The Essential Artificial Intelligence Glossary for Marketers

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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.

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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.

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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.

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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. 

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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.”

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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.

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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.

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A Brief History of Productivity: How Getting Stuff Done Became an Industry

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Anyone who’s ever been a teenager is likely familiar with the question, “Why aren’t you doing something productive?” If only I knew, as an angsty 15-year-old, what I know after conducting the research for this article. If only I could respond to my parents with the brilliant retort, “You know, the idea of productivity actually dates back to before the 1800s.” If only I could ask, “Do you mean ‘productive’ in an economic or modern context?”

Back then, I would have been sent to my room for “acting smart.” But today, I’m a nerdy adult who is curious to know where today’s widespread fascination with productivity comes from. There are endless tools and apps that help us get more done — but where did they begin?  Download our complete guide here for more tips on improving your productivity.

If you ask me, productivity has become a booming business. And it’s not just my not-so-humble opinion — numbers and history support it. Let’s step back in time, and find out how we got here, and how getting stuff done became an industry.

What Is Productivity?

The Economic Context

Dictionary.com defines productivity as “the quality, state, or fact of being able to generate, create, enhance, or bring forth goods and services.” In an economic context, the meaning is similar — it’s essentially a measure of the output of goods and services available for monetary exchange.

How we tend to view productivity today is a bit different. While it remains a measure of getting stuff done, it seems like it’s gone a bit off the rails. It’s not just a measure of output anymore — it’s the idea of squeezing every bit of output that we can from a single day. It’s about getting more done in shrinking amounts of time.

It’s a fundamental concept that seems to exist at every level, including a federal one — the Brookings Institution reports that even the U.S. government, for its part, “is doing more with less” by trying to implement more programs with a decreasing number of experts on the payroll.

The Modern Context

And it’s not just the government. Many employers — and employees — are trying to emulate this approach. For example, CBRE Americas CEO Jim Wilson told Forbes, “Our clients are focused on doing more and producing more with less. Everybody’s focused on what they can do to boost productivity within the context of the workplace.”

It makes sense that someone would view that widespread perspective as an opportunity. There was an unmet need for tools and resources that would solve the omnipresent never-enough-hours-in-the-day problem. And so it was monetized to the point where, today, we have things like $25 notebooks — the Bullet Journal, to be precise — and countless apps that promise to help us accomplish something at any time of day.

But how did we get here? How did the idea of getting stuff done become an industry?

A Brief History of Productivity

Pre-1800s

Productivity and Agriculture

In his article “The Wealth Of Nations Part 2 — The History Of Productivity,” investment strategist Bill Greiner does an excellent job of examining this concept on a purely economic level. In its earliest days, productivity was largely limited to agriculture — that is, the production and consumption of food. Throughout the world around that time, rural populations vastly outnumbered those in urban areas, suggesting that fewer people were dedicated to non-agricultural industry.

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Source: United Nations Department of International Economic and Social Affairs

On top of that, prior to the 1800s, food preservation was, at most, archaic. After all, refrigeration wasn’t really available until 1834, which meant that crops had to be consumed fast, before they spoiled. There was little room for surplus, and the focus was mainly on survival. The idea of “getting stuff done” didn’t really exist yet, suppressing the idea of productivity.

The Birth of the To-Do List

It was shortly before the 19th century that to-do lists began to surface, as well. In 1791, Benjamin Franklin recorded what was one of the earliest-known forms of it, mostly with the intention of contributing something of value to society each day — the list opened with the question, “What good shall I do this day?”

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Source: Daily Dot

The items on Franklin’s list seemed to indicate a shift in focus from survival to completing daily tasks — things like “dine,” “overlook my accounts,” and “work.” It was almost a precursor to the U.S. Industrial Revolution, which is estimated to have begun within the first two decades of the nineteenth century. The New York Stock & Exchange Board was officially established in 1817, for example, signaling big changes to the idea of trade — society was drifting away from the singular goal of survival, to broader aspirations of monetization, convenience, and scale.

1790 – 1914

The Industrial Revolution actually began in Great Britain in the mid-1700s, and began to show signs of existence in the U.S. in 1794, with the invention of the cotton gin — which mechanically removed the seeds from cotton plants. It increased the rate of production so much that cotton eventually became a leading U.S. export and “vastly increased the wealth of this country,” writes Joseph Wickham Roe.

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Source: Gregory Clark

It was one of the first steps in a societal step toward automation — to require less human labor, which often slowed down production and resulted in smaller output. Notice in the table below that, beginning in 1880, machinery added the greatest value to the U.S. economy. So from the invention of the cotton gin to the 1913 unveiling of Ford’s inaugural assembly line (note that “automotive” was added to the table below in 1920), there was a common goal among the many advances of the Industrial Revolution: To produce more in — you guessed it — less time.

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Source: Joel Mokyr

1914 – 1970s

Pre-War Production

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Source: Joel Mokyr

Advances in technology — and the resulting higher rate of production — meant more employment was becoming available in industrial sectors, reducing the agricultural workforce. But people may have also become busier, leading to the invention and sale of consumable scheduling tools, like paper day planners.

According to the Boston Globe, the rising popularity of daily diaries coincided with industrial progression, with one of the earliest known to-do lists available for purchase — the Wanamaker Diary — debuting in the 1900s. Created by department store owner John Wanamaker, the planner’s pages were interspersed with print ads for the store’s catalogue, achieving two newly commercial goals: Helping an increasingly busier population plan its days, as well as advertising the goods that would help to make life easier.

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Source: Boston Globe

World War I

But there was a disruption to productivity in the 1900s, when the U.S. entered World War I, from April 1917 to the war’s end in November 1918. Between 1918 and at least 1920 both industrial production and the labor force shrank, setting the tone for several years of economic instability. The stock market grew quickly after the war, only to crash in 1929 and lead to the 10-year Great Depression. Suddenly, the focus was on survival again, especially with the U.S. entrance into World War II in 1941.

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Source: William D. O’Neil

But look closely at the above chart. After 1939, the U.S. GDP actually grew. That’s because there was a revitalized need for production, mostly of war materials. On top of that, the World War II era saw the introduction of women into the workforce in large numbers — in some nations, women comprised 80% of the total addition to the workforce during the war.

World War II and the Evolving Workforce

The growing presence of women in the workforce had major implications for the way productivity is thought of today. Starting no later than 1948 — three years after World War II’s end — the number of women in the workforce only continued to grow, according to the U.S. Department of Labor.

That suggests larger numbers of women were stepping away from full-time domestic roles, but many still had certain demands at home — by 1975, for example, mothers of children under 18 made up nearly half of the workforce. That created a newly unmet need for convenience — a way to fulfill these demands at work and at home.

Once again, a growing percentage of the population was strapped for time, but had increasing responsibilities. That created a new opportunity for certain industries to present new solutions to what was a nearly 200-year-old problem, but had been reframed for a modern context. And it began with food production.

1970s – 1990s

The 1970s and the Food Industry

With more people — men and women — spending less time at home, there was a greater need for convenience. More time was spent commuting and working, and less time was spent preparing meals, for example.

The food industry, therefore, was one of the first to respond in kind. It recognized that the time available to everyone for certain household chores was beginning to diminish, and began to offer solutions that helped people — say it with us — accomplish more in fewer hours.

Those solutions actually began with packaged foods like cake mixes and canned goods that dated back to the 1950s, when TV dinners also hit the market — 17 years later, microwave ovens became available for about $500 each.

But the 1970s saw an uptick in fast food consumption, with Americans spending roughly $6 billion on it at the start of the decade. As Eric Schlosser writes in Fast Food Nation, “A nation’s diet can be more revealing than its art or literature.” This growing availability and consumption of prepared food revealed that we were becoming obsessed with maximizing our time — and with, in a word, productivity.

The Growth of Time-Saving Technology

Technology became a bigger part of the picture, too. With the invention of the personal computer in the 1970s and the World Wide Web in the 1980s, productivity solutions were becoming more digital. Microsoft, founded in 1975, was one of the first to offer them, with a suite of programs released in the late 1990s to help people stay organized, and integrate their to-do lists with an increasingly online presence.

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Source: Wayback Machine

It was preceded by a 1992 version of a smartphone called Simon, which included portable scheduling features. That introduced the idea of being able to remotely book meetings and manage a calendar, saving time that would have been spent on such tasks after returning to one’s desk. It paved the way for calendar-ready PDAs, or personal digital assistants, which became available in the late 1990s.

By then, the idea of productivity was no longer on the brink of becoming an industry — it was an industry. It would simply become a bigger one in the decades to follow.

The Early 2000s

The Modern To-Do List

Once digital productivity tools became available in the 1990s, the release of new and improved technologies came at a remarkable rate — especially when compared to the pace of developments in preceding centuries.

In addition to Microsoft, Google is credited as becoming a leader in this space. By the end of 2000, it won two Webby Awards and was cited by PC Magazine for its “uncanny knack for returning extremely relevant results.” It was yet another form of time-saving technology, by helping people find the information they were seeking in a way that was more seamless than, say, using a library card catalog.

In April 2006, Google Calendar was unveiled, becoming one of the first technologies that allowed users to share their schedules with others, helping to mitigate the time-consuming exchanges often required of setting up meetings. It wasn’t long before Google also released Google Apps for Your Domain that summer, providing businesses with an all-in-one solution — email, voicemail, calendars, and web development tools, among others.

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Source: Wayback Machine

During the first 10 years of the century, Apple was experiencing a brand revitalization. The first iPod was released in 2001, followed by the MacBook Pro in 2006 and the iPhone in January 2007 — all of which would have huge implications for the widespread idea of productivity.

2008 – Present

Search Engines That Talk — and Listen

When the iPhone 4S was released in 2011, it came equipped with Siri, “an intelligent assistant that helps you get things done just by asking.” Google had already implemented voice search technology in 2008, but it didn’t garner quite as much public attention — most likely because it required a separate app download. Siri, conversely, was already installed in the Apple mobile hardware, and users only had to push the iPhone’s home button and ask a question conversationally.

But both offered further time-saving solutions. To hear weather and sports scores, for examples, users no longer had to open a separate app, wait for a televised report, or type in searches. All they had to do was ask.

By 2014, voice search had become commonplace, with multiple brands — including Microsoft and Amazon — offering their own technologies. Here’s how its major pillars look today:

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The Latest Generation of Personal Digital Assistants

With the 2014 debut of Amazon Echo, voice activation wasn’t just about searching anymore. It was about full-blown artificial intelligence that could integrate with our day-to-day lives. It was starting to converge with the Internet of Things — the technology that allowed things in the home, for example, to be controlled digitally and remotely — and continued to replace manual, human steps with intelligent machine operation. We were busier than ever, with some reporting 18-hour workdays and, therefore, diminishing time to get anything done outside of our employment.

Here was the latest solution, at least for those who could afford the technology. Users didn’t have to manually look things up, turn on the news, or write down to-do and shopping lists. They could ask a machine to do it with a command as simple as, “Alexa, order more dog food.”

Of course, competition would eventually enter the picture and Amazon would no longer stand alone in the personal assistant technology space. It made sense that Google — who had long since established itself as a leader in the productivity industry — would enter the market with Google Home, released in 2016, and offering much of the same convenience as the Echo.

Of course, neither one has the same exact capabilities as the other — yet. But let’s pause here, and reflect on how far we’ve come.

Where We Are Now…and Beyond

We started this journey in the 1700s with Benjamin Franklin’s to-do list. Now, here we are, over two centuries later, with intelligent machines making those lists and managing our lives for us.

Have a look at the total assets of some leaders in this space (as of the writing of this post, in USD):

Over time — hundreds of years, in fact — technology has made things more convenient for us. But as the above list shows, it’s also earned a lot of money for a lot of people. And those figures leave little doubt that, today, productivity is an industry, and a booming one at that.

How do you view productivity today, and what’s your approach to it? Let us know in the comments.

Productivity Guide

 

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The Goldfish Conundrum: How to Create Content for Short Attention Spans

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The creation of mobile devices has made multitasking close to ubiquitous in the modern world. Between social media, live streaming, and digital news, it’s hard to imagine a time of day when we aren’t tempted to look at a screen while we’re doing something else at the same time.

Because of this phenomenon, it should come to no surprise that the average human attention span has fallen to just eight seconds — shorter than that of a goldfish.

What’s more, 59% of people share articles on Twitter without even reading them, and more than half of all pageviews are under a minute in length. It’s clear that people aren’t reading as much as they used to, and content creators need to adapt their strategy to that reality.

In this post, we’ll discuss strategies and resources marketers can use to create content that will generate clicks, shares, and most importantly, more readers.

The Current State of Content Marketing

Back in September, Content Marketing Institute (CMI) and MarketingProfs released their annual B2B content marketing survey results, revealing that 70% of respondents plan to produce more content in 2017 than in 2016. However, while content production continues to rise year over year, content engagement saw a 17% drop in 2016, according to TrackMaven.

This dip could be a reflection of decreased content quality, as proper planning and research tend to fall by the wayside when marketers ramp up their output. But it could also be attributed to that attention span shrinkage we mentioned earlier. After all, today’s readers are more likely to skim blogs, long-form written content, and podcasts rather than thoroughly consume them, according to data from HubSpot Research.

What’s a marketer to do? Let’s dive into our strategies for defying the goldfish attention span, without sacrificing the quality of your content.

How to Create Content That Gets Consumed

You already know that content creation is an integral part of the inbound marketing methodology. It attracts visitors to your blog, cultivates brand awareness, and helps you generate leads for your organization. But what about page views? Here are our strategies for making sure you’re not just creating into the void, but are actually producing content that gets consumed and shared.

Write quality content

We know, this one seems like a no brainer. But with 30% of marketers reporting that they don’t have clarity around what content marketing success looks like, it’s an important issue to stress.

It’s estimated that bad writing costs businesses close to $400 billion per year in inefficiency and productivity loss. And it could also be costing your organization if you’re generating content that isn’t driving any results. So before you start putting fingers to keyboard, implement a few processes to make sure you’re writing quality content that’s also useful to your audience.

Here are a few ideas:

One of the easiest ways to create content that your audience will read? Ask your audience what they want to read about. Conduct social media polls and surveys to find out what topics and content types your subscribers are interested in, and brainstorm ideas based on their feedback.

For example, The Muse publishes content for job seekers about career growth, and they ran a poll asking their Twitter followers what would improve their workday.

Sure enough, shortly after the poll closed on Twitter, they published this article based on the results:

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Clever, right? Experiment with social media polls and ask for engagement from your followers. Encourage your audience to engage with the poll to generate content ideas and more participation on social media, and see what ideas you come up with based on the results.

Invest in thought leadership

When setting your blog editorial calendar for the months ahead, ask yourself: Are there any topics that someone in the organization, such as a founder or executive, is uniquely qualified to write about?

That’s thought leadership — and it’s not as difficult to incorporate into your strategy as you might think it is. In fact, there are a lot of small steps you can take to incorporate more thought leadership into your current editorial.

Here are a few ideas:

At HubSpot, we frequently partner with influential organizations — like Trello — to create content that reflects our combined expertise. By collaborating with the folks behind Trello to put together a comprehensive guide for using the project management tool in your marketing campaigns, we demonstrated our ability to provide credible, helpful tips — straight from the source:

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Brainstorm other organizations in your industry and determine a mutually beneficial way you could collaborate. Whether that’s guest posting, cross-promotion, or working together as in the example above, keep bringing new ideas to the table that your audience can’t help but read.

Create visual content

Your audience wants to see more visual content, and it performs better, too: readers spend more time looking at images than words on a web page, and images promote greater memory recall than text alone.

There are a variety of different types of visual content that you can create to draw attention and promote greater readership, and our blog has a number of step-by-step guides to creating eye-catching infographics, videos, and more.

Vox does a great job of providing written and visual content for its readers. On any given day, it might publish a data visualization, a long-form article, and a video featuring different angles on the same topic — in this case, the Women’s March on Washington — to match different people’s content preferences and to keep things fresh for its audience.

Data Visualization

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Long-Form Article

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Video

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The lesson? Don’t automatically default to writing a blog post simply because it’s a medium you’re comfortable with. Experiment with creating visual content to tell data-driven stories your audience will click, and hopefully share, too.

Meet the reader halfway

Follow the Golden Rule: Treat your reader as you’d like to be treated. Most of us are busy people, and busy people on the internet like to skim-read content. Luckily, you can make it easier for readers to consume your content all the way through with different formatting, layout, and coding choices.

Here are a few ideas:

  • Use headers and bolded text to break up sections and paragraphs so readers can maintain focus.
  • Use bullet points and numbered lists to draw the eye to a new format and pay closer attention. (See what we did there?)
  • Include summary and takeaway sections in your written content to help readers remember what they’ve been reading about and maintain their interest.

Even better, help the reader understand how quickly they’ll be able to read a piece before they get started. Check out how Medium does this in an example from ThinkGrowth.org, HubSpot’s Medium publication:

medium_skimming_example

(For more examples of publications that produce easily consumable content, try reading this blog post for inspiration.)

Publish on a variety of channels

Another challenge to getting people to thoroughly consume your content is they just may not have found it yet, and that’s where off-site content can come in handy. Audiences vary across different platforms, and it’s easier for your content to get discovered, and then read, if it’s published in more places than just your blog.

Medium is one example of where you can publish different content to attract a broader audience. You could create original content for a Medium publication, or repurpose old content by turning text into an infographic or video. As HubSpot Vice President of Marketing Meghan Keaney Anderson notes:

On the open web, people are searching, but on Medium, people come to spend time reading. This leads to much higher engagement on Medium and it’s this engagement, not search behavior, that fuels further discovery.”

Social media platforms also offer a variety of features for publishing original content. For example, you can publish live video on Facebook, ephemeral messages on Snapchat, photos on Instagram, and blog posts on LinkedIn. To ensure you’re publishing on these channels at the most optimal time, check out this guide from ClearVoice on when to publish content on social media for different industries.

Getting Started

We know this is a lot of information, but competition is getting stiff, so experimenting with how you create content now will pay off in the future. If you need help getting started, here’s our list of free tools to make awesome content.

What’s your favorite type of content to read? Share with us in the comments below.

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4 Smart Ways to Keep Up With Google in 2017

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Over the past few years, Google has made a lot of changes that can be challenging for modern marketers and SEO experts to keep up with.

Recently, Google started indexing according to mobile search results, cracked down on intrusive pop-up ads, and removed right-side ads. What do all of these changes have in common?

Google has been making changes to its algorithm, search engine results pages, and policies based on user behavior and preference. They’re observing what users are doing and making the search engine work more intuitively for them. This is great for the frequent Google searchers in all of us — Google processes more than 2 trillion searches per year — but it can make it challenging to adapt your strategy and achieve your SEO goals. >” src=”https://no-cache.hubspot.com/cta/default/53/bd685600-02f9-40f3-a4e7-18488a8d79ba.png”>

At last year’s INBOUND event, Moz Co-founder and former CEO Rand Fishkin unpacked the ways Google is changing and what marketers and SEO pros can do to improve their results in 2017.

4 Ways Marketers Can Strengthen Their SEO Strategy in 2017

1) Diversify your traffic sources to keep earning visits from Google.

Fishkin encourages a distributed content strategy designed to help you earn traffic from a variety of different sources. This approach ensures that your traffic numbers aren’t entirely contingent on people directly searching for your brand name.

Video marketing is an engaging content format that can diversify traffic. For example, Moz videos are published on Wistia first, then on YouTube. This multi-channel strategy allows them to generate traffic from different sources. In the same vein, written content can be published on blogs, LinkedIn Pulse, and Medium publications. However, it’s important to tailor your content to different audiences where they consume content, whether it’s on social media or YouTube or guest posts on other blogs, to keep earning traffic and links from a variety of sources in Google search.

2) Evolve your keyword targeting strategy.

There are many new elements on Google search engine results pages (SERPs) that make it harder to generate clicks. With Google’s Answer Box and Carousel search results, there is less real estate on Google’s search engine results page than ever. Check it out below — if you Google the term “blog,” a large portion of the page is taken up by Google Ads, news stories, and an Answer Box:

blog search results.png

Because Google’s real estate is this competitive, it’s harder to get onto page one of the search engine results page, which is proven to have higher clickthrough rates than other results. In fact, did you know that the first position in Google gets 33% of search traffic?

The solution? Changing your keyword targeting strategy so your content is better geared toward the changing ways people are searching on Google. Use keyword research tools such as Google AdWords and HubSpot’s Keywords App to determine the long-tail, conversational keywords your audience is searching for. Then, plan a blog post strategy based around topic clusters so your website is more likely to rank higher in Google search results.

3) Invest in a link building strategy that can scale.

Inbound links pointing toward your website give it massive levels of authority in search engines, so link building is still an integral part of your inbound marketing strategy. Fisher says that, in order for link building to be successful, you need several elements: link goals, a strategic approach to achieving them, tactical initiatives, and metrics to track. Link building takes a long time to show results, so there is usually a significant time gap between when you start experimenting and conducting initiatives and when you see the benefits of your efforts.

For that reason, SEO marketers need to balance their efforts between long-term investments and short-term hacks so you’re constantly generating links while investing in the future health of your site’s inbound link generation. You’ll want to make sure that these approaches are white hat link building tactics that offer a benefit to your site and sites linking to you, and not spammy tactics that can hurt your site.

Long-term investments have high upfront costs and are slower to return on their investment, but they earn links while you sleep and don’t put you at risk of any spammy behaviors. These tactics primarily consist of publishing high-quality content and promoting it in hopes that other sites will link to it. Short-term hacks can show results more quickly, but these strategies can sometimes be viewed as spammy. Such tactics include guest posting and sponsored content.

With a balance of long-term and short-term strategies, you’ll be able to ensure your domain’s authority immediately while you work to build up your blog to the point where it is newsworthy and linked to organically.

4) Increase searcher engagement with your content.

With the introduction of RankBrain, Google’s new machine-learning algorithm that helps determine search engine rankings, how the searcher interacts with Google is now being weighed as one of hundreds of ranking signals. While Google is still figuring out how RankBrain operates themselves, but metrics such as clickthrough rate and time on-page already contribute to how high (or low) your site ranks in Google.

Here are strategies to think about in order to amp up your site if RankBrain starts to play a greater role in SEO:

  • Think about all of the questions your audience is searching for answers to, and not just individual keywords.
  • Prioritize user experience and consider amplifying or redesigning your website so that it answers multiple search queries and visitors will stay on your site for longer.
  • Design your website so that it delivers content quickly and beautifully across multiple devices.
  • Remove features like pop-up ads that discourage users from reading or returning to your page.
  • Most importantly, invest time and resources in publishing authoritative, comprehensive content that your audience will read and share again and again.

What’s Ahead in Search

Fishkin believes that the name of the SEO game is engagement. “If you deliver dramatically better engagement than your competitors,” he explained, “they will be hard-pressed to catch up with you in SERP.” We already know that Google has changed a lot this year, and will most likely keep changing in the next year, so it’s important to get a plan in motion now for search engine optimization and link building strategies to propel your site’s growth. Watch Fishkin’s INBOUND talk in full below:

 

 

For more ideas on safeguarding your website for search engine optimization, read our blog posts about white hat link building tactics, blog post content ideation, speeding up your blog, and more.

How are you changing your SEO strategy in 2017? Share with us in the comments below.

seo myths 2017

Is Marketing Undergoing a Dramatic Change? [Survey]

Marketing is Changing.jpg

Every year, HubSpot publishes the State of Inbound report, providing benchmarks for the tactics, challenges, and priorities of marketers and salespeople around the world. 

HubSpot has been tracking trends in inbound marketing for eight years, and we’ve recently seen serious disruptions rock the market. In 2016, we began asking marketers more forward thinking questions — and saw a real shift in focus to visual content, like video.

Has your marketing team integrated Snapchat into its marketing strategy? Are you finding yourself more focused on video and less on written content? Or do you think this is all a blip? 

We can’t run this study without the help of marketers like you. Tell us how your envisioning marketing in 2017 by taking our 10-minute survey — it’s completely anonymous so you can be honest. Click here to get started.

We publish the results every year, free of charge. Best of all, as a thank you for participating in our study, you’ll get the report before it’s released to the general public.

The State of Inbound 2017 Survey

How Growth-Driven Design Impacts on Your Sales Process and Why You Shouldn’t Ignore It

ThinkstockPhotos-508056140_1-279716-edited-1.jpgWhen I first got into digital marketing as a career change a few years back (I had previously worked as a financial advisor and had a brief stint starting a study-abroad program for a Chinese government contracted company) I thought the term ‘growth driven design’ was a bit haughty.

It reminded me of an interview I once read with a favorite musician of mine, who was labeled in the music press as an IDM artist, the acronym standing for ‘intelligent dance music’.

The artist’s response, and of course I need to paraphrase here, was: “Intelligent dance music? It makes it sound like other types of dance music aren’t as intelligent as this kind. A bit nasty, innit?” (He was English, and “innit” was the only part I didn’t need to paraphrase, as it’s humorous to read it online when one is a guy born and raised in the US.)

I agreed with his perspective, and felt the same way when ‘growth driven design’ and ‘GDD’ first began exploding on marketing blogs left and right, bandied about like free candy. Does the term imply that other web design methodologies were somehow not aimed at growing the organization? That these companies NOT using GDD were suckers, and we select few were privy to a pot of marketing gold? Should I feel guilty for knowing how to do this?

Well, yes. I should feel guilty, but only if I refuse to share this knowledge with others. Hence – here we are now.

Before we get into how GDD impacts your sales process, we should clear the air beforehand, and this is not something that every marketing professional will let you know from the get-go…but I will because I’m still a rebel at heart.

Growth driven design is not practical for every organization.

If you are Nike or Coca-Cola, you probably don’t need to be a GDD devotee to the degree that the rest of us do. Until my company attains the market salience and multi-billion revenue of a Bloomberg or Macy’s, I am a convert one hundred percent to the value of GDD.

Also, if you run an organization in certain industries (i.e. legal services, construction work) where your online presence needs to be as consistent and predictable as your personal communications, delivery of services, what-have-you, then GDD might not the best way to approach your web design efforts.

Yet, emphasis on might…

However, if you run a dynamic company, breaking ground with your products or services, trying to edge out the competition as one of the new guys, or any other situation where online customer conversions via your website are vital for your sustainability – welcome, friends. Time to dive in.

So, why doesn’t Mercedes-Benz need GDD?

Once your company has been globally recognized as an industry leader for about 100 years, well, you gained market salience during the entire time that many of your customers were birthed and passed on, all before the internet existed.

Yes, admittedly this is a bit grim, but it’s a fact. Rolex doesn’t need GDD for their web presence. Not to say it can’t be a bit of fun to try it out for those guys, but it could very well plainly be a waste of time and (ahem, negligible degree) of resources. 

So, why does your company need GDD?

Because traditional web design approaches are robbing you of potential customers for a crazy number of reasons. I don’t use the word ‘crazy’ as an adjective often, so here’s why it’s warranted:

  • You started to grow your company after the dawn of the internet which, for practical purposes, demands that your web presence is your primary line of contact with customers or
  • You work in an industry for which a web presence is impossibly valuable for your sales
  • The traditional approach of design – a complete overhaul of your website every two years – alienates customers who have not yet had the opportunity to develop brand loyalty to your company

Before I get into the stats (they’re going to happen eventually – this is a blog post about marketing after all), here’s another quick story that illustrates the impact of GDD:

When I was 23 I was department manager at a major domestic chain store outside of NYC. I had retail experience for the preceding five years at four other retail outlets. The one I’m talking about now confused the hell out of me – our merchandisers and designers were spending every single day going over fresh plans from corporate to redesign the entire store bit by bit, and here’s the kicker, during retail hours.

I found the idea ludicrous. Who would want to shop at a store that is always a work in progress? But…it worked. The target audience were Millennials and Generation X’rs, and this redesign perpetuity brought us in measurably greater foot traffic (and thus sales) than any other competing stores in each and every American college town we had property in.

Yes, we did the analytics.

What this retailer was doing was the physical equivalent of web GDD. It didn’t make sense to me as a kid, but it makes tremendous marketing sense to me now as an adult – give the customers what they want deep down from your by testing it out on them while they’re already in your store.

I swear I’ll get into the numbers soon, but here’s the thing – the core audience didn’t mind the sawdust or awkward positioning of fixtures while things were shuffled around; the value of the product was already established. However, the customers kind of on the fence, that walk in once or twice to survey what the brand feels like, are much more enticed to walk in again a few days or weeks later just to see how the store had changed.

And this was all analyzed by corporate…

Color choices for seasons were not simply chosen at random – they had be tested over several years. In tandem, trends were analyzed and incorporated into the store’s design along with what had learned to be the company’s target audience preferences.

Ladies and gentlemen – this was growth driven design in actual physical practice.

The numbers you’ve been waiting for…

You are probably making your own assumptions for why when it comes to web design GDD is a godsend for countless organizations, why it works, and how crucial it is to grow sales. To reference my earlier point, ‘growth driven’ isn’t implying that GDD is what simply grows a company’s sales, but instead that it drives sales when sales need to be driven through your website experience.

If you’re like my agency and in charge of turning SMEs and enterprise-level technology companies into industry dominators through digital marketing, or are one yourself, this is why you need to incorporate GDD or suffer miserable profit margins:

What does this mean for your web design? It means that your potential customers are expecting to know how your product or service is going to solve their pain points, but with less text and prettier pictures.

Remember, your core audience will still be loyal to your brand, because the content that worked in the first place is still there. However, the traffic that is walking through your doors is going to need to know why your brand is the right choice for them.

This is where GDD gives you greater opportunity to convert them than traditional web design. The sales and marketing teams work in conjunction with your designers in testing out what works for the customer, instead of what your brand assumes works for the customer.

In this sense, GDD allows you the freedom to optimize your site to cater only to the customer’s expectations and desires. Through continually redesigning your site, while analyzing what changes are providing the greatest ROI, you are building a site design that speaks exactly to both your core customer base and to those unfamiliar with your brand yet. 

How does this impact your sales process?

When continuously optimizing your website for your target audience, your sales risks are substantially mitigated against consumer preferences and trends. This means that not only are you saving money on an extremely costly overhaul of your site, in at least the tens of thousands, but you are delivering a site that is based not on assumptions, but actual customer behavior regarding their preferences and expectations.

This means that your sales team can now communicate with customers that are already prepped to be closed. Sales staff are kept in the loop month after month of what is working to convert visitors to leads. Your team may already know of your product and its value, but know they are also aware of how informed the leads are via your design; the content and visuals that have spoken to the buyer at that moment in time.

For example, buyer preferences change over time, such as what color car is in fashion and which have fallen out. With an informed sales team, one which knows exactly what pain points are relevant to customers at a given moment in time and how preferences have shifted from one month to the next, well, think of the possibilities.

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63 Web Design Terms Every Marketer Should Know

Design-Glossary-compressor.jpg

When you’re new to marketing, especially on a small team, you might have to do a lot of things at a moment’s notice. And when it comes to things like blogging and social media, sure, you’ve got this. But soon enough, you’re being pulled onto design projects. One day you’re mocking up an infographic; the next, you’re designing an ebook. You feel woefully unprepared — and that design vocabulary? It can feel like a foreign language.

Sound familiar? 

We’ve been there — and we know we’re not the only marketers who have, at some point, needed to become fluent in this vocabulary. So we decided to share a larger glossary, to help us all step up our game a bit. By no means is this the be-all-end-all of design terminology, so feel free to add your definitions in the comments as well. Here’s what we have, organized alphabetically.

The Ultimate Web Design Terms Glossary

A /

B /

C /

D /

E /

F /

G /

H /

J /

K /

L /

M /

N /

O /

P /

R /

S /

T /

V /

W /

X /

Z

A

1) Alignment

The positioning of the elements in your design (e.g. text, images, etc.). These elements can be aligned to both the page and to each other. For example, this paragraph of text is aligned to the left margin, whereas the lines depicted in the image below are aligned to the right.

document-27091_960_720.png

Source: Pixabay

2) Analogous Colors

Colors that appear adjacent to each other on a color wheel.

analogous.jpg

Source: nopira

3) Ascender

A linear extension of a letter that appears above the midline — also see baseline, cap height, descender, and extender.

ascender-1.png

Source: Max Naylor

B

4) Baseline

The even, invisible line on which all letters of a typeface sit — also see ascender, cap height, descender, extender, and midline.

baseline.png

Source: Max Naylor

C

5) Cap Height

The distance between the baseline and the top of uppercase letters — also see ascender, descender, extender, and midline.

cap height.png

Source: Max Naylor

6) CMYK Color Model

Stands for cyan, magenta, yellow, and black. This set of colors is used in print design because of the way paper absorbs light.

CMYK-1.png

Source: Capsoul

7) Color Wheel

A circle of colors that shows relationships between primary, secondary, and tertiary colors.

BYR_color_wheel.svg.png

Source: nopira

8) Color Schemes

The combination of two or more colors from the color wheel — also known as color harmonies.

9) Complementary Colors

Colors that are directly opposite of each other on the color wheel.

complementary.jpg

Source: nopira

10) Compression

Reducing a file size by eliminating excess data. Particularly helpful when emailing or saving large image files. See more on lossy and lossless compression.

11) Contrast

The accentuation of differences between colors, shapes, spacing, or any other design element.

leaves-835488_960_720.jpg

Source: Pixabay

12) Crop

When outer parts of an image are removed to reframe the subject matter, or to resize the image’s aspect ratio.

M6bEaWiUIs.gif

13) CSS

A piece of code that is used to designate the look and feel of a website, separate from the actual content of a web page.

D

14) Descender

An extender on a letter, appearing below the baseline — also see ascender, cap height, and midline.

descender-1.png

Source: Max Naylor

15) Dots per Inch (DPI)

Similar to the pixel for the web, dots are the smallest unit of measurement when printing digital images. The number of DPIs refer to the resolution of a printed digital object — the higher the DPI, the higher the resolution.

16) Drop Shadow

A visual effect that displays a graphic as if it had a shadow behind it.

Blurshadow.png

Source: Tizio

E

17) EPS

A file format used for vector images that contain both text and graphics.

18) Extender

The part of a letter that extends above the x-height or below the baseline — also see ascender, cap height, descender, and midline.

F

19) Feathering

A design technique used to smooth out edges of a feature.

20) Font

A typeface in one specific style and size. An example would include Times New Roman Semi Bold in size 14.

G

21) GIF

An image file format that’s best used for small image files with few colors and designs, or animated images. Below is an example of an animated GIF image:

Humulone-3D-xray.gif

Source: Manuel Almagro Rivas

22) Gradient

A design technique in which one color or portion of an image appears to fade into another.

turquoise-top-gradient-background.jpg

Source: Public Domain Pictures

23) Grid

A purely hypothetical map of vertical and horizontal lines that helps align images and text within a document.

H

24) HEX Code

A code used in HTML and CSS to designate a specific color, often appearing after the pound sign (#). Below is a chart of HEX color codes:

Xterm_color_chart.png

Source: bmdavll

25) HTML

The computer language used to display content like text, images, and links on the web.

26) Hue

What most people think of as “color” — red, orange, yellow, etc.

J

27) JPEG

An image file type that uses lossy or lossless compression, with little perception in a loss of quality. This type of file is best used for photographs and realistic paintings where there are smooth transitions between colors.

K

28) Kerning

The space between individual letters.

1280px-Kerning_EN.svg.png

Source: Sherbyte

L

29) Leading

The space between lines of type.

30) Lossy

A form of data compression where detail is deleted as the file size is decreased. A usual lossy compression method is JPEG.

31) Lossless

As opposed to lossy compression, this format allows the image’s detail to be restored.

M

32) Midline

The distance from the baseline to the top of most lowercase letters, including “e,” “g,” and the curve of “h.” Also know as the “median,” as depicted below. See ascender, cap height, and descender.

midline-1.png

Source: Max Naylor

N

33) Negative Space

The empty space surrounding a design, whether a webpage or single image — also see white space.

negativespace.jpg

Source: Public Domain Pictures

O

34) Open Type Fonts

The current standard in font formats. It contains both the screen and printer versions in a single file, and is compatible for both Windows and Mac. The file extension is .otf.

35) Orphan

An opening line in a paragraph that appears alone at the bottom of a page. An orphan can also be a word or very short line that appears by itself at the end of a paragraph.

2000px-Orphan-typesetting.svg.png

Source: Maat

P

36) Pantone

A color-matching system developed by the Pantone company. Largely used in print design, and used to match printed colors to those that appear on the screen during the digital phase of design.

nuance-1086725_960_720.jpg

Source: Pixabay

37) PDF

A file format best used to represent documents and presentations.

38) Pixel

The smallest element of an image on a computer.

39) Pixels per Inch (PPI)

Another measure of image resolution, according to how many pixels are present within a given section of the image.

40) PNG

An image file format that’s best used when the image has large areas of uniform color, or a transparent background (unlike JPEG).

R

41) Rectangular (or Tetradic) Colors

Four colors that are two pairs of complementary colors.

rectangular.jpg

Source: nopira

42) Resolution

A way of measuring the sharpness and level of detail in an image. A higher resolution usually indicates a larger file size, representing the amount of data — like pixels or dots — within the image.

43) RGB Color Model

An acronym standing for the colors red, green, and blue. The RGB color model is used for web design, because monitors transmit light in these colors.

AdditiveColor.svg.png

Source: Mike Horvath

S

44) Saturation

How bright or intense a color is.

HSV_color_solid_cylinder_alpha_lowgamma.png

Source: SharkD

45) Serif

A small line attached to the end of a stroke in some fonts. “Sans serif” refers to fonts that don’t have this line.

2000px-S_long_serif_et_sans_serif.svg.png

Source: GJo

46) Shade

How much black is mixed in with the hue.

47) Split-Complementary Colors

Colors that consist of a base color, plus the two colors that lie next to its complementary color.

splitcomplementary.jpeg

Source: nopira

48) Square Colors

On the color wheel, four colors are spaced evenly from each other.

SQUARE-4.jpg

Source: nopira

49) Stem

The primary vertical stroke in a letter. It’s used in the letter “B” and the diagonal line of “V.”

50) Strokes

The lines that make up a letter in a typeface.

T

51) Tail

The descending stroke in a letter that’s often decorative — for example, in the letter “Q.”

52) Terminal

The end of a stroke that doesn’t include a serif.

53) Tint

How much white is mixed in with the hue.

54) Triadic Colors

Color scheme in which three colors located at 120 degrees from each other on the color wheel are combined. It’s often considered the best color scheme.

triadic.jpg

Source: nopira

55) Typeface

A design collection of characters, including letters, numbers, and punctuations. Examples include Times New Roman, Helvetica, and Arial.

V

56) Vector Image

Instead of using pixels to represent images, vectors use lines and shapes. Because they do not rely on pixels, enlarged vector images still maintain image clarity and quality.

57) Visual Hierarchy

A design principle that visually orders and emphasizes different parts of your content’s message by using colors, sizes, and layouts.

W

58) Watermark

An easy-to-see marker placed over the top of photos on the web and in print. It is used to identify the owner of an image and prevent visual content theft.

59) Weight

In typefaces, the thickness of the stroke’s width. Some examples include demibold, light, and bold.

60) White Space

The blank space surrounding an object in design — also see negative space.

books-education-school-literature-48126.jpeg

Source: Pexels

61) Widow

The section of text at the end of a paragraph that spills over into the following column or page.

2000px-Widow-typesetting.svg.png

Source: Maat

X

62) X-height

In a letter, the distance between the midline and baseline — also see ascender, cap height, descender, and extender.

xheight.png

Source: Max Naylor

Z

63) ZIP file

A file format that compresses several files and combines them into a single folder. Compressed files do not lose any data to become smaller, and are easily restored by unzipping the ZIP file.

What other web design terms would you add to the list? Let us know in the comments. 

Editor’s Note: This post was originally published in July 2013 and has been updated for accuracy and comprehensiveness.

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