What we need are tools that simplify the creative and content-oriented process to advertising. Good news, friends, we’ve got them.
This month, Shutterstock’s director of innovation solutions, Stefan Britton, was invited to speak as part of ANA’s Thought Leader Series. Below are edited excerpts from his webinar, in which he discussed the future of data in advertising.
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Google says it takes 50 milliseconds for someone to decide if they like an image on an ad. Facebook says if someone does like an image on an ad, they’ll dwell for 1.7 seconds.
What that really means is you have 50 milliseconds to get 1.7 seconds of attention from someone who’s seen 15,000 ads a day—and it’s your creative that has to pull that off.
Tech really isn’t helping as much as it should because, currently, creators have to go to one place to do their research, somewhere else for image storage and asset management, somewhere else to access tools for creation, and then to another set of tools for collaboration and planning.
We’ve got a scenario where multiple people, multiple teams, multiple companies are using entirely different bits of tech, and trying to somehow do it better and quicker than ever before, when there’s more at stake than there’s ever been.
What they need are tools that help simplify the creative process, improve collaboration and asset management, and allow them to make more confident decisions using data.
How Creatives Make Decisions Today
Every creative project starts with what we tend to call the Cold Star: Someone’s given a project and they have to get started. So, where do they go?
Well, most companies and people will perhaps go to Google. Or, they’ll look at their brand guidelines or a past campaign or any previous testing that they’ve done.
But, the problem with this is that campaign performance is looking backwards, it’s not looking forward. And, usually, if we’ve done some testing on content, we’ve probably had too many variables for it to really be a true test.
Perhaps, for instance, we’ve done split testing on content to see which of two pieces of content people liked more or less. But, if we’re running split tests on content that we randomly chose, all we’re really doing is working out what’s the least worst of that random content that we put out into market, at that point.
We’re not really learning what was optimal. We haven’t really got meaningful data we can use to then go on and make creative decisions. And, if you can’t make a decision with data, it’s just noise—and there’s already way too much of that in this creative process.
How Data Can Help
Many of us in the industry are still making decisions the same way as we did in the ’50s and ’60s. It’s guesswork, it’s gut feel, it’s human bias, or it’s the person with the loudest opinion in the room. And, with so much at stake, we really must now bring this process into the 21st century.
By bringing AI tools into the market, Shutterstock will be able to help creators make more confident choices.
Using computer vision, we can analyze all of the elements in an image: objects, color, movement, depths of field. That gives us a rich data source that machine learning can analyze to be able to predict—with confidence—how certain pieces of content will perform in the future.
This has incredible applications, particularly in advertising. Imagine being in programmatic and being able to win an ad slot, and AI can then create the perfect piece of content for you. We can have customers test their ads in advance, so they know exactly what will work and, most importantly, what won’t work.
Commercially, knowing what doesn’t work in advance can be an absolute game-changer. Because, if you knew in advance that an ad is going to under-perform, you could simply not run it.
Content Creation
There are lots of tools to help with creation. But, the challenge with a lot of these tools is they’re not always integrated into the decision-making process, and they’re often quite expensive.
And, given that 70% of all content creation now is for social, we don’t necessarily need a full creative studio just to create a quick social post.
It’s really important to inject data here in this process, because we need to be able to help right at the point where these decisions are being made and where this content is being produced. Because, we can’t ask someone to leave their workflow and look elsewhere, and then come back.
They’re under pressure to get their work done, and they need to get it done quickly.
Improved Workflow
We know the collaboration is hard. Many of us have been working from home remotely for the last couple years, and just organizing meetings is still surprisingly difficult.
So, imagine having to manage this sort of complex workflow—a workflow that’s full of iterations, people from different teams, different companies, who all have opinions and comments that need attending to.
If we can bring data into this creative process, and also hone into one suite of products and applications, we can help streamline that collaboration and that communication piece.
Because, currently, what’s happening is customers are going through their inboxes, looking at slack channels, they might be moving Trello cards on Trello boards. That means they really don’t have much in the way of an audit trail, because if a particular asset has been agreed to be used in a campaign (or rejected), there’s no real track record of who said what or why.
So, what we want to be able to build out with technology is a platform where not only have you got data to help with that creative decision-making, but you’ve also got one place where everyone can communicate, you’ve got one place you can manage that project, where everyone can coalesce around one idea, regardless of their role or their place in that creative process. Where creative campaigns can be planned months in advance and a whole company can see that content calendar.
At the same time, AI can be monitoring trends in the market, looking at what’s working in various places, and bringing in suggestions to these content plans.
So, when someone comes into this content plan or this particular project, they can see that AI has already started giving them some help, it’s already started giving them some concepts and some creative that’ll help that project.
And, of course, when people can make decisions quicker, the project can move forward faster. In addition, people can understand why certain decisions are made.
So, not only does it mean everyone is accountable, it also means everybody gets to have their voice heard. And, in the creative process, that’s important, because creatives can be emotional.
By enabling them to work more efficiently together, you’re putting them under less pressure and setting them up to deliver work as a happier team.
Final Thoughts
We hope this technology is going to usher in this new era of more thoughtful creative marketing, where intuition and data can be married to bring out the absolute best in creativity.
And, for that to work, we need to ensure that data is threaded right through the entire creative process. These technologies need to actually work together—they must integrate and share data.
We’ve got artificial intelligence here that can help creatives, not hinder them. It’s still relatively early days, but we want the industry in the creative world to be open to data and see it very much like spell check for a copywriter—technology that can help.
Cover image via Jakub Kolodynski.
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