Why We Can’t Do Keyword Research Like It’s 2010 – Whiteboard Friday

Posted by randfish

Keyword Research is a very different field than it was just five years ago, and if we don’t keep up with the times we might end up doing more harm than good. From the research itself to the selection and targeting process, in today’s Whiteboard Friday Rand explains what has changed and what we all need to do to conduct effective keyword research today.

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

What do we need to change to keep up with the changing world of keyword research?

Howdy, Moz fans, and welcome to another edition of Whiteboard Friday. This week we’re going to chat a little bit about keyword research, why it’s changed from the last five, six years and what we need to do differently now that things have changed. So I want to talk about changing up not just the research but also the selection and targeting process.

There are three big areas that I’ll cover here. There’s lots more in-depth stuff, but I think we should start with these three.

1) The Adwords keyword tool hides data!

This is where almost all of us in the SEO world start and oftentimes end with our keyword research. We go to AdWords Keyword Tool, what used to be the external keyword tool and now is inside AdWords Ad Planner. We go inside that tool, and we look at the volume that’s reported and we sort of record that as, well, it’s not good, but it’s the best we’re going to do.

However, I think there are a few things to consider here. First off, that tool is hiding data. What I mean by that is not that they’re not telling the truth, but they’re not telling the whole truth. They’re not telling nothing but the truth, because those rounded off numbers that you always see, you know that those are inaccurate. Anytime you’ve bought keywords, you’ve seen that the impression count never matches the count that you see in the AdWords tool. It’s not usually massively off, but it’s often off by a good degree, and the only thing it’s great for is telling relative volume from one from another.

But because AdWords hides data essentially by saying like, “Hey, you’re going to type in . . .” Let’s say I’m going to type in “college tuition,” and Google knows that a lot of people search for how to reduce college tuition, but that doesn’t come up in the suggestions because it’s not a commercial term, or they don’t think that an advertiser who bids on that is going to do particularly well and so they don’t show it in there. I’m giving an example. They might indeed show that one.

But because that data is hidden, we need to go deeper. We need to go beyond and look at things like Google Suggest and related searches, which are down at the bottom. We need to start conducting customer interviews and staff interviews, which hopefully has always been part of your brainstorming process but really needs to be now. Then you can apply that to AdWords. You can apply that to suggest and related.

The beautiful thing is once you get these tools from places like visiting forums or communities, discussion boards and seeing what terms and phrases people are using, you can collect all this stuff up, plug it back into AdWords, and now they will tell you how much volume they’ve got. So you take that how to lower college tuition term, you plug it into AdWords, they will show you a number, a non-zero number. They were just hiding it in the suggestions because they thought, “Hey, you probably don’t want to bid on that. That won’t bring you a good ROI.” So you’ve got to be careful with that, especially when it comes to SEO kinds of keyword research.

2) Building separate pages for each term or phrase doesn’t make sense

It used to be the case that we built separate pages for every single term and phrase that was in there, because we wanted to have the maximum keyword targeting that we could. So it didn’t matter to us that college scholarship and university scholarships were essentially people looking for exactly the same thing, just using different terminology. We would make one page for one and one page for the other. That’s not the case anymore.

Today, we need to group by the same searcher intent. If two searchers are searching for two different terms or phrases but both of them have exactly the same intent, they want the same information, they’re looking for the same answers, their query is going to be resolved by the same content, we want one page to serve those, and that’s changed up a little bit of how we’ve done keyword research and how we do selection and targeting as well.

3) Build your keyword consideration and prioritization spreadsheet with the right metrics

Everybody’s got an Excel version of this, because I think there’s just no awesome tool out there that everyone loves yet that kind of solves this problem for us, and Excel is very, very flexible. So we go into Excel, we put in our keyword, the volume, and then a lot of times we almost stop there. We did keyword volume and then like value to the business and then we prioritize.

What are all these new columns you’re showing me, Rand? Well, here I think is how sophisticated, modern SEOs that I’m seeing in the more advanced agencies, the more advanced in-house practitioners, this is what I’m seeing them add to the keyword process.

Difficulty

A lot of folks have done this, but difficulty helps us say, “Hey, this has a lot of volume, but it’s going to be tremendously hard to rank.”

The difficulty score that Moz uses and attempts to calculate is a weighted average of the top 10 domain authorities. It also uses page authority, so it’s kind of a weighted stack out of the two. If you’re seeing very, very challenging pages, very challenging domains to get in there, it’s going to be super hard to rank against them. The difficulty is high. For all of these ones it’s going to be high because college and university terms are just incredibly lucrative.

That difficulty can help bias you against chasing after terms and phrases for which you are very unlikely to rank for at least early on. If you feel like, “Hey, I already have a powerful domain. I can rank for everything I want. I am the thousand pound gorilla in my space,” great. Go after the difficulty of your choice, but this helps prioritize.

Opportunity

This is actually very rarely used, but I think sophisticated marketers are using it extremely intelligently. Essentially what they’re saying is, “Hey, if you look at a set of search results, sometimes there are two or three ads at the top instead of just the ones on the sidebar, and that’s biasing some of the click-through rate curve.” Sometimes there’s an instant answer or a Knowledge Graph or a news box or images or video, or all these kinds of things that search results can be marked up with, that are not just the classic 10 web results. Unfortunately, if you’re building a spreadsheet like this and treating every single search result like it’s just 10 blue links, well you’re going to lose out. You’re missing the potential opportunity and the opportunity cost that comes with ads at the top or all of these kinds of features that will bias the click-through rate curve.

So what I’ve seen some really smart marketers do is essentially build some kind of a framework to say, “Hey, you know what? When we see that there’s a top ad and an instant answer, we’re saying the opportunity if I was ranking number 1 is not 10 out of 10. I don’t expect to get whatever the average traffic for the number 1 position is. I expect to get something considerably less than that. Maybe something around 60% of that, because of this instant answer and these top ads.” So I’m going to mark this opportunity as a 6 out of 10.

There are 2 top ads here, so I’m giving this a 7 out of 10. This has two top ads and then it has a news block below the first position. So again, I’m going to reduce that click-through rate. I think that’s going down to a 6 out of 10.

You can get more and less scientific and specific with this. Click-through rate curves are imperfect by nature because we truly can’t measure exactly how those things change. However, I think smart marketers can make some good assumptions from general click-through rate data, which there are several resources out there on that to build a model like this and then include it in their keyword research.

This does mean that you have to run a query for every keyword you’re thinking about, but you should be doing that anyway. You want to get a good look at who’s ranking in those search results and what kind of content they’re building . If you’re running a keyword difficulty tool, you are already getting something like that.

Business value

This is a classic one. Business value is essentially saying, “What’s it worth to us if visitors come through with this search term?” You can get that from bidding through AdWords. That’s the most sort of scientific, mathematically sound way to get it. Then, of course, you can also get it through your own intuition. It’s better to start with your intuition than nothing if you don’t already have AdWords data or you haven’t started bidding, and then you can refine your sort of estimate over time as you see search visitors visit the pages that are ranking, as you potentially buy those ads, and those kinds of things.

You can get more sophisticated around this. I think a 10 point scale is just fine. You could also use a one, two, or three there, that’s also fine.

Requirements or Options

Then I don’t exactly know what to call this column. I can’t remember the person who’ve showed me theirs that had it in there. I think they called it Optional Data or Additional SERPs Data, but I’m going to call it Requirements or Options. Requirements because this is essentially saying, “Hey, if I want to rank in these search results, am I seeing that the top two or three are all video? Oh, they’re all video. They’re all coming from YouTube. If I want to be in there, I’ve got to be video.”

Or something like, “Hey, I’m seeing that most of the top results have been produced or updated in the last six months. Google appears to be biasing to very fresh information here.” So, for example, if I were searching for “university scholarships Cambridge 2015,” well, guess what? Google probably wants to bias to show results that have been either from the official page on Cambridge’s website or articles from this year about getting into that university and the scholarships that are available or offered. I saw those in two of these search results, both the college and university scholarships had a significant number of the SERPs where a fresh bump appeared to be required. You can see that a lot because the date will be shown ahead of the description, and the date will be very fresh, sometime in the last six months or a year.

Prioritization

Then finally I can build my prioritization. So based on all the data I had here, I essentially said, “Hey, you know what? These are not 1 and 2. This is actually 1A and 1B, because these are the same concepts. I’m going to build a single page to target both of those keyword phrases.” I think that makes good sense. Someone who is looking for college scholarships, university scholarships, same intent.

I am giving it a slight prioritization, 1A versus 1B, and the reason I do this is because I always have one keyword phrase that I’m leaning on a little more heavily. Because Google isn’t perfect around this, the search results will be a little different. I want to bias to one versus the other. In this case, my title tag, since I more targeting university over college, I might say something like college and university scholarships so that university and scholarships are nicely together, near the front of the title, that kind of thing. Then 1B, 2, 3.

This is kind of the way that modern SEOs are building a more sophisticated process with better data, more inclusive data that helps them select the right kinds of keywords and prioritize to the right ones. I’m sure you guys have built some awesome stuff. The Moz community is filled with very advanced marketers, probably plenty of you who’ve done even more than this.

I look forward to hearing from you in the comments. I would love to chat more about this topic, and we’ll see you again next week for another edition of Whiteboard Friday. Take care.

Video transcription by Speechpad.com

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Reblogged 3 years ago from tracking.feedpress.it

How We Fixed the Internet (Ok, an Answer Box)

Posted by Dr-Pete

Last year, Google expanded the Knowledge Graph to use data extracted (*cough* scraped) from the index to create answer boxes. Back in October, I wrote about a failed experiment. One of my posts, an odd dive
into Google’s revenue, was being answer-fied for the query “How much does Google make?”:

Objectively speaking, even I could concede that this wasn’t a very good answer in 2014. I posted it on Twitter, and
David Iwanow asked the inevitable question:

Enthusiasm may have gotten the best of us, a few more people got involved (like my former Moz colleague
Ruth Burr Reedy), and suddenly we were going to fix this once and for all:

There Was Just One Problem

I updated the post, carefully rewriting the first paragraph to reflect the new reality of Google’s revenue. I did my best to make the change user-friendly, adding valuable information but not disrupting the original post. I did, however, completely replace the old text that Google was scraping.

Within less than a day, Google had re-cached the content, and I just had to wait to see the new answer box. So, I waited, and waited… and waited. Two months later, still no change. Some days, the SERP showed no answer box at all (although I’ve since found these answer boxes are very dynamic), and I was starting to wonder if it was all a mistake.

Then, Something Happened

Last week, months after I had given up, I went to double-check this query for entirely different reasons, and I saw the following:

Google had finally updated the answer box with the new text, and they had even pulled an image from the post. It was a strange choice of images, but in fairness, it was a strange post.

Interestingly, Google also added the publication date of the post, perhaps recognizing that outdated answers aren’t always useful. Unfortunately, this doesn’t reflect the timing of the new content, but that’s understandable – Google doesn’t have easy access to that data.

It’s interesting to note that sometimes Google shows the image, and sometimes they don’t. This seems to be independent of whether the SERP is personalized or incognito. Here’s a capture of the image-free version, along with the #1 organic ranking:

You’ll notice that the #1 result is also my Moz post, and that result has an expanded meta description. So, the same URL is essentially double-dipping this SERP. This isn’t always the case – answers can be extracted from URLs that appear lower on page 1 (although almost always page 1, in my experience). Anecdotally, it’s also not always the case that these organic result ends up getting an expanded meta description.

However, it definitely seems that some of the quality signals driving organic ranking and expanded meta descriptions are also helping Google determine whether a query deserves a direct answer. Put simply, it’s not an accident that this post was chosen to answer this question.

What Does This Mean for You?

Let’s start with the obvious – Yes, the v2 answer boxes (driven by the index, not Freebase/WikiData)
can be updated. However, the update cycle is independent of the index’s refresh cycle. In other words, just because a post is re-cached, it doesn’t mean the answer box will update. Presumably, Google is creating a second Knowledge Graph, based on the index, and this data is only periodically updated.

It’s also entirely possible that updating could cause you to lose an answer box, if the new data weren’t a strong match to the question or the quality of the content came into question. Here’s an interesting question – on a query where a competitor has an answer box, could you change your own content enough to either replace them or knock out the answer box altogether? We are currently testing this question, but it may be a few more months before we have any answers.

Another question is what triggers this style of answer box in the first place? Eric Enge has an
in-depth look at 850,000 queries that’s well worth your time, and in many cases Google is still triggering on obvious questions (“how”, “what”, “where”, etc.). Nouns that could be interpreted as ambiguous also can trigger the new answer boxes. For example, a search for “ruby” is interpreted by Google as roughly meaning “What is Ruby?”:

This answer box also triggers “Related topics” that use content pulled from other sites but drive users to more Google searches. The small, gray links are the source sites. The much more visible, blue links are more Google searches.

Note that these also have to be questions (explicit or implied) that Google can’t answer with their curated Knowledge Graph (based on sources like Freebase and WikiData). So, for example, the question “When is Mother’s Day?” triggers an older-style answer:

Sites offering this data aren’t going to have a chance to get attribution, because Google essentially already owns the answer to this question as part of their core Knowledge Graph.

Do You Want to Be An Answer?

This is where things get tricky. At this point, we have no clear data on how these answer boxes impact CTR, and it’s likely that the impact depends a great deal on the context. I think we’re facing a certain degree of inevitability – if Google is going to list an answer, better it’s your answer then someone else’s, IMO. On the other hand, what if that answer is so complete that it renders your URL irrelevant? Consider, for example, the SERP for “how to make grilled cheese”:

Sorry, Food Network, but making a grilled cheese sandwich isn’t really that hard, and this answer box doesn’t leave much to the imagination. As these answers get more and more thorough, expect CTRs to fall.

For now, I’d argue that it’s better to have your link in the box than someone else’s, but that’s cold comfort in many cases. These new answer boxes represent what I feel is a dramatic shift in the relationship between Google and webmasters, and they may be tipping the balance. For now, we can’t do much but wait, see, and experiment.

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Reblogged 4 years ago from tracking.feedpress.it

Long Tail CTR Study: The Forgotten Traffic Beyond Top 10 Rankings

Posted by GaryMoyle

Search behavior is fundamentally changing, as users become more savvy and increasingly familiar with search technology. Google’s results have also changed significantly over the last decade, going from a simple page of 10 blue links to a much richer layout, including videos, images, shopping ads and the innovative Knowledge Graph.

We also know there are an increasing amount of touchpoints in a customer journey involving different channels and devices. Google’s
Zero Moment of Truth theory (ZMOT), which describes a revolution in the way consumers search for information online, supports this idea and predicts that we can expect the number of times natural search is involved on the path to a conversion to get higher and higher.

Understanding how people interact with Google and other search engines will always be important. Organic click curves show how many clicks you might expect from search engine results and are one way of evaluating the impact of our campaigns, forecasting performance and exploring changing search behavior.

Using search query data from Google UK for a wide range of leading brands based on millions of impressions and clicks, we can gain insights into the how CTR in natural search has evolved beyond those shown in previous studies by
Catalyst, Slingshot and AOL.

Our methodology

The NetBooster study is based entirely on UK top search query data and has been refined by day in order to give us the most accurate sample size possible. This helped us reduce anomalies in the data in order to achieve the most reliable click curve possible, allowing us to extend it way beyond the traditional top 10 results.

We developed a method to extract data day by day to greatly increase the volume of keywords and to help improve the accuracy of the
average ranking position. It ensured that the average was taken across the shortest timescale possible, reducing rounding errors.

The NetBooster study included:

  • 65,446,308 (65 million) clicks
  • 311,278,379 (311 million) impressions
  • 1,253,130 (1.2 million) unique search queries
  • 54 unique brands
  • 11 household brands (sites with a total of 1M+ branded keyword impressions)
  • Data covers several verticals including retail, travel and financial

We also looked at organic CTR for mobile, video and image results to better understand how people are discovering content in natural search across multiple devices and channels. 

We’ll explore some of the most important elements in this article.

How does our study compare against others?

Let’s start by looking at the top 10 results. In the graph below we have normalized the results in order to compare our curve, like-for-like, with previous studies from Catalyst and Slingshot. Straight away we can see that there is higher participation beyond the top four positions when compared to other studies. We can also see much higher CTR for positions lower on the pages, which highlights how searchers are becoming more comfortable with mining search results.

A new click curve to rule them all

Our first click curve is the most useful, as it provides the click through rates for generic non-brand search queries across positions 1 to 30. Initially, we can see a significant amount of traffic going to the top three results with position No. 1 receiving 19% of total traffic, 15% at position No. 2 and 11.45% at position No. 3. The interesting thing to note, however, is our curve shows a relatively high CTR for positions typically below the fold. Positions 6-10 all received a higher CTR than shown in previous studies. It also demonstrates that searchers are frequently exploring pages two and three.

CTR-top-30-730px.jpg

When we look beyond the top 10, we can see that CTR is also higher than anticipated, with positions 11-20 accounting for 17% of total traffic. Positions 21-30 also show higher than anticipated results, with over 5% of total traffic coming from page three. This gives us a better understanding of the potential uplift in visits when improving rankings from positions 11-30.

This highlights that searchers are frequently going beyond the top 10 to find the exact result they want. The prominence of paid advertising, shopping ads, Knowledge Graph and the OneBox may also be pushing users below the fold more often as users attempt to find better qualified results. It may also indicate growing dissatisfaction with Google results, although this is a little harder to quantify.

Of course, it’s important we don’t just rely on one single click curve. Not all searches are equal. What about the influence of brand, mobile and long-tail searches?

Brand bias has a significant influence on CTR

One thing we particularly wanted to explore was how the size of your brand influences the curve. To explore this, we banded each of the domains in our study into small, medium and large categories based on the sum of brand query impressions across the entire duration of the study.

small-medium-large-brand-organic-ctr-730

When we look at how brand bias is influencing CTR for non-branded search queries, we can see that better known brands get a sizable increase in CTR. More importantly, small- to medium-size brands are actually losing out to results from these better-known brands and experience a much lower CTR in comparison.

What is clear is keyphrase strategy will be important for smaller brands in order to gain traction in natural search. Identifying and targeting valuable search queries that aren’t already dominated by major brands will minimize the cannibalization of CTR and ensure higher traffic levels as a result.

How does mobile CTR reflect changing search behavior?

Mobile search has become a huge part of our daily lives, and our clients are seeing a substantial shift in natural search traffic from desktop to mobile devices. According to Google, 30% of all searches made in 2013 were on a mobile device; they also predict mobile searches will constitute over 50% of all searches in 2014.

Understanding CTR from mobile devices will be vital as the mobile search revolution continues. It was interesting to see that the click curve remained very similar to our desktop curve. Despite the lack of screen real estate, searchers are clearly motivated to scroll below the fold and beyond the top 10.

netbooster-mobile-organic-ctr-730px.jpg

NetBooster CTR curves for top 30 organic positions


Position

Desktop CTR

Mobile CTR

Large Brand

Medium Brand

Small Brand
1 19.35% 20.28% 20.84% 13.32% 8.59%
2 15.09% 16.59% 16.25% 9.77% 8.92%
3 11.45% 13.36% 12.61% 7.64% 7.17%
4 8.68% 10.70% 9.91% 5.50% 6.19%
5 7.21% 7.97% 8.08% 4.69% 5.37%
6 5.85% 6.38% 6.55% 4.07% 4.17%
7 4.63% 4.85% 5.20% 3.33% 3.70%
8 3.93% 3.90% 4.40% 2.96% 3.22%
9 3.35% 3.15% 3.76% 2.62% 3.05%
10 2.82% 2.59% 3.13% 2.25% 2.82%
11 3.06% 3.18% 3.59% 2.72% 1.94%
12 2.36% 3.62% 2.93% 1.96% 1.31%
13 2.16% 4.13% 2.78% 1.96% 1.26%
14 1.87% 3.37% 2.52% 1.68% 0.92%
15 1.79% 3.26% 2.43% 1.51% 1.04%
16 1.52% 2.68% 2.02% 1.26% 0.89%
17 1.30% 2.79% 1.67% 1.20% 0.71%
18 1.26% 2.13% 1.59% 1.16% 0.86%
19 1.16% 1.80% 1.43% 1.12% 0.82%
20 1.05% 1.51% 1.36% 0.86% 0.73%
21 0.86% 2.04% 1.15% 0.74% 0.70%
22 0.75% 2.25% 1.02% 0.68% 0.46%
23 0.68% 2.13% 0.91% 0.62% 0.42%
24 0.63% 1.84% 0.81% 0.63% 0.45%
25 0.56% 2.05% 0.71% 0.61% 0.35%
26 0.51% 1.85% 0.59% 0.63% 0.34%
27 0.49% 1.08% 0.74% 0.42% 0.24%
28 0.45% 1.55% 0.58% 0.49% 0.24%
29 0.44% 1.07% 0.51% 0.53% 0.28%
30 0.36% 1.21% 0.47% 0.38% 0.26%

Creating your own click curve

This study will give you a set of benchmarks for both non-branded and branded click-through rates with which you can confidently compare to your own click curve data. Using this data as a comparison will let you understand whether the appearance of your content is working for or against you.

We have made things a little easier for you by creating an Excel spreadsheet: simply drop your own top search query data in and it’ll automatically create a click curve for your website.

Simply visit the NetBooster website and download our tool to start making your own click curve.

In conclusion

It’s been both a fascinating and rewarding study, and we can clearly see a change in search habits. Whatever the reasons for this evolving search behavior, we need to start thinking beyond the top 10, as pages two and three are likely to get more traffic in future. 

 We also need to maximize the traffic created from existing rankings and not just think about position.

Most importantly, we can see practical applications of this data for anyone looking to understand and maximize their content’s performance in natural search. Having the ability to quickly and easily create your own click curve and compare this against a set of benchmarks means you can now understand whether you have an optimal CTR.

What could be the next steps?

There is, however, plenty of scope for improvement. We are looking forward to continuing our investigation, tracking the evolution of search behavior. If you’d like to explore this subject further, here are a few ideas:

  • Segment search queries by intent (How does CTR vary depending on whether a search query is commercial or informational?)
  • Understand CTR by industry or niche
  • Monitor the effect of new Knowledge Graph formats on CTR across both desktop and mobile search
  • Conduct an annual analysis of search behavior (Are people’s search habits changing? Are they clicking on more results? Are they mining further into Google’s results?)

Ultimately, click curves like this will change as the underlying search behavior continues to evolve. We are now seeing a massive shift in the underlying search technology, with Google in particular heavily investing in entity- based search (i.e., the Knowledge Graph). We can expect other search engines, such as Bing, Yandex and Baidu to follow suit and use a similar approach.

The rise of smartphone adoption and constant connectivity also means natural search is becoming more focused on mobile devices. Voice-activated search is also a game-changer, as people start to converse with search engines in a more natural way. This has huge implications for how we monitor search activity.

What is clear is no other industry is changing as rapidly as search. Understanding how we all interact with new forms of search results will be a crucial part of measuring and creating success.

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Reblogged 4 years ago from feedproxy.google.com

The Best of MozCons Past: 7 Future-Facing Videos

Posted by EricaMcGillivray

The countdown to
MozCon—July 14-16 in Seattle—is on! We’ve finalized the agenda and our speaker selection, put in our swag orders, and choreographed happy dances for Roger. We’re also counting down as ticket sales speed up and are getting closer to selling out. That means:

For the best MozCon deal, make sure you 
take a 30-day free trial and register as a Moz Subscriber. If our software’s not for you, cancel at anytime, and we’ll still look forward to seeing you at MozCon.

To get you a little more excited, we’re sharing these seven future-forward videos from talks from our past two MozCons. This is the first time that these videos have been available for free! That’s right, all-new content just for you because we love you. 

If each of these videos doesn’t make you a little more happy to be part of this industry, thrilled to dive into your work, and overly-eager to attend MozCon yourself, then I suggest some
cat video therapy. 😉

1. Building a Winning Video Marketing Strategy with Phil Nottingham

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Want more Phil? He’ll be back on stage with “YouTube: The Most Important Search Engine You Haven’t Optimized For” this year. He also rocked it on the blog last year with a strategy for the kind of videos you should create for your business.


2. The D-Word: Leading the Way to Great Design with Jenny Lam

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You can never spend too much time thinking about your design and how to make it better.


3. Beyond 10 Blue Links: The Future of Ranking with Dr. Pete Meyers

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Noted as the scariest presentation from last year, Dr. Pete takes you on a journey through the SERPs. Don’t miss his “How to Never Run Out of Great Ideas” this year.


4. 35 Ways to Get Links with Paddy Moogan

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And pencils down… Paddy will be bringing his great ideas and beer challenge back this year with “Beyond SEO – Tactics for Delivering an Integrated Marketing Campaign.”


5. Next Level Local Tactics: Making Your SEO Stand Out with Dana DiTomaso

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“Wow.” That’s pretty much what I thought after seeing this presentation live. Dana will be give a talk titled “Prove Your Value” this year.


6. A New Form of CRO with Joanna Lord

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You’ll never look at conversion rate optimization the same way again.


7. Strings to Things: Entities and SEO with Matthew Brown

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Yep, Matthew basically predicted Hummingbird before it hit Google’s Algo.

Now are you ready for MozCon?

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Reblogged 4 years ago from feedproxy.google.com