Episode 15: Optimizing Facebook for eCommerce post iOS 14.6 Updates

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Believe it or not, the media world is still facing data optimization issues with iOS 14.6 (*sigh). We know. Despite all of the confusion, loss of data, and frustration that comes with it, we still have hope. 

In attempts to adapt to the ever-changing art of optimization, we have developed a strategy, especially for eCommerce websites to still thrive when it comes to ROAS.

It’s multi-faceted – take a look!


Success on the optimization side is a science so we are breaking this down on the ad level to give you our exact strategy and formula for seeing more return on ad spend:

Rules of Thumb

1. Test Ads Against Ads

Whenever you’re testing an ad, it should always be tested against another ad. It is important to note that ads with the most data should be kept live. Whether those ads are performing well or nowhere near what you want, the data here is very valuable so you can test against it.

2. Make Sure Each Ad Gets Enough Data

For example, if you have 8 ads live, there is probably not going to be enough data distributed to each one of them. Don’t split your budgets too far! We typically test 3-4 ads at a time and vary between 2-3. We optimize down to 2 ads, then add a third in, and so on.

Note: For really strict and tight budgets, you can have 2 ads and keep a hero ad in. Focus your spend on the 2 ads and test against that hero ad.


Add-to-Cart & Initiate Checkout

So how do you optimize when you test an ad against another ad (given you have 2 different ads live)? Let’s use an example – pretend you have a scenario similar to the following:

Current Avg. Cost Per Sale = $25

Desired Avg. Cost Per Sale = $20

The next step is to ask yourself, how much do I spend on that ad? Typically, we like to spend 1x-1.5x of the desired cost per sale. So for this scenario, that could be ~$30.

Current Avg. Cost Per Sale = $25

Desired Avg. Cost Per Sale = $20

Test Ad Spend = $30

The what? If it gets sales, great! If not, we cut it. However, there are a few “buts” that come into play before you can officially cut:

  • Does it have a Cost per ATC (Add-to-Cart) rate that is at or lower than the next best ad?
  • Does it have an IC (Initiate Checkout) rate that is at or lower than the next best ad?
  • How does the CTR (Click-Through Rate) look compared to the next best ad? (we will cover this in the next section)
  • How does the CPV (Cost Per Page View) look compared to the next best ad? (we will cover this in the next section)

In this case, let’s say you were driving a Cost per ATC at $5 (1 out of every 5 ATC became a sale) from another ad and when you test your $30 ad against it (with no sales), you see that the Cost per ATC for that ad is $3. You also see that you drove more ATC for significantly less money on that ad. For this scenario, we say leave it live!

This is why: once a user gets to the ATC point, the percentage of ATC to purchase is always consistent if it’s left long enough. This is the same thing for IC. Some accounts get more data on the ATC level and some get more on the IC level.

When we let this play out more, typically we spend around 1.5x-2x more on that ad compared to the next best, so in this case, that would be now ~$50.

Note: If we do decide to cut an ad, we usually go back and relaunch it a few days later to see how it does. We also relaunch when it comes to an ad performing well in one audience and not another. In the audience that didn’t perform well, we relaunch that ad in another space.

Click-Through Rate & Cost Per View Content

The next set of metrics we look at is CTR (Click-Through Rate) and CPV (Cost per View Content) or Product Page View. If our CTR is double whatever our next best ad is, we keep spending in this ad the same way we would spend if we saw a really low Cost per ATC.

So, again in this example:

Test Ad Spend = $30

Purchases = 0

CTR = 2x (than the next best ad)

CPV = Equal or lower (than the next best ad)

We would let this play out and leave the ad live. This second layer of CTR + CPV can help you identify an ad that needs just a little more time to start bringing in more conversions later at a better cost.

How iOS 14.6 Affects This

Before iOS 14.6, we didn’t look at CTR too heavily, but with changes to the data being pulled in, we are relying more on CTR more than ever. 

Even with all of the data loss, CTR is a metric on Facebook that hasn’t lost its data! So Facebook is leaning more into CTR data to rank advertisers in the auction, which is something to look out for.

The Significant Number of 2 Metrics

This next tip summarizes our strategy for all formulas. We have determined that if two different metrics together look significantly better than the ad that we are testing against, we will go ahead and leave that live just to see if sales come in later.

Remember, pausing an ad isn’t the same as cutting it! If it needs to be paused, it just means that at that particular time, the ad isn’t performing its best in the auction.

How iOS 14.6 Affects This

It is even more necessary now to pause, relaunch, and let ads play out because iOS 14.6 is delaying our data! We are sometimes waiting 3 days for data so we have developed a process to look back at the last 1 day, 3 days, 7 days, and even 30 days.

Sometimes ads will look terrible, but we get the delayed sales data in and then relaunch that ad.


Did any of these strategies catch your eye? Let us know in the comments!

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