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Invest Wisely in Your Tech Stack With AI

Digital ads are continuing to grow as advertisers utilize online channels to push brand awareness and sales, with advertising spend estimated to increase to $509 billion in 2023, up 6% on last year’s figures. With emerging channels like Connected TV (CTV), digital audio and digital out-of-home (DOOH) driving this growth in particular, digital is now capturing large amounts of ad budgets that were once allocated to offline media. 

It is, however, no secret that the industry is currently facing a number of challenges. In particular, economic uncertainty is making media planning more difficult. Simultaneously, the media landscape is also fragmenting from a buying perspective, and evolving privacy regulations are not making targeting and measurement any easier.

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So, how can brands ensure that they are spending their advertising budgets efficiently during this challenging period? There is one clear solution – they must leverage the power of Artificial Intelligence (AI). And in particular, customizable media-buying algorithms, which are proving critical for the modern programmatic ad stack.

This technology provides a level of ad performance, efficiency and scale that cannot be replicated by human hands or off-the-shelf algorithms alone. Moreover, it enables brands to optimize their campaigns against the business-specific outcomes that matter most to them. 

Supercharging media buying with AI technology stacks

For some time, digital marketers have been effectively pursuing better performance using Demand Side Platforms (DSPs). However, with the sheer volume of data available in the DSP exacerbated by increasingly fragmented reporting, analyzing this data is becoming difficult, meaning digital marketers are missing out on valuable insights. 

As a resolution, the number of DSPs opening up their APIs for the integration of third party software is on the rise. Incorporating advertiser data into the programmatic bidding process in order to optimize campaigns utilizes the most advanced features of the DSP at scale. But this requires a bit more than just the standard ‘off the shelf’ offering. 

This is where more intricate and unique solutions such as customizable AI come to the fore, as their ability to analyze vast amounts of data in near-real time can boost results dramatically. More and more RFPs are being issued for these solutions as brands are increasingly eager to leverage AI and ask more of their DSPs. 

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With the deprecation of third-party cookies getting ever closer, marketers are under increased pressure to pursue privacy-safe data strategies. In this sense, customizable AI makes smarter targeting a possibility, as there is no need for reliance on personal data, as it instead harnesses non-user-specific semantic and contextual metadata for targeting. It also gives brands the option of utilizing their own first-party data, as well as measurement data sets, giving them a unique competitive advantage, as their ad targeting can be more efficient.

This technology is able to ingest and analyze data across the entire media stack, including all parts of the business. This is hugely important as data is often trapped in a customer relationship management (CRM) system and customer data platform (CDPs), including price position, inventory and POS data, which in fact, is hugely valuable and provides actionable business insight. However, this data is often too complicated for a human to draw insights from, which is where customizable AI comes into its own. These tailor-made algorithms make this data readily available to be used and inform the media buying process. In addition, as these algorithms are continually updated and fed back into DSPs, media buying is aligned with the most up to date AI insights almost immediately. 

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Customizing KPIs with AI technology stacks

With slimming budgets marketing teams must be able to show clear results on business-specific objectives. Therefore, by embracing customizable technology, it will ensure that the spend goes towards the KPIs that matter the most.

For example, consider attention, an increasingly important measurement in advertising which is not currently a standard KPI within DSPs. But customizable algorithms can change this by ingesting this complex data to ensure it’s actionable in the media buying process. Marketers therefore have an opportunity to optimize investments against prioritized KPIs and unique business outcomes.

Digital marketers are in the unenviable position of needing to maximize ad spend as the economic climate continues to look challenging, and customizable AI could provide the solution. The ability to analyze and produce actionable insights across the media stack allows brands to begin to plan more definitively for the privacy-centric future. And given its versatility, there is little doubt that this will be utilized more often across digital marketing.

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