This week we’re featuring everything you need to know about using CPA (cost per acquisition) as your testing metric. This metric is simply how much you pay for a conversion.
This a common metric to use for testing in a few different types of accounts:
- Lead generation
- eCommerce when checkout amounts greatly vary
- eCommerce when the checkout amount is always the same
- Subscription sites
Here’s a few examples of business models that should be examining their CPAs:
- Reselling leads and doesn’t want to pay more to acquire a lead than they can sell it for
- Has a long sales cycle and needs to use early funnel proxies to final conversion numbers
- There is a single product to buy (and thus all checkouts are equal)
- The site has a recurring subscription fee that’s fixed each month
Some ecommerce accounts have checkouts that are highly variable and ROAS (return on ad spend) doesn’t work as a testing method, in which case CPA is a good metric to use. For instance, I work with one ecommerce company whose average checkout is roughly $500. However, 5% of their checkouts are for more than $10,000. The keyword and ad that receives those high value checkouts are completely random and there is no pattern. Thus if they were to use ROAS as a bid or testing method the random checkouts would lead to them picking an ad winner that might not lead to the same value the following month; and thus CPA is a better metric for them to use for testing and bid management.
Often with CPA testing, you won’t use just CPA as your testing metric, it’s a great combination metric to use and we’ll address that later in this article.
What is CPA (Cost per Acquisition)?
CPA is how much a conversion costs you.
How CPA is Calculated
CPA is calculated by dividing total cost by total conversions.
CPA = cost / conversions
It is generally displayed as a currency number. Here are some examples:
|4||€ 429.00||11||€ 39.00|
If we don’t correct for the currency differences and assume these were all in the same currency, then ad 4 would have the lowest CPA and ad 2 would have the highest CPA.
The Advantage of using CPA as Your Testing Metric
The primary advantage of using CPA is to control costs and how much a conversion costs you.
For instance, if you are reselling leads for $25, then you might not want to pay more than $15 for a lead.
If you have a long sales cycle, then often you need to do the math throughout the cycle to determine your short term CPAs. For example, let’s say that your sales cycle is:
- Buy clicks to site
- Site’s goal is to collect email address
- If user gives you their email, then you invite them to a webinar
- If user watches 50% of the webinar, then you pass the info to the sales team
- Sales team tries to close lead
Let’s assume we spent $10,000 at $1 CPC and see how much it costs to close the lead:
|Conversion rate||People in funnel||CPA|
|Clicks to site||10,000|
|Accept webinar invite||40%||800||$13|
|Watch 50% of webinar||50%||200||$50|
|Sales team contact rate||25%||50||$200|
|Sales team close rate||20%||10||$1000|
From this information we could work backwards to determine our initial CPA for email collection. If the cost of $1,000 for a new sale is profitable, then we’re in good shape. If we want to increase the people entering the funnel, we can raise the CPA. If the final cost is too low, then we can lower the CPA.
Now, in this particular case, there are many things you can test beyond the ad’s CPA, such as:
- Landing page
- Email invitation
- Webinar sign-up page
- Webinar content
- Sales team contact & follow-up methods
- Sales team script
In some cases, this is much easier. For instance, if you sell a single digital product for $50, you might be OK with a $40 CPA as that will net you a $10 profit on each sale. CPA can be complicated to determine at times; but when you want to watch your costs, CPA is a good metric to use either by itself or in conjunction with other testing metrics.
The Disadvantage of using CPA our Testing Metric
While CPA is great for controlling costs, it isn’t always the best metric to use for testing since it doesn’t take into account the volume of clicks or the conversion rate.
Consider these stats:
For these results, each ad was served the same amount of times (1000). The lowest CPA is ad 4 at $8.25; however, it only has 4 conversions. The ad with the most conversions is ad 5 with 12; but it’s CPA is more than double the lowest CPA ad.
This is often what you fight with CPA – costs versus volume.
Where CPA is a great metric is when you combine it with other metrics.
Combining CPA Other Metrics
In many cases, you don’t want the lowest CPA ad – what you want is to set a threshold for your target CPA and then pick the ad with the most conversions that is at or below your target CPA.
For instance, if our max CPA was $20 this would be our process:
- Eliminate ad 3 as its cost is above our target CPA
- Pick the remaining ad with the most conversions, which is ad 5 in this case
Now, the biggest issue in the real world with this process is that all your ads won’t have the exact same number of impressions. Thus in reality what you usually do is eliminate the ads above your target CPA and then pick the highest CPI (conversion per impression) ad as that will lead to the most conversions at or below your target CPA.
In some cases, you will have a target CPA, but you want the most visitors to see your offer (landing page) and become familiar with your company. This is a common tactic for PPC accounts where many searchers will visit the site multiple times before they convert. In that case, you would use CPA as your filter and then user CTR as your winning ad metric. This is also useful if you are trying to raise quality score (higher CTRs usually mean higher quality scores) but you have a max CPA that you to optimize to at the same time.
If you are trying to filter ads and then pick winners, CPA is always your best filter to use. To see many more examples of using CPA as a filter, please see the article: The Best Ads Testing Metrics for Lead Generation Campaigns.
When testing ads, you often want to create minimum data requirements for some of your metrics before you even examine if a test has achieved statistical significance.
These metrics will vary by your testing metric.
When considering CPA as your metric, there are three considerations to use:
With minimum data requirements; usually you want every ad in the test to reach the minimum data numbers before you examine the tests.
When you consider CPA as a testing metric, you usually care about two metrics: Impressions & conversions from a minimum data standpoint.
Some people are tempted to set a high number of conversions, and only use conversions, as their minimum data when testing by CPA. However, the issue is that if you have an ad with a very low conversion rate, then it might take a long time for that test to hit minimum data since the ad rarely converts. Thus you usually don’t want to set a very high minimum data number on the number of conversions every ad must have before a test is valid.
Now, you always have to temper this by how your account acts. For instance, if you are doing 500 conversions a day in your ad group and you are testing 3-5 ads, then you might set a threshold of 100 conversions per ad. Just remember, if one ad doesn’t achieve that number, then the test will never be complete.
The same issues occurs for setting minimum clicks. If an ad is poorly written and no one clicks on it and you are using high minimum clicks, then you might never see test results. Therefore, you might not set minimum data for clicks at all in testing CPA.
The primary number you want to set as your minimum is impressions. When an ad is displayed, then you have a chance at a click, cost, conversion, and a way of determining your CPA for each ad. If you have a very high volume ad group, you might set a very high minimum impressions (such as 1000). If you have medium volume ad groups; then you might only use 500 impressions. If you have low volume ad groups, then it is recommended that you use multi-ad group testing instead of A/B testing and can once again set higher thresholds.
Just remember, your ad rotation settings will also determine how often an ad is shown. So if you are using optimize for conversions or clicks, it will take longer for each ad to achieve minimum data.
CPA is a very important metric for most accounts (some ecommerce being the exception) as it determines how much you pay for a conversion. You can compare that data to your actual revenue per conversions and ensure that your PPC account is profitable.
The downside of CPA is it doesn’t take volume into account (such as CTR or conversion rates); and thus while it’s a great metric to know and use, it is rarely a metric you will use exclusively in your ad testing. What CPA is great for is filtering ad tests. Use CPA as a filter to remove ads that are above your target CPA; and then you can use another metric, such as CPI or CTR to determine the winner of the ads that are within your target CPA.
This combination of using CPA as a filter and another metric to determine a winner of ads that are left is a great way to ensure that your account is profitable and that you are maximizing the other goals your account has, such as most conversion, most clicks, etc.
If you would like to easily see your Cost per Conversion along with other testing metrics, take a look at what Adalysis has to offer. You can easily see your Conversion Rates along with many other metrics to make quick determinations as to what ad really is the best one to use for your account to ensure that you are hitting your advertising goals.