Most marketing tech stacks are over-complicated and underperforming — AI marketing platforms represent a single, elegant solution to both problems.
The digital revolution has provided marketers with unprecedented access to their target audiences, but it has also made their day-to-day operations dramatically more complicated. In fact, according to Forrester’s AI: The Next Generation of Marketing report, a mere 6% of marketers believe that their current technology stacks are capable of dealing with the complexities presented by modern marketing.
Marketers’ tech stacks aren’t only ineffective, however — they are themselves overly complex. While separate Forrester research indicates that 58% of B2C marketers are looking to reduce the number of tech vendors they use, fewer than 20% of these marketers are confident that they can get all the functionality they require from a single vendor.
Ultimately, marketers need a centralized “brain” designed to operate and orchestrate various tools and functions across solutions in their tech stacks — not more tools, just a smart one to manage what they already have. This is where cutting-edge tools like Albert™, the world’s first autonomous artificial intelligence (AI) marketing platform, come into play.
To get a sense of how a tool like Albert could be integrated into your tech stack, it helps to consider its specific orchestration and optimization capabilities and how they might be used to manage your existing technologies.
Many traditional targeting technologies are designed primarily to identify consumers at the bottom of the sales funnel who only need to be nudged towards a conversion. This final push is obviously important, but the most effective marketers target potential customers far higher up the funnel. AI can be used to identify, test, and optimize countless paths-to-purchase, all of which can be leveraged to increase the precision of ad retargeting and messaging personalization down the line.
Audience targeting point solutions are ideal for management by AI. Such technologies are often limited to data from within the channel for which they were designed, despite the fact that many of these channels are dealing with the same audiences. Giving AI control over these targeting solutions enables organizations to optimize messaging for specific users based on their experiences across channels and devices.
Many available programmatic solutions can make decisions in real-time, but again, only based on data coming from programmatic campaigns. These solutions should also be considered for operation by AI, as an AI platform can not only make real-time decisions based on data from across channels, but autonomously manage challenges like pacing and cross-channel budget allocation.
A good AI marketing platform can sit on top of existing stack components and run them using learnings from other channels like search and social to inform your media buying strategy. “Unintelligent” programmatic media buying solutions can do a passable job given a straightforward set of conditions, but for real-time autonomous media buying that takes not only price and placement, but things like quality and brand safety into account, AI is the only option.
Finally, once marketers have selected their audiences and delivered their pitches, they need to evaluate how it all went — and how they can do better next time. In addition to extensive A/B testing, this entails a great deal of complex multivariate calculations that go beyond probabilistic decision trees. Tools that can only handle dealing with a few factors like copy, image, and timing have limited value today.
The variables that bear upon digital ad campaigns are legion, and only an AI-based platform is capable of sifting through the massive datasets whose nuances constitute the knife-edge on which successful campaigns balance. Albert uses several different forms of sophisticated machine learning to independently assess not only whether a campaign was a success, but will independently take actions to continually optimize future efforts.
Rather than forcing marketers to reconfigure their tech stacks, AI marketing platforms like Albert are placed on top of existing stacks — they then act as virtual super user, ensuring that marketers get the maximum return from their adtech infrastructure and media spend.
Marketers who adopt AI discover that it provides them superhuman capabilities, like the ability to process and analyze huge amounts of data in real time. Working alongside their virtual team member, marketers translate a brand’s business goals into parameters and guidelines against which the AI platform operates.
As the brand’s business goals and strategy shift, marketers find themselves on the front lines of the human/machine frontier, guiding the AI, as well as interpreting the AI’s ongoing customer insights to their colleagues, from creative to product to senior leadership. This represents a substantial leap beyond marketing organizations who remain crippled by the overwhelming number of tools and unwieldy interfaces at their disposal.