Tag Archive | "Learning"

PPC and machine learning: Where do we draw the line on automation?

During SMX Advanced, Frederick Vallaeys and Brad Geddes examine automation and need to understand the potential impact of unintended consequences.



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How McDonald’s Is Using Data, Machine Learning, and AI to Accelerate Growth

“Our acquisition of Dynamic Yield has brought us a lot of excitement,” says McDonald’s CEO Steve Easterbrook. “Very simply put, in the online world when we’re shopping and we pick an item and put it into our shopping basket, any website will automatically suggest two or three things to go along with it. We’re the first business that we’re aware of that can bring that into the physical world. It’s really just taking data and machine learning and AI, all these sorts of technical capabilities.”

Steve Easterbrook, CEO of McDonald’s, discusses how the company is using technology to elevate the customer experience and accelerate growth in an interview on CNBC:

Continue To See How We Can Elevate the Customer Experience

As we’ve executed the growth plan we’ve spent the first two years, three or four years ago, turning the business around. Now we’ve had a couple of years of growth. We’re confident now that we’re beginning to identify further opportunities to further accelerate growth. That takes a little bit of research and development cost. It means you’ve got to bring some expertise into the business to help us do that. We’re still managing to effectively run the business. G&A is staying the same and we’re putting a little bit more into innovation.

We continue to see how can we help continue to elevate the experience for customers. With this pace of change in the world and with different technology and different innovations, whether it’s around food, technology, or design, we’re seeing opportunities that we think can either make the experience more fun and enjoyable or smoother for customers. If we can find that we’re going to go hard at it.

We need to continue growing. If where we are investing that money is helping drive growth across 38,000 restaurants then I think the shareholders and investors would be satisfied. We want to bring our owner-operators along with us as well. They’re investing their hard-earned dollars so that always means we got a business case. The owner-operators will want to see a return on their investment just the same as a shareholder would. We’ve got a wonderful check and balance in the system to help us make sure we spend that innovative money in the right way.

Using Data, Machine Learning, and AI to Accelerate Growth

Our acquisition of Dynamic Yield has brought us a lot of excitement. It was our first acquisition for 20 years. It was an acquisition in a way that was different from the past. It wasn’t looking at different restaurant businesses to try and expand our footprint. It’s bringing a capability, an IP and some talent, into our business that can help us accelerate the growth model. We completed the deal mid-April and within two weeks we had that technical capability in 800 drive-throughs here in the U.S. It’s a very rapid execution and implementation.

Very simply put, in the online world when we’re shopping and we pick an item and put it into our shopping basket, any website we’re on these days will automatically suggest two or three things to go along with it. People who buy that tend to like these things as well. We’re the first business that we’re aware of that can bring that into the physical world. As customers are at the menu board, maybe they’re ordering a coffee and we can suggest a dessert or they’re ordering a quarter pounder with cheese and we can suggest making that into a meal. It’s really just taking data and machine learning and AI, all these sorts of technical capabilities.

Mining All of the Data Will Improve the Business

The best benefit for customers is we’re more likely to suggest things they do want and less likely to suggest things they don’t. It’ll just be a nicer experience for the customer. But yes, for the restaurant itself, because we can put our drive-thru service lines in there, for example, the technical capability by mining all of the data will be to suggest items are easier to make at our busier times. That’ll help smooth the operation as well. The immediate result will be some ticket (increases). But frankly, if the overall experience is better customers come back more often. That’s ultimately where the success will be, driving repeat visits and getting people back more often.

Across the entire sector, traffic is tight right now and people are eating out less. They have been progressively eating out less for a number of years. Whether it’s the advent of home delivery, for example, which is something we participate in, but at the moment it’s just a little bit tight out there. It’s a fight for market share. Anyone who is getting growth, typically it’s because they’re adding new units. People are finding it hard to (increase) guest count growth. It’s something that we have stated as an ambition of ours. We think that’s a measure of the true health of the business. Last quarter, we did grow traffic and we’ve grown traffic for the last couple of years, but only modestly. We want to be stronger than that.

How McDonald’s Is Using Data, Machine Learning, and AI to Accelerate Growth

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Machine Learning Should Be Used to Deliver Great Brand Experiences, Says PagerDuty CEO

PagerDuty began trading on the New York Stock Exchange for the first time this morning and is now trading at more than 60% above their IPO price of $ 24. That gives the company a market capitalization of more than $ 2.7 billion. PagerDuty offers a SAAS platform that monitors IT performance. The company had sales of $ 118 million for its last fiscal year, up close to 50% over the previous year.

The company uses machine learning to inform companies in real-time about technical issues. “Our belief is that machine learning and data should be used in the service of making people better, helping people do their jobs more effectively, and delivering those great brand experiences every time,” says PagerDuty CEO Jennifer Tejada. “PagerDuty is really about making sure that our users understand that this could be a good thing, being woken up in the middle of the night if it’s for the right problem. It’s a way that can help you deliver a much better experience for your customers.”

Jennifer Tejada, CEO of PagerDuty, discusses their IPO and how machine learning should be used to deliver great brand experiences in an interview on CNBC:

It’s Gotten Harder for Human’s to Manage the Entire IT Ecosystem

If you think about the world today, it’s an always-on world. We as consumers expect every experience to be perfect. Every time you wake up in the morning, you order your coffee online, you check Slack to communicate with your team, and maybe you take a Lyft into work. Sitting behind all of that is a lot of complexity, many digital and infrastructure based platforms, that don’t always work together the way you’d expect them to. As that complexity has proliferated over the years and because developers can deploy what they like and can use the tools that they want it’s gotten harder for human beings to really manage the entire ecosystem even as your demands increase.

You want it perfect, you want it right now and you want it the way you’d like it to be. PagerDuty is the platform that brings the right problem to the right person at the right time. We use machine learning, sitting on ten years of data, data on humans behavior and data on all these signals there that are happening through the system, and it really helps the developers that sit behind these great experiences to deliver the right experience all the time.

Machine Learning Should Be Used to Deliver Great Brand Experiences

Going public is the right time for us right now because there’s an opportunity for us to deliver the power of our platform to users all over the world. We are a small company and we weren’t as well-known as we could be and this is a great opportunity to extend our brand and help developers and employees across teams and IT security and customer support to deliver better experiences for their end customers all the time.

At PagerDuty we take customer trust and user trust very seriously. We publish our data policy and we will not use data in a way other than what we describe online. We care deeply about the relationship between our users in our platform. Our belief is that machine learning and data should be used in the service of making people better, helping people do their jobs more effectively, and delivering those great brand experiences every time. PagerDuty is really about making sure that our users understand that this could be a good thing, being woken up in the middle of the night if it’s for the right problem. It’s a way that can help you deliver a much better experience for your customers.

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How Palo Alto Networks Blocks 30,000 New Pieces of Malware Daily Via AI, Machine Learning, and Big Data

“The platform we have uses big data analytics and machine learning in the cloud to process and find all of the unknown malware, make it known and be able to block it,” says Scott Stevens, SVP, Global  Systems Engineering at Palo Alto Networks. “We find 20-30 thousand brand new pieces of malware every day. We’re analyzing millions and millions of files every day to figure out which ones are malicious. Once we know, within five minutes we’re updating the security posture for all of our connected security devices globally.”

Scott Stevens, SVP, Global  Systems Engineering at Palo Alto Networks, discusses how the company uses AI, machine learning, and big data to find and block malware for its customers in an interview with Jeff Frick of theCUBE which is covering RSA Conference 2019 in San Francisco:

We Find 20-30 Thousand New Pieces of Malware Every Day

There are two ways to think about artificial intelligence, machine learning, and big data analytics. The first is if we’re looking at how are we dealing with malware and finding unknown malware and blocking it, we’ve been doing that for years. The platform we have uses big data analytics and machine learning in the cloud to process and find all of the unknown malware, make it known and be able to block it.

We find 20-30 thousand brand new pieces of malware every day. We’re analyzing millions and millions of files every day to figure out which ones are malicious. Once we know, within five minutes we’re updating the security posture for all of our connected security devices globally.

Whether it’s endpoint software or it’s our inline next gen firewalls we’re updating all of our signatures so that the unknown is now known and the known can be blocked. That’s whether we’re watching to block the malware coming in or the command-and-control that’s using via DNS and URL to communicate and start whatever it’s going to do. You mentioned crypto lockers and there are all kinds of things that can happen. That’s one vector of using AI NML to prevent the ability for these attacks to succeed.

Machine Learning Uses Data Lake to Discover Malware

The other side of it is how do we then take some of the knowledge and the lessons we’ve learned for what we’ve been doing now for many years in discovering malware and apply that same AI NML locally to that customer so that they can detect very creative attacks very and evasive attacks or that insider threat that employee who’s behaving inappropriately but quietly.

We’ve announced over the last week what we call the cortex XDR set of offerings. That involves allowing the customer to build an aggregated data lake which uses the Zero Trust framework which tells us how to segment and also puts sensors in all the places of the network. This includes both network sensors an endpoint as we look at security the endpoint as well as the network links. Using those together we’re able to stitch those logs together in a data lake that machine learning can now be applied to on a customer by customer basis.

Maybe somebody was able to evade because they’re very creative or that insider threat again who isn’t breaking security rules but they’re being evasive. We can now find them through machine learning. The cool thing about Zero Trust is the prevention architecture that we needed for Zero Trust becomes the sensor architecture for this machine learning engine. You get dual purpose use out of the architecture of Zero Trust to solve both the in-line prevention and the response architecture that you need.

How Palo Alto Networks Blocks 30,000 New Pieces of Malware Daily

>> Read a companion piece to this article here:

Zero Trust Focuses On the Data That’s Key to Your Business

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AI, Machine Learning, and Robotics Are Fundamentally Changing Healthcare, Says Johnson & Johnson CEO

AI, machine learning, and robotics are fundamentally changing healthcare, says Johnson & Johnson CEO Alex Gorsky. “One of the most exciting parts of my job right now is to see the technology that’s usually equated with California and the West Coast,” said Gorsky. “Whether it’s AI, machine learning or robotics, you’re seeing it more and more being integrated into healthcare.”

Alex Gorsky, Johnson & Johnson CEO, discusses the reinvention of healthcare via technological innovation in an interview on CNBC’s Mad Money:

AI, Machine Learning, Robotics Integrated Into Healthcare

One of the most exciting parts of my job right now is to see the technology that’s usually equated with California and the West Coast. Whether it’s AI, machine learning or robotics, you’re seeing it more and more being integrated into healthcare. With this remarkable partnership that we have now with Apple where we’re taking this technology built into the iWatch to help detect things like atrial fibrillation or when you get a heart fluttering earlier. We know that there are over 35 million people around the world that suffer from this condition.

If we can detect that earlier we can get them to write medication and we can help them be compliant on these medications over a longer period of time. Ultimately we’re going to save lives. I think it really shows how some of this new technology is coming to healthcare in new, innovative, and unique ways. We couldn’t have even imagined this just a few years ago. We’re talking about algorithms that are built into the watch that are monitoring health in real-time. It can detect these anomalies far before something really manifests itself that the patient’s going to recognize in the terms of symptoms.

Robotics Technology Fundamentally Changing Healthcare

Auris (recently acquired) is another great example of how this technology is fundamentally changing the way we’re thinking about healthcare. Today, less than five percent of surgeries are done with a robot or digitally. In the future, we think that’s going to be significantly greater. What we’re so excited about is just as technology has changed the way that we drive a car, where you pull up your map system or you see that light go on if you start to change lanes, think about that in surgery.

Robotics Technology via Auris is Fundamentally Changing Healthcare

Suddenly, a surgeon can go in preoperatively, utilized imaging to help him or her really navigate their way specifically to the lesion, and they can actually get guidance. We know that’s going to lead to better precision, better outcomes for the patient, and better value overall for the healthcare system.

Healthcare Being Reinvented by Technology

Think of it, for example, with our Auris Monarch Platform which is used for something called bronchoscopy. Now, if you happen to have a lesion or a tumor at a very far out section in your lung, they, of course, would have to go in through minimally invasive surgery to do a biopsy to better diagnose what you have. Imagine we take a tree and turn it upside down and that tree is your lung. We can run this wire down through the system, way out to the outer ends of the leaf. Think of it almost like the acorn.

Once we get there we can do a biopsy or we can use imaging in the future to actually determine what kind of a cancer it is, or we could deliver a therapeutic, perhaps a new kind of immuno-oncology agent to that specific lesion, or we could go ahead and cut it out. Those are the kinds of things are being made possible by this new technology at a company like Auris.

AI, Machine Learning, and Robotics Are Fundamentally Changing Healthcare

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How LinkedIn is Using Machine Learning to Determine Skills

One of the more interesting reveals that Dan Francis, Senior Product Manager for LinkedIn Talent Insights, provided in a recent talk about the Talent Insights tool is how LinkedIn is using machine learning to determine skills of people. He says that there are now over 575 million members in the LinkedIn database and there are 35,000 standardized skills in LinkedIn’s skills taxonomy. The way LinkedIn is figuring out what skills a member has is via machine learning technology.

Dan Francis, Senior Product Manager, LinkedIn Talent Insights, discussed Talent Insights in a recent LinkedIn video embedded below:

LinkedIn Using Machine Learning to Determine Skills

The skills data in Talent Insights comes from a variety of sources, mainly from a member’s profile. There are over 35,000 standardized skills that we have in LinkedIn’s skills taxonomy, and the way we’re figuring out what skills a member has is using machine learning. We can identify skills that a member has that’s based on things that they explicitly added to their profile.

The other thing that we’ll do is look at the text of the profile. There’s a field of machine learning called natural language processing and we’re basically using that. It’s scanning through all the words that are on a member’s profile, and when we can determine that it’s pertaining to the member, as oppose the company or another subject, we’ll say okay, we think that this member has this skill. We also look at other attributes, like their title or the company, to make sure they actually are very likely to have that skill.

The last thing that we’ll do is look at the skills a member has and figure out what are skill relationships. So as an example, let’s say that a member has Ember, which is a type of JavaScript framework, since we know that they know Ember, they also know JavaScript. So if somebody’s running a search like that, we’ll surface them in the results. I think that the most important reason why this is helpful and the real benefit to users of the platform is when you’re searching, you want to get as accurate a view of the population as possible. What we’re trying to do is look at all the different signals that we possibly have to represent that view.  

575 Million People on LinkedIn Globally and Adding 2 Per Second

Today, LinkedIn has over 575 million members that are on the platform globally. This is actually growing at a pretty rapid clip, so we’re adding about two members per second. One of the great things about LinkedIn is that we’re actually very well represented in terms of the professional workforce globally. If you look at the top 30 economies around the world, we actually have the majority of professionals in all of those economies.

LinkedIn is the World’s Largest Aggregator of Jobs

I think there’s often a perception that most of the data’s directly from LinkedIn, stuff that’s posted on LinkedIn and job status is one notable exception to that. Plenty of companies and people will post jobs on LinkedIn, and that’s information that does get surfaced. However, we’re also the world’s largest aggregator of jobs. At this point there are over 20 million jobs that are on LinkedIn.

The way that we’re getting that information is we’re working with over 40,000 partners. These are job boards, ATS’s, and direct customer relationships. We’re collecting all of those jobs, standardizing them, and showing them on our platform. The benefit is not just for displaying the data in Talent Insights, the benefit is also when members are searching on LinkedIn.com, we’re giving them as representative a view of the job market as possible.

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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.

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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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