The budget floodgates are finally opening up for marketers after five consecutive quarters of cuts. According to the latest IPA Bellwether report, Q2 2021 brought the first post-pandemic spending increase for UK companies, as well as marking the sharpest hike since early 2019. Immediate prospects are also looking positive, with forecasts of 7% growth for the rest of this year — but that doesn’t necessarily mean there is no longer a need for shrewd investment.

Maximising the returns generated by campaigns will continue to depend on harnessing the right opportunities. To make effective use of newly expanded budgets, marketers must ensure spend is directed where it’s most likely to drive maximum impact by anchoring activity to key objectives from the get-go. Embracing outcome-based advertising will enable them to instantly enhance ad delivery in line with pre-defined, desired results.

Isn’t that performance marketing?

Not quite. As highlighted by Gartner, however, performance marketing is often mostly about using brand objectives to set agency payment terms, with fees released when campaigns hit their parameters. Outcome-based advertising is more a mode of ongoing operations. Its aim is to accurately identify the uplift potential of each ad and drive decisions accordingly, on a consistent basis. The greatest similarity is a shared emphasis on how ads perform. 

Using advanced analysis of audience data, outcomes-based advertising fuels efficient advertising against brand goals, such as awareness, purchase intent, consideration, foot traffic, and sales. Moreover, it also goes beyond many traditional performance marketing efforts by covering metrics that can be measured in relation to specific user actions.

What makes adoption essential?

Many of today’s core marketing challenges can be traced back to a well-worn issue: overuse of proxies. Despite previous pushes from the Internet Advertising Bureau (IAB) for this to evolve, metrics such as click-through rates (CTRs) and impressions remain a common feature of campaign analysis — and persistently cause problems with tracking real-time and incremental results, in addition to optimising what really matters.  

The chief reason why these metrics are now considered outmoded is largely down to their imprecision. By nature, proxies are stand-ins – served neither ad impressions nor clicks offer definitive signals of user engagement and interest. This means they aren’t a solid foundation for determining ROI. But it’s also important to note that limited visibility of true ad impact makes it difficult to stay in sync with current audience tastes, trends, and habits.

Outcome-based advertising swaps proxies for reliable, actionable measurement. By applying tangible metrics from the start, such as purchases or requests for information, marketers can 

gain an exact view of how users react to each ad. Armed with a granular understanding of what drives the best outcomes, they will then be able to make informed strategic and spending decisions that enhance performance, both for in-flight and future campaigns. 

How does it work? 

In short, the overall process creates a closed loop of optimisation. The longer explanation requires a closer look at how and where artificial intelligence (AI) facilitates this cycle. AI abilities stretch much further than simply lightening the analytical load for marketers by speeding through complex data processing with instructional algorithms. Leveraged in tandem with outcome-focused metrics, sophisticated models can run deep assessments of ad effectiveness, even before budgets are distributed. 

Typically, these implementations tend to be rooted in using AI subsets, such as reinforced learning. This involves training algorithms to find the best way of achieving specific goals, whatever the situation – for instance, by combining incoming ad request data with historic information, algorithms can pinpoint the probable influence many contextual variables will have on users completing desired outcomes, such as booking test drives or signing up to subscription services. 

The immediate picture this provides of predicted ad performance can then be used as a guide for campaign execution. For example, marketers might concentrate their investment on ad requests most likely to hit their goal outcomes, or those with the highest probability of reaching receptive audiences, simultaneously saving on wastage and bolstering ROI.

Taking the longer-term perspective

The other beauty of self-learning AI is that its knowledge and accuracy increase over time. By moving further into the realm of supervised learning, marketers can use outcome-focused advertising to get ahead of users and proactively anticipate how they will behave and what they will want next; paving the way for anticipatory ads that spark delight and strong results. 

By learning from past data about what has and hasn’t worked, AI engines can predict the way multiple future scenarios will play out by teaching themselves how to construct bespoke models for an array of data sets and potential variables. Thanks to their capacity for agile analysis, they can also factor in information as it arises and rapidly fine-tune models; be that first-party data about customer interactions, or insights about user sentiment towards brands or products from opt-in intelligence surveys. 

Although not new, outcomes-based advertising is a perfect fit for the multi-faceted, modern marketing climate. As brands set their sights on dialling up budgets and seizing the emerging economic recovery, there is an enduring need to consider investments with care. 

Accurately measuring performance and steering smart investment means stepping away from proxy metrics to truly quantify ad impact and optimise strategies, at all stages of campaigns. Only by tying evaluation to defined outcomes can marketers be nimble enough to pivot strategies amid uncertainty, optimise campaigns to improve performance, and achieve brand goals.