During the last week or so, I’ve been talking with people from all sectors of the business for a series of predictions for 2016. It’s been fun to speak with so many smart people who are thinking hard about the issues in the real-time and programmatic universe, and I've been learning a lot.
My most recent conversation led me to Adam Weinroth, CMO of Austin-based OneSpot, a provider of content-marketing technology. OneSpot has worked with a variety of B2C and B2B brands including Delta Faucet, Johnson & Johnson, Intel, The Kraft Heinz Co., Mondelez International, Microsoft and Whole Foods.
Weinroth described OneSpot as a “cross-channel engagement and personalization platform” focused on branded editorial content that appears in all media channels. “It’s a tech solution built on content sequencing.” In case you don’t know what that means: essentially, for every prospect you’re trying to reach, there’s a unique and ideal sequence of content that you want to deliver over time, and in the right order. Make sense?
For example, Whole Foods uses OneSpot’s platform to make recommendations on content that you might like, and those recommendations appear personally, tailored to you when you log in. It’s kind of like Amazon's recommendations, but it’s content vs. product recommendations. “We make this work across different channels — display, social, mobile, a company’s own Web properties and email — in a highly automated way," Weinroth said.
Machine learning and predictive modeling enable all of this in real time. Weinroth says OneSpot is doing a trillion content placement predictions per day. With that backgrounder, here are his five predictions for 2016:
1.Content marketing will become a first-class citizen: “Brands will continue to go beyond the experimental campaign stage, to implement always-on, cross-platform disciplinary efforts. Content will move from the edges of marketing to become the centerpiece -- and in many cases, it will upstage paid media.”
Weinroth said content has been going through the same journey in marketing that we've seen in social. First, social was the shiny new object, and now it’s “oxygen” for a lot of brands. It’s a given. “You almost can’t do marketing without content. You have to tell a compelling story, and you can’t tell a story without a lot of content. And for us, you can’t tell it in one sitting.” That’s where the sequential piece comes in.
2.Content will shake up the traditional tech stack: “Marketing leaders and CTOs will collaborate more to rethink how they have organized their tech stacks to enable content to play a more central role.”
Weinroth sees significant and potentially disruptive ramifications for the ascent of content marketing. He says Web content management, CRM, data layers, social media, promotion and distribution methods have all been set up in such a way that content marketing has been bolted on, so to speak. But because content is moving toward the center of marketing strategy, he maintains that it needs to be in the center of the marketing tech stack.
Brands will need content amplification and editorial tools for work flows. “I think it will work differently between publishers and brands. Brands are trying to build relationships, action and activity that lead to business results. Publishers are looking to monetize through advertising and premium subscription fees.”
3.New areas of marketing will use machine learning and predictive modeling more pervasively: “As content, social and mobile become more mainstream, pressure to better scale and provide automated and real-time solutions will increase exponentially.”
Weinroth says that if you look at all the fervor around Big Data during the last two years, which he calls machine learning, it’s actionable learning that you get from Big Data. It involves real-time decisioning and treating people as individuals vs. members of segments. Machine learning, he says, is one of the best ways to put all your Big Data to work.
An aside: I asked Weinroth exactly what he meant by “machine learning” because I’ve been substituting “marketing technology” and “marketing automation” whenever I see "machine learning." In his view, machine learning is something that can be applied to marketing technology and marketing automation but it isn't the same thing.
In Weinroth's view, machine learning is an area of artificial intelligence (AI) that leverages lots of data and real-time decisioning. In effect, machine learning is not the exclusive province of marketing, it’s the part of AI that’s used in applications such as driverless cars and the IoT. Machine learning allows you to automate interactions with people at an individual level — unlike segment-based marketing automation or rules-based marketing, types of personalization that aren’t all that personal.
Brands have thousands of pieces of content! How can you possibly pick from each of these pieces to meet individuals' specific needs and passions? Weinroth believes machine learning can allow marketers to do that.
4.Adblockalypse causes marketers to mitigate over-reliance on any one channel: “Marketers will assess how content overlays with email, social platforms and other owned areas as part of a more holistic, serious view of distribution.”
I love the use of “adblockalypse" and haven’t seen that word before. Weinroth is referring to all the hysteria around the threat of widespread adoption of ad-blocking technology.
He maintains: “If you look at what’s happened with fraud and viewability, the value proposition for online advertising and display as stand-alones is at risk. Because of that, if brands and marketers overly rely on those tactics and sets of channels, they could be disproportionately exposed to risk. 2016 will be the year that marketers look at how they can broaden and diversify their marketing mix and try to achieve the things they do via online advertising, via other means. ...I think the whole ad blocking thing could be the straw that breaks the camel’s back in terms of prompting marketers to zoom out and look at how all their channels should be interconnected and interoperating.”
5.Personalization will get a facelift: Marketers will dive deeper to get to a one-to-one relationship with consumers, which means moving beyond existing segmentation approaches, to truly understand data tied to individuals.”
Weinroth explained that this prediction goes back to what he said about machine learning, which is if brands and publishers are going to have so much data, what are they actually going to do with all of it? In his view, they need to apply machine learning to the data to deliver true personalization to their customers.
For example, in email, personalization may mean tailoring email copy or rules-based personalization."If someone takes a certain action, then I want to show them x, y or z.” Approaches to personalization will become even more individually tailored — the combination of big data and machine learning is making this possible right now, he says.