Maybe you haven’t noticed, but artificial intelligence has arrived. Gradually, over the last decade or so, it’s become so ingrained into our everyday lives that most people don’t think twice about it. Spotify assembles a “Discover Weekly” playlist tailored to your tastes. Netflix serves you options for your next binge-watching marathon. Alexa answers your questions and plays that nifty Spotify playlist on command. All with the power of content AI.
Pop culture often paints a much more sinister picture of AI—Minority Report, I, Robot and 2001: A Space Odyssey comes to mind. Some in the real world, like Silicon Valley mogul Elon Musk, have also expressed concern about the rapid development of AI. Yet, in practice, it’s been a boon to the average person.
Without thinking about it, we’re already taking advantage of AI’s ability to streamline data and simplify ordinary processes. We’ve become accustomed, specifically, to content AI automatically putting the best, most relevant content in front of us. Employing it in your marketing department is a natural extension of the technology. Forward-thinking marketers have already realized this, and they’re starting to use content AI to improve their effectiveness.
While it might be in its infancy now, AI in marketing isn’t going away. It’s poised to become an important tool going forward, and marketers would be wise to start paying attention now rather than playing catch up later.
To appreciate AI marketing’s potential, let’s first examine AI broadly and a sampling of what applications are currently being offered.
What is AI?
There is no single definition of what AI, or artificial intelligence, is. Generally, it’s thought of as machines or programs that mirror the functions of the human mind.
To be more specific, the Marketing Artificial Intelligence Institute defines artificial intelligence as, “technologies and processes that augment human knowledge and capabilities.” Amazon has a similar view on AI. The digital retailer, which has applied AI technology successfully in several areas, describes it as, “dedicated to solving cognitive problems commonly associated with human intelligence, such as learning, problem-solving, and pattern recognition.”
AI concepts associated with these ideas include:
- Machine learning: A method of linear data analysis allowing computers and computer systems to “learn” (or improve the performance of a specific task based on data) without being explicitly programmed by humans.
- Deep learning: Software that attempts to mimic activity in the human brain’s neocortex, creating a digital neural network. It learns to recognize things like language, sounds and images by being fed information and then being tested on it. As more information is processed, it creates a hierarchy of increasing complexity and abstraction.
- Computer vision: A computer system that seeks to automate the tasks of the human visual system. This involves automatic extraction, analysis and understanding of information from an image, sequence of images or video.
- Image recognition: An application of computer vision where software detects or identifies places, people, writing and actions in a digital image or video.
- Natural-language generation: A process that automatically turns data input into data-driven, human-friendly narratives like memos, financial reports and product descriptions.
- Cognitive computing: An umbrella term that includes the previous terms. Broadly, it’s shorthand for hardware and software designed to simulate human thought processes and the functions of the human brain.
In a field driven by data, it’s easy to see why AI is an attractive investment. It can parse complex sets of data at a rate unmatched by any person and then deliver actionable insights. Here’s how people are already using content AI.
What Content AI is available?
There is a variety of exciting, and useful, AI applications that promise to make marketers more effective by cultivating insights, delivering predictions, making recommendations, creating content and communicating with consumers.
Predictive analysis is incredibly helpful when planning your editorial calendar. With it, you become more efficient by predicting what your customers want and tailoring your content to their needs.
Providing the most relevant content possible will put you in a position to create quality leads and, eventually, conversions. AI makes lead scoring—ranking prospective customers against a scale of their perceived value to see what action you take next—incredibly easy.
By using predictive lead scoring powered by artificial intelligence, you can identify who is an ideal target for conversion based on previous data. Of course, marketers can do this themselves, but interpreting data in a consistent and accurate way can be challenging, not to mention time-consuming.
However, with machine learning, the predictive lead scoring software creates algorithms that make precise predictions about new customers using data from customers and external sources. AI finds patterns and trends that humans using traditional lead scoring would probably miss. There are a number of options for predictive lead scoring including Adobe Analytics, Lattice and 6Sense.
Don’t miss our favorite AI Content Marketing tools, listed here!
Competitive analysis is another valuable use of Content AI. An AI-driven competitive analysis will present you with the digital footprint of your competitors, making it easier to strategically plan your topics.
PowerPost’s own content intelligence integration features a share of voice (SOV) dashboard. SOV can be defined in many ways, but generally, it’s the percentage of the conversation a brand holds in the market over a certain period of time compared to competitors. You’re able to visualize where you’re leading the conversation where others are drowning you out.
PowerPost offers one solution with SOV, but there are many others. Crayon monitors more than 100 different variables from competitors’ feature and price changes to their employees’ sentiments on social media. Brandwatch Analytics crawls the web for reviews, articles, comments and conversations and segments the data.
But what about the content you’ve already produced?
AI content analysis offers a way to evaluate your content so that you can continue to make informed decisions about what you write in the future. IBM Watson Analytics can be used for a range of purposes, including content analysis. It can look at a set of data (your content catalog) and tell you how individual authors are performing and how articles are performing by word count, among many other possibilities.
Optimized content still needs to be promoted properly, though. Managing cross-channel promotions to drive engagement can be a significant task. Content AI tools like Cortex simplify your decision-making process by making recommendations about the content you’re promoting on social media.
Cortex helps you create content that encourages people to take action. The software suggests colors, hashtags, keywords, images and then schedules posts at the perfect date and time to reach your audience. It’s estimated that these recommendations save marketers 8.5 hours a week—a whole workday.
Still, others are using AI to actually create content in addition to using it for analysis and predictive insights.
Right now, Narrative Science is leading the pack with its natural language generation (NLG) tool, Quill. According to Narrative Science, it is “…humanizing data like never before, with technology that interprets your data, then transforms it into Intelligent Narratives at unprecedented speed and scale.”
So far, financial businesses have been Quill’s biggest market because it can create 10-plus page financial reports in a very short amount of time. But it also counts brands like Groupon, Forbes and USAA among its clients.
Even respected journalism outlets are using AI content creation to bolster their production. The Washington Post created a proprietary AI content creation system called Heliograf. It can churn out simple stories like sports recaps and social media posts. During the 2016 Summer Olympics, it created around 300 articles and alerts to aid the Post’s coverage.
The Associated Press is using Automated Insights’ Wordsmith to increase its output of corporate earnings reports. After implementing artificial intelligence NLG, stories on corporate earnings reports increased 12 times.
That being said, Wordsmith still requires a lot of human interaction for its NLG algorithm to work. First, data is added to the software and then you write or choose a template for the story. Next, there’s a preview of the NLG story. Finally, you can hit publish.
NLG is primarily restricted to simple, data-driven stories, but there’s clearly a market. Brands in 50 different industries used Wordsmith to create more than 1.5 billion articles and reports in 2017. For now, content creators can rest easy knowing humans are still needed for more creative pieces and to provide greater context to articles.
As you can see, there are already endless ways to utilize content AI. It’s sure to keep evolving going forward, and marketers will need to keep up with change—and the resulting implications.
Are you interested in learning more about how AI is revolutionizing content marketing? We partnered with Mike Kaput of the Marketing Artificial Intelligence Institute for a webinar you don’t want to miss. Watch the recording here.
What’s next and how will it affect content marketing?
What the future holds always takes a bit of speculation, and sometimes our visions for what’s next are closer to the mark than others. For instance, sci-fi creators and tech enthusiasts predicted things that sounded much like the internet and mobile phones we know today. But we still aren’t headed for a four-hour workday in personal jetpacks.
Right now, one of the frontiers ripe for innovation is voice artificial intelligence, like Amazon’s Alexa and Apple’s Siri. More people are trading talking for typing, and thus brands will have to reckon with that. Expect to see more brands incorporating voice into their digital experiences. This could take the form of voice notifications, instant customer service solutions and personalized recommendations for content or eCommerce.
Similarly, virtual reality (VR) and augmented reality (AR) are likely to become more prevalent in the future, and they’re on a “crash course” with AI according to Adweek. AR apps are already available on mobile phones (Pokemon GO anyone?), and industry experts expect them to become a common feature.
“In two years it’s going to become second nature to hold your phone up to a billboard and expect it to give you more information, or to roll your phone over an orange to see what citrus grove it came from,” Fred Schonenberg, CEO of VentureFuel, told Adweek.
And if you think marketing is personal now, wait until immersive VR and AR environments are the norm. Right now, heat-mapping and eye-tracking technology can illustrate generally what caught someone’s attention. But with more immersive experiences, it will become even more granular.
Another interesting path forward is meta AI analytics. Even now, it wouldn’t be unusual for a marketing department to use more than one AI analytics tool. This year and beyond, marketers will only be more spoiled for choice on tools, data sources and analytics packages. Making sense of all the insights gleaned from these sources isn’t easy.
This opens the door for “analytics on analytics,” or “meta-analysis.” AI will be used to look at the insights and data from all of your tools and platforms to recognize new patterns. This would give your marketing team a broad, integrated view of everything you’re doing.
Something else to consider regarding analytics is the amount of time that will be saved. Predictive and prescriptive AI, tools that can either make decisions or give tailored recommendations and insights, will make it easier to automate simple tasks and make decisions, freeing up the time senior staff members.
More attention can then be spent on creative marketing projects, which will, theoretically, improve the general quality of your content. Plus, more time can be spent on the human aspects of marketing like communicating with customers and clients.
When you consider the combined implications of voice AI, VR and AR, the synthesization of big data and time savings, it adds up to a truly personalized experience for consumers. If used properly together, marketers could do away with—or at least reduce—broadcasting general marketing messaging that have little value to individuals.
Find out how you can use AI to strengthen your brand messaging!
To get to that point, though, marketers need to realize that we’re at a turning point. AI will continue to become more sophisticated, and unlocking its power has the potential to totally reshape how marketers and consumers interact.
So, will you take a step forward, or stay where you are?
Are you using AI in your content marketing yet? Let us know in the comments! Download our free eBook the Brand Publishing Roadmap to find out how you can turn your brand into a full-fledged media property!