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Thank goodness for live chat. If you’re anything like me, you look back at the days of corded phones and 1-800 numbers with anything but fondness.

But as you’re chatting with a customer service agent on Facebook Messenger to see if you can change the shipping address on your recent order, sometimes it’s tempting to ask, am I really talking to a human? Or is this kind, speedy agent really just a robot in disguise?

Believe it or not, this question is older than you might think. The game of trying to decipher between human and machine goes all the way back to 1950 and a computer scientist named Alan Turing.

In his famous paper, Turing proposed a test (now referred to as the Turing Test) to see if a machine’s ability to exhibit intelligent behavior is indistinguishable from that of a human. An interrogator would ask text-based questions to subject A (a computer) and subject B (a person), in hopes of trying to figure out which was which. If the computer successfully fooled the interrogator into thinking it was a human, the computer was said to successfully have artificial intelligence.

Since the days of Alan Turing, there’s been decades and decades of debate on if his test really is an accurate method for identifying artificial intelligence. However, the sentiment behind the idea remains: As AI gains traction, will we be able to tell the difference between human and machine? And if AI is already transforming the way we want customer service, how else could it change our jobs as marketers?

Why Artificial Intelligence Matters for Marketers

As Turing predicted, the concepts behind AI are often hard to grasp, and sometimes even more difficult recognize in our daily lives. By its very nature, AI is designed to flow seamlessly into the tools you already use to make the tasks you do more accurate or efficient. For example, if you’ve enjoyed Netflix movie suggestions or Spotify’s personalized playlists, you’re already encountering AI.

In fact, in our recent HubSpot Research Report on the adoption of artificial intelligence, we found that 63% of respondents are already using AI without realizing it.

When it comes to marketing, AI is positioned to change nearly every part of marketing — from our personal productivity to our business’s operations — over the next few years. Imagine having a to-do list automatically prioritized based on your work habits, or your content personalized based on your target customer writes on social media. These examples are just the beginning of how AI will affect the way marketers work.

No matter how much AI changes our job, we’re not all called to be expert computer scientists. However, it’s still crucial to have a basic understanding how AI works, if only to get a glimpse of the possibilities with this type of technology and to see how it could make you a more efficient, more data-driven marketer.

Below we’ll break down the key terms you’ll need to know to be a successful marketer in an AI world. But first, a disclaimer …

This isn’t meant to be the ultimate resource of artificial intelligence by any means, nor should any 1,500-word blog post. There remains a lot of disagreement around what people consider AI to be and what it’s not. But we do hope these basic definitions will make AI and its related concepts a little easier to grasp and excite you to learn more about the future of marketing.

13 Artificial Intelligence Terms Marketers Need to Know

Algorithm

An algorithm is a formula that represents the relationship between variables. Social media marketers are likely familiar, as Facebook, Twitter, and Instagram all use algorithms to determine what posts you see in a news feed. SEO marketers focus specifically on search engine algorithms to get their content ranking on the first page of search results. Even your Netflix home page uses an algorithm to suggest new shows based on past behavior.

When you’re talking about artificial intelligence, algorithms are what machine learning programs use to make predictions from the data sets they analyze. For example, if a machine learning program were to analyze the performance of a bunch of Facebook posts, it could create an algorithm to determine which blog titles get the most clicks for future posts.

Artificial Intelligence

In the most general of terms, artificial intelligence refers to an area of computer science that makes machines do things that would require intelligence if done by a human. This includes tasks such as learning, seeing, talking, socializing, reasoning, or problem solving.

However, it’s not as simple as copying the way the human brain works, neuron by neuron. It’s building flexible computers that can take creative actions that maximize their chances of success to a specific goal.

Bots

Bots (also known as “chatbots” or “chatterbots”) are text-based programs that humans communicate with to automate specific actions or seek information. Generally, they “live” inside of another messaging application, such as Slack, Facebook Messenger, WhatsApp, or Line.

Bots often have a narrow use case because they are programmed to pull from a specific data source, such as a bot that tells you the weather or helps you register to vote. In some cases, they are able to integrate with systems you already use to increase productivity. For example, GrowthBot — a bot for marketing and sales professionals — connects with HubSpot, Google Analytics, and more to deliver information on a company’s top-viewed blog post or the PPC keywords a competitor is buying.

Some argue that chatbots don’t qualify as AI because they rely heavily on pre-loaded responses or actions and can’t “think” for themselves. However, others see bots’ ability to understand human language as a basic application of AI.

Cognitive Science

Zoom out from artificial intelligence and you’ve got cognitive science. It’s the interdisciplinary study of the mind and its processes, pulling from the foundations of philosophy, psychology, linguistics, anthropology, and neuroscience.

Artificial intelligence is just one application of cognitive science that looks at how the systems of the mind can be simulated in machines.

Computer Vision

Computer vision is an application of deep learning (see below) that can “understand” digital images.

For humans, of course, understanding images is one of our more basic functions. You see a ball thrown at you and you catch it. But for a computer to see an image and then describe it makes simulating the way the human eye and brain work together pretty complicated. For example, imagine how a self-driving car would need to recognize and respond to stop lights, pedestrians, and other obstructions to be allowed on the road.

However, you don’t have to own a Tesla to experience computer vision. You can put Google’s Quick Draw to the test and see if it recognizes your doodles. Because computer vision uses machine learning that improves over time, you’ll actually help teach the program just by playing.

Data Mining

Data mining is the process of computers discovering patterns within large data sets. For example, an ecommerce company like Amazon could use data mining to analyze customer data and give product suggestions through the “customers who bought this item also bought” box. 

Deep Learning

On the far end of the AI spectrum, deep learning is a highly advanced subset of machine learning. It’s unlikely you’ll need to understand the inner workings of deep learning, but know this: Deep learning can find super complex patterns in data sets by using multiple layers of correlations. In the simplest of terms, it does this by mimicking the way neurons are layered in your own brain. That’s why computer scientists refer to this type of machine learning as a “neural network.”

Machine Learning

Of all the subdisciplines of AI, some of the most exciting advances have been made within machine learning. In short, machine learning is the ability for a program to absorb huge amounts of data and create predictive algorithms.

If you’ve ever heard that AI allows computers to learn over time, you were likely learning about machine learning. Programs with machine learning discover patterns in data sets that help them achieve a goal. As they analyze more data, they adjust their behavior to reach their goal more efficiently.

That data could be anything: a marketing software full of email open rates or a database of baseball batting averages. Because machine learning gives computers to learn without being explicitly programmed (like most bots), they are often described as being able to learn like a young child does: by experience.

Natural Language Processing

Natural language processing (NLS) can make bots a bit more sophisticated by enabling them to understand text or voice commands. For example, when you talk to Siri, she’s transposing your voice into text, conducting the query via a search engine, and responding back in human syntax.

On a basic level, spell check in a Word document or translation services on Google are both examples of NLS. More advanced applications of NLS can learn to pick up on humor or emotion.

Semantic Analysis

Semantic analysis is, first and foremost, a linguistics term that deals with process of stringing together phrases, clauses, sentences, and paragraphs into coherent writing. But it also refers to building language in the context of culture.

So, if a machine that has natural language processing capabilities can also use semantic analysis, that likely means it can understand human language and pick up on the contextual cues needed to understand idioms, metaphors, and other figures of speech. As AI-powered marketing applications advance in areas like content automation, you can imagine the usefulness of semantic analysis to craft blog posts and ebooks that are indistinguishable than that of a content marketer.

Supervised Learning

Supervised learning is a type of machine learning in which humans input specific data sets and supervise much of the process, hence the name. In supervised learning, the sample data is labeled and the machine learning program is given a clear outcome to work toward.

Training Data

In machine learning, the training data is the data initially given to the program to “learn” and identify patterns. Afterwards, more test data sets are given to the machine learning program to check the patterns for accuracy. 

Unsupervised Learning

Unsupervised learning is another type of machine learning that uses very little to no human involvement. The machine learning program is left to find patterns and draw conclusions on its own.

Have an artificial intelligence definition to add? Let us know in the comment below.

Supermodels may have ruled the world in the 1990s, but today it’s the creatives. Everyone wants in on disruptive, viral, [insert your own buzz word here] intersecting worlds of design and technology. Especially the savvy students who are looking for challenging, fun, and economically rewarding professions.

Design and coding academies are rising to meet these opportunities, but there’s a lot of noise swirling around. If you want your academy’s message to shout out and reach your prospective students, you need to be using inbound and content marketing.

Fortunately, your creative natures and enterprises are great fits to achieve great results with content marketing and inbound.

You’re Already Flush with Content

For a lot of marketing teams, creating quality content on a consistent basis is a big challenge. Not so for design and coding academies. Your faculty and students already create amazing content daily. Student projects, faculty lectures, and documented curricula are all content sources ready to be tapped.

You can video individual lectures from different courses and post clips online. You’re naturals for developing some of the best visual content out there. Take some screenshots of the design or coding tools you teach students to use and add some eye-popping captions as a mini-tutorial. Share your faculty’s expertise by publishing their work paired with a short back story or interview with that teacher.

Getting creative is your jam. Review the wealth of content your community is already creating through the lens of your content strategy to attract new students. You’ll see your opportunities.

Improve Your SEO by Repurposing Your Content

The first step with the inbound methodology is attracting your target audience. This requires an SEO strategy based on relevant keywords and topics. A blog talking about things your personas don’t care about isn’t going to help your academy get found.

After you’ve done some SEO research, freshen up blog posts, newsletter articles, and other content you already have. Let’s say your research tells you that prospective students are curious about mobile UX design best practices. Now you can add new a summary, keywords and tags to a lecture video or presentation you’ve posted on this topic that are more relevant. Instead of captioning it “Introductory Lecture on Mobile Design,” you can change it to “Mobile UX Design: Learning Best Practices for Mobile Apps” (or whatever your research indicates).

Understanding what your SEO research is telling you will also help you select the most useful and on-point content to repurpose. Your academy does have a wealth of content, but that doesn’t mean you want to throw all of it up to see what sticks. You want to make strategic selections of what content to look for, what to create, and how to optimize it for SEO so you make best use of your resources.

Your Content Will Build Your Reputation

The linchpin of finding success with inbound marketing is using your content to build trust with your target personas. Academies and bootcamps don’t have the brand recognition that traditional schools enjoy. The waters are also muddied by the explosion in your direct competition.

The number of coding academies grew by nearly 50% in 2016 and is projected to continue growing rapidly. If you want to carve out a spot on the leader board, then you need to boost your brand recognition and make sure your brand connotes credibility and authority.

Prospective students won’t trust putting their professional training in your hands if they don’t see you as a go-to source on topics related to coding or design. Use your content to build up their confidence that your academy is at the front edge of your field and has the chops to make them employer-magnets after you graduate them.

You can share recent alumni stories that show how easily your graduates transition from students into lucrative careers. Quintupling your salary post-graduation? That’s not a bad haul.

Build your reputation as masters of your field by regularly publishing content that addresses its most pressing issues and trends. Publish your academy’s own insights on everything from your field’s fundamentals to controversial issues. The phrase is cliché, but you need to become a thought leader, not just as an academic institution, but within the professions you’re training students to enter.

Your prospective students are out there, dying for clear guidance and answers to their most pressing questions. If you can be resource that answers them, you’ll be the academy they choose. 

Here at HubSpot, we’re not shy about our fondness of Snapchat. Heck, we even devoted an entire day to recruiting via Snapchat. But maybe not everyone is as crazy about the app as we are. Personal feelings aside, it’s time to start taking it seriously.

If nothing else, there’s something to be said for Snapchat’s off-the-charts growth. It’s shown a 12% average year-over-year increase in revenue since 2014, and is estimated to earn just short of $1 billion is 2017. Why is it worth so much? Because people are listening and watching. In fact, content posted to the app gets, in total, 10 million views every day. 

So, are you ready to start listening and watching, too? If you’re not on board with Snapchat yet, have a look at this helpful infographic from our friends at WebpageFX. Think about what these figures might mean to your own organization — and start snapping.

As the number of email senders fighting for recipients’ attention continues to climb, many marketers are seeing their engagement rates steadily decrease — even if they’re using an approach that worked well a year or two ago. But that’s the problem: If you haven’t shaken up your email program in over a year, your emails are probably getting stale.

Whether you’re an email marketing veteran or are just getting started, you may be operating under certain common misconceptions about email that have been disproved by research. Even worse, your email may be getting dull due to a lack of experimentation or inspiration. Maybe you’re aware that it’s time to switch up your email marketing, but it can still be hard to know where to begin.

HubSpot and SendGrid are here to help. We’ve joined forces to jumpstart your effort to redefine your email program. In doing so, we created a guide with over 100 rigorously-researched tips on how to avoid common misconceptions, figure out what works for your audience, and bring new inspiration to your email program. Inside, you’ll find:

• Facts to help you push past the newest and most stubborn email marketing myths
• Test and experiment ideas you can use to optimize your email campaigns
• Tips and inspiration to help you identify ways to keep your emails fresh and engaging
• Extra juicy pro tips you can share with your network with one click

Click here to download 104 Email Marketing Myths, Experiments, and Inspiration

In 2015, GE ran an amusing ad campaign featuring Owen, a newly hired programmer trying to explain to family and friends why they should be excited about his new job. They were baffled that a programmer would work at what they believed was just a manufacturing company, offering only condolences as he tried to outline the innovative nature of a more modern GE.

The spot was a clever way for GE to spread the word about its position as a digital industrial company, and not just a manufacturing one. Simultaneously, it helped boost the recruiting of young, tech-savvy developers into the fold. After the ad campaign aired, GE’s online recruitment site received 66 percent more visits month after month.

With the exception of GE — whose goal was to let people know that it does more than manufacture — the top agencies often offer similar services with little or no variation. Beyond that, what separates the greats is their personalities: their people, their values, their approach to business, and their interactions with their communities.

In today’s culture, who you work with is becoming more important than who you work for, and digital marketing is the best way to show prospective employees who you are as an agency, from the bottom up. By aligning digital marketing with HR, marketing agencies have the potential to create a sense of community that draws potential employees, clients, and market influencers closer than ever before.

Blurring the Lines Between HR and Marketing

Before the “Owen” ads brought more attention to GE, recent trends among other companies were already blurring the lines between HR and digital marketing. For instance, leaders are recognizing that employees are the best influencers, and have started leveraging them on social media channels, where employees can share company posts with their networks.

Certain corporate campuses even have designated areas for Instagram moments, allowing people to take photos with specific corporate campus attributes in the backdrop. The trend has spread all the way to the top, with CEOs like Meg Whitman posting pictures of their workspaces to promote more transparency.

By aligning HR’s and marketing’s methods and goals, you place your agency’s HR personnel in a more strategic position to achieve their recruiting goals. It also forces the creation of a single, consistent brand that accurately portrays your agency’s values and community.

Using Social Media to Attract New Employees

Most consumers today wouldn’t make a large purchase without first researching the brand. Likewise, practical job candidates will likely research multiple agencies’ brands before applying or accepting a job at any of them. In fact, company websites are a top resource for candidates on the job hunt. Keep your agency’s online representation consistent — and therefore more attractive to interested parties — with these five tips:

1) Highlight your agency’s culture on social media.

Every social media platform caters to a different need, and understanding the gains of each will help you use social media to its utmost potential. For instance, use Snapchat to tell quick visual stories that showcase the agency’s inner culture. Use Instagram to share photos of daily office life, and Twitter to showcase your agency’s unique personality and content.

If your agency doesn’t have a strong social media presence in 2017, you not only limit your chances of getting discovered by potential job seekers, you also risk turning off candidates in the process of researching your agency. Job seekers want to be able to get a good sense of what working for your agency is like on a daily basis. Without sharing content on social media that highlights this, they could get the impression that you don’t value transparency.

2) Build employee advocacy.

In addition to using your agency’s social media presence to showcase your teams’s culture, it’s important to build up employee advocacy. Social media isn’t going away, and empowering employees to use it to the agency’s advantage isn’t difficult.

Encourage employees to share their own insights about working for your agency on LinkedIn, Facebook, Twitter, Instagram, and other relevant social media platforms. Their extended networks will see their posts and spread awareness of your agency’s brand. By leveraging your employee’s personal and professional networks, your agency has the potential to reach people you wouldn’t have otherwise been able to with your business presence alone.

3) Connect with influencers in your agency’s niche.

Digital marketing also allows your recruiting team to more easily interact with potential candidates, as well as the most influential individuals and companies in your market. Like them on Facebook, interact with their tweets, and follow the trends that research shows are proving the most successful.

4) Network freely.

Networking is at the heart of social media and digital marketing, so use media extensively to connect and network with potential candidates, clients, and companies that you work with. Capital One, for example, offers another idea of how increased networking boosted HR performance. Just this year, Capital One’s CIO attended the Grace Hopper Celebration of Women in Technology, specifically promoting Capital One’s digital products and, most importantly, its company culture.

5) Aim to amplify your message.

Connecting and networking with the right influencers also gives you the opportunity to amplify your message to a significantly larger audience. If you build employee advocacy, utilize social platforms to your advantage, and master the art of networking, you’ll see your brand spread exponentially every time someone posts, tweets, or Snapchats something about you. Others will see it, too, including the hundreds of thousands of followers your connections will be able to reach.

When employees share content about their company, those shares receive up to eight times more engagement and are reshared up to 25 times more frequently than the content on the brand’s page. Research shows that such strong engagement and company culture helps companies outperform their peers in “profitability, productivity, customer satisfaction, employee turnover,””and more.

And by participating in social media and mobile apps, companies like Target have been able to attract and retain top talent while demonstrating its success with digital marketing. Because its digital marketing efforts are so extensive, those seeking new jobs already have an idea of what Target’s initiatives are.

GE’s engagement campaign, along with those of many other companies, offer a glimpse into how focusing more on value and results can boost an agency’s image, performance, and recruitment efforts across the board. Follow their example, introduce your agency’s personality to the world, and let top-level talent know who you are and how well they would fit into your agency’s culture.