6 Errores de investigación de palabras clave que podría hacer

Error al deserializar el cuerpo del mensaje de respuesta para la operación 'Translate'. Se superó la cuota de longitud del contenido de cadena (8192) al leer los datos XML. Esta cuota se puede aumentar cambiando la propiedad MaxStringContentLength en el objeto XmlDictionaryReaderQuotas que se usa para crear el lector XML. Línea 1, posición 9048.
Error al deserializar el cuerpo del mensaje de respuesta para la operación 'Translate'. Se superó la cuota de longitud del contenido de cadena (8192) al leer los datos XML. Esta cuota se puede aumentar cambiando la propiedad MaxStringContentLength en el objeto XmlDictionaryReaderQuotas que se usa para crear el lector XML. Línea 1, posición 9031.

Keyword research is an all too often under-appreciated aspect of SEO.

I've written a few keyword research posts here on SEOmoz and that's because I believe it to be the blueprint of any successful SEO campaign.

Here are some of the more common mistakes that I see people make with their keyword research.

#1 – You're being Unrealistic

"It is better to have a bigger slice of a few smaller pies rather than not getting even a slither of a much bigger pie."

Keyword research appears to be a very straightforward task. You fire up your keyword research tool of choice and find the keywords that relate to your industry with the highest search volumes. Sadly, that's not the way to do it if you want to see real results.

To many businesses, high-competition keywords are simply out of reach – at least in the short and medium term. Part of good keyword research is about being realistic and selecting appropriate keywords for targeting that take into account the site's age, current authority and any future optimisation that will take place.

Targeting one word keywords is quite often unrealistic but it may also prove unprofitable – someone searching for 'Toshiba l670 laptop' is likely to be much further along in the purchasing process that someone who searches for 'laptops' – think about which searcher is likely to have their credit card out already.

There's nothing wrong with targeting generic keywords, I'm simply saying that if your campaign has limited budget and you need results in the short to medium term then targeting less trafficked, less competitive keywords is a much better way to utilise resources.

Lower traffic but lower competition keywords might not seem as exciting to target but if your website can dominate these areas fairly quickly then you are going to see far more traffic from the search engines than failing to effectively target a much more competitive term.

#2 – You're looking at broad match instead of exact match

A seemingly simple mistake but one which many people continue to make...

Search volume is of course a very important metric when it comes to keyword research but all too often people make the mistake of looking at broad search volumes rather than the exact match figure when using tools like Google's Keyword Tool.

There can be a huge difference between broad match and exact match search traffic for example:

There are 135,000 broad match searches each month in the UK for 'dog kennels' but only 14,800 exact match searches for the same keyword. Still, this wouldn't prove particularly problematic as this is obviously still a keyword worth targeting – it would knock traffic and ROI projections way off kilter if you do these kinds of things though.

The real problem comes when you choose to target a keyword like 'ladies leather handbags' which has a broad match search volume of 2,400 but an exact match search volume of only 260 – failing to base your research on exact match data might mean you think you are targeting a reasonably well-trafficked keyword when in actual fact, once you've factored in data inaccuracies, you could be looking at a very low search volume keyword indeed.

It is widely accepted that Google's Keyword Tool isn't entirely accurate when it comes to search volumes but using exact match gives you the best data available when assessing how viable a keyword is to target.

#3 – You're targeting plural instead of singular

It is very common to see a website targeting the plural version of a keyword but in most cases, it is the singular version of a keyword that people are searching for.

I see this most often on eCommerce websites where the site owner optimises category pages and because they sell more than one product, they naturally focus on the pluralised keywords for example "tablet PCs" which actually gets 91% less searches than "tablet PC".

I will readily admit that Google is much better at determining that a singular and plural version of a keyword are one and the same, but in many cases there are still differences in the search results. Failing to target the singular keyword can be the difference between your search listing being highlighted in the SERPs (=higher clickthrough) and it can also mean your website appears lower (even slightly) than marginally better targeted pages – that could be the difference between making a sale and not.

#4 – You're ignoring conversion

This one could easily turn into a rant for me because so often I come up against clients who want to rank for [insert trophy keyword] when in actual fact they'd do better (financially) targeting a different keyword or set of keywords. I try to explain that a keyword that brings in traffic is wasted bandwidth if that traffic doesn't convert. You don't hire my company to get traffic for traffic's sake...you presumably hire us to help you ultimately make more sales.

The online world is competitive and it's only going to get more competitive, therefore making the most of every penny being invested is vital.

This makes conversion and language analysis a vital part of keyword research. The human mind is the only software capable of performing a good quality 'conversion audit' of a keyword list because whilst there are programmes out there that can filter and sort keywords to make your life easier, there's no real substitute for industry experience and SEO knowledge.

There are some very basic indicators for example prefixes such as 'buy' might be a clear indicator that the traffic from this keyword is going to convert.

A keyword conversion audit is more complex than that however since each situation and market is individual. I find existing data to be a very useful way to determine which keywords are likely to convert well. If you have goal tracking setup with Google Analytics, you can easily determine the highest converting keywords your site currently gets traffic from, try to identify patterns in your highest converting keywords and then translate and apply this knowledge to other areas of keyword research.

#5 – You're selecting keywords that are out of context

This is yet more rationale to further humanise the keyword research process because most keyword tools struggle to compute words and their meaning in the way a human would.

For example, a searcher looking for 'storage' could be looking for a self-storage centre, boxes and other storage furniture for the home or even professional storage solutions for a warehouse or office.

Opportunities for confused targeting are abundant which is why it is essential the keywords you decide to target are highly-relevant and laser-focused towards what your business offers.

A good way to do this is to search manually for the keywords in Google and see the kinds of results that come up, you will likely be able to get a feel for whether the keyword is applicable to the product or service you intended to target.

#6 – You're failing to conduct keyword reviews

It is accepted that SEO is an on-going process but rarely are target keywords reviewed and audited. If a marketplace is shifting over time then you would also expect customer search behaviour to develop and evolve over time too – this makes regular keyword reviews essential.

In most markets, I find an annual review is perfectly adequate. Any time period shorter than this and there is a risk that targeting becomes a bit chaotic with efforts focused on new keywords before results on old keywords have been achieved or evaluated.

That being said, in some competitive and very fast moving markets a more regular keyword review may be required.

The aim of a keyword review is to:

Weed out poor performing keywordsIdentify opportunities and areas for growthShape your SEO strategy for the future

To do a strategic and actionable keyword review you can use this adapted version of the Boston Matrix that I like to use.

Large brands use the Boston Matrix to assess the health of their product portfolio and to identify where to concentrate their resources.

You can do the same thing for your keyword portfolio.

Sort your keywords into four categories in order to better shape your search strategy for the future.

Question marks – these are keywords in areas where growth is likely but at present you're not getting the performance you'd expect. These are very often untapped keyword opportunities and you should plan how you are going to improve performance on these kinds of keywords.Stars – high-performance keywords and loads of room for growth – find ways to capitalise on growth. My advice is to focus your resources of gaining results in these areas for maximum ROI in a short period of time.Dogs – the poor performing keywords with little or no chance of growth – bin these in favour of other keywords, reallocate any resources to other areas.Cash cows – the high performing keywords that show little opportunity for growth – look for ways to enhance and maintain performance whilst identifying patterns and translating this learning to other areas or verticals.

What mistakes do you see happening in the keyword research process? Please share them in the comments section below...

By James Agate, founder of Skyrocket SEO and a regular SEO contributor to leading blogs and publications across the web.

View the original article here

This post was made using the Auto Blogging Software from WebMagnates.org This line will not appear when posts are made after activating the software to full version.

6 Errores de investigación de palabras clave que podría hacer

Error al deserializar el cuerpo del mensaje de respuesta para la operación 'Translate'. Se superó la cuota de longitud del contenido de cadena (8192) al leer los datos XML. Esta cuota se puede aumentar cambiando la propiedad MaxStringContentLength en el objeto XmlDictionaryReaderQuotas que se usa para crear el lector XML. Línea 1, posición 9048.
Error al deserializar el cuerpo del mensaje de respuesta para la operación 'Translate'. Se superó la cuota de longitud del contenido de cadena (8192) al leer los datos XML. Esta cuota se puede aumentar cambiando la propiedad MaxStringContentLength en el objeto XmlDictionaryReaderQuotas que se usa para crear el lector XML. Línea 1, posición 9029.

Keyword research is an all too often under-appreciated aspect of SEO.

I've written a few keyword research posts here on SEOmoz and that's because I believe it to be the blueprint of any successful SEO campaign.

Here are some of the more common mistakes that I see people make with their keyword research.

#1 – You're being Unrealistic

"It is better to have a bigger slice of a few smaller pies rather than not getting even a slither of a much bigger pie."

Keyword research appears to be a very straightforward task. You fire up your keyword research tool of choice and find the keywords that relate to your industry with the highest search volumes. Sadly, that's not the way to do it if you want to see real results.

To many businesses, high-competition keywords are simply out of reach – at least in the short and medium term. Part of good keyword research is about being realistic and selecting appropriate keywords for targeting that take into account the site's age, current authority and any future optimisation that will take place.

Targeting one word keywords is quite often unrealistic but it may also prove unprofitable – someone searching for 'Toshiba l670 laptop' is likely to be much further along in the purchasing process that someone who searches for 'laptops' – think about which searcher is likely to have their credit card out already.

There's nothing wrong with targeting generic keywords, I'm simply saying that if your campaign has limited budget and you need results in the short to medium term then targeting less trafficked, less competitive keywords is a much better way to utilise resources.

Lower traffic but lower competition keywords might not seem as exciting to target but if your website can dominate these areas fairly quickly then you are going to see far more traffic from the search engines than failing to effectively target a much more competitive term.

#2 – You're looking at broad match instead of exact match

A seemingly simple mistake but one which many people continue to make...

Search volume is of course a very important metric when it comes to keyword research but all too often people make the mistake of looking at broad search volumes rather than the exact match figure when using tools like Google's Keyword Tool.

There can be a huge difference between broad match and exact match search traffic for example:

There are 135,000 broad match searches each month in the UK for 'dog kennels' but only 14,800 exact match searches for the same keyword. Still, this wouldn't prove particularly problematic as this is obviously still a keyword worth targeting – it would knock traffic and ROI projections way off kilter if you do these kinds of things though.

The real problem comes when you choose to target a keyword like 'ladies leather handbags' which has a broad match search volume of 2,400 but an exact match search volume of only 260 – failing to base your research on exact match data might mean you think you are targeting a reasonably well-trafficked keyword when in actual fact, once you've factored in data inaccuracies, you could be looking at a very low search volume keyword indeed.

It is widely accepted that Google's Keyword Tool isn't entirely accurate when it comes to search volumes but using exact match gives you the best data available when assessing how viable a keyword is to target.

#3 – You're targeting plural instead of singular

It is very common to see a website targeting the plural version of a keyword but in most cases, it is the singular version of a keyword that people are searching for.

I see this most often on eCommerce websites where the site owner optimises category pages and because they sell more than one product, they naturally focus on the pluralised keywords for example "tablet PCs" which actually gets 91% less searches than "tablet PC".

I will readily admit that Google is much better at determining that a singular and plural version of a keyword are one and the same, but in many cases there are still differences in the search results. Failing to target the singular keyword can be the difference between your search listing being highlighted in the SERPs (=higher clickthrough) and it can also mean your website appears lower (even slightly) than marginally better targeted pages – that could be the difference between making a sale and not.

#4 – You're ignoring conversion

This one could easily turn into a rant for me because so often I come up against clients who want to rank for [insert trophy keyword] when in actual fact they'd do better (financially) targeting a different keyword or set of keywords. I try to explain that a keyword that brings in traffic is wasted bandwidth if that traffic doesn't convert. You don't hire my company to get traffic for traffic's sake...you presumably hire us to help you ultimately make more sales.

The online world is competitive and it's only going to get more competitive, therefore making the most of every penny being invested is vital.

This makes conversion and language analysis a vital part of keyword research. The human mind is the only software capable of performing a good quality 'conversion audit' of a keyword list because whilst there are programmes out there that can filter and sort keywords to make your life easier, there's no real substitute for industry experience and SEO knowledge.

There are some very basic indicators for example prefixes such as 'buy' might be a clear indicator that the traffic from this keyword is going to convert.

A keyword conversion audit is more complex than that however since each situation and market is individual. I find existing data to be a very useful way to determine which keywords are likely to convert well. If you have goal tracking setup with Google Analytics, you can easily determine the highest converting keywords your site currently gets traffic from, try to identify patterns in your highest converting keywords and then translate and apply this knowledge to other areas of keyword research.

#5 – You're selecting keywords that are out of context

This is yet more rationale to further humanise the keyword research process because most keyword tools struggle to compute words and their meaning in the way a human would.

For example, a searcher looking for 'storage' could be looking for a self-storage centre, boxes and other storage furniture for the home or even professional storage solutions for a warehouse or office.

Opportunities for confused targeting are abundant which is why it is essential the keywords you decide to target are highly-relevant and laser-focused towards what your business offers.

A good way to do this is to search manually for the keywords in Google and see the kinds of results that come up, you will likely be able to get a feel for whether the keyword is applicable to the product or service you intended to target.

#6 – You're failing to conduct keyword reviews

It is accepted that SEO is an on-going process but rarely are target keywords reviewed and audited. If a marketplace is shifting over time then you would also expect customer search behaviour to develop and evolve over time too – this makes regular keyword reviews essential.

In most markets, I find an annual review is perfectly adequate. Any time period shorter than this and there is a risk that targeting becomes a bit chaotic with efforts focused on new keywords before results on old keywords have been achieved or evaluated.

That being said, in some competitive and very fast moving markets a more regular keyword review may be required.

The aim of a keyword review is to:

Weed out poor performing keywordsIdentify opportunities and areas for growthShape your SEO strategy for the future

To do a strategic and actionable keyword review you can use this adapted version of the Boston Matrix that I like to use.

Large brands use the Boston Matrix to assess the health of their product portfolio and to identify where to concentrate their resources.

You can do the same thing for your keyword portfolio.

Sort your keywords into four categories in order to better shape your search strategy for the future.

Question marks – these are keywords in areas where growth is likely but at present you're not getting the performance you'd expect. These are very often untapped keyword opportunities and you should plan how you are going to improve performance on these kinds of keywords.Stars – high-performance keywords and loads of room for growth – find ways to capitalise on growth. My advice is to focus your resources of gaining results in these areas for maximum ROI in a short period of time.Dogs – the poor performing keywords with little or no chance of growth – bin these in favour of other keywords, reallocate any resources to other areas.Cash cows – the high performing keywords that show little opportunity for growth – look for ways to enhance and maintain performance whilst identifying patterns and translating this learning to other areas or verticals.

What mistakes do you see happening in the keyword research process? Please share them in the comments section below...

By James Agate, founder of Skyrocket SEO and a regular SEO contributor to leading blogs and publications across the web.

View the original article here

This post was made using the Auto Blogging Software from WebMagnates.org This line will not appear when posts are made after activating the software to full version.

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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