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Artificial Intelligence and Machine Learning for Marketing and Public Relations - B-AIM PICK SELECTS

The rise of Artificial Intelligence in marketing and public relations has become an important way to automate routine tasks to save time and money as well as to increase the success of marketing initiatives. AI can help marketers in many ways, such as analyzing which blog or email newsletter topics has the greatest chance of getting seen and shared, the best ways to write headlines for maximum exposure, the best time and day to post it, which channels are the best to share it on, and what hashtags are appropriate to use.

In the seventh edition of The New Rules of Marketing and PR which released this month, I included a new chapter on AI because I think it is that important for entrepreneurs, marketers, and PR pros to understand. This post is based partly on the opening of that chapter.

AI is everywhere in our lives

When I write, I frequently use web-based Artificial Intelligence transcription software to help me turn recorded audio interviews into a text transcription. As I research new ideas my books, I conduct interviews either in person or virtually, recording those conversations on my iPhone. Recording the conversation allows me to focus on what my source is saying, so I can ask better follow-up questions. I’m not distracted by the need to produce accurate notes to pull quotes from later.

I upload these audio files from my iPhone to the transcription app and then speech-to-text algorithm quickly generates a quality first-pass transcript of the interview.

Behind the scenes, the digital transcription starts with AI, including automated speech recognition and natural language processing. The software is tuned to interpret the sounds that make up human speech and then match those sounds to the corresponding word in its multi-language dictionary.

That’s just one example of an AI-powered application I use frequently. Artificial Intelligence is all around us, helping power many of the tools we use every day. For example, I use my smartphone map application countless times a month (well, not as often during the pandemic). I plug in the address of where I want to go, and the AI-powered algorithms analyze huge sets of data, taking into account the time of day, day of the week, holiday schedule, road construction, accidents, and other factors to compute the optimal route. The app provides an estimated time of arrival and, if things change while I’m on the road, it will suggest detours or recompute the ETA. A human could never analyze such a massive amount of data.

What is Artificial Intelligence?

Artificial Intelligence is the umbrella term for the algorithms, technologies, and mathematics that make machines smarter. AI is typically used to perform tasks that have previously been done by humans, and done frequently, but that take lots of time, e.g., audio transcription and route-planning. AI-powered applications require a huge amount of data to do their jobs effectively, so the social networking, online retail, and online entertainment companies that are most invested in AI technologies tend to collect as much as they can get their hands on.

The term machine learning (ML) is often used interchangeably with artificial intelligence, although technically ML is just one aspect of AI (the part where computer systems learn from data to make decisions without explicitly being programmed how to make them). An example of an ML program is a system that looks at a huge number of photos and learns to decide which ones depict cats. In my own mind I don’t make a distinction between AI and ML, so I won’t try to do so in this post.

Here are a few other ways AI is powering my daily routine: The recommendation engines on services like YouTube, Amazon, and Netflix take in the massive amounts of data generated by tens of millions of users to figure out what videos, books, and movies I might like. Similarly, social networks like Facebook and LinkedIn use AI-powered algorithms to figure out what updates to show me — updates I’m likely to engage with and that will keep me coming back to these sites. And when somebody sends me an email, the AI within Gmail will suggest several responses, such as “Sounds good to me!” and “Great!” and “I’m not sure.” (An aside: It’s interesting to me that most of the suggested Gmail responses end in an explanation point! I wonder why?!)

AI-Powered Marketing and PR

By now you realize that much of our online lives already have AI-powered components. We don’t see the actual programs, but they are actively working in the background. The same is increasingly true in the marketing and public relations worlds, because marketing is increasing a data-driven activity. For example, the ads that appear on many websites are powered by AI engines. Some media companies are even using AI to “read” corporate press releases to create first drafts of stories for reporters to edit.

So, what else is AI good at that can help with your marketing and PR? It can automate routine tasks and let you focus on marketing and PR strategy. AI works best on solving simple problems with help from large amounts of data. Many worry that AI will take away people’s jobs. It’s true that certain repetitive jobs, like screening thousands of resumes for a great new marketing hire and testing dozens of web pages to find the one that performs best, probably won’t exist in the future. However, many new and exciting opportunities will likely arise as our ability to use this computing power leads to new opportunities for smart people to develop strategy.

As you consider AI in your organization, think about the routine tasks that drive business value and might be possible to automate.

“Machines have no inherent abilities that humans have,” says Paul Roetzer, founder of the Marketing Artificial Intelligence Institute. Paul’s organization educates modern marketers on the potential of AI and connects them with appropriate technologies. “Machines can’t see, they can’t hear, and they can’t understand language. They don’t have movement. Artificial intelligence is the science of giving those things to a machine, so it can do things that are more human-like.”

Paul says well-defined and repetitive processes are the best candidates for AI solutions. “For example, if I write a blog, I want to know what topics I should write about, based on what has the greatest chance of getting seen and shared,” he says. “I also want to know the best headline to use. From that blog post, I now want to predict what are the best excerpts to pull out of it to share on social media — I’m trying to predict what’s the most shareable content. I also want to predict what hashtags to use, what’s the best image to use with the post, what’s the best time to share it, and which channels are the best to share it on. With every one of those things, I’m trying to make a prediction based largely on instinct and sometimes on analytics. Each of these can be done by a machine. What you want to look for across all marketing and PR categories is what requires prediction, what’s repetitive, and what’s data-driven.”

In Paul’s blog post example, there is no magical AI platform that a marketer can purchase to instantly do all those things. Rather than look for one overarching solution, it’s always best to consider specific, component tasks within the process that a machine can help with.

“We advise making a list of all the things you do in your marketing department in a given week or month, and write out the activities,” Paul says. “You might write email marketing messages, figure out the subject lines of those emails, decide when in the calendar to send them, and find an image to use with each one. Then you go through and create another column that values intelligently automating each task. On a one to five scale, how valuable would it be to you to automate each of those things? You can consider how much time or money you might save. Then consider artificial intelligence platforms to help with the most important tasks. For example, you can find AI services to tag images and AI services to write email subject lines.”

Besides saving time and money, Paul also suggests considering how AI can help make certain processes more effective. “Lead scoring is used to predict the likelihood that someone’s going to buy,” he says. “Historically, most lead scoring is based on a human putting in factors that give points for each attribute that indicates a likelihood to buy. If you do that, you’re trying to make the prediction based on your knowledge and experience. But the machine, if it has enough data, can go through and figure out what a great lead looks like and then continually evolve that prediction as new data becomes available.”

Your life is already AI-assisted. Your marketing should be too.

As I was listening to Paul, I was thinking about ways I could potentially use AI in my own marketing and public relations efforts. I’d like to be able to use AI to figure out what call-to-action offers would be best for each blog post I write, how to write better emails and headlines based on opt-outs that occur for each email marketing message I’ve sent, and what kinds of tweets to compose based on the content of blog posts.

Paul says the first step is education — understanding what AI actually is (and is not) so you’re not afraid of it. Paul’s organization, Marketing Artificial Intelligence Institute, offers a ton of free information for beginners who want to learn. It’s how I first got educated, and I highly recommend the resources. Some AI-powered tools include the ability to get started building a test application at no cost. For example, IBM’s Watson Personality Insights predicts customer needs, values, and personality characteristics based on written text. This service uses linguistic analytics to infer people’s interest in different products, as well as people’s preferred digital communications channels (email, blogs, tweets, forum posts, etc.).

“You’re not going to just flip a switch and become ‘all AI, all the time’ as a marketing team,” Paul says. “The key is once you’ve done the education and you understand the basics of being able to identify opportunities for intelligent automation, then you can go about prioritizing.”

Larger enterprises have more opportunity to apply artificial intelligence, simply because they tend to have more data and more resources available to figure out what to do with that data. But that doesn’t mean small and midsize businesses shouldn’t be paying very close attention and taking steps to be prepared.

“Right now, you can get a competitive advantage in the market by using a smarter AI solution,” Paul says. “Simply knowing that you spend 20 hours a month tagging content on your website using a taxonomy, and realizing this task might be a potential use for AI, you can get a massive head start on your peers in the industry. If you’re a marketer, don’t just sit back and wait. Everything around you will get smarter with AI.”

There’s lots more on AI in the new seventh edition of The New Rules of Marketing and PR where I explain the basics of what you need to know about AI and how to make it a part of your business. Or for a deep dive, head on over to the Marketing Artificial Intelligence Institute to learn from Paul and his colleagues.

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