How To Get 100% ACCURATE Facebook Ads Data (Really!)

TL;DR

Learn methods to achieve accurate Facebook ads data post iOS 14.

Transcript

in this video i'm going to show you how to get 100 accurate facebook ads data yes it really is possible and there's a couple of different ways to do it i'm also going to share a facebook ads conspiracy theory that might help explain some of the weird stuff that's been happening to do with facebook ads over the past 12 months or so make sure you sti... Read More

Key Insights

• The iOS 14 update has significantly impacted the accuracy of Facebook ads data, complicating campaign optimization and profitability assessments.
• To achieve accurate data, advertisers can transition to in-app campaigns, keeping users within Facebook or Instagram, which provides more reliable tracking.
• Lead generation campaigns using instant forms offer a low-effort way to gather accurate data, though they may result in lower-quality leads due to reduced user friction.
• Facebook shops are another in-app option for e-commerce businesses, providing better data accuracy but with fewer features compared to platforms like Shopify.
• A high-effort approach involves creating unique landing pages for each ad, allowing precise tracking through Google Analytics but not feeding data back into Facebook's machine learning.
• Facebook may incentivize in-app advertising by offering lower CPMs, encouraging advertisers to keep users within the platform.
• The video suggests a conspiracy theory that Facebook might prefer the iOS 14 changes as they push advertisers towards in-app solutions, which benefit the platform.
• Businesses need to evaluate whether the benefits of accurate data outweigh the effort required for these strategies, considering their specific needs and advertising goals.

Questions & Answers

Q: How has iOS 14 affected Facebook ads data accuracy?

The iOS 14 update has significantly reduced the accuracy of Facebook ads data. This change makes it challenging for advertisers to accurately track leads and sales generated by their campaigns, complicating performance assessments and optimization efforts. The lack of precise data also affects Facebook's machine learning capabilities, potentially leading to a drop in campaign results.

Q: What are the low-effort methods to achieve accurate Facebook ads data?

Low-effort methods to achieve accurate Facebook ads data include transitioning to in-app campaigns. This involves using lead generation campaigns with instant forms or setting up Facebook shops for e-commerce businesses. Keeping users within the Facebook or Instagram app allows for more reliable data tracking, as opposed to sending users to external websites where data accuracy drops.

Q: What are the benefits and drawbacks of using instant forms for lead generation?

Instant forms for lead generation provide the benefit of lower lead costs due to reduced user friction, making it easier for users to submit their information. However, this ease can result in lower-quality leads, as it does not filter out less interested individuals. Meta is working on tools to improve lead filtering, which may enhance the quality of leads over time.

Q: How do Facebook shops compare to other e-commerce platforms like Shopify?

Facebook shops offer more accurate data tracking by keeping transactions within the app, but they currently have fewer features compared to platforms like Shopify. This limitation might be significant for businesses that rely on advanced tools for store setup and optimization. However, Facebook shops can reduce user friction, potentially improving the user experience.

Q: What is the high-effort method for achieving accurate Facebook ads data?

The high-effort method involves creating unique landing pages for each ad, campaign, or ad set, allowing precise tracking through Google Analytics. This approach provides accurate data on user behavior and conversion rates but does not feed data back into Facebook's machine learning, which could affect campaign optimization. It's suitable for businesses that prioritize using their website.

Q: What is the proposed conspiracy theory regarding Facebook's stance on iOS 14 changes?

The conspiracy theory suggests that Facebook might not be opposed to the iOS 14 changes, as they encourage advertisers to transition to in-app campaigns, which benefit the platform. By keeping users within Facebook or Instagram, Facebook can maintain user engagement and potentially offer advertisers lower CPMs as an incentive for in-app advertising.

Q: Why might Facebook offer lower CPMs for in-app campaigns?

Facebook might offer lower CPMs for in-app campaigns to incentivize advertisers to keep users within the platform. By doing so, Facebook can ensure users spend more time on their apps, enhancing user engagement and benefiting the platform's overall ecosystem. Lower CPMs make in-app advertising more cost-effective, encouraging advertisers to adopt this approach.

Q: How should businesses decide between the low-effort and high-effort methods?

Businesses should evaluate the importance of sending users to their website versus keeping them in-app. If using a website is crucial, the high-effort method of unique landing pages might be suitable. However, if data accuracy and cost-effectiveness are priorities, the low-effort in-app campaigns could be more beneficial. Testing both methods can help determine the best approach for a business's specific needs.

Summary & Key Takeaways

• The video discusses the challenges advertisers face with Facebook ads data accuracy post-iOS 14 and offers solutions to improve data reliability. It highlights in-app campaigns as a way to achieve accurate data, with options like instant forms and Facebook shops, each with its pros and cons.

• A conspiracy theory is proposed, suggesting Facebook might favor the iOS 14 changes as they encourage advertisers to keep users in-app, benefiting the platform. Lower CPMs for in-app campaigns could be an incentive for advertisers.

• The video also covers a high-effort strategy of creating unique landing pages for better tracking, though it doesn't aid Facebook's machine learning. Advertisers must weigh the importance of data accuracy against the effort required for these methods.