Artificial Intelligence and Content Marketing in 2017
7 AI Implications for Your Marketing Strategy
July 8, 2017
Some futurists believe as artificial intelligence (AI) becomes smarter, it will lead to a technological singularity: humans and machines melding into one digital, biological entity. Such an explosion of intelligence would lead to an entirely new way of life.
This prediction isn’t fantastical. Computer scientists forecast that by 2029 AI could be at about the level of intelligence of adult humans. In October 2016 the US National Science and Technology Council released their first ever document on preparing for the future of artificial intelligence. This document outlines the potential impact AI could have on the world economically, including an increase in the wage gap—and significant job reduction.
A quick Google search for “Content Marketing and Artificial Intelligence” reveals how big a concern this is. Top results include: Will Artificial Intelligence Kill Content Marketing?, Will Artificial Intelligence (AI) Take Over Content Marketing? and Is Artificial Intelligence Taking Over Content Marketing?
For now, AI isn’t causing a robot apocalypse, or even taking marketing jobs, although a study at Karlstads University determined that people are not able to tell the difference between content written by journalists and those generated by software. AI does however, hold significance as a content marketing trend for 2017.
As digital behemoths acquire AI companies and start bringing the technology to a wider audience, AI is changing the course of the content marketing industry. Among other things, AI is starting to eliminate the need for manual messaging and segmentation, is optimizing the personalization and automation features of content strategy, and driving predictive lead scoring and data analysis at the click of a mouse.
This article details seven ways AI is impacting content marketing, and how you can adapt for 2017 and beyond.
1. Content Creation
Journalist robots are already creating millions of pieces of basic sports content for the Associated Press, as well as Samsung, Comcast, and Yahoo. This technology can create content at a rate of 2,000 pieces per second, and as it improves, robots will become capable of creating more complicated content. Right now however, there are limited programs available for the average content marketer, but what is available saves time on simpler writing tasks so you can devote more time to more meaningful projects. Some examples include WorldAI, Automated Insight’s Wordsmith, and Narrative Science.
2. Empathy and Personalization at Scale
According to Forrester, 40 percent of loyalty marketers struggle with personalization. Marketing automation software company Emarsys is addressing this challenge, announcing a new artificial intelligence-driven platform (Emarsys AIM) in November 2016 that aims to let marketers focus on one-to-one engagement and personalization with their customers at scale. Using over two billion Emarsys unified customer profiles, and enabled by AI that automates the timing, content, and communication channel, AIM removes the burden of operational and execution tasks, allowing marketers to focus more on strategy and content. This technology is already pointing marketers to start thinking of content in terms of how it can be personalized by AI software.
One example is WayBlazer, a cognitive travel platform using IBM Watson’s AI to personalize images, recommendations, and travel insights based on customer data. It illustrates how AI can make content intelligent for your audience, allowing you to focus on quality and more personalized messaging.
3. Easing the Transition From Email to Messaging
With the increasing popularity of messaging apps for the office such as Slack, Yammer, and HipChat, combined with messenger platforms such as Facebook Messenger and WhatsApp, chat might be killing email.
There’s obvious appeal in receiving an immediate, custom answer from a company without dealing with the frustration and wasted time of waiting on the phone for a customer service representative. AI not only allows companies to create steadily better chat experiences at scale, but could allow for automated improvements to the product and business model at hand. Beer company IntelligentX Brewing is employing this tactic with AI that changes the recipe of its beer based on customer feedback given through a bot on Facebook.
Just as email, Twitter, and other mediums have impacted content creation, messaging is too. In the case of instant messaging marketing, company to customer messaging results in a quicker, more personalized content strategy, with a higher percentage of reach, and higher engagement and conversion rates.
4. Recommended Content
The Netflix Tech Blog says 75% of what people watch on Netflix is from an algorithm-generated recommendation. Facebook and Twitter are both investing in AI to help match users to relevant content.
Non-tech brands are starting to implement similar strategies for recommended content. According to artificial intelligence & machine learning think tank AI Business, activewear brand Under Armour is working with IBM Watson to combine “... user data from its record app with third-party data and research on fitness, nutrition etc. The result is the ability for the brand to offer up relevant training and lifecycle advice based on aggregated wisdom.”
Companies looking to create their own recommendation engine should investigate machine learning software such as Seldon, or software with a prepackaged recommendation engine such as Apptus, Clerk, or RichRelevance.
5. Image Analysis
Facebook, Amazon, and Apple have all acquired image recognition AI software of some kind recently (Faciometrics, Orbeus, and Emotient respectively). Audience reaction measurement could open an entirely new way of thinking about how content success is measured and how marketers should engage their audience to interact with content. For example, this technology could enable you to measure the success of your next piece by basing it on how many people laughed or smiled while reading it.
6. Strategy and Data Analysis
IBM’s Deep Blue, a computer designed to beat chess master Garry Kasparov, lost when they battled in 1996. It beat him in the rematch in 1997. Since then, AI has become a commonplace tool to analyze data and inform strategy. Airbnb uses AI to determine how much a host should charge for a stay at their house based on the time of year, location, proximity to holidays and amount other lodging is charging in that area.
Adobe Sensei, launched in November 2016, is an example of how this concept can scale and influence marketing strategy. According to the Adobe website, Adobe Sensei:
... harnesses trillions of content and data assets—from high-resolution images to customer clicks—all within a unified AI and machine learning framework. From image matching across millions of assets, to understanding the meaning and sentiment of documents, to finely targeting important audience segments, Adobe Sensei does it all. Adobe Sensei crunches numbers and notifies you when it finds something interesting. Like a new look-alike audience that you should approach. Or a specific message that will resonate with a customer. And it also offers predictive modeling, so you can anticipate market changes and make better decisions."
Software such as Adobe Sensei has enormous potential to enable you to hone in on strategy and trends that are right for your organization.
7. Customer Success Improvements
Salesforce’s AI machine, Einstein, is geared toward improving customer success. The platform uses “advanced machine learning, deep learning, predictive analytics, natural language processing and smart data discovery” to optimize for each customer and their interactions with a company’s CRM. The aim is to give content marketers a better understanding of how your content impacts existing customers, and what you can due to pivot your strategy to support customer success.
As AI advances, it is enabling the rapid development of content intelligence to drive content marketing. (Read all about this new wave of content marketing technology here.) Content intelligence technology will move content strategy further away from foggy guesses and closer to exact prognosis, forecasting precisely what to create, and preemptively giving you the analytics you need, right down to predicting the revenue generated by a particular blog post. As computational creativity advances, maybe someone won’t need to predict the future of AI in a few years—AI could already have it covered.
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