Facebook has published a new research paper that outlines its proposed new approach which would enable it to essentially offer Campaign Budget Optimization across multiple platforms from one ad campaign, as opposed to being confined to a single app.
The new process seeks to provide more, simplified alternatives to automated ad bidding, thereby optimizing ad spend across various apps from a single budget stream.
As explained by Facebook:
“Consider an advertiser who uses the Facebook platform to advertise a product. They have a daily budget that they would like to spend on our platform. Advertisers want to reach users where they spend time, so they spread their budget over multiple platforms, like Facebook, Instagram, and others. They want an algorithm to help bid on their behalf on the different platforms and are increasingly relying on automation products to help them achieve it.”
The problem, Facebook says, is that as the digital ad landscape becomes more crowded, advertisers are increasingly looking to diversify their ad spend, based on where their audience is active. So ideally, they would be able to ensure they’re allocating budget to the right platforms to reach their target market, as opposed to spending too much on one or the other.
The concept of Campaign Budget Optimization (CBO) is that it automatically allocates your assigned ad budget across your chosen Facebook ad sets, in order to ensure that the best performers see the most spend, thereby giving you the best bang for your ad buck.
But with this new process, you would also be able to ensure the same, across Facebook, Instagram, WhatsApp and Messenger (theoretically), all from a single, streamlined campaign.
“The algorithm is given a total budget (e.g., the daily budget) and a time horizon over which this budget should be spent. At each time-step, the algorithm should decide the bid it will associate with each of the platform, which will be input into the auctions for the next set of requests on each of the platforms. At the end of a round (i.e., a sequence of requests), the algorithm sees the total reward it obtained (e.g., number of clicks) and the total budget that was consumed in the process, on each of the different platforms. Based on just this history, the algorithm should decide the next set of bid multipliers it needs to place.”
The full 24-page research paper is technically dense, with lots of references to data modeling and ‘Stochastic Bandits’:
“Let yt(i) = λt(i)/k λt k1 , i = 1, . . . , d be the normalized cost of the resources. For a parameter ∈ [0, 1], for every vector y, for any sequence of payoff vectors c1, . . . , cτ ∈ [0, 1]d , Hedge’s guarantee gives.”
Yeah, a lot of that, so it’s difficult for non-experts to ascertain a full understanding of the process, but the basics are that the option, if it’s fully implemented, will provide more ways for advertisers to maximize their ad spend, while also decreasing workload.
“Adoption of automated products that perform many of the targeting, placement, and creative optimization elements on advertisers’ behalf is rapidly rising. […]The advantage of using the proposed algorithm is that the bidding is near-optimal thus, getting the most value for their spend. This has benefits for both the individual advertiser and the overall ecosystem.”
Indeed, many advertisers are seeing significantly better results when relying on automated bidding, as the ad systems at Google and Facebook, in particular, get better at understanding the key signals that will drive improved performance.
That means simpler ad processes, delivering better results, and while all such processes could potentially be impacted by the coming IDFA changes on iOS, being able to utilize CBO across platforms could be a valuable addition to your approach.