In digital media, the last 12 months was a year of planning. Now, 2019 is set to be a year of execution.

That is to say, last year several big new initiatives, challenges and opportunities came to light, already causing convulsions across the industry. But it is this year when those convulsions will become louder.

From new policies to new competitors, 2019 is when marketers will need to make good on the new world they glimpsed last year.

GDPR’s Long Tail

Europe’s new consumer data privacy regulation caused chaos within the digital ad ecosystem. Limits on processing audience data prompted a shrinkage in programmatic inventory supply – and that was no wonder, with publishers like The New York Times ceasing programmatic trading in Europe and others like Hearst’s newspapers ending all publication here.

But GDPR was not a one-time event, it was the start of a process which will be felt more keenly through 2019. The pivot of ad-tech vendors like Drawbridge and Tapad out of a Europe that has become more hostile to identity processing, coupled with the elimination of third-party dataset processing by Google DoubleClick and Facebook, has left many marketers needing to either fill a supplier gap or pivot themselves. 2019 is when an answer must be found.

Customer Data Is The New Black

The likeliest answer had been gathering steam before GDPR hit. Use of a brand’s own data on its own customer and prospects has often sat in the “marketing” function rather than the “advertising” one, and having a marketing relationship with your own audience always seemed like a good idea. But 2018’s new regulations have further reframed the benefits of a “mar-tech” that is now fusing with “ad-tech”.

In 2019, this new emphasis on customer data management will place a greater focus on Customer Data Platform (CDP) software. Brands which may already have implemented a Data Management Platform (DMP) strategy are going to need to reassess the way they integrate advertising and marketing data for privacy-compliant effectiveness in the year ahead.

Consolidation Necessitates A Supplier Review

Last year’s raft of media and telco mega-mergers has set the cat amongst the pigeons – and it’s not over yet.

AT&T’s acquisition of AppNexus, a programmatic marketplace that competes with Google and Facebook, means an environment which had billed itself as the “independent” challenger is now part of a group which has also gobbled up publishers like Turner, CNN, and HBO. The re-integration of Oath into Verizon Media Group continues the dizzying consolidation of the former Oath’s Yahoo, AOL, and the procession of ad-tech suppliers under their wing.

All these major holders are vying to buy up their own ad-tech arsenal. As they do so, suppliers advertisers have come to depend on can change strategy overnight, forcing them to again consider the alliances they need to build.

Into The Amazon

2018 was the year when everyone realized Amazon had a significant advertising business. 2019, then, is when brands must ask themselves: “What’s our Amazon strategy?”

Agencies have been calling for a third leg to the duopoly of Google and Facebook. And they’ve certainly got it – by last October, Amazon’s “other” earnings, largely comprising ad sales, was worth $2.5 billion. But the addition of this new frenemy to this “tripoly” should be scrutinized as closely as all other suppliers.

Marketers will need to understand Amazon’s ad offerings, assess the unique opportunities afforded by its shopper data, and consider the technology ecosystem that makes buying Amazon as easy as the rest of the pack.

Machine Learning Gets Real

If you thought that programmatic made advertising smart, wait until you see what machine learning (ML) can do. Despite the hype, there should be no mystique – machine learning algorithms simply suck up historical data to find patterns that can inform future decisions. Applied to advertising, that means code can use historical conversion patterns to find the best inventory or audience for a campaign – and continually re-optimize those choices, mid-flight, for a huge effectiveness bump.

But machine learning’s complexity can double-down when buyers are faced with selecting and implementing the technology. ML effectiveness got refined last year thanks to training on big datasets. But kicking the tires of algorithms that, by necessity, could operate a million different ways can bamboozle buyers, while some advertisers may need to go beyond off-the-shelf solutions – necessitating they appraise themselves of how data science fundamentals can apply to their business.