How AI is an ad stack supercharge. 

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In the face of a challenging macroeconomic climate, the UK digital advertising market remains remarkably strong, and is expected to reach $40.37bn in 2023. Indeed, with advertisers increasingly relying on digital channels for driving brand awareness and sales, platforms like Connected TV (CTV), digital audio and digital out-of-home (DOOH) are picking up a larger slice of the ad spend pie. In contrast to just a few years ago, this investment would have traditionally been allocated to offline media.

This is not to say that the industry is without its problems however. The economic situation, amongst other geopolitical pressures, is having an adverse effect on the sector, forcing media planners to think more short-term and reactively. The digital media landscape itself is also looking increasingly complicated, with the development of privacy regulations making ad targeting and measurement less transparent, useful and understandable for media managers. 

Thankfully, there are solutions today that digital marketers can utilize to make their advertising more effective than ever – namely, Artificial Intelligence (AI). AI solutions have been recognized for a few years as presenting huge opportunities for digital marketers, but it is customizable algorithms in particular which are transforming media-buying, and are essential for any competitive ad stack moving forward. 

This is because AI solutions offer a significant improvement on human intelligence in media-buying, utilizing the full potential of data across the ad stack to supercharge ad performance and scale while reducing waste. 


For several years, digital marketers have enjoyed being able to pursue better performance with Demand Side Platforms (DSPs). A key part of the open, digital ecosystem, marketers are nevertheless faced with growing challenges in this area. The volume of data available in a DSP, along with increasingly fragmented reporting, is making analyzing this data a big obstacle. This means that the valuable insights that can be gained are eluding digital marketers. 

To solve this, more and more DSPs are opening up their APIs to enable brands to bring in their own third-party software. Here, customizable AI, and its ability to analyze data at scale, is supercharging the optimisation process. This explains the interest we are seeing brands express in customizable AI solutions, with more and more RFPs being issued for this purpose, and we expect further DSPs to recognise their worth as the industry moves forwards. 


Customizable AI is also being sought after in solving the cookie issue. Indeed, with the deprecation of cookies on the horizon, marketers are eager to explore ways that can ensure they maintain effective targeting without user tracking and profiling. 

Certain solutions can provide this targeting by forgoing the ‘dirty fuel’ of third-party cookies in favor of the non-user specific semantic and contextual metadata. Coupled with the ability of customizable AI to combine this with their accrued first-party data and consented third-party data, the result for digital marketers is increased media productivity through more effective ad targeting. 


Customizable AI’s capability with ingesting and analyzing data extends across the entire media stack, including the data traditionally siloed within different sections of the business. For example, a large retailer will likely have within its customer relationship management (CRM) systems and customer data platforms (CDPs) huge amounts of actionable data across categories like price position, inventory and POS data. The problem is that this data is often too massive and complicated for a human to draw insights from. 

Customizable AI meanwhile can make it more readily available and therefore inform media-buying. Furthermore, as these algorithms are constantly being updated and fed back into the DSPs, media buying remains aligned with the latest business information in near real time. 


With marketing teams under growing pressure to show measurable business outcomes from their budgets, customizable AI solutions can be leveraged to ensure that this spend is quantifiable against the KPIs that matter. For example, a metric which is of increasing worth to marketers is attention. However, this is not available as a standard metric within the DSPs; they require measurement providers to deliver these. Only through customizable algorithms can this type of data be actionable in the media buying process. This offers marketers the twin opportunity to further optimize investments against these more useful KPIs, as well as highlight their worth to key stakeholders more clearly. 

As digital marketers contend with justifying and maximizing media spend in an increasingly difficult economic context, customizable AI will be key to achieving success. Its ability to analyze and make actionable data from across the media stack will serve brands well as they look to prepare themselves for future challenges. And for its sheer versatility, it is likely we will see customizable AI leveraged across digital marketing practices.

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Louisa Jones

UK Senior Sales Director, Scibids UK

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