Ad monetization has been witnessing a rapid increase in app-generated revenues. With estimates disclosing a 60% increase in the number of applications monetizing via in-app advertisements in 2020.

In-app advertising enables you to get the most out of users who do not pay, by taking advantage of ad impressions, clicks, and installs when users engage with your ads within the apps.

This means that proper analyses of your Return On Ad Spend (ROAS) metrics are of critical importance; but the question to be asking is which ROAS model should you embrace? 

First things first, let’s look at some definitions!

What are ROAS?

ROAS, or Return On Ad Spend, is an online marketing metric that identifies and measures your business earnings in revenue for every dollar you spend on marketing.

Unlike ROI (Return On Investment), ROAS tracks the cost of a specific ad campaign instead of the entire investment.

It helps answer one specific question central to your ad campaign: Are you spending your marketing budget effectively?

That said, there are multiple ROAS models to consider; choosing the wrong model can make successful campaigns look unsuccessful. For example, ROAS models that depend on In-App Purchases data could be inaccurate and might not be enough to determine how efficient your marketing spend is.

With advert monetization, a more efficient model would be User-Level Ad data.

User-Level Ad Data

While you can efficiently measure your marketing efforts using ROAS metrics alone, a proper use of those metrics at the user-level can be a game changer.

With User-Level Ad data, you can identify which users engage the most with your ad spend, helping you successfully optimize your monetization-strategy efforts.

However, the problem lies in trying to get User-Level ROAS, because it’s not easy to split user-ad revenue (UAR). You can track your marketing spend and non-ad earnings via third-party monitoring platforms; however, ad-monetization data comes from a multitude of networks.

An ad network usually delivers up to 2 dimensions of your data through API: (1) country and (2) ad unit. It won’t give you any User-Level insight, though.

How to use User-Level Ad data to improve your ROAS

Without User-Level data on ad revenue, you can’t determine your “ad whales,” (high-quality users); but what does this mean for you as an advertiser?

Not having User-Level Ad data deprives you of identifying the marketing channels, campaigns, or creatives that brought in your high-quality users.

Here are some tips to help you use User-Level Ad data and ultimately improve your ROAS.

Create a proper strategy and drive more engagement

Today, online businesses create a plethora of monetization strategies to achieve their end goals. One of these goals is revenue that stems from in-app advertisements.

Having User-Level Ad revenue data will provide you with granular insight, which will enable you to dive deeper and build predictive models (eCPM and eCPC models).

You can, then, use these data to have a deeper understanding of how you can maximize your ROAS and optimize your Customer Lifetime Value (CLV or LTV).

Let’s consider a gaming example in which the user-acquisition team reported that their ad whales come from Snap. They also engage with video content extremely well.

The monetization manager can readjust her monetization strategy to fit the new channel that attracted the target users. Therefore, she can direct the advertising team to display a video ad to Snap-gained users as their first ad impression.

Measure your Lifetime Value metrics

A customer Lifetime Value is a metric that marketers use so they could predict the net profit attributed to your entire relationship with the user. Knowing your LTV will help you develop killer strategies to land new clients and nurture the ones you already have.

Naturally, there are complex calculations at play, but you only need to focus on the value the customer provides over your lifetime relationship with each other.

Remember to figure out the customer experience and measure feedback at every vital point of contact. This way you will get a hold of what drives your LTV.

Leverage User-Level Ad revenue using A/B testing

You can use A/B testing to better your user experience with ads. This way, you can discover rewarding monetization strategies that boost your ROAS.

A/B testing can be used with ads in different ways. Here are some examples:

• Add a new bidder
• Consider different bidding forms: In-app, traditional, waterfall, and hybrid
• Mind the value of a new network or new network placement
• Adjust ads frequency
• Determine price floors to balance user experience and revenue decisions
• Identify In-App Purchase vs ads balance

Real-time analytics is the force that empowers the A/B testing feature to increase your ROAS steadily.

Make improvements to your future ad campaigns

While real-time analytics are of great significance to Return on Investment and your Return On Ad Spend, sincerely consider putting your entire ad revenues in one place.

Ease of access to these past and present analytics will provide you with a map that guides your future steps.

User acquisition teams rarely have access to ad revenue. With the rapid growth of ad revenue and the major role it’s starting to play in the app revenue, blocking that access will only block your success.

Without ad revenue, user acquisition teams lack knowledge of what it costs to land a cohort of users as opposed to the revenue a cohort has landed.

Consider the following improvements to your future ad campaign:

• Segment users by User-Level Ad revenues so you can find similar audiences
• Figure out how users engage with in-app ads
• Measure and analyze ad revenue in cohorts

Analyze your ad strategy and spot weak performance

When you analyze your ad campaign strategy to reach your business full potential and retain clients, use A/B testing to determine advertisements that are performing weaker than predicted.

Next, take massive action to reallocate your time, effort, and budget where appropriate. 

When you can see your ROAS for each ad campaign, it’s not only easier to measure your future ad spend, but also more productive.