Many creatives bristle at the idea of data informing their choices. But, data can help solve some of their biggest problems. Here, Shutterstock’s VP of Innovation Solutions, Chip Schenck, explains how.
The old creatives, if I’m thinking about a professional creative on the agency side, weren’t necessarily using data. They were getting research from a strategist who had been given a brief from a brand. The brief might have said, “I need to reach women 18-34, and my brand wants to be associated with ‘sullen, but not sexy’” or whatever. But, as the strategist, it’s like, “Okay, how the hell do I turn that into something?”
So, they do a bunch of digging. They have access to research and insights and ad-server data, like, “How did X, Y, and Z audience respond with a number of clicks to this kind of thing last time?”
Then, they have to take their human brain and translate that information into what they think is relevant. They’re using data that’s offline and online, collated from a bunch of different places, but it’s not connected. It needs a human translation.
There’s nothing wrong with that. But it takes time to execute and, at the speed of creation needed, how can you do enough research to make a quick decision?
People tend to focus on one data point that helps drive their entire view, which means there’s really nothing to back up their choices. “I believe that this picture of the beach is more representative of ‘sullen’ than it is ‘sexy.’” Well, who the—what? “It’s my gut. That’s what it’s telling me.”
Besides taking too long, because I am expected to create five versions, I am questioning, “Am I delivering the right thing?” Where I used to rely on my gut, things change so fast now, I need some assurance.
The way that data can impact the creative process, and the way that Shutterstock sees it impacting the creative process, can be much greater. Here, we’ve been asking, “How can we provide data in a couple of different forms that, at the end of the day, gives the user more confidence that the image they’re selecting is going to be the right image for their goal?”
The difference between a tool like Canva or Adobe and what Shutterstock wants to be by using data, is that those tools help you design something. Shutterstock helps you achieve something. Because your goal isn’t just to finish a piece, your goal is to finish a piece that’s going to resonate with the consumer.
With that in mind, here are three ways data can improve the creative process.
1. Better Search
Shutterstock is heavily search driven (as many companies are), so providing a better search experience is providing a better customer experience. How do you provide a better search experience? Well, search is an algorithm, and an algorithm only improves with a combination of more experience and more data.
2. Better Output
How can we take search results to the next level? The first way is by scoring them based on their relevance. One image may score a 99, another a 91, and a third an 83. You might decide to go with the 83. Why? Well, that’s your choice. Maybe the results didn’t jive with your gut. But scoring is directional.
Once the algorithm has more input from the user about what they’re looking for, it can offer recommendations. It can say, “Based on the parameters of this information and the 200,000 similar things that I’ve seen, I think these are the right assets.”
The ultimate offering is predictive performance, where you’ve given the algorithm a fair amount of information, it understands what you’re trying to do, what’s happening in the market, and past performance. And, it can say, “There’s an 82% chance of you hitting your KPI with one of these two images.”
So, it’s a recommendation, but with a predictive value.
3. Better Portability
There are two ways to look at data and technology: You can have a walled garden, which is when you force people into your own ecosystem and whatever they use in that ecosystem can’t leave that ecosystem.
Or you can have an open ecosystem, which basically means that instead of forcing people into your ecosystem and having everything face inward with connections outward that are only piped one way, it’s almost like a flower. You open it up, and all of these petals are connections to anywhere someone wants to go.
The open model is important because people don’t want to go to sixteen different platforms. If you don’t want to force people to the mountain, how can you bring the mountain to them? You can build models from the data and learnings of your existing algorithms and make them available through an Application Programming Interface (API), so that anyone who can write to an API can directly access the actual recommendation/scoring/prediction engine.
That’s a really important concept. You’re not forcing a customer to go to another place to get your insights and information—you’re solving a problem and streamlining their creative process.
At the end of the day, that’s what using data is all about.
Data-driven creativity is growing. Just take a peek at these articles:
How to Create the Most Clickable Video Content, According to AIThe Most Clickable Foods, According to DataWhy Retailers Need to Adopt AR Technology Right NowHow Marketers Should Deal With the Delta Variant According to DataThe Color Pink’s Enduring Nature and Impact on Culture
Cover image via irin-k.
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