Apr 29, 2020

3 trends are shaping advertising in the 2020s (not coronavirus!)

Credera Team

Credera Team

3 trends are shaping advertising in the 2020s (not coronavirus!)

For advertising, the dawn of the current decade was not just an arbitrary chunking of time. Before coronavirus got us all into lockdown, advertising in the 2020s was already facing significant change.

The trends of the previous decade have triggered a step-change in how advertisers and publishers approach buying, selling, targeting, placing and even creating adverts.

It will be a time of continual change and publishers, like everyone, need to be able to adapt constantly. Such ceaseless change is impossible to predict. But here are three trends already shaping the new decade in advertising.

AI and machine learning: making big data accessible

Perhaps the biggest trend already emerging is the growing acceptance of Artificial Intelligence (AI) and machine learning in advertising circles.

The shift will principally be a result of AI platforms becoming friendlier. AI and machine learning once seemed like forbidding black boxes to the less technically minded. Now, interfaces are becoming more accessible.

AI has already been behind much of programmatic advertising, quietly determining the outcome of AdWords bids, the frequency of Facebook serves and so on. The difference will be the permeation of AI into other areas of advertising.

AI for targeting

The use of AI and machine learning for targeting is the most obvious use case in advertising. Consider how we use current CRM systems to identify high-value targets. We might consider those who downloaded an ebook, for example, as warmer leads than those that didn’t perform that action.

AI can take that data and balance it against more detailed information regarding longer-term actions. It might reveal that downloading an ebook isn’t as valuable as we previously assumed. Instead, it may discover a pattern of users who visited a specific subpage at a specific time of day and bought product x an average of 29 days later – uncovering a higher value segment you weren’t even aware of. This simple, illustrative example only hints at the possibilities.

One case study cited by the Marketing Artificial Intelligence Institute described how Australian experience company RedBalloon averaged a 1,100% return on ad spend while cutting marketing costs by 25%. By parsing volumes of data no human could, AI identified customer segments that RedBalloon didn’t know they appealed to. This gave them a reality check on who their core customer base was.

AI for content

In late 2018, Lexus debuted the first advert entirely scripted by artificial intelligence. The AI was fed data from 15 years of award-winning car adverts, plus additional data to ensure the result stayed on-brand and didn’t just repeat successes of the past. 

On a more prosaic level, Google has already introduced AI recommendations into its AdWords platform. Machine learning fuels campaign recommendations about bid strategies and so forth, but it also generates copy variations based on past performance, existing ad copy, and material from your landing pages.

If you’ve used the ‘smart compose’ feature of Gmail, you’ll have some understanding of the quality currently achievable with Google’s AI, making it a basic, but serviceable early example. 

However, as the technology improves, expect machine learning to create personalised ad variations based on consumers’ individual personality profiles. 

Diversification: of channels, content and revenue streams

Another already-present trend that will undoubtedly continue its trajectory is diversification. Of everything.

More and more channels are constantly opening up. Mobile gaming. Connected TVs. VR. Digital audio is of increasing importance. A 2019 survey of UK media and ad agency managers found 85% were intending to increase spend on podcast ads over the coming year. Home voice assistants are extending the digital audio segment.

In an environment of perpetually increasing competition, publishers are already experimenting with diversified revenue streams. We can see early case studies in the traditional newspaper industry. Papers like The Guardian and The New York Times have been experimenting with a mixture of paywalls, reader contributions, events, sponsored content and innumerable other sources of income.

For publishers, such diversification is a matter of survival. For advertisers, each new stream is another opportunity to reach consumers – and another opportunity to more precisely target potential customers, aided by publishers’ valuable 1st party data.

But accommodating such a mix also requires a diversification of content. The same display ad won’t work in every context.

Advertisers have already begun – rightfully – to experiment with producing variations of the same ad, which they want to deliver in different contexts. Cold sore relief medication company Abreva ran 119 variations of the same YouTube ad to appeal to differing audiences.

Data unification: the solution to fragmentation

Advertisers already find themselves in an increasingly fragmented world. They must run campaigns across more channels, with more individual content variations – and do so with more restricted access to customer data than before.

Orchestrating such nuanced, omnichannel, personalised campaigns requires contiguous, 360 customer data – especially if AI is to parse through it all.

As such, the importance of dismantling data silos will become more important as time goes on. Publishers will increasingly converge their data in order to serve buyers looking to place cross-channel ads with near-infinite variety. The more precisely they can target their content to your audience, the higher value your inventory becomes.

Serving this demand will catalyse more and more publishers to overhaul their backend systems to make it easier for advertisers to use AI-assisted modelling to create lookalike audiences from your data, place multichannel ad orders in a single click, and run increasingly sophisticated variate testing.

This data convergence will yield more sophisticated use of data, such that consumer unease at the blunt ad personalisation of today gives way to an appreciation of highly targeted content, that provides more utility – with a precisely matched aesthetic – to its viewer.

Practical steps for publishers

These three trends are all present today. Before coronavirus, many saw them as ahead of the curve. Now it is clear that those who were already ahead of the curve will be better off than those who were not ready. By the time 2030 comes around, no one can know what we’ll need to be prepared for.

The common factor of these trends is perpetual, accelerating evolution. Publishers must reshape themselves into organisations capable of rapid adaptation, able to respond to each new development as it looms on the horizon.

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