Will Machine Learning Algorithms Encroach on Content Marketers?
Nate Nead is the President & CEO of DEV.co, a custom software development company based in Seattle.
Since its inception, the field of online content marketing has been in a constant state of flux. This tendency is built into it — since it operates in an ever changing online world, all it can ever do is change along with the internet. For better or for worse, the rapid advent of machine learning algorithms in online processes will only lead to more change in the field of content marketing
This means it’s up to content marketers to adapt — and adapt they should. While marketers who move against the current of innovation are bound to run into obstacles, those who use machine learning as a tool will stand to benefit. In an increasingly automated market, core tenets of content marketing and artificial intelligence should be used in tandem to gain an advantage. With new streams of potential being uncovered every year, marketers are now in the position to streamline several key aspects of their workflow.
Let’s take a closer look at the role machine learning plays in automating virtual processes, and what it means for content marketing.
What is Machine Learning?
This AI subset is becoming an increasingly mainstream subject. In 2018, for example, Deep Mind released Alpha Zero, a self-learning algorithm designed to play chess. Given only the rules of the game and a few days to practice, Alpha Zero played millions of rounds against itself in order to achieve mastery. In its test run, it demonstrated techniques to optimize the game by defeating the world’s most powerful chess engine. The algorithm then moved on to master Go, then Starcraft, leaving each demographic reeling at the ingenuity of its discoveries.
The potential applications of machine learning is vast and constantly growing through heavy activity on the development end. Marketers who factor its applications into their business strategy can streamline numerous key processes while optimizing the customer experience.
The Purpose of Machine Learning in Content Marketing
Marketers can use machine learning to generate written content. This application has been around for some time and continues to gain accuracy every year. Many of us have already read simple articles — such as sports updates and stock reports — without knowing that they were written by machines. As far as natural language generation has come, these algorithms are still in their infancy.
Up and coming models are showing a lot of promise. Open AI’s GPT-2 is a glowing example, being capable of producing full news stories using a human prompt and data set. In its current state, the text still lacks a human touch, though it’s only a revision cycle or two away from being publish-able. A few upgrades down the line, when GPT-2 becomes capable of producing seamless guest blogs with minimal supervision, making the algorithm a content marketers best friend or a ghastly enemy. The only gap in the process for the would-be is finding guest blogging opportunities
, if those even will make sense in five years.
Curating the right content for a client’s specific output isn’t as straightforward as it might seem. It’s important for the content to be relevant, engaging and aimed at a specific target audience.
Using AI’s streamlined capacity to collect data, content marketers can gain a comprehensive understanding of what their client’s demographic thinks about their business, what sort of experience they’re looking to have, and the type of questions they want answered. Machine learning helps marketers form data sets that define a target audience, so they’ll be better equipped to curate the right content for their clients.
This is a more recent innovation, though its progression is on a constant upward trend. As time goes on, an increasing number of marketers are using it to their advantage.
Computer vision algorithms analyze visual data to help brands determine where their products are being shown — for example, in videos and images online.
An algorithm can scan through a set of users’ content on social media in order to locate images related to a certain brand. If the user set is large enough, there’s no limit to the amount of data this process can derive. What would be nearly impossible for a human researcher can take an algorithm a matter of days.
We’ve all had our run-ins with a chatbot at some point or another. Traditionally, it’s been easy to tell when the other end of the conversation is algorithmically generated. However, as time goes on it becomes increasingly difficult to know whether we’re speaking to an AI or human being.
Conversational AI in its current stage is used as a networking tool for marketers, applied in online forums and social media interactions. As the technology continues to advance, with AI’s conversational skills incorporating more diversity of thought and emotional recognition, the algorithms will eventually be let loose with a decreasing amount of supervision.
Machine learning can help identify a demographic’s specific set of preferences
. Once that data is collected, it can also generate specific recommendations and relay them on an automated schedule.
This is an invaluable tool that savvy marketers should keep close at hand. Knowing what a customer wants based on their online history is key. The more relevant the product suggestions, the higher the conversion ratio.
AI is growing in its capacity for IA — Intelligent Assistance.
IBM’s Watson computer is a great example. Watson takes the information it receives and applies it with a layer of complexity that exceeds the user experience of its peers, Cortana and Siri. When speaking with Watson, users get the sense that it resonates with their needs and interacts with them accordingly.
AI’s applications will lean increasingly toward managing many aspects of the customer experience. While customers have traditionally worked with AI through menial tasks like filling forms, bots like Watson will soon be helping them find solutions to their problems.
Moving Forward With (Not Against) Artificial Intelligence
Rather than move against the flow of innovation coming from the AI sector, a savvy marketer will use machine learning as an advancement tool.
Artificial intelligence is greatly beneficial in allowing marketers to divert their attention away from automatic processes, instead focusing on aspects that advance their company’s vision. In a rapidly evolving climate, the definition of “automatic processes” continues to grow. Tasks that we traditionally thought were unfeasible for algorithms to handle are becoming fully automated right before our eyes.
It’s impossible to see what the future of technology holds for us in the long run. In the short term, though, it’s safe to say that machine learning will continue to be an invaluable tool in a content marketer’s arsenal. For now, the inherent problem solving and creative capabilities of human marketers provides enough job security for now.
Whether complex algorithms will eventually replace human management in content marketing — well, that will only be clear whenever AI grows conscious enough to start a marketing campaign of its own.
Subscribe to get your daily round-up of top tech stories!