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Why You Shouldn’t Duplicate Your Facebook Campaigns


There are many ways to optimize your Facebook advertising campaigns. An entire industry was created based on how to effectively run Facebook ads. The lack of official recommendations by Facebook created a gap for ongoing discussions and debates on the best ways to scale and optimize your ads. Unsupported theories and secret hacks became a big part of the narrative of running effective ads. One of the most common strategies that I’ve come across is the duplication method. 

If you are not already familiar with it, the duplication method is when you take an existing campaign or ad set and duplicate it to get better performance and larger spend. Advertisers who use the duplication method claim to use it for the following reasons:

  • Use duplication when ads are not delivering. If a specific ad doesn’t get delivery because it’s in the same ad set with another ad that gets all the reach, they duplicate it to force delivery.
  • Duplicate a high performing ad set or campaign to extend performance and spend.
  • The fear that if they increase their campaign budgets too quickly, performance will suffer. Instead, they choose to duplicate.

To explain why duplicating a campaign is a bad idea, it’s important to understand the fundamentals of the learning system which the algorithm uses to optimize your ads. 

Facebook defines the learning phase as the period when the optimization algorithm still has a lot to learn about an ad set. During the learning phase, the delivery system is exploring the best way to deliver your ad set, so performance is less stable and cost-per-action (CPA) is usually worse. The learning phase occurs when you create a new ad or ad set or make a significant edit to an existing one. The learning phase is complete once Facebook has enough data to predict future performance and delivery. When learning is complete, you can scale your ads more aggressively and expect relatively consistent delivery. 

Learning is stored in two places. The first is in your ad set that is running the ads. The second is within your pixel that’s placed on every page of your website. Facebook uses a combination of both data sources to perfect the learning and deliver the best performance. 

Your goal as an advertiser is to have as much data as possible. More data in the ad sets means a more stable and effective delivery. If you want to leverage Facebook’s machine learning capabilities, you need to set up your campaigns in the most effective ways. This means that you should aim for the fewest amount of campaigns possible and combine as much data into as few ad sets as possible. This will help you provide turbulence-free ads with more stable and scalable results.

 

Here is evidence to support the above claim: 

 

 

As you can see, Facebook recommends minimizing the number of ads running and focusing the budgets on the bigger ad sets.

 

 Here are the top reasons why you shouldn’t use the duplication method to scale your campaigns.

 

  • You’ll miss out on the snowball effect. The snowball effect happens when your ad set receives a lot of conversions and performance rapidly increases. The more data the ad set has, the bigger the snowball becomes. One ad set with 50 conversions will perform better than two ad sets with 25 conversions each. The snowball effect works off your ad set data. The more data it has, the more accurately it will be able to deliver your ads at the best rate possible. When you duplicate your ad set to expand delivery, you are resetting the data in the new ad set and starting from zero.
  • Multiply your learning phases. Similar to the point above, when you use duplication as a regular method to scale your campaigns, you will have a lot of ads that are stuck in the learning phase. Because they will each get less delivery, it will take longer for ad sets to complete their learning phase. Having the majority of ad sets in the learning phase will result in unsteady performance, which will make it more challenging to scale your daily or monthly spending.
  • Audience overlap. Having overlapping audiences is not always a bad thing, but it can lead to poor delivery or performance of your ad sets. The more ad sets you to have, the more likely they are to perform against the same audience in the same auctions. Theoretically, it can impact your bidding and the actual price of impressions.
  • Budget and bidding management. Instead of having to worry about optimizing your budgets and bids for a few campaigns, duplicating your ad sets or campaigns will require more work. You’ll have to analyze each ad set and campaign based on different timeframes and adjust the bids and budgets for them individually. It’s not just about lowering budgets when some campaigns underperform. You can also miss out on opportunities. Some of your ad sets might have reached their full daily budget and had a great day, but you didn’t manage to increase them on time because you had so many to manage.

 

What’s the alternative to duplicating? 

Instead of using the duplication strategy to try to scale your campaigns horizontally, use the best practices to grow your campaigns vertically. Less is more. Create a limited number of campaigns and ad sets. Have faith in the algorithm and let your ads run without interference until the learning phase has been completed. Once it has been completed, instead of duplicating, grow your current ad sets by increasing their budgets and bids. Remember that there are no secret hacks that can deliver better performance. Work with the algorithm and rationally increase bids and budgets.

You should only duplicate your campaigns if you want to test a different style of creatives, audiences, or products. Another reason to use duplication is if your previous well-performing campaign has stopped delivering because you made too many changes or external factors impacted the performance of the ad set. 

When you use the best practices you are leveraging the power of machine learning instead of working against it. 

In summary: 

There are many tactics for optimizing your ads. Different advertisers use different methods. One common method for scaling campaigns is called the duplication method. Rather than increasing your ad set or campaign budget, you would instead duplicate your campaign and aim to grow your campaigns horizontally. Using the duplication method harms the algorithm’s performance, creates an overlap in the audience, impacts the learning system, and requires more work in terms of managing campaign bidding and budgets. I highly recommend reviewing the best practices and focusing on fewer ad sets to accumulate more data-rich ad sets for best performance



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