad Platforms

• Facebook Meta Business
• Google Ads
• LinkedIn Ads

Many brands are now complementing traditional advertising with increased investment in digital advertising to reach their target audiences faster, more economically, and with measurable results.

But how exactly do these algorithms reach the target audience? Let’s explore the Learning Phase concept introduced by Facebook (Meta) to optimize ad performance.

What is the Learning Phase?

The Learning Phase is an optimization period designed by Facebook to maximize ad performance. During this phase, new ad campaigns or ad sets with modified targeting collect sufficient data to achieve optimal results.

In this process, the algorithm begins to gather data by showing ads to various individuals to identify the audience most likely to engage with the product or service. The Learning Phase refers to this data collection period, which allows the algorithm to determine the most effective factors for reaching the target audience and enhancing ad performance.

During this phase, since the ad optimization is incomplete, the Cost Per Acquisition (CPA) is higher. As the phase progresses, ad performance improves, and conversions become more cost-efficient over time.

What Extends the Learning Phase, and How Can It Be Prevented?

Factors Affecting the Learning Phase

1. Campaign Adjustments
   • Budget Changes
     Adjusting campaign budgets by more than 25% can prolong the learning phase. Ad sets require sufficient optimization activity over a 7-day period to exit the learning phase successfully. The campaign budget must allow for approximately 50 conversions within this timeframe. Budget caps or limits preventing this can delay optimization.
   • Bid Strategies
     Changing bid strategies within an ad set, such as shifting from engagement-focused to reach-focused strategies, resets the optimization objective and restarts the learning phase.
2. Ad Set Adjustments
   • Targeting Adjustments
     Changes to the target audience, location, or narrowing focus to specific interests can impact the learning phase. A broader audience can lead to better conversion opportunities and faster exit from the learning phase.
   • Pausing Ads for Over 7 Days
     Stopping ad sets for extended periods can hinder active campaigns.
   Multiple ad sets within a campaign can split the delivery, delaying optimization. Consolidating ad sets and increasing the overall budget for a single ad set can reduce the learning time.
3. Ad-Level Adjustments
   • Changes to Visuals, Text, or CTA
     Altering creative elements within an ad set affects the learning phase. While such changes can enhance results, any modifications during the early stages of a campaign can disrupt the machine learning process.

Outcome of the Learning Phase

The learning phase optimizes bids and budgets for higher performance. By considering metrics such as user behavior, relevance, time, and day, the process ensures more effective targeting and cost efficiency.

To achieve the best results, refrain from modifying ad sets or campaigns during the learning phase. Plan and implement all changes collectively to minimize disruptions to the optimization process.

By adhering to these guidelines, advertisers can maximize the benefits of the learning phase, ensuring their campaigns deliver optimal results.