Meta advertising plays a significant role in businesses expanding into overseas markets, with many companies choosing to advertise on the Meta platform to reach global users.
However, advertisers have been seeking more precise and efficient methods during the ad placement process, but the lack of proper placement advice leads to time and effort consumption.
Issue 1
Image Size Adaptation
During the Facebook ad placement process, different positions have their own size requirements. The same image often cannot adapt well to multiple positions, leading to the image being stretched or distorted. This not only affects the visual effect of the advertisement but may also create a negative impression of the product or service to potential customers.
For example, a originally exquisite product image, when stretched, becomes blurry or disproportioned, severely affecting the visual impact of the advertisement and reducing user engagement.
Issue 2
Uniform Ad Copy Across Different Positions
The ad copy is uniform across different positions, lacking personalized customization and creativity, leading to issues such as lack of concept, visual and content discordance. This makes the advertisement hard to stand out among many, fails to spark user interest, cannot effectively convey value information, and lacks interactivity and emotional resonance, thus limiting views and resulting in low click-through rates, while also increasing the cost per effectiveness.
Issue 3
Creative Fatigue
The same material is used for a long period, yet it fails to complete the machine learning phase, leading to creative fatigue. When ad creativity is repeatedly presented, user interest gradually decreases, along with click-through and conversion rates.
Issue 4
Audience Fragmentation
Ad campaigns use similar settings and creatives, but target different audiences. These campaigns may need more time to exit the machine learning phase and require more budget to start optimizing ad performance.
Meta data shows that A+SC campaigns reduce average costs by 17% compared to manually set campaigns and maximize effects by matching the best ad combinations with the audience in an automated system mode.
Addressing these challenges in ad placement, advertisers previously needed to spend a lot of time and effort on checking and optimizing advertisements.
