How custom algorithms will shape the future of media buying – AI News
The digital advertising industry ingests and processes millions of data signals per second, generating immense volumes of data. While the industry is hyper-focused on the cookie deprecation, the third-party cookie is actually only one marketing input, there are many other data signals, both on and offline, available to optimise media buying.
Algorithms based on artificial-intelligence (AI) can be tailored to brands’ unique goals, allowing marketers to find pockets of performance within vast amounts of data and optimise media buying to drive real business outcomes. By combining custom AI approaches that integrate a brand’s key performance indicators (KPIs), and shaking off our third-party cookie dependence, we can welcome a new era of transparent and effective programmatic media.
User matching via first-party data signals
One way AI and custom algorithms will shape media buying, is by matching converted consumers with prospects that have similar digital patterns. Rather than focussing on who consumers are – their age and gender, or where they live – AI looks beyond basic characteristics to focus on the most important behavioural signals of a likely customer. Two consumers can have completely different profiles but ultimately want the same thing. Where traditional audience targeting would miss this opportunity, algorithmic matching enables brands to identify and take advantage of these similar needs.
Algorithmic consumer matching is currently based on first-party data signals, from retailers, brands or publishers. Moving forward, an explosion in new types of data is expected from connected cars and homes, internet-of-things devices, virtual and augmented reality, and biometrics, which will all feed into this process. AI will be vital to manage this data, and there must always be an emphasis on balancing the relationship between AI and ethics to ensure advertising works better for everyone while individual identities are protected.
Aligning media buying with brand objectives
A second way tailored algorithms will make media buying more effective is by aligning activity with brand objectives to deliver real business performance. Brands decide on the outcomes they want to achieve, allowing multi-metric KPIs and offline data inputs to be integrated into customised algorithms and ensure media buying is focussed on attaining those goals.
AI can increase efficiency by automatically directing spend towards areas of strong performance. The technology constantly checks itself to shift delivery and improve execution. Algorithms can predict which impressions will perform well, based on a huge variety of factors such as the length of time since a user visited an advertiser’s website, and generate far better conversion rates than can be achieved though manual optimisation.
In addition, once desired outcomes are established, custom algorithms can run thousands of real-time tests to determine the exact bid required to win media placements in an ad exchange. The performance of media buys can be continually measured, with results fed back into algorithms to create a closed loop of optimisation.
While AI is vital to enhance and streamline digital media buying, it doesn’t remove people from the process by any means. Success relies on the initial input and continuous management of campaigns by highly skilled people from data scientists to media planners. Algorithmic success is about finding harmony between man and machine by optimising towards goals set and overseen by real people to ensure ethical application of technology.
Dynamically optimising creative for performance
The role of custom algorithms doesn’t end with buying the right impression at the right price, it also includes ad execution, and specifically optimising ad creative to maximise the chances of conversion. Sophisticated algorithms are used to select the most relevant and effective creative elements, according to a variety of data points, and to assemble ads that appeal to individuals at different stages of the purchase journey.
Volvo, for instance, recently used AI to generate cost-effective conversions from a digital advertising campaign in Norway. Custom algorithms were used to test creative elements such as logos, layouts, and messaging at scale to determine which creative versioning drove the most conversions at the lowest cost. As a result, Volvo saw a 440% increase in audiences configuring new cars and booking test-drives and made more efficient use of its marketing budget with a 66% reduction in cost-per-acquisition (CPA).
As technologies evolve and volumes of data increase in digital advertising, the creative applications of custom algorithms will continue to grow in ways we may not be able to imagine yet. What we can be sure of, is that AI will be a necessary component in the toolbox of any marketer looking to optimise media buying, and better deliver business outcomes.
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