Tag Archive | "Learning"

Etsy CEO: Machine Learning is Opening Up a Whole New Opportunity

Etsy CEO Josh Silverman says that “machine learning is opening up a whole new opportunity” for the company to organize 50 million items into a discovery platform that makes buying an enjoyable experience and also is profitable for sellers.

Josh Silverman, CEO of Etsy, recently talked about their much-improved business and why it is working so well with Jim Cramer on CNBC:

Our Mission is Keeping Commerce Human

Our mission is keeping commerce human. It’s really about in a world where automation is changing the nature of work and we’re all buying more and more commoditized things from the same few fulfillment centers. Allowing someone to harness their creative energy and turn that creativity into a business and then connect with someone in the other part of the country or in another part of the world, that’s really special. We think there’s an ever-increasing need for that in this world.

It’s about value. We’ve been really focused on delivering more value for our makers. Etsy really is a platform that brings buyers to sellers and that’s very valuable. We raised our commission from 3.5 to 5 percent commission which was I think is fair value for our sellers, particularly because we’re reinvesting 80 percent of that into the growth of the platform.

Free shipping is pretty much table stakes today. Yet only about 20 percent of items have free shipping. About half of all the items on Etsy buyers say have shipping prices that are too high and yet we grew GMS at 20 percent last quarter.

Machine Learning is Opening Up a Whole New Opportunity

Machine learning is opening up a whole new opportunity for us to take 50 million items from two million makers and make sense of that for people. We have 37 million active buyers now and many of them come just for discovery, just to see what they can find, and that is exactly the right thing for someone out there. Our job is to create that love connection. Etsy over the past 14 years, with a large team effort, has I think done a great job.

One thing I want to emphasize is the quality and the craftsmanship with so many of the products on Etsy. That’s something that has been such a delight for me. People like Kringle Workshops that make these incredible products. What we have been doing a better job and need to continue to do a better job of really surfacing the beautiful artisanally crafted products that are available at a really fair price. You’re not having to pay for warehousing, you’re not having to pay for all the other things that mass-produce things have to pay for, you’re buying directly from the person who made it. So it can be both beautiful, handcrafted, and well priced.

There are 2 million sellers, 87 percent of them are women, over 90 percent are working from home or are businesses of one, who can create a global business from their garage or their living room. Etsy does provide a real sense of community for them and that’s really powerful.

Amazon May Open New HQ in Queens Near Etsy

We feel great about our employee value proposition and come what may. Here’s what we have going for us. We think we’ve got the best team, certainly in tech companies on the eastern seaboard. We think ours is the best and we continue to attract great talent. The reason is, first and foremost, our mission is really a meaningful important mission and that matters. Great people want to work in a place with a great mission.

Second, our technology challenges are interesting. For example, search and using machine learning to make sense of 50 million items that don’t map to a catalog. Third, our culture is really special. We have been a company that’s authentically cared about diversity from the beginning. Over 50 percent of our executive staff are women, we have a balanced board, 50 percent male and female, and 32 percent of our engineers are female, which is twice the industry average. People who care about diversity and inclusion really want to come to work at Etsy. All of that is going for us and we’re happy to compete with whoever we need to.

Earnings Call Comments by Etsy CEO:

Active Buyers Grew 17 Percent

Etsy’s growth accelerated again in the third quarter to nearly 21% on a constant-currency basis. Revenue growth exceeded 41%, fueled by the launch of our new pricing structure, and our adjusted EBITDA margins grew to nearly 23%, while we also increased our investments in the business.

Active buyers grew 17% to 37 million worldwide. This is the fourth consecutive quarter that GMS has grown faster than active buyers, evidence that we are seeing increased buyer activity on the platform, which is a key proxy for improvement in frequency. We grew the number of active sellers by 8% and GMS per active seller is also increasing.

Two principal levers contributed to our progress this past quarter. The first is our continued product investment, focused on improving the shopping experience on Etsy. By making it easier to find and buy the great products available for sale on Etsy, we’re doing a better job converting visits into purchases. The second lever was our new pricing structure, which enabled us to ramp up investments in marketing, shipping improvements and customer support.

Successful Cloud Migration

We achieved a significant milestone in our cloud migration this quarter, successfully migrating our marketplace, Etsy.com, and our mobile applications to the Google Cloud with minimal disruption to buyers and sellers. This increases our confidence that the migration will be complete by the end of 2019.

Once fully migrated, we expect to dramatically increase the velocity of experiments and product development to iterate faster and leverage more complex search and machine learning models with the goal of rapidly innovating, improving search and ultimately driving GMS growth.

In fact, we’re beginning to see some of those benefits today based on the systems we’ve already migrated. I’d like to thank our engineering team for their incredible work to get this – get us to this point.

 

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SearchCap: Facebook food ordering, Google Posts automation & machine learning

Below is what happened in search today, as reported on Search Engine Land and from other places across the web.

The post SearchCap: Facebook food ordering, Google Posts automation & machine learning appeared first on Search Engine Land.



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Moz’s Brand-New SEO Learning Center Has Landed!

Posted by rachelgooodmanmoore

CHAPTER 1: A New Hope

A long time ago in a galaxy far, far away, marketers who wanted to learn about SEO were forced to mine deep into the caverns of Google search engine result pages to find the answers to even the most simple SEO questions.

Then, out of darkness came a new hope (with a mouthful of a name):

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…the Learn SEO and Search Marketing hub!

The SEO and Search Marketing hub housed resources like the Beginner’s Guide to SEO and articles about popular SEO topics like meta descriptions, title tags, and robots.txt. Its purpose was to serve as a one-stop-shop for visitors looking to learn what SEO was all about and how to use it on their own sites.

The Learn SEO and Search marketing hub would go on to serve as a guiding light for searchers and site visitors looking to learn the ropes of SEO for many years to come.

CHAPTER 2: The Learning Hub Strikes Back

Since its inception in 2010, this hub happily served hundreds of thousands of Internet folk looking to learn the ropes of SEO and search marketing. But time took its toll on the hub. As marketing and search engine optimization grew increasingly complex, the Learning Hub lapsed into disrepair. While new content was periodically added, that content was hard to find and often intermingled with older, out-of-date resources. The Learning Hub became less of a hub and more of a list of resources… some of which were also lists of resources.

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Offshoots like the Local Learning Center and Content Marketing Learning Center sprung up in an effort to tame the overgrown learning hub, but ‘twas all for naught: By autumn of 2016, Moz’s learning hub sites were a confusing nest of hard-to-navigate articles, guides, and 404s. Some articles were written for SEO experts and explained concepts in extensive, technical detail, while others were written for an audience with less extensive SEO knowledge. It was impossible to know which type of article you found yourself in before you wound up confused or discouraged.

What had once been a useful resource for marketers of all backgrounds was languishing in its age.

CHAPTER 3: The Return of the Learning Center

The vision behind the SEO and Search Marketing Hub had always been to educate SEOs and search marketers on the skills they needed to be successful in their jobs. While the site section continued to serve that purpose, somewhere along the along the way we started getting diminishing returns.

Our mission, then, was clear: Re-invent Moz’s learning resources with a new structure, new website, and new content.

As we set off on this mission, one thing was clear: The new Learning Center should serve as a home base for marketers and SEOs of all skill levels to learn what’s needed to excel in their work: from the fundamentals to expert-level content, from time-tested tenets of SEO success to cutting-edge tactics and tricks. If we weren’t able to accomplish this, our mission would all be for naught.

We also believed that a new Learning Center should make it easy for visitors of all skill levels and learning styles to find value: from those folks who want to read an article then dive into their work; to those who want to browse through libraries of focused SEO videos; to folks who want to learn from the experts in hands-on webinars.

So, that’s exactly what we built.

May we introduce to you the (drumroll, please) brand new, totally rebuilt SEO Learning Center!

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Unlike the “list of lists” in the old Learn SEO and Search Marketing hub, the new Learning Center organizes content by topic.

Each topic has its own “topic hub.” There are eleven of these and they cover:

Each of the eleven topic hubs host a slew of hand-picked articles, videos, blog posts, webinars, Q&A posts, templates, and training classes designed to help you dive deeper into your chosen SEO topic.

All eleven of the hubs contain a “fundamentals” menu to help you wrap your brain around a topic, as well as a content feed with hundreds of resources to help you go even further. These feed resources are filterable by topic (for instance, content that’s about both ranking & visibility AND local SEO), SEO skill level (from beginner to advanced), and format.

Use the Learning Center’s filters to zero in on exactly the content you’re looking for.

And, if you’re brand new to a topic or not sure where to start, you can always find a link to the Beginner’s Guide to SEO right at the top of each page.

But we can only explain so much in words — check it out for yourself:

Visit the new SEO Learning Center!

CHAPTER 4: The Content Awakens

One of the main motivations behind rebuilding the Learning Center website was to make it easier for folks to find and move through a slew of educational content, be that a native Learning Center article, a blog post, a webinar, or otherwise. But it doesn’t do any good to make content easier to find if that content is totally out-of-date and unhelpful.

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In addition to our mission to build a new Learning Center, we’ve also been quietly updating our existing articles to include the latest best practices, tactics, strategies, and resources. As part of this rewrite, we’ve also made an effort to keep each article as focused as possible around specifically one topic — a complete explanation of everything someone newer to the world of SEO needs to know about the given topic. What did that process look like in action? Check it out:

As of now we’ve updated 50+ articles, with more on the way!

Going forward, we’ll continue to iterate on the search experience within the new Learning Center. For example, while we always have our site search bar available, a Learning Center-specific search function would make finding articles even easier — and that’s just one of our plans for the future. Bigger projects include a complete update of the Beginner’s Guide to SEO (keep an eye on the blog for more news there, too), as well as our other introductory guides.

Help us, Moz-i Wan Community, you’re our only hope

We’ve already telekinetically moved mountains with this project, but the Learning Center is your resource — we’d love to hear what you’d like to see next, or if there’s anything really important you think we’ve missed. Head over, check it out, and tell us what you think in the comments!

Explore the new SEO Learning Center!

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Learning to Re-Share: 4 Strategies to Renew, Refresh, and Recycle Content for Bigger Reach

Posted by jcar7

In the nearly three years the MeetEdgar blog went live, we’ve published more than 250 posts, written over 300,000 words, searched for hundreds of .gifs, and used our own tool to share our content 2,600 times to over 70,000 fans on social media.

After all that work, it seems silly to share a post just once. Nobody crumples up an oil painting and chucks it in the trash after it’s been seen one time — and the same goes for your content.

You’ve already created an “art gallery” for your posts. Resharing your content just lets the masses know what you’ve got on display. Even if hundreds or thousands of people have seen it all before, there’s always someone new to your content.

In a social media landscape that’s constantly changing, building a solid foundation of evergreen content that can be shared and shared again should be a key part of your social media strategy.

Otherwise, your art gallery is just another building in the city.

But wait… aren’t we supposed to be writing fresh content?

Yes! One of the biggest misconceptions about resharing is that it’s a spammy tactic. This is just not true — provided that you’re resharing responsibly. We’ll explain how to do that in just a moment.

Resharing actually does double-duty for your brand. It not only gets the content that you spent your valuable time creating in front of more eyeballs (and at optimal times, if you want to get fancy), it also frees you up to have more authentic, real-time social interactions that drive people to your site from social media — since you’ve got content going out no matter what.

Did we mention that resharing is good for SEO? Moz Blog readers know that the more people engage with a post, the better your blog or site looks to search engines. And that’s only one facet of the overall SEO boost (and traffic boost!) resharers can see.

How resharing impacts SEO

Big brands are probably the most prolific content resharers. Heck, they don’t even think twice about it:

BuzzFeed is a perfect example of the value of repeating social updates, because they don’t necessarily NEED to.

So why do they do it anyway? Because it gets results.

Social sharing alone has an impact on SEO, but social engagement is really where it’s at. Quality content is totally worth the up-front time and cost, but only if it gets engagement! You up your chances of engagement with your content if you simply up your content’s exposure. That’s what resharing does awesomely.

With literally zero tweaks to the content itself, BuzzFeed made each of those social posts above double in value. Chances are, the people who saw these posts the first time they were shared are not the same people who saw them when they were reshared.

But simply resharing social posts isn’t the only way to get more engagement with your content. This post covers how companies large and small do resharing right, and highlights some of the best time-saving content strategies you can implement for your brand right now.

1 – Start at the source: Give old posts a new look

Lots has changed in five years — the world got three new Fast & Furious movies and LKR Social Media transformed from a consulting service into social media automation software.

We’ve done the math: three months is one Internet year and five years is basically another Internet epoch. (This may be a slight exaggeration.) So when we transferred some of our founder’s older evergreen blog posts to the new MeetEdgar blog, we took stock of which of those posts had picked up the most organic traffic.

One thing that hadn’t changed in five years? A blog post about how Vin Diesel was winning the social media game was still insanely popular with our readers:

Screen Shot 2017-07-24 at 11.53.06 AM.pngScreen Shot 2017-07-24 at 11.54.34 AM.png

Writing blog posts with an eye toward making them as evergreen as possible is one of the smartest, most time-saving-est content marketing strategies out there.

There weren’t a ton of tweaks to make, but we gave this popular post some love since so many people were finding it. We pepped up the headline, did a grammar and content rundown, refreshed links and images, updated social share buttons, and added more timely content. The whole process took less time than writing a brand new post, and we got to share it with tens of thousands of followers who hadn’t seen it when it was originally published.

So… check your metrics! Which evergreen posts have performed the best over time? Which have lots of awesome organic traffic? Make a list, do a content audit, and start updating!

2 – Find your social sharing “sweet spot” by repackaging your content

When you read studies that say many social media users reshare social posts without ever clicking through to the content itself… it can be a little disheartening.

Okay, a LOT disheartening.

You’ve probably spent tons of time creating your content, and the thought that it’s not getting read NEARLY as often as it could be is a recipe for content marketing burnout. (We’ve all been there.)

But it’s not all for naught — you might just need to experiment until you find the “sweet spot” that gets people to read and share. One way to do that is to simply repackage content you’ve already written.

The tried-and-true “best of” post offers a reprieve from the content-creation grind while still delivering tons of value to your fans and readers.

Repackaging is best when it reframes your content with a new focus — like rounding up similar posts based on a theme. (You can do this in reverse, too, and turn one great post into a bunch of fresh content to then share and reshare!)

If you can get people to your site, a “best of” post encourages readers to stay longer as they click links for the different articles you’ve gathered up, and engage with content they may never have thought to look up separately.

Most fun of all, you can repackage your content to target new or different subsets of your audience on social media. (More on that in the next section.)

3 – Social shake-up: Reaching and testing with different audiences

“What if the same person recognizes something that I’ve already posted in the past?” you might be asking right about now. “I don’t want to annoy my followers! I don’t want to be spammy!”

Forget about people resharing social posts without reading the content behind the links — most people don’t see your social posts at all in the first place.

This is just one of those uncomfortable facts about the Internet, like how comment sections are always a minefield of awful, and how everyone loves a good startled cat .gif.

That doesn’t mean you should repeat yourself, word-for-word, all the time. Chances are, you have more than one type of reader or customer, so it’s important not just to vary your content, but also to vary how you share it on social media.

Savvy marketers are all over this tactic, marketing two sides (or more) of the same coin. Here are a couple of examples of social sharing images from a Mixpanel blog post:

Option A

Option B

Both Option A and Option B go to the same content, but one highlights a particularly juicy stat (problem statement: “97% of users churn”) and the other hits the viewer with an intriguing subheader (solution statement: “behavior-based messaging”). In this way, Mixpanel can find out what pulls in the most readers and tweak and promote that message as needed.

Pull a cool anecdote from your post or highlight a different stat that gets people excited. It can be as easy as changing up the descriptions of your posts or just using different images. There’s so much to test and try out — all using the same post.

4 – Automate, automate, automate

Remember, your best posts are only as good as the engagement they get. That fact, however, doesn’t mean you have to keep manually resharing them on social media day in and day out.

Unless, of course, you’re into that boring busywork thing.

Automating the whole process of resharing evergreen content saves tons of time while keeping your brand personality intact. It also frees you up to have real-time interactions with your fans on social media, brainstorm new post ideas, or just go for a walk, and it solves the time crunch and the hassle of manually re-scheduling posts, while actually showcasing more of your posts across the massive social media landscape. Just by spacing out your updates, you’ll be able to hit a wider range of your followers.

(This is probably a good time to check whether your social media scheduling tool offers automatic resharing of your content.)

Now, social media automation isn’t a substitute for consistently creating great new content, of course, but it does give your existing evergreen content an even better opportunity to shine.

Win with quality, get things DONE with resharing

It’s noisy out there. The law of diminishing returns — as well as declining social reach — means that a lot of what you do on social media can feel like shouting into the void.

And there’s not a huge ROI for shouting into voids these days.

Responsible resharing is an important part of your overall content marketing strategy. As long as you keep your content fresh, create new quality content regularly, and talk to your fans where and when they’re most active, chances are people won’t see the same thing twice. The data shows you’ll get more clicks, more traffic, and better SEO results — not a bad bonus to that whole “saving lots of time” thing.

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Machine learning for large-scale SEM accounts

Can machine learning be applied to your PPC accounts to make them more efficient? Columnist David Fothergill describes how he utilized machine learning to find new keywords for his campaigns.

The post Machine learning for large-scale SEM accounts appeared first on Search Engine Land.



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SearchCap: Machine learning, content marketing & search rankings

Below is what happened in search today, as reported on Search Engine Land and from other places across the web.

The post SearchCap: Machine learning, content marketing & search rankings appeared first on Search Engine Land.



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Why Learning to Write Is the Toughest and Best Thing You’ll Do

why better writing is worth the effort

Trigger warning: I’m about to list some terms that might give you nightmares. Do you remember these?

  • Gerunds
  • Participles
  • Sentence diagrams
  • Split infinitives
  • Absolute modifiers

Just talking about them might cause you to flash back to middle school. You’re sitting in a sweaty classroom, listening to the chalk squeak as your teacher writes the definition for each term on a dusty chalkboard.

You, in the meantime, are mentally calculating how many minutes are left before lunchtime.

Here’s the thing about learning to write: It’s not about the terms above. Yes, you need to be aware of them. But if you think learning to write well is about mastering grammar, you’re missing the point.

Learning to write goes beyond masterful handling of the parts of speech. They’re just the paper that wraps the gift.

Today, we’re going to cover what writing well really looks like and why it might be the hardest and best skill you’ll ever master. It’s the gift that keeps on giving: read on to learn why.

Well-written ideas are easier to circulate

You’re reading Copyblogger. And you probably read paper books, ebooks, news sites, long posts on social media, and more.

When we want our ideas to spread, we start by making them look good in writing.

With the surge in popularity of podcasting and the widespread use of visual platforms like Instagram, Pinterest, and even YouTube, you might wonder if the written word matters as much as it used to.

But most podcasts and videos start out as words in one form or another. They begin life as a written outline, a thoroughly-planned script, or notes on an index card.

When you’re a proficient writer, those first-draft-quality notes will do a better job getting your ideas out of your head and into a new format.

Jerod Morris, co-host of both The Showrunner and The Digital Entrepreneur podcasts, starts 75 percent of his episodes with some type of written outline. Written outlines help you plan, pace, and express your information.

And any medium will benefit when you write well.

That headline you want to add to your Pinterest image? That quote for the image you plan to post on Instagram?

When you know how to write well, you can count on finding the perfect words more easily and expressing them in a way that’s compelling and gets noticed.

Your ideas stand a better chance of spreading when they’re well-written.

Writing builds discipline (and not just for writing)

Here’s the worst-kept secret about becoming a better writer: To get good at it, you have to write — more than you think and on a regular basis. And you’ll need to keep it up for longer than you may expect.

You may find that in order to keep your writing chops in the best possible shape, you need to write almost every single day.

Our own Sonia Simone, for example, has written something every day for thirty years, with the exception of a short stint in the hospital while she recovered from major surgery. (We’ll let that one slide.)

There aren’t too many things in life that promise the kind of return that writing on most days will give you. (More on that below.)

And the discipline you’ll build from steadily working to improve your writing will build your character.

You may even find yourself looking around for more to write about once you’re in the habit of writing most days.

Clearer thoughts are born from your writing structure

The process of writing clearly usually involves starting with some sort of basic outline.

But since “outline” is another one of those scary words from English class, I want to offer you the phrase I use to describe the initial stage of writing — building the backbone.

Building the backbone refers to the process of working out the basics of the idea you want to express by deciding on a topic, then hashing out the underlying structure of how you’ll present your information. It forces you to bring your ideas into focus and clarify them so they are strong enough to support the concepts you’ll hang on them.

There’s nothing like figuring out your supporting arguments to help you clarify your ideas.

This process can spill over into many other areas of your life.

Structuring your thoughts before you share them in writing will get you into the habit of structuring your thoughts before you share them anywhere else as well. It will help you clarify your message and put it into a form that’s easier to understand.

How can you become a better writer?

Start with the posts below. They’ll cover the basics and help you establish a strong writing habit that you can use to structure and share your ideas.

You can also download and print out this poster (3.3 MB) to help motivate you to write on a regular basis.

And consider joining us inside Authority: it’s where people who want to become better writers get weekly education, support, and encouragement so they can get there faster.

Become a better writer inside Authority

Authority is our content marketing training and networking community designed to help you build the skills you need to profit online.

Put your name on the Authority interest list by clicking on the button below. We’ll let you know when we open our doors.

Join the Authority interest list

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The Machine Learning Revolution: How it Works and its Impact on SEO

Posted by EricEnge

Machine learning is already a very big deal. It’s here, and it’s in use in far more businesses than you might suspect. A few months back, I decided to take a deep dive into this topic to learn more about it. In today’s post, I’ll dive into a certain amount of technical detail about how it works, but I also plan to discuss its practical impact on SEO and digital marketing.

For reference, check out Rand Fishkin’s presentation about how we’ve entered into a two-algorithm world. Rand addresses the impact of machine learning on search and SEO in detail in that presentation, and how it influences SEO. I’ll talk more about that again later.

For fun, I’ll also include a tool that allows you to predict your chances of getting a retweet based on a number of things: your Followerwonk Social Authority, whether you include images, hashtags, and several other similar factors. I call this tool the Twitter Engagement Predictor (TEP). To build the TEP, I created and trained a neural network. The tool will accept input from you, and then use the neural network to predict your chances of getting an RT.

The TEP leverages the data from a study I published in December 2014 on Twitter engagement, where we reviewed information from 1.9M original tweets (as opposed to RTs and favorites) to see what factors most improved the chances of getting a retweet.

My machine learning journey

I got my first meaningful glimpse of machine learning back in 2011 when I interviewed Google’s Peter Norvig, and he told me how Google had used it to teach Google Translate.

Basically, they looked at all the language translations they could find across the web and learned from them. This is a very intense and complicated example of machine learning, and Google had deployed it by 2011. Suffice it to say that all the major market players — such as Google, Apple, Microsoft, and Facebook — already leverage machine learning in many interesting ways.

Back in November, when I decided I wanted to learn more about the topic, I started doing a variety of searches of articles to read online. It wasn’t long before I stumbled upon this great course on machine learning on Coursera. It’s taught by Andrew Ng of Stanford University, and it provides an awesome, in-depth look at the basics of machine learning.

Warning: This course is long (19 total sections with an average of more than one hour of video each). It also requires an understanding of calculus to get through the math. In the course, you’ll be immersed in math from start to finish. But the point is this: If you have the math background, and the determination, you can take a free online course to get started with this stuff.

In addition, Ng walks you through many programming examples using a language called Octave. You can then take what you’ve learned and create your own machine learning programs. This is exactly what I have done in the example program included below.

Basic concepts of machine learning

First of all, let me be clear: this process didn’t make me a leading expert on this topic. However, I’ve learned enough to provide you with a serviceable intro to some key concepts. You can break machine learning into two classes: supervised and unsupervised. First, I’ll take a look at supervised machine learning.

Supervised machine learning

At its most basic level, you can think of supervised machine learning as creating a series of equations to fit a known set of data. Let’s say you want an algorithm to predict housing prices (an example that Ng uses frequently in the Coursera classes). You might get some data that looks like this (note that the data is totally made up):

In this example, we have (fictitious) historical data that indicates the price of a house based on its size. As you can see, the price tends to go up as house size goes up, but the data does not fit into a straight line. However, you can calculate a straight line that fits the data pretty well, and that line might look like this:

This line can then be used to predict the pricing for new houses. We treat the size of the house as the “input” to the algorithm and the predicted price as the “output.” For example, if you have a house that is 2600 square feet, the price looks like it would be about $ xxxK ?????? dollars.

However, this model turns out to be a bit simplistic. There are other factors that can play into housing prices, such as the total rooms, number of bedrooms, number of bathrooms, and lot size. Based on this, you could build a slightly more complicated model, with a table of data similar to this one:

Already you can see that a simple straight line will not do, as you’ll have to assign weights to each factor to come up with a housing price prediction. Perhaps the biggest factors are house size and lot size, but rooms, bedrooms, and bathrooms all deserve some weight as well (all of these would be considered new “inputs”).

Even now, we’re still being quite simplistic. Another huge factor in housing prices is location. Pricing in Seattle, WA is different than it is in Galveston, TX. Once you attempt to build this algorithm on a national scale, using location as an additional input, you can see that it starts to become a very complex problem.

You can use machine learning techniques to solve any of these three types of problems. In each of these examples, you’d assemble a large data set of examples, which can be called training examples, and run a set of programs to design an algorithm to fit the data. This allows you to submit new inputs and use the algorithm to predict the output (the price, in this case). Using training examples like this is what’s referred to as “supervised machine learning.”

Classification problems

This a special class of problems where the goal is to predict specific outcomes. For example, imagine we want to predict the chances that a newborn baby will grow to be at least 6 feet tall. You could imagine that inputs might be as follows:

The output of this algorithm might be a 0 if the person was going to shorter than 6 feet tall, or 1 if they were going to be 6 feet or taller. What makes it a classification problem is that you are putting the input items into one specific class or another. For the height prediction problem as I described it, we are not trying to guess the precise height, but a simple over/under 6 feet prediction.

Some examples of more complex classifying problems are handwriting recognition (recognizing characters) and identifying spam email.

Unsupervised machine learning

Unsupervised machine learning is used in situations where you don’t have training examples. Basically, you want to try and determine how to recognize groups of objects with similar properties. For example, you may have data that looks like this:

The algorithm will then attempt to analyze this data and find out how to group them together based on common characteristics. Perhaps in this example, all of the red “x” points in the following chart share similar attributes:

However, the algorithm may have trouble recognizing outlier points, and may group the data more like this:

What the algorithm has done is find natural groupings within the data, but unlike supervised learning, it had to determine the features that define each group. One industry example of unsupervised learning is Google News. For example, look at the following screen shot:

You can see that the main news story is about Iran holding 10 US sailors, but there are also related news stories shown from Reuters and Bloomberg (circled in red). The grouping of these related stories is an unsupervised machine learning problem, where the algorithm learns to group these items together.

Other industry examples of applied machine learning

A great example of a machine learning algo is the Author Extraction algorithm that Moz has built into their Moz Content tool. You can read more about that algorithm here. The referenced article outlines in detail the unique challenges that Moz faced in solving that problem, as well as how they went about solving it.

As for Stone Temple Consulting’s Twitter Engagement Predictor, this is built on a neural network. A sample screen for this program can be seen here:

The program makes a binary prediction as to whether you’ll get a retweet or not, and then provides you with a percentage probability for that prediction being true.

For those who are interested in the gory details, the neural network configuration I used was six input units, fifteen hidden units, and two output units. The algorithm used one million training examples and two hundred training iterations. The training process required just under 45 billion calculations.

One thing that made this exercise interesting is that there are many conflicting data points in the raw data. Here’s an example of what I mean:

What this shows is the data for people with Followerwonk Social Authority between 0 and 9, and a tweet with no images, no URLs, no @mentions of other users, two hashtags, and between zero and 40 characters. We had 1156 examples of such tweets that did not get a retweet, and 17 that did.

The most desirable outcome for the resulting algorithm is to predict that these tweets not get a retweet, so that would make it wrong 1.4% of the time (17 times out of 1173). Note that the resulting neural network assesses the probability of getting a retweet at 2.1%.

I did a calculation to tabulate how many of these cases existed. I found that we had 102,045 individual training examples where it was desirable to make the wrong prediction, or for just slightly over 10% of all our training data. What this means is that the best the neural network will be able to do is make the right prediction just under 90% of the time.

I also ran two other sets of data (470K and 473K samples in size) through the trained network to see the accuracy level of the TEP. I found that it was 81% accurate in its absolute (yes/no) prediction of the chance of getting a retweet. Bearing in mind that those also had approximately 10% of the samples where making the wrong prediction is the right thing to do, that’s not bad! And, of course, that’s why I show the percentage probability of a retweet, rather than a simple yes/no response.

Try the predictor yourself and let me know what you think! (You can discover your Social Authority by heading to Followerwonk and following these quick steps.) Mind you, this was simply an exercise for me to learn how to build out a neural network, so I recognize the limited utility of what the tool does — no need to give me that feedback ;->.

Examples of algorithms Google might have or create

So now that we know a bit more about what machine learning is about, let’s dive into things that Google may be using machine learning for already:

Penguin

One approach to implementing Penguin would be to identify a set of link characteristics that could potentially be an indicator of a bad link, such as these:

  1. External link sitting in a footer
  2. External link in a right side bar
  3. Proximity to text such as “Sponsored” (and/or related phrases)
  4. Proximity to an image with the word “Sponsored” (and/or related phrases) in it
  5. Grouped with other links with low relevance to each other
  6. Rich anchor text not relevant to page content
  7. External link in navigation
  8. Implemented with no user visible indication that it’s a link (i.e. no line under it)
  9. From a bad class of sites (from an article directory, from a country where you don’t do business, etc.)
  10. …and many other factors

Note that any one of these things isn’t necessarily inherently bad for an individual link, but the algorithm might start to flag sites if a significant portion of all of the links pointing to a given site have some combination of these attributes.

What I outlined above would be a supervised machine learning approach where you train the algorithm with known bad and good links (or sites) that have been identified over the years. Once the algo is trained, you would then run other link examples through it to calculate the probability that each one is a bad link. Based on the percentage of links (and/or total PageRank) coming from bad links, you could then make a decision to lower the site’s rankings, or not.

Another approach to this same problem would be to start with a database of known good links and bad links, and then have the algorithm automatically determine the characteristics (or features) of those links. These features would probably include factors that humans may not have considered on their own.

Panda

Now that you’ve seen the Penguin example, this one should be a bit easier to think about. Here are some things that might be features of sites with poor-quality content:

  1. Small number of words on the page compared to competing pages
  2. Low use of synonyms
  3. Overuse of main keyword of the page (from the title tag)
  4. Large blocks of text isolated at the bottom of the page
  5. Lots of links to unrelated pages
  6. Pages with content scraped from other sites
  7. …and many other factors

Once again, you could start with a known set of good sites and bad sites (from a content perspective) and design an algorithm to determine the common characteristics of those sites.

As with the Penguin discussion above, I’m in no way representing that these are all parts of Panda — they’re just meant to illustrate the overall concept of how it might work.

How machine learning impacts SEO

The key to understanding the impact of machine learning on SEO is understanding what Google (and other search engines) want to use it for. A key insight is that there’s a strong correlation between Google providing high-quality search results and the revenue they get from their ads.

Back in 2009, Bing and Google performed some tests that showed how even introducing small delays into their search results significantly impacted user satisfaction. In addition, those results showed that with lower satisfaction came fewer clicks and lower revenues:

The reason behind this is simple. Google has other sources of competition, and this goes well beyond Bing. Texting friends for their input is one form of competition. So are Facebook, Apple/Siri, and Amazon. Alternative sources of information and answers exist for users, and they are working to improve the quality of what they offer every day. So must Google.

I’ve already suggested that machine learning may be a part of Panda and Penguin, and it may well be a part of the “Search Quality” algorithm. And there are likely many more of these types of algorithms to come.

So what does this mean?

Given that higher user satisfaction is of critical importance to Google, it means that content quality and user satisfaction with the content of your pages must now be treated by you as an SEO ranking factor. You’re going to need to measure it, and steadily improve it over time. Some questions to ask yourself include:

  1. Does your page meet the intent of a large percentage of visitors to it? If a user is interested in that product, do they need help in selecting it? Learning how to use it?
  2. What about related intents? If someone comes to your site looking for a specific product, what other related products could they be looking for?
  3. What gaps exist in the content on the page?
  4. Is your page a higher-quality experience than that of your competitors?
  5. What’s your strategy for measuring page performance and improving it over time?

There are many ways that Google can measure how good your page is, and use that to impact rankings. Here are some of them:

  1. When they arrive on your page after clicking on a SERP, how long do they stay? How does that compare to competing pages?
  2. What is the relative rate of CTR on your SERP listing vs. competition?
  3. What volume of brand searches does your business get?
  4. If you have a page for a given product, do you offer thinner or richer content than competing pages?
  5. When users click back to the search results after visiting your page, do they behave like their task was fulfilled? Or do they click on other results or enter followup searches?

For more on how content quality and user satisfaction has become a core SEO factor, please check out the following:

  1. Rand’s presentation on a two-algorithm world
  2. My article on Term Frequency Analysis
  3. My article on Inverse Document Frequency
  4. My article on Content Effectiveness Optimization

Summary

Machine learning is becoming highly prevalent. The barrier to learning basic algorithms is largely gone. All the major players in the tech industry are leveraging it in some manner. Here’s a little bit on what Facebook is doing, and machine learning hiring at Apple. Others are offering platforms to make implementing machine learning easier, such as Microsoft and Amazon.

For people involved in SEO and digital marketing, you can expect that these major players are going to get better and better at leveraging these algorithms to help them meet their goals. That’s why it will be of critical importance to tune your strategies to align with the goals of those organizations.

In the case of SEO, machine learning will steadily increase the importance of content quality and user experience over time. For you, that makes it time to get on board and make these factors a key part of your overall SEO strategy.

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Why Digital Marketing Podcasts Belong in Your Learning Routine

woman-listening-to-podcast

Marketers are always looking for new and efficient ways to learn. As a marketer, I’ve recently begun working podcasts into my ongoing learning routine. I’ll admit, I resisted for awhile (which was dumb). I think it’s because I have an aversion to books on tape/CD, which comes from being forced to listen to children’s books on repeat during annual family trips up and down the Oregon coast.

A realization hit me one day when I was reading entertainment “news” and taking a quiz to find out my spirit animal. I spend an exorbitant amount of time consuming a lot of content-light when I’m on the go, when I should be using that time to keep up to speed with what’s going on in my industry.

Do any of you feel the same way?

What Makes A Good Podcast?

For me, that answer is partially dependent on what setting I’m in while consuming the content. If I’m driving to work I enjoy listening to a podcast that offers quick tips and news highlights. However, If I’m walking my dog or sitting at my desk, I prefer listening to interviews or more in-depth analysis of marketing topics.

Ultimately though, the format is not as important as the foundation. A good podcast should have the same elements as any other form of content marketing and should answer the following questions:

  • What is it? Defining the topic that will be covered and the people involved.
  • How does it help the listener? Provide valuable information that helps solve a marketing problem that you may have.
  • What’s the next step? Encourage the reader to listen to more podcasts, implement the tips learned and then come back for more or visit other content marketing assets created by the podcaster.

Expert podcaster Jerod Morris believes that there are four elements that create a remarkable podcast:

  • Authenticity
  • Usefulness
  • Sustainability
  • Profitability

My (Current) Top 5 Favorite Digital Marketing Podcasts

Understanding what makes a good podcast is important because you can very easily begin going down the rabbit hole and spend an entire hour listening to information that gets you nowhere. Below are some of my favorite digital marketing podcasts:

  1. This Old Marketing Podcast: Joe Pulizzi and Robert Rose have great banter and provide a healthy mix of content marketing smarts and lively discussions about the latest news.
  2. Marketing Smarts: MarketingProfs’ podcast features interviews with some of the top marketing experts in their fields. The in-depth interviews provide a real look at the subjects and their journey to success.
  3. The Sophisticated Marketer’s Podcast: Jason Miller of LinkedIn has only aired two episodes of his new podcast, but I’m already a fan. The way that Jason approaches interviewing his guests incorporates a lot of humor and natural conversation.
  4. Online Marketing Made Easy: Amy Porterfield is so easy to listen to. When she’s talking about social media, lead generation or content marketing, it feels like she’s speaking directly to me which makes it hard to tear myself away from listening.
  5. Unpodcast: Alison Kramer and Scott Stratten’s podcast is reminiscent of your favorite morning radio talk show, filled with humor, awkward tangents and oh yeah, marketing!

Best & Worst Times for Listening to Podcasts

Unfortunately, I now want to listen to podcasts all the time. Through a series of trial and error, I was able to uncover some of the best and worst times to integrate podcasts into your routine:

Best Times for Listening to Podcasts

  • When you’re sitting in seemingly endless traffic: hook up your smartphone to your car stereo and you’re good to go.
  • Hitting the pavement or the treadmill at the gym: catching up on Keeping up with the Kardashians can wait.
  • Walking your pet: I’ve found that listening to a podcast while walking my Puggle is better than music.
  • During your lunch hour: It’s finally nice out, so I’ve been enjoying spending some time taking a walk outside, listening to digital marketing podcasts and soaking up some sun.

Worst Times for Listening to Podcasts

  • While trying to write emails, marketing content or your name: podcasts and writing are not a good mix.
  • When you’re in an important meeting: this one should be obvious right?
  • When you’re reading a news article, book or pretty much anything: unfortunately you won’t retain much from either.

What Are Your Go-To Podcasts?

We asked, and you answered! Thank you to the members of our online community that shared your favorite podcasts with us. Below are some of the responses:

Amber Jones: Oh yes, I love marketing podcasts! They let me “tune in” and learn something new while I continue to be productive. My faves are Why I Social (hosted by Christopher Barrows), iSocialTalks (hosted by Brian Fanzo), and the UnMarketing podcast (hosted by Scott Stratten and Alison Kramer)

Craig Johnson: Marketing Smarts and HBR Ideacast

Doug & Emily Allison: Home Business Profits with Ray Higdon!

If you didn’t have a chance to tune-in earlier, now is your time to share. What are your favorite digital marketing podcasts and what keeps you coming back?

Disclosure: LinkedIn is a TopRank Marketing client.

Image: Shutterstock


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Learning How To Be A Manager

Posted by caitlin.krumdieck

Introduction from Will Critchlow:

I want to introduce the post that follows for two reasons. First, it’s a little different to the majority of posts we write for the SEOmoz blog, and second, it’s Caitlin’s first post here. Caitlin Krumdieck is our Director of Client Development at Distilled. Until she joined the company (as a sales executive), I had sold every piece of work that Distilled had done. She (supposedly) joined the company to assist me in responding to leads and putting together proposals. When she out-sold me in her third month, it became clear that I should be making way for her to do her thing and her growth at Distilled has continued from there. Along the way, she’s learned some interesting things about herself and the various roles she’s held in the company. I hope you enjoy reading about Caitlin’s growth and development and take away something useful for your own career and company.


Throwing myself in the deep end (aka learning how to be a manager)

I always thought I wanted to be a manager. Growing up naturally bossy and bit of a control freak, it just seemed like the natural spot for me to end up. So when I stepped into my first management position at Distilled, I was surprised at how hard the transition was. Moving from consultant to manager of a team required a complete change of mindset and challenged me in ways I never expected. Today, I'll be sharing the four things I believe are worth thinking about if you are looking to make the move into management. 

Gut check: make sure you actually want to be a manager

About three months after my transition from London Sales Exec into the Head of Sales role, I had a very frank conversation with Will Critchlow (Distilled's Co-Founder) about my role. He then asked me point blank if I actually wanted to be a manager.

For me, this was a career-changing question. At the time, I was having a tough time letting go of my old responsibilities and moving forward into management responsibilities. I had been working in sales for over six years. I loved the buzz of talking to clients and closing deals. I liked the fact that I was personally responsible for bringing in revenue for Distilled, and I still valued my contribution to the company by the amount of money I could generate. So instead of focusing all my time and energy on how to make my team awesome, I was still spending at least 70% of my time trying to bring in new business. This meant I was essentially doing two jobs, over working myself, and not giving my team the management support they needed.

My answer to Will was, “Let me think about it.” I surprised myself by not going right back to him with a, “Hell yeah, I want to be a manager” response. I spent a few days really thinking about the changes I would need to make if I really wanted to step into a management position. To help me evaluate both opportunities, I made a list of the responsibilities for each. I thought about what it would mean to my day-to-day work, and I asked myself quite frankly, “Will I be happy as a manager?”

I think a lot of people make the mistake of skipping this step. They think that, because management seems like a step up, it is the natural progression they should strive for. But the truth is that management isn’t for everyone. It is a somewhat thankless job that requires a lot of patience, focus, determination, and self-motivation. It isn’t just a progression from a consulting role; it’s a complete job change.

In the end, I decided to challenge myself and devote myself fully to becoming a great manager. I would love to say that from the moment I made that decision everything changed, but to be honest, it took about another nine months before I made the full transition.

So before you eagerly put yourself forward for that management position, ask yourself, “Do I really want to be a manager?” If you are currently a consultant and love working on accounts, would you be happy if your daily responsibilities shifted from being at the heart of the action to becoming the person setting team targets, having line manager meetings, and generally solving problems? Would you miss the thrill of the discovery that only comes from day-in, day-out work with clients? These aren't easy questions, and it is well worth taking the time out to really think about what a move into management means. Rand wrote a great post covering the management vs contributor conundrum, highlighting how management isn't everything and shouldn't be the only growth path within a company.

Transitioning: re-learning how to be a team player

When I was in high school, I was the goalie for my school’s water polo team. This role requires a lot of the same characteristics of a great manager. While everyone knows that it is the goalie’s job to stop the ball from going in the net, it is also the goalie’s responsibility to set plays into motion. However, once the ball is in play, they need to get their ass back to the goal and provide support. From the vantage point in the goal, you can see the whole pool, so it is your job to let the other members of the team know what's going on, but you can’t actually get involved. A goalie is the ultimate support position. Sure, you get credit for any major saves, but you never get credit for the goals your offense scores.

Management is very similar. At Distilled, we subscribe to the belief that good management means being the support for the whole rest of the team, not the other way around. We are avid believers of Joel Spolsky’s support function approach to management.

http://www.avc.com/a_vc/2012/02/the-management-team-guest-post-from-joel-spolsky.html

As a manager, you have to be constantly aware of everything happening and make yourself available to help, but you need to let your team score their own goals.  A good manager doesn’t take all the great leads/clients; they share their experience and knowledge so their team is able to step up and perform on their own.

Another big mind shift for me in going from a consultant to a manager, was learning to see my team’s success as my success. While I wasn’t out there directly making clients happy, I was supporting a team that was getting results. That is the management win.

Learning to lead: don’t dictate, start a flywheel

We talk about the power of flywheels a lot at Distilled. Building a great team should be approached with the same ideology and methodology as starting a flywheel. The goal is the same: ideally, when you push hard in a consistent direction for a length of time, it seems to get easier and easier to build momentum. With a small team and big targets, it was essential for me to think about how, as the manager, I could push my team to get the best possible results and continued growth for Distilled.

It’s easy to assume that you know what all the right answers are and that your team should do things your way. This was a mistake I made when I first started managing my team. As the first sales person at Distilled, I created a lot of our original sales material. I thought the most successful approach would be to get my team to just use what I built and go out and sell the way I would sell things. That approach worked OK for a while, but it was short-sighted and didn’t allow us to leverage the talent within our team. It also meant I had to be involved with every major deal we did, which limited our ability to speak with a larger number of clients.

So I took a step back. I stopped telling people how I thought they should approach working with a new client, and I started asking them what they thought they should do. I forced myself to stop getting involved in every conversation, and gave my team the space and responsibility to own all the client relationships, only bringing me in when they really need me. Instead of bulldozing in when trying to solve problems, I started to refuse to give my team advice until they told me what they thought a solution looked like.

The results have been amazing. My team has grown in confidence and the work they are doing now is more than twice as good as it was when I was forcing my approach on them. We are talking to more clients than ever before, and were able to double business last year without growing the size of our team.

Getting results: make sure your team knows what is expected of them

As a sales team, it was easy to focus target setting on revenue, but that only looks at part of the picture. If you only focus on the money coming in, you might miss some crucial areas of personal development that need to also be addressed as a manager. While I could use our sales reporting system to see how my team was performing, I couldn’t see if they were happy or achieving what they wanted to in their roles.

The first step I took was to redefine the roles within our team and to set out clear responsibilities of the roles my team currently filled and what progression into more senior roles would look like. I made sure to focus not just on their sales targets, but also team development responsibilities within the role. I put in more ownership-based responsibilities so the team could see how they were a part of the big picture and not just a cog. This helped my team to see exactly what is expected of them and what they can start working on to progress to the next level within the team. It also allowed me to open up conversations with my team on what sideways steps might look like, should someone on the team choose to move in a new direction.

Once I had the roles clearly defined, I sent out a happiness survey to each member of my team. Here are the questions I asked my team.

  1. On a scale of 1-5 with 5 being the best, how happy are you in your roll at the moment? On a scale from 1-5 with 5 being the best, how do you feel you are performing in your role?        
  2. Do you feel like you know what is expected of you in your role?      
  3. On a scale from 1-5 with 5 being the best, do you feel that you are well supported in your roll?        
  4. On a scale of 1-5 with 5 being the best, do you feel you get the support you need from Caitlin?  
  5. What do you feel is your biggest accomplishment in the past 12 months?     
  6. Where do you think you have failed or would like to improve?          
  7. What do you think of the targets set for 2012/2013 (this past year)? 
  8. What are areas you feel like you could use more support in?
  9. What is one thing Caitlin can do for you to support you in your role?           
  10. Do you understand what Caitlin's role is?      
  11. What is one thing you would like to see improve/change/grow for the Client Development team for the New Year?
  12. How would you rank the general quality of leads you have received in the past 3 months?

My line manager Duncan Morris (Distilled CEO) had used a similar tactic with me in our line manager meetings and I found it was a great way to open up conversations about happiness and personal development. In the past when asking my team, “How are you doing?” I tended to get half thought-out answers. Giving them the space to write at length about it and asking them to assign a number to how they felt about how things were going, meant I got much more critical responses. It also allowed me to ask them what I needed to do as their manager to get them to the next level, which forced them to give me critical feedback. This really opened up conversations and has led to better personal development, increased team happiness, and improvements in openness across the team.

Wrapping up

Every company is going to demand different things from its management team, but I found getting the team management side of things right is one of the most important steps I took. It wasn’t until I got that right that I really started to feel like a manager. There have been a lot of lessons along the way and I could probably write another whole post on the challenges of setting targets, managing difficult consultants and clients, and the importance of communication. However, I felt these three things really sum up the major lessons I learned as a person when moving into a management role and are the most transferable, regardless of the type of manager you are looking to be.

If you would like some more references, I found these resources very helpful:

One of the great things about being a manager is that you are always learning and there is always more to think about when trying to help your team grow. I hope sharing my own learning experinces has helped and I would love to hear from others who have advice on how to manage a team effectively.

I'll leave you with an aswer I had to give recently, when someone I was interviewing asked me what I love about my job: For the past four years, I have found my self doing something brand new and challanging every day. No week is the same. Finally, while a manager may not get a lot of credit for all the behind the scenes work you do supporting the team, seeing your team be successful can be supremely rewarding and fulfilling. 

Good luck!

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