Are digital marketers prepared to make the most of AI?

AI-based marketing tools have the potential to revolutionise critical digital marketing tasks like content personalisation, but only if marketers learn to use them properly.

While much of the early conversation about commercial applications of AI has been shaped by concerns about automation-driven job loss, the consensus is that these concerns are largely unfounded. McKinsey reports that fewer than 5 per cent of jobs could be fully automated using current technology. Meanwhile, Gartner Research Director Manjunath Bhat declares that “robots are not here to take away our jobs, they’re here to give us a promotion.” As countless expertshave made clear, AI tools for marketing are not designed to replace human workers, but to augment them. This is especially true in fields like marketing, where creativity and complex data analysis are in constant conversation with one another. Despite this clear alignment of interests, a recent Conductor survey found that a significant portion of marketers (34 per cent) rank AI as the industry trend for which they feel most unprepared. Much of this uncertainty stems from marketers’ lack of clarity about what an AI tool can and cannot do, and how adopting one will affect the particulars of their day-to-day work.

AI needs onboarding too

The most important thing for a digital marketer to understand is that an AI tool must be trained before it can really contribute to an organisation’s marketing efforts. Most AI marketing tools are powered by machine learning algorithms — strings of code that aren’t programmed to execute an explicit series of commands, but rather, “learn” from the datasets they’re provided. It’s therefore a marketer’s responsibility to feed their AI tool not just large quantities of data, but large quantities of high-quality data — especially during the early stages of integration. This can be a big ask for less data-literate marketers (over half of marketers admit to being overwhelmed by the amount of data in their marketing stack). That’s why the best AI tools are designed to be incredibly user-friendly. For instance, with Albert, the world’s first fully-autonomous AI marketing platform, marketers are able to create a straightforward rule set that shapes how the platform learns and adjusts in the future. Albert takes established KPIs like overall budget, daily spending limits, and frequency caps and develops a nuanced understanding of when and where to cut off and/or redirect spending for various campaigns to maximise results while remaining within those boundaries.

Revolutionising personalisation in digital marketing

Once a machine learning algorithm has been properly trained, personalisation is arguably the first task marketers should delegate to their AI tool. Many modern consumers demand highly tailored ad experiences, but only a little more then one in ten marketers are either “very” or “extremely” satisfied with their current level of personalisation. Traditionally, marketers have approached digital personalisation in a way that reinforces existing silos within their department. If a marketer is crafting a campaign for Facebook, for example, they typically rely on lookalike audiences assembled by the social media giant’s internal teams. Tracking the quality of this lookalike audience requires a great deal of intensive cross-referencing between datasets, making it effectively impossible to accomplish in real time. An AI tool, however, can seamlessly process immense volumes of data drawn from any number of marketplaces in a matter of minutes. This not only delivers truly 1:1 experiences for all targets in cross-channel campaigns, but helps break down long-standing silos within a marketing team, as well.

A new way forward for digital marketing

When it comes to the potential impact of AI on digital marketing, these improved personalisation capabilities are just the tip of the iceberg. Tools like Albert make marketers better at their craft and free them up to work with their human colleagues on higher-level, more creative projects and processes — provided that these marketers train their AI partners with the proper care.


Original Article By   | Jan 12, 2019