RoAS Pricing Model.

Return on Ad Spend (ROAS) is the amount of revenue a company receives for every dollar spent on an advertising source. This is a gauge of the effectiveness of online advertising campaigns.

To determine RoAS, divide revenue derived from the ad source by the cost of that ad source. Values less than one indicate that less revenue is generated than is spent on the advertising.

The Return on Ad Spend pricing model helps you focus on sales optimization and thus allows you to set how much revenue you would like to generate at a minimum compared to advertising spend.

The difference between RoAS and the existing CPA optimization is that with the latter, all conversions are treated in the same way, regardless of how much a particular customer purchases.  ROAS, on the other hand, uses historical sales data to predict the expected revenue of a bid request – bidding higher on requests that are expected to bring more revenue.

Right, So, How does it work?

“Return on Ad Spend” refers to revenue per advertising cost, just think of it as “how much revenue I would like to get for a unit of spend”.

The pricing model takes into account historical purchase values logged in tracking points and through machine learning, makes predictions on which impressions are likely to convert into higher value customers. This data then informs the bidding engine about which impressions it should bid more for.


Ok, How Does this Look in Practice?

Let’s say you’re an advertiser running a brand campaign and want to optimize the campaign for RoAS.

If your goal is to have 500% ROAS, while your campaign budget is 100 EUR, the algorithm will be bidding to maximize conversion revenue, aiming to bring at least 500EUR. The right level of RoAS depends on your desired and attainable profit margin.

For each bid, the pricing model will aim to purchase impressions that can achieve the minimum ROAS, within the set CPM bid price.



What Does this Mean for my Campaigns?

With the RoAS pricing model, advertisers can purchase inventory with less risk. By using machine learning, you can act on the previous purchasing behaviour of your customers and try to ensure your future spending is intelligent, thrifty, and optimized to enhance sales.


amardeep kaushal

Blogger, Marketer & Data Analyst.

Leave a Reply

This site uses Akismet to reduce spam. Learn how your comment data is processed.