Any seasoned marketer will happily tell you that retargeting is one of the best ways to engage with potential customers on the open web. The approach allows you to find and connect with people who have previously shown interest and is a powerful tool when used correctly. But how do you measure and optimize a retargeting campaign? Well, that’s where the real fun starts—we use retargeting statistics.
This article will explain:
- Why all successful retargeting campaigns rely on retargeting statistics for optimization.
- What the most commonly used retargeting ad statistics are, and what they actually mean.
- Why you should focus on the final result, rather than other metrics, during a retargeting campaign.
- How you can leverage Deep Learning to use retargeting statistics in campaign optimization more effectively.
Table of Contents:
- Why Retargeting Matters
- Retargeting ads statistics help you to understand and optimize your campaign
- How Should Marketers Use Retargeting Statistics?
- What Should Marketers Watch Out For?
- RTB House Can Help You Leverage Retargeting Statistics Using Deep Learning
Why Retargeting Matters
Retargeting is important because it enables brands to effectively connect with users who have previously shown interest in their products or services.
The goal of a retargeting campaign is to reach the most interested users with the most relevant messaging. This is done using a combination of permissioned data collected about users and third-party cookies or other tracking methods to identify where to show ads to a user. By maintaining a connection with the customer over the entire buying process, brands are more likely to be able to entice them into their store and ultimately convert them into a paying customer.
Unfortunately, it’s not quite as simple as just hitting the start button and letting the campaign run. Marketers need to carefully balance the cost of ads with the final outcome in order to ensure that a campaign makes financial sense. Additionally, monitoring retargeting ad statistics helps to give you peace of mind that retargeting partners are hitting their agreed goals, or allows you to compare the performance of multiple retargeting partners.
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Retargeting ads statistics help you to understand and optimize your campaign
As a general rule of thumb, you need to make between $1.5 – $5 for every $1 you spend for a campaign to be considered successful, but keep in mind that this range varies wildly depending on industry, margins, and specific campaign goals. Whatever your aim, to achieve a good Return On Ad Spend, you need to be able to understand a number of retargeting ads statistics, often displayed as a dizzying array of acronyms. These statistics offer deep insights into your campaign, and provide you with a way to measure the actual success of your campaign at a granular level.
To help you understand them, we’ve compiled a cheat sheet of the most important retargeting statistics you’ll see in 2023, along with a brief explanation of what they are and why they’re important.
Impressions are the first statistic you’re likely to come across. They represent the number of times an ad is shown to all users.
Clicks are quite simply the number of times an advertisement was clicked on, redirecting a user to the advertiser’s website.
Click-Through Rate (CTR)
Click-Through Rate (CTR) represents the total percentage of users who have seen your advert, and then clicked it. It is calculated by dividing the number of clicks vs. impressions and expressed as a percentage like so:
Clicks / impressions = CTR %
What represents a good CTR varies from industry to industry, but higher is always better. However, marketers should remember that CTR is not a hugely important metric in a retargeting campaign but rather a piece of the puzzle. It can be beneficial to have a lower CTR if the overall value of each eventual conversion is higher, so in general, don’t worry too much about your CTR.
The number of conversions
This is the raw number of generated conversions, that is, the number of conversions which users made after they clicked on your banner ad.
Conversion Rate (CR)
Conversion Rate (CR) is one of the key statistics. It represents the total percentage of times users clicked through your ad and then converted to a sale. It is calculated by taking the total number of conversions and dividing that by the total number of clicks from a given campaign, including users who did not convert, like this:
The number of conversions / Clicks = CR %
For example, if you had 20 conversions and 1 K clicks, your CR would be 2%. Again, higher is usually better, in a raw sense. However, while CR provides a useful way for you to track how well your ad is performing in a pure sense of effectivity, it should be noted that the end result, specifically ROAS or Conversions, is the most important, and you shouldn’t obsess too much over CR %.
This is the value of all conversions, always expressed in a currency amount. Conversion value is the first step towards understanding how cost-effective your campaign actually is.
It is possible to get a quick overview of your average conversion value by using the following calculation:
Average order value = Conversion value / conversions = average value of a single conversion
Cost Per Action (CPA)
This refers to the amount of money you need to spend in order to get a user to perform a desired action, which is the business goal of the campaign, for example purchasing a product, or signing up for a newsletter.
This is calculated by taking the total cost of your ad and dividing it by the number of conversions, expressed as a currency value, like so:
Cost of Ads / Number of conversions: = $CPA
For example, if your ads cost $10 and you had five conversions, you would have a CPA of $2. In this case, lower is usually better.
Cost Per Sale (CPS)
Cost Per Sale (CPS) measures the relation between the cost of your adverts to the value of your sales. It’s calculated by taking in the total cost of an ad campaign and dividing that against the total value of all sales that can be attributed to the campaign, expressed in percentages like so:
Total ad costs / sum of attributed sales (or conversion value) = CPS [%]
For example, if you had an ad campaign that cost $1 K but generated $4 K in sales, you would have a CPS of 25%. Again, in this case, lower is better.
Return on Ad Spend (ROAS)
Return on Ad Spend (ROAS) is probably the most important retargeting statistic. It is designed to help marketers instantly understand how effective their campaign has been. ROAS is typically calculated by taking your total revenue from an advertising campaign and dividing that by your total ad spend, like this:
Revenue (AKA attributed sales, AKA attributed conversion value) / Costs = ROAS
It is also possible to define ROAS as a percentage, like so:
ROAS x 100 = ROAS %
ROAS is mathematically connected to CPS. Indeed they are two sides of the same coin. ROAS enables you to understand how effectively spend was converted into revenue, while CPS allows you to understand what percentage of your revenue is dedicated to campaign costs.
Due to the connection between these two statistics, one can be used to calculate the other like this:
ROAS = 1 / CPS
CPS = 1 / ROAS
So, using our previous example, a CPS of 25% would equate to a ROAS of 1 /0.25 = 4, or 400%.
How Should Marketers Use Retargeting Statistics?
Statistics can be a funny thing. They tell a story, but if we’re looking at the wrong statistics, we can often find ourselves lost in the woods. In a retargeting context, marketers should always be looking at statistics that give you more information about the end result or your target metric.
This means that most marketers should not worry about the number of impressions or their CTR. Instead, you should focus on metrics that matter to your bottom line, such as CPS/CPA and ROAS.
How does this work? Let’s imagine that you have a closed budget, and you’re naturally trying to get as much revenue as you can within this allowance. Rather than wasting energy focusing on CPA and the number of conversions, you should instead focus on any retargeting ad statistics related to ROAS. This is because ROAS directly impacts the final outcome of your campaign, and it is entirely possible that a campaign with more conversions and lower CPA will still have higher ROAS because of higher AOV, or the inverse.
What Should Marketers Watch Out For?
It is important for marketers to remember that not all stats are presented equally. Different providers may display retargeting stats in different ways, and this can have a major impact on results. For example, there are two main models used to measure conversions:
- Last-Click—which gives all credit for the conversion to the last-clicked ad (or other paid traffic source).
- Post-Click—gives all ads and actions after an interaction a full conversion attribution.
- Data Driven—attribution models can be confusing as they allocate credit based on the relative significance of touchpoints, or specific interactions, defined by the provider. They can be opaque, as there often is not full clarity on the inner workings of these models.
Unfortunately, it’s not always this simple, as retargeting providers will sometimes have a default setting in their reporting panels that presents stats according to post-click attribution models because they always look much better than last-click, even if you’re actually paying for last-click conversions. This can lead to significant misunderstandings, so it’s important to check what attribution model you’re actually looking at.
If you’re still confused by the results you’re seeing, it can be helpful to compare your retargeting provider’s data with Google Analytics. There’s always going to be some difference there, but if you note large discrepancies, it’s important to investigate further.
What model does RTB house use?
Generally, at RTB House, we prefer to use last-click attribution models, and our Deep Learning-powered algorithms are built around this model. This is due to the fact that last-click attribution is transparent, used by many other providers, and has a clear definition that is understood by all parties, making it a simple and useful way to measure performance.
RTB House Can Help You Leverage Retargeting Statistics Using Deep Learning
Now that you understand how some of these statistics work, let’s dive into how RTB House can help you use them to take your campaign to the next level. Our Deep Learning-powered solution was created to automatically optimize your retargeting campaign in order to maximize the delivery of your desired final outcome.
For example, if you want to get as many conversions as possible within an open budget scenario, while keeping CPS to a set level, or you would like to generate as big revenue as possible with your closed budget (target = ROAS), our algorithm will figure out the best way to obtain those goals. This helps your team eliminate some of the legwork necessary to run a retargeting campaign and gets you better results than you could obtain on your own. In the meantime, you can focus on monitoring the campaign performance, which shall be easier with a good understanding of retargeting stats. For example, we helped Baur Versand to boost average order value and CR without compromising on ROAS.
If you’re interested in learning more about how you can use RTB House Deep Learning-powered solutions to boost campaign performance, talk to our team today!