Machine Learning: A competitor email experiment

A firm belief of the dotmailer team is that marketing automation allows marketers to focus more of their time on the strategic thinking and worry less about the manual tasks and execution. And software like dotmailer is needed to do this in various facets, from sorting and cleansing data, to designing marketing messages, scheduling them and so on. This is why we appreciate when our partners work on projects that apply the same belief. Enter Other Media, a digital agency we work with…

If you’re considering how to make the most of your email marketing, chances are you’ve been investigating personalisation and automation. As an agency defining and designing digital solutions for clients, the machine learning techniques that underpin this intelligent approach to marketing have caught our interest and we wanted to investigate their potential a little further.

We partner with dotmailer (an email marketing automation software) to help our clients, and Other Media as an agency, to get to grips with understanding our customers, but what about our competitors?

It is absolutely the right thing to focus on your customers and understanding their needs and behaviours from analysing their interactions with your brands. However, we can all benefit from taking a more outward looking approach – remembering to look at the market in which you operate.

It can be very useful to take the same way of thinking and processes that you apply to your customer marketing and apply them to your competitors. Getting hung up on what your competitors are doing isn’t healthy but keeping an eye on what they are doing, with minimal input and effort required, can be a very useful thing.

So, how did Other Media do it?

Ok, so we didn’t actually train a puppy, we trained an algorithm to sort documents, but that doesn’t sound as fun or useful. Useful, though, is hugely underplaying the importance of our latest experimentation.  

At Other Media, we thrive on delving into new trends and technologies by experimenting with what is available to help us, and our clients, to provide the best solutions for business needs. And this time the trend we focused on is Artificial intelligence (AI).

The last few weeks have seen us utilising machine learning, a particular branch of AI, to enhance our ambient data technologies. We wanted to see what AI could do for us in terms of analysing vast swathes of data in order to help us understand competing brands.

The ambient data technology, JustNow, collects real-time or near real-time data from multiple sources including Google Analytics, social media, news feeds and email newsletter campaigns. Our custom data displays help our clients to see the bigger picture of how their marketing activities, and those of their competitors, might be driving visitors, sales and value.

We used machine learning technology to distil insight from data and present it through this ambient, real-time display. We looked at the collection and display of competitor newsletters and asked ourselves this question:

Can we build a picture of what is going on in a competitive market by using artificial intelligence and machine learning techniques to automate the reading and classification of news?

Well the spoiler is that, yes, we can.

To find out more about machine learning (what it is, what its applications and implications for businesses are), and see what our resulting data screen looked like and get a feel for how it could look for your organisation, download the experiment report here.

The post Machine Learning: A competitor email experiment appeared first on The Email Marketing Blog.

Reblogged 2 years ago from blog.dotmailer.com

Becoming Better SEO Scientists – Whiteboard Friday

Posted by MarkTraphagen

Editor’s note: Today we’re featuring back-to-back episodes of Whiteboard Friday from our friends at Stone Temple Consulting. Make sure to also check out the second episode, “UX, Content Quality, and SEO” from Eric Enge.

Like many other areas of marketing, SEO incorporates elements of science. It becomes problematic for everyone, though, when theories that haven’t been the subject of real scientific rigor are passed off as proven facts. In today’s Whiteboard Friday, Stone Temple Consulting’s Mark Traphagen is here to teach us a thing or two about the scientific method and how it can be applied to our day-to-day work.

For reference, here’s a still of this week’s whiteboard.
Click on it to open a high resolution image in a new tab!

Video transcription

Howdy, Mozzers. Mark Traphagen from Stone Temple Consulting here today to share with you how to become a better SEO scientist. We know that SEO is a science in a lot of ways, and everything I’m going to say today applies not only to SEO, but testing things like your AdWords, how does that work, quality scores. There’s a lot of different applications you can make in marketing, but we’ll focus on the SEO world because that’s where we do a lot of testing. What I want to talk to you about today is how that really is a science and how we need to bring better science in it to get better results.

The reason is in astrophysics, things like that we know there’s something that they’re talking about these days called dark matter, and dark matter is something that we know it’s there. It’s pretty much accepted that it’s there. We can’t see it. We can’t measure it directly. We don’t even know what it is. We can’t even imagine what it is yet, and yet we know it’s there because we see its effect on things like gravity and mass. Its effects are everywhere. And that’s a lot like search engines, isn’t it? It’s like Google or Bing. We see the effects, but we don’t see inside the machine. We don’t know exactly what’s happening in there.

An artist’s depiction of how search engines work.

So what do we do? We do experiments. We do tests to try to figure that out, to see the effects, and from the effects outside we can make better guesses about what’s going on inside and do a better job of giving those search engines what they need to connect us with our customers and prospects. That’s the goal in the end.

Now, the problem is there’s a lot of testing going on out there, a lot of experiments that maybe aren’t being run very well. They’re not being run according to scientific principles that have been proven over centuries to get the best possible results.

Basic data science in 10 steps

So today I want to give you just very quickly 10 basic things that a real scientist goes through on their way to trying to give you better data. Let’s see what we can do with those in our SEO testing in the future.

So let’s start with number one. You’ve got to start with a hypothesis. Your hypothesis is the question that you want to solve. You always start with that, a good question in mind, and it’s got to be relatively narrow. You’ve got to narrow it down to something very specific. Something like how does time on page effect rankings, that’s pretty narrow. That’s very specific. That’s a good question. Might be able to test that. But something like how do social signals effect rankings, that’s too broad. You’ve got to narrow it down. Get it down to one simple question.

Then you choose a variable that you’re going to test. Out of all the things that you could do, that you could play with or you could tweak, you should choose one thing or at least a very few things that you’re going to tweak and say, “When we tweak this, when we change this, when we do this one thing, what happens? Does it change anything out there in the world that we are looking at?” That’s the variable.

The next step is to set a sample group. Where are you going to gather the data from? Where is it going to come from? That’s the world that you’re working in here. Out of all the possible data that’s out there, where are you going to gather your data and how much? That’s the small circle within the big circle. Now even though it’s smaller, you’re probably not going to get all the data in the world. You’re not going to scrape every search ranking that’s possible or visit every URL.

You’ve got to ask yourself, “Is it large enough that we’re at least going to get some validity?” If I wanted to find out what is the typical person in Seattle and I might walk through just one part of the Moz offices here, I’d get some kind of view. But is that a typical, average person from Seattle? I’ve been around here at Moz. Probably not. But this was large enough.

Also, it should be randomized as much as possible. Again, going back to that example, if I just stayed here within the walls of Moz and do research about Mozzers, I’d learn a lot about what Mozzers do, what Mozzers think, how they behave. But that may or may not be applicable to the larger world outside, so you randomized.

We want to control. So we’ve got our sample group. If possible, it’s always good to have another sample group that you don’t do anything to. You do not manipulate the variable in that group. Now, why do you have that? You have that so that you can say, to some extent, if we saw a change when we manipulated our variable and we did not see it in the control group, the same thing didn’t happen, more likely it’s not just part of the natural things that happen in the world or in the search engine.

If possible, even better you want to make that what scientists call double blind, which means that even you the experimenter don’t know who that control group is out of all the SERPs that you’re looking at or whatever it is. As careful as you might be and honest as you might be, you can end up manipulating the results if you know who is who within the test group? It’s not going to apply to every test that we do in SEO, but a good thing to have in mind as you work on that.

Next, very quickly, duration. How long does it have to be? Is there sufficient time? If you’re just testing like if I share a URL to Google +, how quickly does it get indexed in the SERPs, you might only need a day on that because typically it takes less than a day in that case. But if you’re looking at seasonality effects, you might need to go over several years to get a good test on that.

Let’s move to the second group here. The sixth thing keep a clean lab. Now what that means is try as much as possible to keep anything that might be dirtying your results, any kind of variables creeping in that you didn’t want to have in the test. Hard to do, especially in what we’re testing, but do the best you can to keep out the dirt.

Manipulate only one variable. Out of all the things that you could tweak or change choose one thing or a very small set of things. That will give more accuracy to your test. The more variables that you change, the more other effects and inner effects that are going to happen that you may not be accounting for and are going to muddy your results.

Make sure you have statistical validity when you go to analyze those results. Now that’s beyond the scope of this little talk, but you can read up on that. Or even better, if you are able to, hire somebody or work with somebody who is a trained data scientist or has training in statistics so they can look at your evaluation and say the correlations or whatever you’re seeing, “Does it have a statistical significance?” Very important.

Transparency. As much as possible, share with the world your data set, your full results, your methodology. What did you do? How did you set up the study? That’s going to be important to our last step here, which is replication and falsification, one of the most important parts of any scientific process.

So what you want to invite is, hey we did this study. We did this test. Here’s what we found. Here’s how we did it. Here’s the data. If other people ask the same question again and run the same kind of test, do they get the same results? Somebody runs it again, do they get the same results? Even better, if you have some people out there who say, “I don’t think you’re right about that because I think you missed this, and I’m going to throw this in and see what happens,” aha they falsify. That might make you feel like you failed, but it’s success because in the end what are we after? We’re after the truth about what really works.

Think about your next test, your next experiment that you do. How can you apply these 10 principles to do better testing, get better results, and have better marketing? Thanks.

Video transcription by Speechpad.com

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Reblogged 4 years ago from tracking.feedpress.it

8 Ways Content Marketers Can Hack Facebook Multi-Product Ads

Posted by Alan_Coleman

The trick most content marketers are missing

Creating great content is the first half of success in content marketing. Getting quality content read by, and amplified to, a relevant audience is the oft overlooked second half of success. Facebook can be a content marketer’s best friend for this challenge. For reach, relevance and amplification potential, Facebook is unrivaled.

  1. Reach: 1 in 6 mobile minutes on planet earth is somebody reading something on Facebook.
  2. Relevance: Facebook is a lean mean interest and demo targeting machine. There is no online or offline media that owns as much juicy interest and demographic information on its audience and certainly no media has allowed advertisers to utilise this information as effectively as Facebook has.
  3. Amplification: Facebook is literally built to encourage sharing. Here’s the first 10 words from their mission statement: “Facebook’s mission is to give people the power to share…”, Enough said!

Because of these three digital marketing truths, if a content marketer gets their paid promotion* right on Facebook, the battle for eyeballs and amplification is already won.

For this reason it’s crucial that content marketers keep a close eye on Facebook advertising innovations and seek out ways to use them in new and creative ways.

In this post I will share with you eight ways we’ve hacked a new Facebook ad format to deliver content marketing success.

Multi-Product Ads (MPAs)

In 2014, Facebook unveiled multi-product ads (MPAs) for US advertisers, we got them in Europe earlier this year. They allow retailers to show multiple products in a carousel-type ad unit.

They look like this:

If the user clicks on the featured product, they are guided directly to the landing page for that specific product, from where they can make a purchase.

You could say MPAs are Facebook’s answer to Google Shopping.

Facebook’s mistake is a content marketer’s gain

I believe Facebook has misunderstood how people want to use their social network and the transaction-focused format is OK at best for selling products. People aren’t really on Facebook to hit the “buy now” button. I’m a daily Facebook user and I can’t recall a time this year where I have gone directly from Facebook to an e-commerce website and transacted. Can you remember a recent time when you did?

So, this isn’t an innovation that removes a layer of friction from something that we are all doing online already (as the most effective innovations do). Instead, it’s a bit of a “hit and hope” that, by providing this functionality, Facebook would encourage people to try to buy online in a way they never have before.

The Wolfgang crew felt the MPA format would be much more useful to marketers and users if they were leveraging Facebook for the behaviour we all demonstrate on the platform every day, guiding users to relevant content. We attempted to see if Facebook Ads Manager would accept MPAs promoting content rather than products. We plugged in the images, copy and landing pages, hit “place order”, and lo and behold the ads became active. We’re happy to say that the engagement rates, and more importantly the amplification rates, are fantastic!

Multi-Content Ads

We’ve re-invented the MPA format for multi-advertisers in multi-ways, eight ways to be exact! Here’s eight MPA Hacks that have worked well for us. All eight hacks use the MPA format to promote content rather than promote products.

Hack #1: Multi-Package Ads

Our first variation wasn’t a million miles away from multi-product ads; we were promoting the various packages offered by a travel operator.

By looking at the number of likes, comments, and shares (in blue below the ads) you can see the ads were a hit with Facebook users and they earned lots of free engagement and amplification.

NB: If you have selected “clicks to website” as your advertising objective, all those likes, comments and shares are free!

Independent Travel Multi Product Ad

The ad sparked plenty of conversation amongst Facebook friends in the comments section.

Comments on a Facebook MPA

Hack #2: Multi-Offer Ads

Everybody knows the Internet loves a bargain. So we decided to try another variation moving away from specific packages, focusing instead on deals for a different travel operator.

Here’s how the ads looked:

These ads got valuable amplification beyond the share. In the comments section, you can see people tagging specific friends. This led to the MPAs receiving further amplification, and a very targeted and personalised form of amplification to boot.

Abbey Travel Facebook Ad Comments

Word of mouth referrals have been a trader’s best friend since the stone age. These “personalised” word of mouth referrals en masse are a powerful marketing proposition. It’s worth mentioning again that those engagements are free!

Hack #3: Multi-Locations Ads

Putting the Lo in SOLOMO.

This multi-product feed ad was hacked to promote numerous locations of a waterpark. “Where to go?” is among the first questions somebody asks when researching a holiday. In creating this top of funnel content, we can communicate with our target audience at the very beginning of their research process. A simple truth of digital marketing is: the more interactions you have with your target market on their journey to purchase, the more likely they are to seal the deal with you when it comes time to hit the “buy now” button. Starting your relationship early gives you an advantage over those competitors who are hanging around the bottom of the purchase funnel hoping to make a quick and easy conversion.

Abbey Travel SplashWorld Facebook MPA

What was surprising here, was that because we expected to reach people at the very beginning of their research journey, we expected the booking enquiries to be some time away. What actually happened was these ads sparked an enquiry frenzy as Facebook users could see other people enquiring and the holidays selling out in real time.

Abbey Travel comments and replies

In fact nearly all of the 35 comments on this ad were booking enquiries. This means what we were measuring as an “engagement” was actually a cold hard “conversion”! You don’t need me to tell you a booking enquiry is far closer to the money than a Facebook like.

The three examples outlined so far are for travel companies. Travel is a great fit for Facebook as it sits naturally in the Facebook feed, my Facebook feed is full of envy-inducing friends’ holiday pictures right now. Another interesting reason why travel is a great fit for Facebook ads is because typically there are multiple parties to a travel purchase. What happened here is the comments section actually became a very visible and measurable forum for discussion between friends and family before becoming a stampede inducing medium of enquiry.

So, stepping outside of the travel industry, how do other industries fare with hacked MPAs?

Hack #3a: Multi-Location Ads (combined with location targeting)

Location, location, location. For a property listings website, we applied location targeting and repeated our Multi-Location Ad format to advertise properties for sale to people in and around that location.

Hack #4: Multi-Big Content Ad

“The future of big content is multi platform”

– Cyrus Shepard

The same property website had produced a report and an accompanying infographic to provide their audience with unique and up-to-the-minute market information via their blog. We used the MPA format to promote the report, the infographic and the search rentals page of the website. This brought their big content piece to a larger audience via a new platform.

Rental Report Multi Product Ad

Hack #5: Multi-Episode Ad

This MPA hack was for an online TV player. As you can see we advertised the most recent episodes of a TV show set in a fictional Dublin police station, Red Rock.

Engagement was high, opinion was divided.

TV3s Red Rock viewer feedback

LOL.

Hack #6: Multi-People Ads

In the cosmetic surgery world, past patients’ stories are valuable marketing material. Particularly when the past patients are celebrities. We recycled some previously published stories from celebrity patients using multi-people ads and targeted them to a very specific audience.

Avoca Clinic Multi People Ads

Hack #7: Multi-UGC Ads

Have you witnessed the power of user generated content (UGC) in your marketing yet? We’ve found interaction rates with authentic UGC images can be up to 10 fold of those of the usual stylised images. In order to encourage further UGC, we posted a number of customer’s images in our Multi-UGC Ads.

The CTR on the above ads was 6% (2% is the average CTR for Facebook News feed ads according to our study). Strong CTRs earn you more traffic for your budget. Facebook’s relevancy score lowers your CPC as your CTR increases.

When it comes to the conversion, UGC is a power player, we’ve learned that “customers attracting new customers” is a powerful acquisition tool.

Hack #8: Target past customers for amplification

“Who will support and amplify this content and why?”

– Rand Fishkin

Your happy customers Rand, that’s the who and the why! Check out these Multi-Package Ads targeted to past customers via custom audiences. The Camino walkers have already told all their friends about their great trip, now allow them to share their great experiences on Facebook and connect the tour operator with their Facebook friends via a valuable word of mouth referral. Just look at the ratio of share:likes and shares:comments. Astonishingly sharable ads!

Camino Ways Mulit Product Ads

Targeting past converters in an intelligent manner is a super smart way to find an audience ready to share your content.

How will hacking Multi-Product Ads work for you?

People don’t share ads, but they do share great content. So why not hack MPAs to promote your content and reap the rewards of the world’s greatest content sharing machine: Facebook.

MPAs allow you to tell a richer story by allowing you to promote multiple pieces of content simultaneously. So consider which pieces of content you have that will work well as “content bundles” and who the relevant audience for each “content bundle” is.

As Hack #8 above illustrates, the big wins come when you match a smart use of the format with the clever and relevant targeting Facebook allows. We’re massive fans of custom audiences so if you aren’t sure where to start, I’d suggest starting there.

So ponder your upcoming content pieces, consider your older content you’d like to breathe some new life into and perhaps you could become a Facebook Ads Hacker.

I’d love to hear about your ideas for turning Multi-Product Ads into Multi-Content Ads in the comments section below.

We could even take the conversation offline at Mozcon!

Happy hacking.


*Yes I did say paid promotion, it’s no secret that Facebook’s organic reach continues to dwindle. The cold commercial reality is you need to pay to play on FB. The good news is that if you select ‘website clicks’ as your objective you only pay for website traffic and engagement while amplification by likes, comments, and shares are free! Those website clicks you pay for are typically substantially cheaper than Adwords, Taboola, Outbrain, Twitter or LinkedIn. How does it compare to display? It doesn’t. Paying for clicks is always preferable to paying for impressions. If you are spending money on display advertising I’d urge you to fling a few spondoolas towards Facebook ads and compare results. You will be pleasantly surprised.

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How Much Has Link Building Changed in Recent Years?

Posted by Paddy_Moogan

I get asked this question a lot. It’s mainly asked by people who are considering buying my link building book and want to know whether it’s still up to date. This is understandable given that the first edition was published in February 2013 and our industry has a deserved reputation for always changing.

I find myself giving the same answer, even though I’ve been asked it probably dozens of times in the last two years—”not that much”. I don’t think this is solely due to the book itself standing the test of time, although I’ll happily take a bit of credit for that 🙂 I think it’s more a sign of our industry as a whole not changing as much as we’d like to think.

I started to question myself and if I was right and honestly, it’s one of the reasons it has taken me over two years to release the second edition of the book.

So I posed this question to a group of friends not so long ago, some via email and some via a Facebook group. I was expecting to be called out by many of them because my position was that in reality, it hasn’t actually changed that much. The thing is, many of them agreed and the conversations ended with a pretty long thread with lots of insights. In this post, I’d like to share some of them, share what my position is and talk about what actually has changed.

My personal view

Link building hasn’t changed as much we think it has.

The core principles of link building haven’t changed. The signals around link building have changed, but mainly around new machine learning developments that have indirectly affected what we do. One thing that has definitely changed is the mindset of SEOs (and now clients) towards link building.

I think the last big change to link building came in April 2012 when Penguin rolled out. This genuinely did change our industry and put to bed a few techniques that should never have worked so well in the first place.

Since then, we’ve seen some things change, but the core principles haven’t changed if you want to build a business that will be around for years to come and not run the risk of being hit by a link related Google update. For me, these principles are quite simple:

  • You need to deserve links – either an asset you create or your product
  • You need to put this asset in front of a relevant audience who have the ability to share it
  • You need consistency – one new asset every year is unlikely to cut it
  • Anything that scales is at risk

For me, the move towards user data driving search results + machine learning has been the biggest change we’ve seen in recent years and it’s still going.

Let’s dive a bit deeper into all of this and I’ll talk about how this relates to link building.

The typical mindset for building links has changed

I think that most SEOs are coming round to the idea that you can’t get away with building low quality links any more, not if you want to build a sustainable, long-term business. Spammy link building still works in the short-term and I think it always will, but it’s much harder than it used to be to sustain websites that are built on spam. The approach is more “churn and burn” and spammers are happy to churn through lots of domains and just make a small profit on each one before moving onto another.

For everyone else, it’s all about the long-term and not putting client websites at risk.

This has led to many SEOs embracing different forms of link building and generally starting to use content as an asset when it comes to attracting links. A big part of me feels that it was actually Penguin in 2012 that drove the rise of content marketing amongst SEOs, but that’s a post for another day…! For today though, this goes some way towards explain the trend we see below.

Slowly but surely, I’m seeing clients come to my company already knowing that low quality link building isn’t what they want. It’s taken a few years after Penguin for it to filter down to client / business owner level, but it’s definitely happening. This is a good thing but unfortunately, the main reason for this is that most of them have been burnt in the past by SEO companies who have built low quality links without giving thought to building good quality ones too.

I have no doubt that it’s this change in mindset which has led to trends like this:

The thing is, I don’t think this was by choice.

Let’s be honest. A lot of us used the kind of link building tactics that Google no longer like because they worked. I don’t think many SEOs were under the illusion that it was genuinely high quality stuff, but it worked and it was far less risky to do than it is today. Unless you were super-spammy, the low-quality links just worked.

Fast forward to a post-Penguin world, things are far more risky. For me, it’s because of this that we see the trends like the above. As an industry, we had the easiest link building methods taken away from us and we’re left with fewer options. One of the main options is content marketing which, if you do it right, can lead to good quality links and importantly, the types of links you won’t be removing in the future. Get it wrong and you’ll lose budget and lose the trust if your boss or client in the power of content when it comes to link building.

There are still plenty of other methods to build links and sometimes we can forget this. Just look at this epic list from Jon Cooper. Even with this many tactics still available to us, it’s hard work. Way harder than it used to be.

My summary here is that as an industry, our mindset has shifted but it certainly wasn’t a voluntary shift. If the tactics that Penguin targeted still worked today, we’d still be using them.

A few other opinions…

I definitely think too many people want the next easy win. As someone surfing the edge of what Google is bringing our way, here’s my general take—SEO, in broad strokes, is changing a lot, *but* any given change is more and more niche and impacts fewer people. What we’re seeing isn’t radical, sweeping changes that impact everyone, but a sort of modularization of SEO, where we each have to be aware of what impacts our given industries, verticals, etc.”

Dr. Pete

 

I don’t feel that techniques for acquiring links have changed that much. You can either earn them through content and outreach or you can just buy them. What has changed is the awareness of “link building” outside of the SEO community. This makes link building / content marketing much harder when pitching to journalists and even more difficult when pitching to bloggers.

“Link building has to be more integrated with other channels and struggles to work in its own environment unless supported by brand, PR and social. Having other channels supporting your link development efforts also creates greater search signals and more opportunity to reach a bigger audience which will drive a greater ROI.

Carl Hendy

 

SEO has grown up in terms of more mature staff and SEOs becoming more ingrained into businesses so there is a smarter (less pressure) approach. At the same time, SEO has become more integrated into marketing and has made marketing teams and decision makers more intelligent in strategies and not pushing for the quick win. I’m also seeing that companies who used to rely on SEO and building links have gone through IPOs and the need to build 1000s of links per quarter has rightly reduced.

Danny Denhard

Signals that surround link building have changed

There is no question about this one in my mind. I actually wrote about this last year in my previous blog post where I talked about signals such as anchor text and deep links changing over time.

Many of the people I asked felt the same, here are some quotes from them, split out by the types of signal.

Domain level link metrics

I think domain level links have become increasingly important compared with page level factors, i.e. you can get a whole site ranking well off the back of one insanely strong page, even with sub-optimal PageRank flow from that page to the rest of the site.

Phil Nottingham

I’d agree with Phil here and this is what I was getting at in my previous post on how I feel “deep links” will matter less over time. It’s not just about domain level links here, it’s just as much about the additional signals available for Google to use (more on that later).

Anchor text

I’ve never liked anchor text as a link signal. I mean, who actually uses exact match commercial keywords as anchor text on the web?

SEOs. 🙂

Sure there will be natural links like this, but honestly, I struggle with the idea that it took Google so long to start turning down the dial on commercial anchor text as a ranking signal. They are starting to turn it down though, slowly but surely. Don’t get me wrong, it still matters and it still works. But like pure link spam, the barrier is a lot more lower now in terms what of constitutes too much.

Rand feels that they matter more than we’d expect and I’d mostly agree with this statement:

Exact match anchor text links still have more power than you’d expect—I think Google still hasn’t perfectly sorted what is “brand” or “branded query” from generics (i.e. they want to start ranking a new startup like meldhome.com for “Meld” if the site/brand gets popular, but they can’t quite tell the difference between that and https://moz.com/learn/seo/redirection getting a few manipulative links that say “redirect”)

Rand Fishkin

What I do struggle with though, is that Google still haven’t figured this out and that short-term, commercial anchor text spam is still so effective. Even for a short burst of time.

I don’t think link building as a concept has changed loads—but I think links as a signal have, mainly because of filters and penalties but I don’t see anywhere near the same level of impact from coverage anymore, even against 18 months ago.

Paul Rogers

New signals have been introduced

It isn’t just about established signals changing though, there are new signals too and I personally feel that this is where we’ve seen the most change in Google algorithms in recent years—going all the way back to Panda in 2011.

With Panda, we saw a new level of machine learning where it almost felt like Google had found a way of incorporating human reaction / feelings into their algorithms. They could then run this against a website and answer questions like the ones included in this post. Things such as:

  • “Would you be comfortable giving your credit card information to this site?”
  • “Does this article contain insightful analysis or interesting information that is beyond obvious?”
  • “Are the pages produced with great care and attention to detail vs. less attention to detail?”

It is a touch scary that Google was able to run machine learning against answers to questions like this and write an algorithm to predict the answers for any given page on the web. They have though and this was four years ago now.

Since then, they’ve made various moves to utilize machine learning and AI to build out new products and improve their search results. For me, this was one of the biggest and went pretty unnoticed by our industry. Well, until Hummingbird came along I feel pretty sure that we have Ray Kurzweil to thank for at least some of that.

There seems to be more weight on theme/topic related to sites, though it’s hard to tell if this is mostly link based or more user/usage data based. Google is doing a good job of ranking sites and pages that don’t earn the most links but do provide the most relevant/best answer. I have a feeling they use some combination of signals to say “people who perform searches like this seem to eventually wind up on this website—let’s rank it.” One of my favorite examples is the Audubon Society ranking for all sorts of birding-related searches with very poor keyword targeting, not great links, etc. I think user behavior patterns are stronger in the algo than they’ve ever been.

– Rand Fishkin

Leading on from what Rand has said, it’s becoming more and more common to see search results that just don’t make sense if you look at the link metrics—but are a good result.

For me, the move towards user data driving search results + machine learning advanced has been the biggest change we’ve seen in recent years and it’s still going.

Edit: since drafting this post, Tom Anthony released this excellent blog post on his views on the future of search and the shift to data-driven results. I’d recommend reading that as it approaches this whole area from a different perspective and I feel that an off-shoot of what Tom is talking about is the impact on link building.

You may be asking at this point, what does machine learning have to do with link building?

Everything. Because as strong as links are as a ranking signal, Google want more signals and user signals are far, far harder to manipulate than established link signals. Yes it can be done—I’ve seen it happen. There have even been a few public tests done. But it’s very hard to scale and I’d venture a guess that only the top 1% of spammers are capable of doing it, let alone maintaining it for a long period of time. When I think about the process for manipulation here, I actually think we go a step beyond spammers towards hackers and more cut and dry illegal activity.

For link building, this means that traditional methods of manipulating signals are going to become less and less effective as these user signals become stronger. For us as link builders, it means we can’t keep searching for that silver bullet or the next method of scaling link building just for an easy win. The fact is that scalable link building is always going to be at risk from penalization from Google—I don’t really want to live a life where I’m always worried about my clients being hit by the next update. Even if Google doesn’t catch up with a certain method, machine learning and user data mean that these methods may naturally become less effective and cost efficient over time.

There are of course other things such as social signals that have come into play. I certainly don’t feel like these are a strong ranking factor yet, but with deals like this one between Google and Twitter being signed, I wouldn’t be surprised if that ever-growing dataset is used at some point in organic results. The one advantage that Twitter has over Google is it’s breaking news freshness. Twitter is still way quicker at breaking news than Google is—140 characters in a tweet is far quicker than Google News! Google know this which is why I feel they’ve pulled this partnership back into existence after a couple of years apart.

There is another important point to remember here and it’s nicely summarised by Dr. Pete:

At the same time, as new signals are introduced, these are layers not replacements. People hear social signals or user signals or authorship and want it to be the link-killer, because they already fucked up link-building, but these are just layers on top of on-page and links and all of the other layers. As each layer is added, it can verify the layers that came before it and what you need isn’t the magic signal but a combination of signals that generally matches what Google expects to see from real, strong entities. So, links still matter, but they matter in concert with other things, which basically means it’s getting more complicated and, frankly, a bit harder. Of course, on one wants to hear that.”

– Dr. Pete

The core principles have not changed

This is the crux of everything for me. With all the changes listed above, the key is that the core principles around link building haven’t changed. I could even argue that Penguin didn’t change the core principles because the techniques that Penguin targeted should never have worked in the first place. I won’t argue this too much though because even Google advised website owners to build directory links at one time.

You need an asset

You need to give someone a reason to link to you. Many won’t do it out of the goodness of their heart! One of the most effective ways to do this is to develop a content asset and use this as your reason to make people care. Once you’ve made someone care, they’re more likely to share the content or link to it from somewhere.

You need to promote that asset to the right audience

I really dislike the stance that some marketers take when it comes to content promotion—build great content and links will come.

No. Sorry but for the vast majority of us, that’s simply not true. The exceptions are people that sky dive from space or have huge existing audiences to leverage.

You simply have to spend time promoting your content or your asset for it to get shares and links. It is hard work and sometimes you can spend a long time on it and get little return, but it’s important to keep working at until you’re at a point where you have two things:

  • A big enough audience where you can almost guarantee at least some traffic to your new content along with some shares
  • Enough strong relationships with relevant websites who you can speak to when new content is published and stand a good chance of them linking to it

Getting to this point is hard—but that’s kind of the point. There are various hacks you can use along the way but it will take time to get right.

You need consistency

Leading on from the previous point. It takes time and hard work to get links to your content—the types of links that stand the test of time and you’re not going to be removing in 12 months time anyway! This means that you need to keep pushing content out and getting better each and every time. This isn’t to say you should just churn content out for the sake of it, far from it. I am saying that with each piece of content you create, you will learn to do at least one thing better the next time. Try to give yourself the leverage to do this.

Anything scalable is at risk

Scalable link building is exactly what Google has been trying to crack down on for the last few years. Penguin was the biggest move and hit some of the most scalable tactics we had at our disposal. When you scale something, you often lose some level of quality, which is exactly what Google doesn’t want when it comes to links. If you’re still relying on tactics that could fall into the scalable category, I think you need to be very careful and just look at the trend in the types of links Google has been penalizing to understand why.

The part Google plays in this

To finish up, I want to briefly talk about the part that Google plays in all of this and shaping the future they want for the web.

I’ve always tried to steer clear of arguments involving the idea that Google is actively pushing FUD into the community. I’ve preferred to concentrate more on things I can actually influence and change with my clients rather than what Google is telling us all to do.

However, for the purposes of this post, I want to talk about it.

General paranoia has increased. My bet is there are some companies out there carrying out zero specific linkbuilding activity through worry.

Dan Barker

Dan’s point is a very fair one and just a day or two after reading this in an email, I came across a page related to a client’s target audience that said:

“We are not publishing guest posts on SITE NAME any more. All previous guest posts are now deleted. For more information, see www.mattcutts.com/blog/guest-blogging/“.

I’ve reworded this as to not reveal the name of the site, but you get the point.

This is silly. Honestly, so silly. They are a good site, publish good content, and had good editorial standards. Yet they have ignored all of their own policies, hard work, and objectives to follow a blog post from Matt. I’m 100% confident that it wasn’t sites like this one that Matt was talking about in this blog post.

This is, of course, from the publishers’ angle rather than the link builders’ angle, but it does go to show the effect that statements from Google can have. Google know this so it does make sense for them to push out messages that make their jobs easier and suit their own objectives—why wouldn’t they? In a similar way, what did they do when they were struggling to classify at scale which links are bad vs. good and they didn’t have a big enough web spam team? They got us to do it for them 🙂

I’m mostly joking here, but you see the point.

The most recent infamous mobilegeddon update, discussed here by Dr. Pete is another example of Google pushing out messages that ultimately scared a lot of people into action. Although to be fair, I think that despite the apparent small impact so far, the broad message from Google is a very serious one.

Because of this, I think we need to remember that Google does have their own agenda and many shareholders to keep happy. I’m not in the camp of believing everything that Google puts out is FUD, but I’m much more sensitive and questioning of the messages now than I’ve ever been.

What do you think? I’d love to hear your feedback and thoughts in the comments.

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Reblogged 4 years ago from tracking.feedpress.it

Using Term Frequency Analysis to Measure Your Content Quality

Posted by EricEnge

It’s time to look at your content differently—time to start understanding just how good it really is. I am not simply talking about titles, keyword usage, and meta descriptions. I am talking about the entire page experience. In today’s post, I am going to introduce the general concept of content quality analysis, why it should matter to you, and how to use term frequency (TF) analysis to gather ideas on how to improve your content.

TF analysis is usually combined with inverse document frequency analysis (collectively TF-IDF analysis). TF-IDF analysis has been a staple concept for information retrieval science for a long time. You can read more about TF-IDF and other search science concepts in Cyrus Shepard’s
excellent article here.

For purposes of today’s post, I am going to show you how you can use TF analysis to get clues as to what Google is valuing in the content of sites that currently outrank you. But first, let’s get oriented.

Conceptualizing page quality

Start by asking yourself if your page provides a quality experience to people who visit it. For example, if a search engine sends 100 people to your page, how many of them will be happy? Seventy percent? Thirty percent? Less? What if your competitor’s page gets a higher percentage of happy users than yours does? Does that feel like an “uh-oh”?

Let’s think about this with a specific example in mind. What if you ran a golf club site, and 100 people come to your page after searching on a phrase like “golf clubs.” What are the kinds of things they may be looking for?

Here are some things they might want:

  1. A way to buy golf clubs on your site (you would need to see a shopping cart of some sort).
  2. The ability to select specific brands, perhaps by links to other pages about those brands of golf clubs.
  3. Information on how to pick the club that is best for them.
  4. The ability to select specific types of clubs (drivers, putters, irons, etc.). Again, this may be via links to other pages.
  5. A site search box.
  6. Pricing info.
  7. Info on shipping costs.
  8. Expert analysis comparing different golf club brands.
  9. End user reviews of your company so they can determine if they want to do business with you.
  10. How your return policy works.
  11. How they can file a complaint.
  12. Information about your company. Perhaps an “about us” page.
  13. A link to a privacy policy page.
  14. Whether or not you have been “in the news” recently.
  15. Trust symbols that show that you are a reputable organization.
  16. A way to access pages to buy different products, such as golf balls or tees.
  17. Information about specific golf courses.
  18. Tips on how to improve their golf game.

This is really only a partial list, and the specifics of your site can certainly vary for any number of reasons from what I laid out above. So how do you figure out what it is that people really want? You could pull in data from a number of sources. For example, using data from your site search box can be invaluable. You can do user testing on your site. You can conduct surveys. These are all good sources of data.

You can also look at your analytics data to see what pages get visited the most. Just be careful how you use that data. For example, if most of your traffic is from search, this data will be biased by incoming search traffic, and hence what Google chooses to rank. In addition, you may only have a small percentage of the visitors to your site going to your privacy policy, but chances are good that there are significantly more users than that who notice whether or not you have a privacy policy. Many of these will be satisfied just to see that you have one and won’t actually go check it out.

Whatever you do, it’s worth using many of these methods to determine what users want from the pages of your site and then using the resulting information to improve your overall site experience.

Is Google using this type of info as a ranking factor?

At some level, they clearly are. Clearly Google and Bing have evolved far beyond the initial TF-IDF concepts, but we can still use them to better understand our own content.

The first major indication we had that Google was performing content quality analysis was with the release of the
Panda algorithm in February of 2011. More recently, we know that on April 21 Google will release an algorithm that makes the mobile friendliness of a web site a ranking factor. Pure and simple, this algo is about the user experience with a page.

Exactly how Google is performing these measurements is not known, but
what we do know is their intent. They want to make their search engine look good, largely because it helps them make more money. Sending users to pages that make them happy will do that. Google has every incentive to improve the quality of their search results in as many ways as they can.

Ultimately, we don’t actually know what Google is measuring and using. It may be that the only SEO impact of providing pages that satisfy a very high percentage of users is an indirect one. I.e., so many people like your site that it gets written about more, linked to more, has tons of social shares, gets great engagement, that Google sees other signals that it uses as ranking factors, and this is why your rankings improve.

But, do I care if the impact is a direct one or an indirect one? Well, NO.

Using TF analysis to evaluate your page

TF-IDF analysis is more about relevance than content quality, but we can still use various precepts from it to help us understand our own content quality. One way to do this is to compare the results of a TF analysis of all the keywords on your page with those pages that currently outrank you in the search results. In this section, I am going to outline the basic concepts for how you can do this. In the next section I will show you a process that you can use with publicly available tools and a spreadsheet.

The simplest form of TF analysis is to count the number of uses of each keyword on a page. However, the problem with that is that a page using a keyword 10 times will be seen as 10 times more valuable than a page that uses a keyword only once. For that reason, we dampen the calculations. I have seen two methods for doing this, as follows:

term frequency calculation

The first method relies on dividing the number of repetitions of a keyword by the count for the most popular word on the entire page. Basically, what this does is eliminate the inherent advantage that longer documents might otherwise have over shorter ones. The second method dampens the total impact in a different way, by taking the log base 10 for the actual keyword count. Both of these achieve the effect of still valuing incremental uses of a keyword, but dampening it substantially. I prefer to use method 1, but you can use either method for our purposes here.

Once you have the TF calculated for every different keyword found on your page, you can then start to do the same analysis for pages that outrank you for a given search term. If you were to do this for five competing pages, the result might look something like this:

term frequency spreadsheet

I will show you how to set up the spreadsheet later, but for now, let’s do the fun part, which is to figure out how to analyze the results. Here are some of the things to look for:

  1. Are there any highly related words that all or most of your competitors are using that you don’t use at all?
  2. Are there any such words that you use significantly less, on average, than your competitors?
  3. Also look for words that you use significantly more than competitors.

You can then tag these words for further analysis. Once you are done, your spreadsheet may now look like this:

second stage term frequency analysis spreadsheet

In order to make this fit into this screen shot above and keep it legibly, I eliminated some columns you saw in my first spreadsheet. However, I did a sample analysis for the movie “Woman in Gold”. You can see the
full spreadsheet of calculations here. Note that we used an automated approach to marking some items at “Low Ratio,” “High Ratio,” or “All Competitors Have, Client Does Not.”

None of these flags by themselves have meaning, so you now need to put all of this into context. In our example, the following words probably have no significance at all: “get”, “you”, “top”, “see”, “we”, “all”, “but”, and other words of this type. These are just very basic English language words.

But, we can see other things of note relating to the target page (a.k.a. the client page):

  1. It’s missing any mention of actor ryan reynolds
  2. It’s missing any mention of actor helen mirren
  3. The page has no reviews
  4. Words like “family” and “story” are not mentioned
  5. “Austrian” and “maria altmann” are not used at all
  6. The phrase “woman in gold” and words “billing” and “info” are used proportionally more than they are with the other pages

Note that the last item is only visible if you open
the spreadsheet. The issues above could well be significant, as the lead actors, reviews, and other indications that the page has in-depth content. We see that competing pages that rank have details of the story, so that’s an indication that this is what Google (and users) are looking for. The fact that the main key phrase, and the word “billing”, are used to a proportionally high degree also makes it seem a bit spammy.

In fact, if you look at the information closely, you can see that the target page is quite thin in overall content. So much so, that it almost looks like a doorway page. In fact, it looks like it was put together by the movie studio itself, just not very well, as it presents little in the way of a home page experience that would cause it to rank for the name of the movie!

In the many different times I have done an analysis using these methods, I’ve been able to make many different types of observations about pages. A few of the more interesting ones include:

  1. A page that had no privacy policy, yet was taking personally identifiable info from users.
  2. A major lack of important synonyms that would indicate a real depth of available content.
  3. Comparatively low Domain Authority competitors ranking with in-depth content.

These types of observations are interesting and valuable, but it’s important to stress that you shouldn’t be overly mechanical about this. The value in this type of analysis is that it gives you a technical way to compare the content on your page with that of your competitors. This type of analysis should be used in combination with other methods that you use for evaluating that same page. I’ll address this some more in the summary section of this below.

How do you execute this for yourself?

The
full spreadsheet contains all the formulas so all you need to do is link in the keyword count data. I have tried this with two different keyword density tools, the one from Searchmetrics, and this one from motoricerca.info.

I am not endorsing these tools, and I have no financial interest in either one—they just seemed to work fairly well for the process I outlined above. To provide the data in the right format, please do the following:

  1. Run all the URLs you are testing through the keyword density tool.
  2. Copy and paste all the one word, two word, and three word results into a tab on the spreadsheet.
  3. Sort them all so you get total word counts aligned by position as I have shown in the linked spreadsheet.
  4. Set up the formulas as I did in the demo spreadsheet (you can just use the demo spreadsheet).
  5. Then do your analysis!

This may sound a bit tedious (and it is), but it has worked very well for us at STC.

Summary

You can also use usability groups and a number of other methods to figure out what users are really looking for on your site. However, what this does is give us a look at what Google has chosen to rank the highest in its search results. Don’t treat this as some sort of magic formula where you mechanically tweak the content to get better metrics in this analysis.

Instead, use this as a method for slicing into your content to better see it the way a machine might see it. It can yield some surprising (and wonderful) insights!

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Reblogged 4 years ago from tracking.feedpress.it

The Nifty Guide to Local Content Strategy and Marketing

Posted by NiftyMarketing

This is my Grandma.

She helped raised me and I love her dearly. That chunky baby with the Gerber cheeks is
me. The scarlet letter “A” means nothing… I hope.

This is a rolled up newspaper. 

rolled up newspaper

When I was growing up, I was the king of mischief and had a hard time following parental guidelines. To ensure the lessons she wanted me to learn “sunk in” my grandma would give me a soft whack with a rolled up newspaper and would say,

“Mike, you like to learn the hard way.”

She was right. I have
spent my life and career learning things the hard way.

Local content has been no different. I started out my career creating duplicate local doorway pages using “find and replace” with city names. After getting whacked by the figurative newspaper a few times, I decided there had to be a better way. To save others from the struggles I experienced, I hope that the hard lessons I have learned about local content strategy and marketing help to save you fearing a rolled newspaper the same way I do.

Lesson one: Local content doesn’t just mean the written word

local content ecosystem

Content is everything around you. It all tells a story. If you don’t have a plan for how that story is being told, then you might not like how it turns out. In the local world, even your brick and mortar building is a piece of content. It speaks about your brand, your values, your appreciation of customers and employees, and can be used to attract organic visitors if it is positioned well and provides a good user experience. If you just try to make the front of a building look good, but don’t back up the inside inch by inch with the same quality, people will literally say, “Hey man, this place sucks… let’s bounce.”

I had this experience proved to me recently while conducting an interview at
Nifty for our law division. Our office is a beautifully designed brick, mustache, animal on the wall, leg lamp in the center of the room, piece of work you would expect for a creative company.

nifty offices idaho

Anywho, for our little town of Burley, Idaho it is a unique space, and helps to set apart our business in our community. But, the conference room has a fluorescent ballast light system that can buzz so loudly that you literally can’t carry on a proper conversation at times, and in the recent interviews I literally had to conduct them in the dark because it was so bad.

I’m cheap and slow to spend money, so I haven’t got it fixed yet. The problem is I have two more interviews this week and I am so embarrassed by the experience in that room, I am thinking of holding them offsite to ensure that we don’t product a bad content experience. What I need to do is just fix the light but I will end up spending weeks going back and forth with the landlord on whose responsibility it is.

Meanwhile, the content experience suffers. Like I said, I like to learn the hard way.

Start thinking about everything in the frame of content and you will find that you make better decisions and less costly mistakes.

Lesson two: Scalable does not mean fast and easy growth

In every sales conversation I have had about local content, the question of scalability comes up. Usually, people want two things:

  1. Extremely Fast Production 
  2. Extremely Low Cost

While these two things would be great for every project, I have come to find that there are rare cases where quality can be achieved if you are optimizing for fast production and low cost. A better way to look at scale is as follows:

The rate of growth in revenue/traffic is greater than the cost of continued content creation.

A good local content strategy at scale will create a model that looks like this:

scaling content graph

Lesson three: You need a continuous local content strategy

This is where the difference between local content marketing and content strategy kicks in. Creating a single piece of content that does well is fairly easy to achieve. Building a true scalable machine that continually puts out great local content and consistently tells your story is not. This is a graph I created outlining the process behind creating and maintaining a local content strategy:

local content strategy

This process is not a one-time thing. It is not a box to be checked off. It is a structure that should become the foundation of your marketing program and will need to be revisited, re-tweaked, and replicated over and over again.

1. Identify your local audience

Most of you reading this will already have a service or product and hopefully local customers. Do you have personas developed for attracting and retaining more of them? Here are some helpful tools available to give you an idea of how many people fit your personas in any given market.

Facebook Insights

Pretend for a minute that you live in the unique market of Utah and have a custom wedding dress line. You focus on selling modest wedding dresses. It is a definite niche product, but one that shows the idea of personas very well.

You have interviewed your customer base and found a few interests that your customer base share. Taking that information and putting it into Facebook insights will give you a plethora of data to help you build out your understanding of a local persona.

facebook insights data

We are able to see from the interests of our customers there are roughly 6k-7k current engaged woman in Utah who have similar interests to our customer base.

The location tab gives us a break down of the specific cities and, understandably, Salt Lake City has the highest percentage with Provo (home of BYU) in second place. You can also see pages this group would like, activity levels on Facebook, and household income with spending habits. If you wanted to find more potential locations for future growth you can open up the search to a region or country.

localized facebook insights data

From this data it’s apparent that Arizona would be a great expansion opportunity after Utah.

Neilson Prizm

Neilson offers a free and extremely useful tool for local persona research called Zip Code Lookup that allows you to identify pre-determined personas in a given market.

Here is a look at my hometown and the personas they have developed are dead on.

Neilson Prizm data

Each persona can be expanded to learn more about the traits, income level, and areas across the country with other high concentrations of the same persona group.

You can also use the segment explorer to get a better idea of pre-determined persona lists and can work backwards to determine the locations with the highest density of a given persona.

Google Keyword Planner Tool

The keyword tool is fantastic for local research. Using our same Facebook Insight data above we can match keyword search volume against the audience size to determine how active our persona is in product research and purchasing. In the case of engaged woman looking for dresses, it is a very active group with a potential of 20-30% actively searching online for a dress.

google keyword planner tool

2. Create goals and rules

I think the most important idea for creating the goals and rules around your local content is the following from the must read book Content Strategy for the Web.

You also need to ensure that everyone who will be working on things even remotely related to content has access to style and brand guides and, ultimately, understands the core purpose for what, why, and how everything is happening.

3. Audit and analyze your current local content

The point of this step is to determine how the current content you have stacks up against the goals and rules you established, and determine the value of current pages on your site. With tools like Siteliner (for finding duplicate content) and ScreamingFrog (identifying page titles, word count, error codes and many other things) you can grab a lot of information very fast. Beyond that, there are a few tools that deserve a more in-depth look.

BuzzSumo

With BuzzSumo you can see social data and incoming links behind important pages on your site. This can you a good idea which locations or areas are getting more promotion than others and identify what some of the causes could be.

Buzzsumo also can give you access to competitors’ information where you might find some new ideas. In the following example you can see that one of Airbnb.com’s most shared pages was a motiongraphic of its impact on Berlin.

Buzzsumo

urlProfiler

This is another great tool for scraping urls for large sites that can return about every type of measurement you could want. For sites with 1000s of pages, this tool could save hours of data gathering and can spit out a lovely formatted CSV document that will allow you to sort by things like word count, page authority, link numbers, social shares, or about anything else you could imagine.

url profiler

4. Develop local content marketing tactics

This is how most of you look when marketing tactics are brought up.

monkey

Let me remind you of something with a picture. 

rolled up newspaper

Do not start with tactics. Do the other things first. It will ensure your marketing tactics fall in line with a much bigger organizational movement and process. With the warning out of the way, here are a few tactics that could work for you.

Local landing page content

Our initial concept of local landing pages has stood the test of time. If you are scared to even think about local pages with the upcoming doorway page update then please read this analysis and don’t be too afraid. Here are local landing pages that are done right.

Marriott local content

Marriot’s Burley local page is great. They didn’t think about just ensuring they had 500 unique words. They have custom local imagery of the exterior/interior, detailed information about the area’s activities, and even their own review platform that showcases both positive and negative reviews with responses from local management.

If you can’t build your own platform handling reviews like that, might I recommend looking at Get Five Stars as a platform that could help you integrate reviews as part of your continuous content strategy.

Airbnb Neighborhood Guides

I not so secretly have a big crush on Airbnb’s approach to local. These neighborhood guides started it. They only have roughly 21 guides thus far and handle one at a time with Seoul being the most recent addition. The idea is simple, they looked at extremely hot markets for them and built out guides not just for the city, but down to a specific neighborhood.

air bnb neighborhood guides

Here is a look at Hell’s Kitchen in New York by imagery. They hire a local photographer to shoot the area, then they take some of their current popular listing data and reviews and integrate them into the page. This idea would have never flown if they only cared about creating content that could be fast and easy for every market they serve.

Reverse infographicing

Every decently sized city has had a plethora of infographics made about them. People spent the time curating information and coming up with the concept, but a majority just made the image and didn’t think about the crawlability or page title from an SEO standpoint.

Here is an example of an image search for Portland infographics.

image search results portland infographics

Take an infographic and repurpose it into crawlable content with a new twist or timely additions. Usually infographics share their data sources in the footer so you can easily find similar, new, or more information and create some seriously compelling data based content. You can even link to or share the infographic as part of it if you would like.

Become an Upworthy of local content

No one I know does this better than Movoto. Read the link for their own spin on how they did it and then look at these examples and share numbers from their local content.

60k shares in Boise by appealing to that hometown knowledge.

movoto boise content

65k shares in Salt Lake following the same formula.

movoto salt lake city content

It seems to work with video as well.

movoto video results

Think like a local directory

Directories understand where content should be housed. Not every local piece should be on the blog. Look at where Trip Advisor’s famous “Things to Do” page is listed. Right on the main city page.

trip advisor things to do in salt lake city

Or look at how many timely, fresh, quality pieces of content Yelp is showcasing from their main city page.

yelp main city page

The key point to understand is that local content isn’t just about being unique on a landing page. It is about BEING local and useful.

Ideas of things that are local:

  • Sports teams
  • Local celebrities or heroes 
  • Groups and events
  • Local pride points
  • Local pain points

Ideas of things that are useful:

  • Directions
  • Favorite local sports
  • Granular details only “locals” know

The other point to realize is that in looking at our definition of scale you don’t need to take shortcuts that un-localize the experience for users. Figure and test a location at a time until you have a winning formula and then move forward at a speed that ensures a quality local experience.

5. Create a content calendar

I am not going to get into telling you exactly how or what your content calendar needs to include. That will largely be based on the size and organization of your team and every situation might call for a unique approach. What I will do is explain how we do things at Nifty.

  1. We follow the steps above.
  2. We schedule the big projects and timelines first. These could be months out or weeks out. 
  3. We determine the weekly deliverables, checkpoints, and publish times.
  4. We put all of the information as tasks assigned to individuals or teams in Asana.

asana content calendar

The information then can be viewed by individual, team, groups of team, due dates, or any other way you would wish to sort. Repeatable tasks can be scheduled and we can run our entire operation visible to as many people as need access to the information through desktop or mobile devices. That is what works for us.

6. Launch and promote content

My personal favorite way to promote local content (other than the obvious ideas of sharing with your current followers or outreaching to local influencers) is to use Facebook ads to target the specific local personas you are trying to reach. Here is an example:

I just wrapped up playing Harold Hill in our communities production of The Music Man. When you live in a small town like Burley, Idaho you get the opportunity to play a lead role without having too much talent or a glee-based upbringing. You also get the opportunity to do all of the advertising, set design, and costuming yourself and sometime even get to pay for it.

For my advertising responsibilities, I decided to write a few blog posts and drive traffic to them. As any good Harold Hill would do, I used fear tactics.

music man blog post

I then created Facebook ads that had the following stats: Costs of $.06 per click, 12.7% click through rate, and naturally organic sharing that led to thousands of visits in a small Idaho farming community where people still think a phone book is the only way to find local businesses.

facebook ads setup

Then we did it again.

There was a protestor in Burley for over a year that parked a red pickup with signs saying things like, “I wud not trust Da Mayor” or “Don’t Bank wid Zions”. Basically, you weren’t working hard enough if you name didn’t get on the truck during the year.

Everyone knew that ol’ red pickup as it was parked on the corner of Main and Overland, which is one of the few stoplights in town. Then one day it was gone. We came up with the idea to bring the red truck back, put signs on it that said, “I wud Not Trust Pool Tables” and “Resist Sins n’ Corruption” and other things that were part of The Music Man and wrote another blog complete with pictures.

facebook ads red truck

Then I created another Facebook Ad.

facebook ads set up

A little under $200 in ad spend resulted in thousands more visits to the site which promoted the play and sold tickets to a generation that might not have been very familiar with the show otherwise.

All of it was local targeting and there was no other way would could have driven that much traffic in a community like Burley without paying Facebook and trying to create click bait ads in hope the promotion led to an organic sharing.

7. Measure and report

This is another very personal step where everyone will have different needs. At Nifty we put together very custom weekly or monthly reports that cover all of the plan, execution, and relevant stats such as traffic to specific content or location, share data, revenue or lead data if available, analysis of what worked and what didn’t, and the plan for the following period.

There is no exact data that needs to be shared. Everyone will want something slightly different, which is why we moved away from automated reporting years ago (when we moved away from auto link building… hehe) and built our report around our clients even if it took added time.

I always said that the product of a SEO or content shop is the report. That is what people buy because it is likely that is all they will see or understand.

8. In conclusion, you must refine and repeat the process

local content strategy - refine and repeat

From my point of view, this is by far the most important step and sums everything up nicely. This process model isn’t perfect. There will be things that are missed, things that need tweaked, and ways that you will be able to improve on your local content strategy and marketing all the time. The idea of the cycle is that it is never done. It never sleeps. It never quits. It never surrenders. You just keep perfecting the process until you reach the point that few locally-focused companies ever achieve… where your local content reaches and grows your target audience every time you click the publish button.

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Reblogged 4 years ago from tracking.feedpress.it

What Deep Learning and Machine Learning Mean For the Future of SEO – Whiteboard Friday

Posted by randfish

Imagine a world where even the high-up Google engineers don’t know what’s in the ranking algorithm. We may be moving in that direction. In today’s Whiteboard Friday, Rand explores and explains the concepts of deep learning and machine learning, drawing us a picture of how they could impact our work as SEOs.

For reference, here’s a still of this week’s whiteboard!

Video transcription

Howdy, Moz fans, and welcome to another edition of Whiteboard Friday. This week we are going to take a peek into Google’s future and look at what it could mean as Google advances their machine learning and deep learning capabilities. I know these sound like big, fancy, important words. They’re not actually that tough of topics to understand. In fact, they’re simplistic enough that even a lot of technology firms like Moz do some level of machine learning. We don’t do anything with deep learning and a lot of neural networks. We might be going that direction.

But I found an article that was published in January, absolutely fascinating and I think really worth reading, and I wanted to extract some of the contents here for Whiteboard Friday because I do think this is tactically and strategically important to understand for SEOs and really important for us to understand so that we can explain to our bosses, our teams, our clients how SEO works and will work in the future.

The article is called “Google Search Will Be Your Next Brain.” It’s by Steve Levy. It’s over on Medium. I do encourage you to read it. It’s a relatively lengthy read, but just a fascinating one if you’re interested in search. It starts with a profile of Geoff Hinton, who was a professor in Canada and worked on neural networks for a long time and then came over to Google and is now a distinguished engineer there. As the article says, a quote from the article: “He is versed in the black art of organizing several layers of artificial neurons so that the entire system, the system of neurons, could be trained or even train itself to divine coherence from random inputs.”

This sounds complex, but basically what we’re saying is we’re trying to get machines to come up with outcomes on their own rather than us having to tell them all the inputs to consider and how to process those incomes and the outcome to spit out. So this is essentially machine learning. Google has used this, for example, to figure out when you give it a bunch of photos and it can say, “Oh, this is a landscape photo. Oh, this is an outdoor photo. Oh, this is a photo of a person.” Have you ever had that creepy experience where you upload a photo to Facebook or to Google+ and they say, “Is this your friend so and so?” And you’re like, “God, that’s a terrible shot of my friend. You can barely see most of his face, and he’s wearing glasses which he usually never wears. How in the world could Google+ or Facebook figure out that this is this person?”

That’s what they use, these neural networks, these deep machine learning processes for. So I’ll give you a simple example. Here at MOZ, we do machine learning very simplistically for page authority and domain authority. We take all the inputs — numbers of links, number of linking root domains, every single metric that you could get from MOZ on the page level, on the sub-domain level, on the root-domain level, all these metrics — and then we combine them together and we say, “Hey machine, we want you to build us the algorithm that best correlates with how Google ranks pages, and here’s a bunch of pages that Google has ranked.” I think we use a base set of 10,000, and we do it about quarterly or every 6 months, feed that back into the system and the system pumps out the little algorithm that says, “Here you go. This will give you the best correlating metric with how Google ranks pages.” That’s how you get page authority domain authority.

Cool, really useful, helpful for us to say like, “Okay, this page is probably considered a little more important than this page by Google, and this one a lot more important.” Very cool. But it’s not a particularly advanced system. The more advanced system is to have these kinds of neural nets in layers. So you have a set of networks, and these neural networks, by the way, they’re designed to replicate nodes in the human brain, which is in my opinion a little creepy, but don’t worry. The article does talk about how there’s a board of scientists who make sure Terminator 2 doesn’t happen, or Terminator 1 for that matter. Apparently, no one’s stopping Terminator 4 from happening? That’s the new one that’s coming out.

So one layer of the neural net will identify features. Another layer of the neural net might classify the types of features that are coming in. Imagine this for search results. Search results are coming in, and Google’s looking at the features of all the websites and web pages, your websites and pages, to try and consider like, “What are the elements I could pull out from there?”

Well, there’s the link data about it, and there are things that happen on the page. There are user interactions and all sorts of stuff. Then we’re going to classify types of pages, types of searches, and then we’re going to extract the features or metrics that predict the desired result, that a user gets a search result they really like. We have an algorithm that can consistently produce those, and then neural networks are hopefully designed — that’s what Geoff Hinton has been working on — to train themselves to get better. So it’s not like with PA and DA, our data scientist Matt Peters and his team looking at it and going, “I bet we could make this better by doing this.”

This is standing back and the guys at Google just going, “All right machine, you learn.” They figure it out. It’s kind of creepy, right?

In the original system, you needed those people, these individuals here to feed the inputs, to say like, “This is what you can consider, system, and the features that we want you to extract from it.”

Then unsupervised learning, which is kind of this next step, the system figures it out. So this takes us to some interesting places. Imagine the Google algorithm, circa 2005. You had basically a bunch of things in here. Maybe you’d have anchor text, PageRank and you’d have some measure of authority on a domain level. Maybe there are people who are tossing new stuff in there like, “Hey algorithm, let’s consider the location of the searcher. Hey algorithm, let’s consider some user and usage data.” They’re tossing new things into the bucket that the algorithm might consider, and then they’re measuring it, seeing if it improves.

But you get to the algorithm today, and gosh there are going to be a lot of things in there that are driven by machine learning, if not deep learning yet. So there are derivatives of all of these metrics. There are conglomerations of them. There are extracted pieces like, “Hey, we only ant to look and measure anchor text on these types of results when we also see that the anchor text matches up to the search queries that have previously been performed by people who also search for this.” What does that even mean? But that’s what the algorithm is designed to do. The machine learning system figures out things that humans would never extract, metrics that we would never even create from the inputs that they can see.

Then, over time, the idea is that in the future even the inputs aren’t given by human beings. The machine is getting to figure this stuff out itself. That’s weird. That means that if you were to ask a Google engineer in a world where deep learning controls the ranking algorithm, if you were to ask the people who designed the ranking system, “Hey, does it matter if I get more links,” they might be like, “Well, maybe.” But they don’t know, because they don’t know what’s in this algorithm. Only the machine knows, and the machine can’t even really explain it. You could go take a snapshot and look at it, but (a) it’s constantly evolving, and (b) a lot of these metrics are going to be weird conglomerations and derivatives of a bunch of metrics mashed together and torn apart and considered only when certain criteria are fulfilled. Yikes.

So what does that mean for SEOs. Like what do we have to care about from all of these systems and this evolution and this move towards deep learning, which by the way that’s what Jeff Dean, who is, I think, a senior fellow over at Google, he’s the dude that everyone mocks for being the world’s smartest computer scientist over there, and Jeff Dean has basically said, “Hey, we want to put this into search. It’s not there yet, but we want to take these models, these things that Hinton has built, and we want to put them into search.” That for SEOs in the future is going to mean much less distinct universal ranking inputs, ranking factors. We won’t really have ranking factors in the way that we know them today. It won’t be like, “Well, they have more anchor text and so they rank higher.” That might be something we’d still look at and we’d say, “Hey, they have this anchor text. Maybe that’s correlated with what the machine is finding, the system is finding to be useful, and that’s still something I want to care about to a certain extent.”

But we’re going to have to consider those things a lot more seriously. We’re going to have to take another look at them and decide and determine whether the things that we thought were ranking factors still are when the neural network system takes over. It also is going to mean something that I think many, many SEOs have been predicting for a long time and have been working towards, which is more success for websites that satisfy searchers. If the output is successful searches, and that’ s what the system is looking for, and that’s what it’s trying to correlate all its metrics to, if you produce something that means more successful searches for Google searchers when they get to your site, and you ranking in the top means Google searchers are happier, well you know what? The algorithm will catch up to you. That’s kind of a nice thing. It does mean a lot less info from Google about how they rank results.

So today you might hear from someone at Google, “Well, page speed is a very small ranking factor.” In the future they might be, “Well, page speed is like all ranking factors, totally unknown to us.” Because the machine might say, “Well yeah, page speed as a distinct metric, one that a Google engineer could actually look at, looks very small.” But derivatives of things that are connected to page speed may be huge inputs. Maybe page speed is something, that across all of these, is very well connected with happier searchers and successful search results. Weird things that we never thought of before might be connected with them as the machine learning system tries to build all those correlations, and that means potentially many more inputs into the ranking algorithm, things that we would never consider today, things we might consider wholly illogical, like, “What servers do you run on?” Well, that seems ridiculous. Why would Google ever grade you on that?

If human beings are putting factors into the algorithm, they never would. But the neural network doesn’t care. It doesn’t care. It’s a honey badger. It doesn’t care what inputs it collects. It only cares about successful searches, and so if it turns out that Ubuntu is poorly correlated with successful search results, too bad.

This world is not here yet today, but certainly there are elements of it. Google has talked about how Panda and Penguin are based off of machine learning systems like this. I think, given what Geoff Hinton and Jeff Dean are working on at Google, it sounds like this will be making its way more seriously into search and therefore it’s something that we’re really going to have to consider as search marketers.

All right everyone, I hope you’ll join me again next week for another edition of Whiteboard Friday. Take care.

Video transcription by Speechpad.com

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Cortex SEO Toolkit Demo

Using big data and machine learning to help your SEO within the Cortex ecommerce platform.

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