Artículo Marketing: Principalmente una estafa - pizarra el viernes

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Article marketing is mostly a scam. Well, wait... some types of article marketing are really scammy. Guest blogging, legitimate article sharing, and similar tactics are great and sustainable linkbuilding practices, but making up terrible article content and passing it off as something people should read or link to is both bad for users and bad for long-term SEO. This week, Rand discusses some of the reasons article marketing is so nefarious and some alternatives that are more user-friendly. Have any alternatives or tactics you're fond of? Let us know in the comments below!

Howdy SEOmoz fans! Welcome to another edition of Whiteboard Friday. This week we're talking about one of the scummiest, lowdownest, dirtiest, ugliest, messiest, nastiest, no goodnesst things of all in the SEO world, it is called article marketing.

Now, there are some good, authentic, legitimate forms of article marketing. They're usually called guest blogging or guest authorship, guest writing. What article marketing has come to mean in the field that we are in is something just sickeningly awful. So what I want to do today is talk about it a little bit. You can probably feel some of my pain. Then, we can get into it in the comments and talk more. I know one of the issues, too, is that some folks have success with this, especially early on in their careers, and then think, oh, this is how I can do SEO. I can just do article marketing.

Let me first, for those of you who aren't familiar, walk you through how article marketing or article spinning, article republishing is done. Basically, we have our friend here. I don't know, let's call him Fred. Fred is clacking away on his keyboard. He's like, "Oh, you know what? I am going to make a useless, fairly painful to read, crap article about why cats are the best pets, and in that article I am going to link back to pages on my site that are about cat ownership or cat food or whatever it is that I am trying to rank for. Those links are going to pass me some nice juice. They'll go over to my website. That'll be real nice there." But instead of just publishing on my site, I might publish it on my site, but I am also going to either take it with me and submit it to a bunch of article directories, article portals, article resource sites, sometimes they're called article publishers. They have all sorts of different names - article portals or something like that sometimes.

Or even better, I'm going to use the article spinning robot software that I downloaded which will go and submit it to all these different article sites for me. By the way, one of the great features of it is it bypasses the CAPTCHA by reading it or they have special arrangements and it only cost me $299. How can I go wrong? My god! It sounds like an amazing deal. Who wouldn't want to spin their article with Article Spinning Robot 5000 for $300? What a . . . sure, that's totally going to work.

So, once you get your article published up on all these different sites, the goal is, the idea is that hopefully when I search Google for why cats are the best pets, I see hundreds of different results. Oh, look at all these article sites that I submitted to, they're all getting indexed, and that must mean they're passing link juice back to me, and hey some of these article sites have a nice home page PageRank, maybe a 4 or a 5 or even 3. Super exciting. Clearly going to be incredibly valuable and useful for my SEO practices. So the goal is I am going to get these hundreds of sites that are all linking back to me with the anchor text that I have optimized from my article and that's going to help me rank.

You know what the problem is? The thing that sucks about this is that sometimes it works. In fact, sometimes it works for months at a time or even a year or two at a time.

I was just in New York. I was speaking at an affiliate conference event, and there were some people in the room. One of the people there asked me, she said, "You know, Rand, I do a lot of article marketing, and I am wondering, instead of writing unique content pieces, entirely unique, I heard that Google only duplicate content checks the first and the last paragraph. So can I just leave the middle paragraphs the same and produce hundreds of different articles, send them out to all the different sites? Because usually the editors, they don't even have editors or they are crappy. They don't review anything. So, if the first paragraph is unique, they usually accept the article and I can get them reposted. Do you think that will work well?"

I don't even know how to tell you what's wrong with your frame of mind when you ask these questions. It's incredibly frustrating. I tried to be very empathetic and explain, hey, search engines use these Markov chain analyses, they can detect duplicate content, very similar content pretty easily, and these sites tend to be very low quality anyway. She's sort of like, "Well, okay. I hear you, but I did get my rankings up quite a bit when I used the article spinner." It's sort of like, yeah, the problem with all of this stuff, with low quality tactics like this is that sometimes they work in the short term and you have to decide whether it is worth the risks.

Let's talk about a few of those. First off, does Google really want to count those links? Is that what search engineers feel like are going to provide the best results? When I search for something in Google and they say, "Ah, well, you know what, looks like Rand's article on white cats are the best pets, that's been spun on 300 article sites, so he must be the very best resource in the whole world on that topic." Can you possibly imagine a Googler thinking that way? So, instead they're going to be writing algorithms to try and prevent this stuff from working. They do all the time. Some of them fall out of favor. You can see they'll sometimes publicly lose their PageRank, or they won't but they'll lose their ability to pass link juice, or the sites will be completely penalized and they won't rank anywhere in the top 5 or 10 results and your site won't rank anywhere. One day Google just wakes up, does an algorithm change. You wake up in the morning, and boom, all your rankings are gone. You're way down in the penalty box. You go, "What did I do wrong? I've been a good article spinner. How could they do this to me?"

Another big risk is the duplicate content side. If you've submitting any content that you'd actually like to rank for, it's going to be pretty tough because some of these article sites are going to claim it's their own. They're going to earn links to their site more than you're able to earn links to your site. You might be penalized and they stay unpenalized, meaning that they're going to essentially cannibalize the traffic that you could have earned. If you are writing anything really good, you should be wanting to put it on one of two sites - your own 90% of the time, or maybe 10% of the time on a guest posting on another blog, on another website, on a content site, that has great reach, great reputation, that's going to earn you some trust and authority, not just from the links you're going to get. That's not the goal. The goal is to get readership and trust over to your site from real people who enjoy that content.

Of course, the content itself. Most of the time when people are talking about doing article marketing or article spinning, they're talking about the worst quality, lowest junk crap. As you can see with updates like Panda and Big Daddy and Vince a little bit, Google is just getting so much smarter about content analysis, and they're able to determine what matters in a block of text and what actual people like. They use user and usage data to do this now. Trying to game that system with low quality junk is not going to get you very far.

Finally, the thing that I think people forget about the most is they'll spend weeks or months, hours and hours on end, trying to spin the right things and find the right directories and getting their articles submitted here and generating some junky crap over there. I think to myself, imagine, imagine if you were doing something authentic. Imagine if you were doing real high quality SEO and inbound marketing. Imagine if instead of doing that, you got 50 more Twitter followers that day and you shared a bunch of good stuff and you wrote one guest post that maybe only went to one site, but that link lasted for the next 10 years. Imagine what you've lost when you spend time doing this kind of crap.

So, are there some alternatives? I was talking to some people at the affiliate show about the alternatives they are using. They're like, "Yeah, you know, when article marketing stopped working for me, I went with some directory link submission stuff. Then I tried some do follow commenting, and that seemed to work all right." They are sort of like, "Oh, you know, maybe some nofollow comments. That could work as well. It seems like sometimes nofollow comments do work. I'll do some reciprocal link exchanges." No. No. No. No. No. No. No. No. Just no. Okay.

How about you try something, anything real? Real and useful. If you think that you can manipulate the search engines or that search engine optimization, that the practice of improving your rankings and gaining traffic is going to be done through this kind of stuff, you're living a decade ago man. This is not going to work. One of the worst parts about this is that when you do this, the impression you create on users, on visitors who do find you, even if you manage to win . . . let's imagine that you got your content up to number one using article spinning and article robots and article marketing. Good for you. Imagine what's going to happen when I come to your site, I visit, and I am, like, "God, this is totally junky." Then I see a bunch of nofollow comment spam that you've left on the Web, and I see the articles of low quality that you've submitted everywhere. What am I going to think about your brand? How is your conversion rate possibly going to match up to the high rankings that you've achieved? If it doesn't, why are you even bothering? Isn't it so much easier to get 100 visitors and convert 10 of them than to get 10,000 visitors and convert 1 out of 1,000? It always is.

So, I really want to suggest it's not that article marketing is evil. This isn't a moral thing. This is about wasting your time and energy as a marketer and doing things that just detract from our profession.

I hope that you will avoid the classic forms of article marketing and consider some real authentic alternatives. I certainly hope that you'll join me again next week for another edition of Whiteboard Friday. Take care.

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Estadísticas: No cometer estos errores - pizarra el viernes

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 Statistics can be very powerful tools for SEOs, but it can be hard to extract the right information from them. People understandably make a lot of mistakes in the process of taking raw data and turning it into actionable numbers. If you know what potential mistakes can be made along the way, you're less likely to make them yourself - so listen up! Will Critchlow, the co-founder and chief strategist at Distilled, has a few tips on how to use valid and sound statistics reliably. Use a lot of statistics yourself? Let us know what you've learned in the comments below!

Howdy, Whiteboard fans. Different look this week. Will Critchlow from Distilled. I am going to be talking to you about some ways you can avoid some common statistical errors.

I am a huge fan of the power of statistics. I studied it. I have forgotten most of the technical details, but I use it in my work. We use it a lot at Distilled. But it is so easy to make really easy to avoid mistakes. Most of it comes from the natural way that humans aren't really very good at dealing with numbers generally, but statistics and probability in particular.

The example I like to use to illustrate that is imagine that we have a disease that we are testing for, a very rare disease, suppose. So, we have a population of people, and some small, some tiny proportion of these people have this rare disease. There is just this tiny, tiny sliver at the top. Maybe that is 1 in 10,000 people, something like that. We have a test that is 99% accurate at diagnosing when people have or don't have this disease. That sounds very accurate, right? It's only wrong 1 time in 100. But let's see what happens.

We run this test, and out of the main body of the population, I am going to exaggerate slightly, most people are correctly diagnosed as not having the disease. But 1% of them, this bit here, are incorrectly diagnosed as having the disease. That's the 99% correct but 1% incorrect. Then we have the tiny sliver at the top, which is a very small number of people, and again 99% correct, a small percent are incorrectly told they don't have it. Then, if we just look at this bit in here, zoom in on there, what we see is actually of all the people who are diagnosed as having this disease, more of them don't have it than do. Counterintuitive, right? That's come from the fact that, yes, our test is 99% accurate, but that still means it is wrong 1 in 100 times, and we're actually saying it is only 1 in 10,000 people who have this disease. So, if you are diagnosed as having it, it is actually more likely that is an error in the diagnosis than that you actually have this very rare disease. But we get this wrong. Intuitively people, generally, everyone would be likely to get this kind of question wrong. Just one example of many.

Some things that may not be immediately intuitively obvious, but if you are working with statistics, you should bear in mind.

Number one, independence is very, very important. If I toss a coin 100 times and get 100 heads, then if those were independent coin flips, there is something very, very odd going on there. If that is a coin that has two heads on it, in other words, they're not in fact independent, the chance of me getting a head is the same on every one, then they're completely different results. So, make sure that whatever it is you are testing, if you are expecting to do analysis over the whole set of trials, that the results are actually independent. The common ways this falls down are when you are dealing with people, humans.

If you want reproducible results, if you accidently manage to include the same person multiple times, their answers to a questionnaire, for example, will be skewed the second time they answer it if they have already seen the site previously, if you are doing user testing or those kinds of things. Be very careful to set up your trials, whatever it is that you are testing, for independence. Don't over worry about this, but realize that it is a potential problem. One of the things we test a lot is display copy on PPC ads. Here you can't really control who is seeing those, but just realize there is not a pure analysis going on there because many of the same people come back to a site regularly and have therefore seen the ad day after day. So there is a skew, a lack of independence.

On a similar note, all kinds of repetition can be problematic, which is unfortunate because repetition is kind of at the heart of any kind of statistical testing. You need to do things multiple times to see how they pan out. The thing I am talking about here particularly is you often have seen confidence intervals given. You have seen situations where somebody says we're 95% sure that advert one is better than advert two or that this copy converts better than that copy or that putting the checkout button here converts better than putting the checkout button there. That 95% number is coming from a statistical test. What it is saying is, it is assuming a whole bunch of independence of the trials, but it is essentially saying the chance of getting this extreme a difference in results by chance if these two things were identical is less than 5%. In other words, fewer than 1 in 20 times would this situation arise by chance.

Now, the problem is that we tend to run lots of these tests in parallel or sequentially. It doesn't really matter. So, imagine you are doing conversion rate optimization testing and you tweak 20 things one after another. Each time you test it against this model and you say, first of all I am going to change the button from red to green. Then I am going to change the copy that is on the button. Then I am going to change the copy that is near the button. Then I am going to change some other thing. You just keep going down the tree. Each time it comes back saying, no, that made no difference, or statistically insignificant difference. No, that made no difference. No, that made no difference. You get that 15 times, say. On the 16th time, you get a result that says, yes, that made a difference. We are 95% sure that made a difference. But think about what that is actually saying. That is saying the chance of this having happened randomly, where the two things you are testing between are actually identical, is 1 in 20. Now, we might expect something that would happen 1 in 20 times possibly to come up by the 16th time. There is nothing unusual about that. So actually, our test is flawed. All we've shown is we just waited long enough for some random occurrence to take place, which would have happened definitely at some point. So, you actually have to be much more careful if you are doing those kinds of trials.

One thing that works very well, which scuppers a lot of these things, is be very, very careful of this kind of thing. If you run these trials sequentially and you get a result like that, don't go and tell your boss right then. Okay? I've made this mistake with a client, rather than the boss. Don't get excited immediately because all you may be seeing is what I was just talking about. The fact that you run these trials often enough and occasionally you are going to find one that looks odd just through chance. Stop. Rerun that trial. If it comes up again as statistically significant, you are now happy. Now you can go and whoop and holler, ring the bell, jump and shout, and tell your boss or your clients. Until that point, you shouldn't because what we very often see is a situation where you get this likelihood of A being better than B, say, and we're like we're 95% sure here. You go and tell your boss. By the time you get back to your desk, it has dropped a little bit. You're like, "Oh, um, I'll show in a second." By the time he comes back over, it has dropped a little bit more. Actually, by the time it has been running for another day or two, that has actually dropped below 50% and you're not even sure of anything anymore. That's what you need to be very careful of. So, rerun those things.

Kind of similar, don't train on your sample data. If you are looking for a correlation between, or suppose you're trying to model search ranking factors, for example. You're going to take a whole bunch of things that might influence ranking, see which ones do, and then try to predict some other rankings. If you get 100 rankings, you train a model on those rankings, and then you try and predict those same rankings, you might do really well, because if you have enough variables in your model, it will actually predict it perfectly because it has just learned, it has effectively remembered those rankings. You need to not do that. I have actually made this mistake with a little thing that was trying to model the stock market. I was, like, yes, I am going to be rich. But, in fact, all it could do was predict stock market movements it had already seen, which it turns out isn't quite as useful and doesn't make you rich. So, don't train on your sample data. Train on a set of data over here, and then much like I was saying with the previous example, test it on a completely independent set of data. If that works, then you're going to be rich.

Finally, don't blindly go hunting for statistical patterns. In much the same way that when you run a test over and over again and eventually the 1 in 20, the 1 in 100 chance comes in, if you just go looking for patterns anywhere in anything, then you're definitely going to find them. Human brains are really good at pattern recognition, and computers are even better. If you just start saying, does the average number of letters in the words I use affect my rankings, and you find a thousand variables like that, that are all completely arbitrary and there is no particular reason to think they would help your rankings, but you test enough of them, you will find some that look like they do, and you'll probably be wrong. You'll probably be misleading yourself. You'll probably look like an idiot in front of your boss. That's what this is all about, how not to look like an idiot.

I'm Will Critchlow. It's been a pleasure talking to you on Whiteboard Friday. I'm sure we'll be back to the usual scheduled programming next week. See you later.

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