This week we’re featuring everything you need to know about using RPI (Revenue per Impression) as your testing metric. This is sometimes called profit per impression (PPI).
The difference between RPI or PPI isn’t in the metric calculations within your PPC account. The difference has to do with how you are passing data to your AdWords account and if you are using ROAS or ROI in your data. To see the difference, please take a look at this article: Understanding Your Testing Metrics: ROAS vs ROI vs Conv. Value / Cost.
In this article, instead of constantly saying revenue/profit per impression (depending on how you are passing data) – we’re going to be consistent and just use RPI and revenue. However, if you are taking out the cost of goods (or don’t have any hard costs) before you pass your revenue data to AdWords; then you can substitute the word profit instead of revenue throughout this article.
RPI (revenue per impression) is a metric that shows you the ratio between your impressions and the amount of money you make. This is very similar to conversion per impression (CPI) with the exception that we are adding actual revenue into the equation and not just using conversion data.
When you consider ad testing, which combination is better?
It’s impossible to say which is better since that information relies on three different metrics: CTR, Conversion Rate, Revenue (or RPS – revenue per sale).
When you have variable checkout amounts, instead of using conversions, using revenue gives you a more accurate picture of how much money you are making and lets you accurately determine ROAS and Conv. Value/Cost. For items that are consistently sold – this is the best metric in most cases to use as its your actual sales data.
There is a time when using CPI or plain conversion data is better than using revenue in your testing and management: when you have random outlier sales that skew the data drastically and are not repeatable.
For example, an early ecommerce client of ours makes about 300 sales a month. Their average sale is roughly $500. However, of those 300 sales, roughly 10-20 of them are for orders that are over $10,000. Month over month – they get 10-20 high value orders that are much higher than almost any other sale on their site. The ads and keywords that bring in these sales are never the same month over month. The fact they will get a sale from a keyword or ad is predictable; however, the amount from the sale is unpredictable. Therefore, using revenue for bidding or ad testing metrics is a bad idea since the data one month will not be consistent with the data the following month. However, since the fact they will get a sale is predictable, just not the revenue from the sale, they are best off to use CPI (conversion per impression) in their ad testing and bid management.
Another exception is when you want the most customers possible regardless of their checkout amounts. For instance, if you are trying to build a customer base then you would be happier with 1000 sales at $10 ($10,000 in revenue) than 500 sales at $30 ($15,000 in revenue). This is also an exception case and not the common management for most companies.
Outside of those edge case scenarios, if you are in ecommerce or have variable checkout amounts (such as a hosting company, domain name, or even consulting packages), then using your actual revenue allows you to maximize your ad testing towards higher revenue and not just conversions.
We should understand that measuring revenue and what you are actually making is more important to most companies than just measuring conversions. As your ads can affect average order value, upsales, cross sales, etc – you want to measure how much the ads are actually making you and not just how many conversions they are bringing to your PPC account.
The question for most people is: why should we measure from the impression?
The problem with using conversion rate as a testing metric is that it assumes you received the click. It doesn’t care about how often an ad is clicked – it only cares about how often someone converted after clicking on your ad. It is a good testing metric for landing pages since the page itself does not attract the click. However, in ad testing, we’re testing how we achieve the traffic, the volume possible, and what message helps to prequalify a user to convert. Therefore, measuring from just the click ignores how often an ad can be clicked and thus it completely ignores the volume of possible sales.
If you think about it, every time your ad is displayed – you have a chance of a conversion. You picked a keyword. Someone searched for your keyword. At this point in time there’s a chance of a conversion. The user must both click on your ad and then convert to receive the actual conversion, but measuring from the impression shows you the total conversions and revenue possible.
RPI is calculated by dividing the total revenue by the total impressions.
RPI = revenue / impressions
It is generally displayed as a currency type. Here are some examples:
|Ad||Impressions||Conversions||Revenue||Average Sale Amount||RPI|
To make this an easy illustration, we used the exact same number of impressions for every ad. Rarely is this the case. However, since we used the same impressions, the highest revenue (ad 4) is the highest RPI (0.84).
This ad is not the highest ratio of conversions (ad 3) or the highest average sale amount (ad 5). Ad 4 is the highest revenue per impression – meaning when ad 4 is displayed, you make more money than any other ad.
If your focus is to maximize your revenue, then you would want to use ad 4 as your winner.
The Advantage of using RPI as Your Testing Metric
The main reason to use RPI is when you want the most revenue possible. As this metric takes into account both CTR and actual revenue, it’s a simple metric that will show you which ad will lead to the most total revenue possible.
There are times that when you examine the full metrics behind various ads, you might not always pick the highest CPI winners. This usually happens for a few reasons.
Let’s take a look at a full chart of data and then examine how we’d pick the various winners (click the chart to see a larger version).
If we just want the most conversions possible; then ad 3 is our clear winner. It receives 15 conversions for every 10,000 impressions. If our goal is the most conversions – this is our winner. However, it has a lower ROAS and RPI than some other ads.
If our goal is ROAS; then ad 1 is our winner. It’s also our highest converting ad. However, it has a low CTR and thus is going to have a poorer quality score than some of the other ads. It has a lower RPI and makes less money than some other ads. This is why ROAS is a good metric for bid management, but rarely for ad testing.
If we want the most revenue possible, then ad 4 is our clear winner. It’s the highest revenue and highest RPI (because the impressions are equal among all the ads).
A common way to also use RPI, since it doesn’t care about ROAS, is to use ROAS as a filter. For instance, you might have a goal of 500% ROAS. Therefore, any ads underneath that ROAS target can’t be a winner and you eliminate them. Among the ads that are left, the highest RPI would be your winner. If your goal was a 600% ROAS, then you’d eliminate ad 4 and of the ads left, ad 1 becomes the winner since it has the highest RPI among ads with at least a 600% ROAS.
You don’t want to throw away your data for all the losers. You always want to examine it to find other ideas to test. For instance, our highest converting ads are 6 and 1, and even ad 3 is higher than the highest RPI ad. Therefore, we’d want to take a look at those ads to see what in them is bringing in better qualified clicks.
We’d want to take a look at ad 5 as it has the highest average order value and see why. Does it have different cross sale or upsell items on the landing page or what about it is affecting average order value.
Ad 2 is a clear conversion rate loser. What about it is bringing in such terrible clicks for us? We’d want to make note of that ad and its idea as a warning in the future that the idea or promotion for that ad doesn’t work well.
Once we’ve examined the data, then we can pause all the losers and create a new ad to test against the winning ad.
The main time not to use RPI is when average order value isn’t a consideration. In those cases, you can rely on CPI as your main testing metric.
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 RPI as your metric, the main considerations are:
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 RPI 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 RPI. 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.
For some actual numbers and examples of minimum data, please see this article: How Much Data Should You Have Before Examining an Ad Test Result?
As for minimum clicks, this is usually a number you can ignore. You don’t want very low CTR ads to stop your test. As RPI is based upon only impressions and conversions; usually RPI testing completely ignores minimum clicks since clicks aren’t even used in calculating that metric.
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 RPI for each ad. If you have a very high volume ad group, you might set a very high minimum impression number (such as 1000 or even 10,000). If you have medium volume ad groups; then you might only use 500-1000 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.
Revenue or profit per impression is one of the best ad testing metrics you can use since it is a simple number that lets you see which ad will lead to the absolute most revenue.
The largest downside of RPI testing is that it doesn’t look at account or ad level ROAS goals and if your revenue can vary widely across orders then it can get skewed by outliers. If you have hard ROAS targets, then you can use ROAS as a filter to remove ads that are below your targets and then pick your highest RPI ad as your winner; so many companies should use both ROAS and RPI testing metrics to choose their winners.
The other issue with RPI is that it relies on consistent data. If your orders are highly inconsistent or you have random outlier orders, then CPI may be a better testing metric for you.
If you would like to easily see your Revenue per Impression metrics along with other testing metrics (such as ROAS), take a look at what Adalysis has to offer. You can easily see your RPI 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.
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