Meet Dan Morris, Executive Vice President, North America

  1. Why did you decide to come to dotmailer?

The top three reasons were People, Product and Opportunity. I met the people who make up our business and heard their stories from the past 18 years, learned about the platform and market leading status they had built in the UK, and saw that I could add value with my U.S. high growth business experience. I’ve been working with marketers, entrepreneurs and business owners for years across a series of different roles, and saw that I could apply what I’d learned from that and the start-up space to dotmailer’s U.S. operation. dotmailer has had clients in the U.S. for 12 years and we’re positioned to grow the user base of our powerful and easy-to-use platform significantly. I knew I could make a difference here, and what closed the deal for me was the people.  Every single person I’ve met is deeply committed to the business, to the success of our customers and to making our solution simple and efficient.  We’re a great group of passionate people and I’m proud to have joined the dotfamily.

Dan Morris, dotmailer’s EVP for North America in the new NYC office

      1. Tell us a bit about your new role

dotmailer has been in business and in this space for more than 18 years. We were a web agency, then a Systems Integrator, and we got into the email business that way, ultimately building the dotmailer platform thousands of people use daily. This means we know this space better than anyone and we have the perfect solutions to align closely with our customers and the solutions flexible enough to grow with them.  My role is to take all that experience and the platform and grow our U.S. presence. My early focus has been on identifying the right team to execute our growth plans. We want to be the market leader in the U.S. in the next three years – just like we’ve done in the UK –  so getting the right people in the right spots was critical.  We quickly assessed the skills of the U.S. team and made changes that were necessary in order to provide the right focus on customer success. Next, we set out to completely rebuild dotmailer’s commercial approach in the U.S.  We simplified our offers to three bundles, so that pricing and what’s included in those bundles is transparent to our customers.  We’ve heard great things about this already from clients and partners. We’re also increasing our resources on customer success and support.  We’re intensely focused on ease of on-boarding, ease of use and speed of use.  We consistently hear how easy and smooth a process it is to use dotmailer’s tools.  That’s key for us – when you buy a dotmailer solution, we want to onboard you quickly and make sure you have all of your questions answered right away so that you can move right into using it.  Customers are raving about this, so we know it’s working well.

  1. What early accomplishments are you most proud of from your dotmailer time so far?

I’ve been at dotmailer for eight months now and I’m really proud of all we’ve accomplished together.  We spent a lot of time assessing where we needed to restructure and where we needed to invest.  We made the changes we needed, invested in our partner program, localized tech support, customer on-boarding and added customer success team members.  We have the right people in the right roles and it’s making a difference.  We have a commercial approach that is clear with the complete transparency that we wanted to provide our customers.  We’ve got a more customer-focused approach and we’re on-boarding customers quickly so they’re up and running faster.  We have happier customers than ever before and that’s the key to everything we do.

  1. You’ve moved the U.S. team to a new office. Can you tell us why and a bit about the new space?

I thought it was very important to create a NY office space that was tied to branding and other offices around the world, and also had its own NY energy and culture for our team here – to foster collaboration and to have some fun.  It was also important for us that we had a flexible space where we could welcome customers, partners and resellers, and also hold classes and dotUniversity training sessions. I’m really grateful to the team who worked on the space because it really reflects our team and what we care about.   At any given time, you’ll see a training session happening, the team collaborating, a customer dropping in to ask a few questions or a partner dropping in to work from here.  We love our new, NYC space.

We had a spectacular reception this week to celebrate the opening of this office with customers, partners and the dotmailer leadership team in attendance. Please take a look at the photos from our event on Facebook.

Guests and the team at dotmailer's new NYC office warming party

Guests and the team at dotmailer’s new NYC office warming party

  1. What did you learn from your days in the start-up space that you’re applying at dotmailer?

The start-up space is a great place to learn. You have to know where every dollar is going and coming from, so every choice you make needs to be backed up with a business case for that investment.  You try lots of different things to see if they’ll work and you’re ready to turn those tactics up or down quickly based on an assessment of the results. You also learn things don’t have to stay the way they are, and can change if you make them change. You always listen and learn – to customers, partners, industry veterans, advisors, etc. to better understand what’s working and not working.  dotmailer has been in business for 18 years now, and so there are so many great contributors across the business who know how things have worked and yet are always keen to keep improving.  I am constantly in listening and learning mode so that I can understand all of the unique perspectives our team brings and what we need to act on.

  1. What are your plans for the U.S. and the sales function there?

On our path to being the market leader in the U.S., I’m focused on three things going forward: 1 – I want our customers to be truly happy.  It’s already a big focus in the dotmailer organization – and we’re working hard to understand their challenges and goals so we can take product and service to the next level. 2 – Creating an even more robust program around partners, resellers and further building out our channel partners to continuously improve sales and customer service programs. We recently launched a certification program to ensure partners have all the training and resources they need to support our mutual customers.  3 – We have an aggressive growth plan for the U.S. and I’m very focused on making sure our team is well trained, and that we remain thoughtful and measured as we take the steps to grow.  We want to always keep an eye on what we’re known for – tools that are powerful and simple to use – and make sure everything else we offer remains accessible and valuable as we execute our growth plans.

  1. What are the most common questions that you get when speaking to a prospective customer?

The questions we usually get are around price, service level and flexibility.  How much does dotmailer cost?  How well are you going to look after my business?  How will you integrate into my existing stack and then my plans for future growth? We now have three transparent bundle options with specifics around what’s included published right on our website.  We have introduced a customer success team that’s focused only on taking great care of our customers and we’re hearing stories every day that tells me this is working.  And we have all of the tools to support our customers as they grow and to also integrate into their existing stacks – often integrating so well that you can use dotmailer from within Magento, Salesforce or Dynamics, for example.

  1. Can you tell us about the dotmailer differentiators you highlight when speaking to prospective customers that seem to really resonate?

In addition to the ones above – ease of use, speed of use and the ability to scale with you. With dotmailer’s tiered program, you can start with a lighter level of functionality and grow into more advanced functionality as you need it. The platform itself is so easy to use that most marketers are able to build campaigns in minutes that would have taken hours on other platforms. Our customer success team is also with you all the way if ever you want or need help.  We’ve built a very powerful platform and we have a fantastic team to help you with personalized service as an extended part of your team and we’re ready to grow with you.

  1. How much time is your team on the road vs. in the office? Any road warrior tips to share?

I’ve spent a lot of time on the road, one year I attended 22 tradeshows! Top tip when flying is to be willing to give up your seat for families or groups once you’re at the airport gate, as you’ll often be rewarded with a better seat for helping the airline make the family or group happy. Win win! Since joining dotmailer, I’m focused on being in office and present for the team and customers as much as possible. I can usually be found in our new, NYC office where I spend a lot of time with our team, in customer meetings, in trainings and other hosted events, sales conversations or marketing meetings. I’m here to help the team, clients and partners to succeed, and will always do my best to say yes! Once our prospective customers see how quickly and efficiently they can execute tasks with dotmailer solutions vs. their existing solutions, it’s a no-brainer for them.  I love seeing and hearing their reactions.

  1. Tell us a bit about yourself – favorite sports team, favorite food, guilty pleasure, favorite band, favorite vacation spot?

I’m originally from Yorkshire in England, and grew up just outside York. I moved to the U.S. about seven years ago to join a very fast growing startup, we took it from 5 to well over 300 people which was a fantastic experience. I moved to NYC almost two years ago, and I love exploring this great city.  There’s so much to see and do.  Outside of dotmailer, my passion is cars, and I also enjoy skeet shooting, almost all types of music, and I love to travel – my goal is to get to India, Thailand, Australia and Japan in the near future.

Want to find out more about the dotfamily? Check out our recent post about Darren Hockley, Global Head of Support.

Reblogged 2 years ago from blog.dotmailer.com

Deconstructing the App Store Rankings Formula with a Little Mad Science

Posted by AlexApptentive

After seeing Rand’s “Mad Science Experiments in SEO” presented at last year’s MozCon, I was inspired to put on the lab coat and goggles and do a few experiments of my own—not in SEO, but in SEO’s up-and-coming younger sister, ASO (app store optimization).

Working with Apptentive to guide enterprise apps and small startup apps alike to increase their discoverability in the app stores, I’ve learned a thing or two about app store optimization and what goes into an app’s ranking. It’s been my personal goal for some time now to pull back the curtains on Google and Apple. Yet, the deeper into the rabbit hole I go, the more untested assumptions I leave in my way.

Hence, I thought it was due time to put some longstanding hypotheses through the gauntlet.

As SEOs, we know how much of an impact a single ranking can mean on a SERP. One tiny rank up or down can make all the difference when it comes to your website’s traffic—and revenue.

In the world of apps, ranking is just as important when it comes to standing out in a sea of more than 1.3 million apps. Apptentive’s recent mobile consumer survey shed a little more light this claim, revealing that nearly half of all mobile app users identified browsing the app store charts and search results (the placement on either of which depends on rankings) as a preferred method for finding new apps in the app stores. Simply put, better rankings mean more downloads and easier discovery.

Like Google and Bing, the two leading app stores (the Apple App Store and Google Play) have a complex and highly guarded algorithms for determining rankings for both keyword-based app store searches and composite top charts.

Unlike SEO, however, very little research and theory has been conducted around what goes into these rankings.

Until now, that is.

Over the course of five studies analyzing various publicly available data points for a cross-section of the top 500 iOS (U.S. Apple App Store) and the top 500 Android (U.S. Google Play) apps, I’ll attempt to set the record straight with a little myth-busting around ASO. In the process, I hope to assess and quantify any perceived correlations between app store ranks, ranking volatility, and a few of the factors commonly thought of as influential to an app’s ranking.

But first, a little context

Image credit: Josh Tuininga, Apptentive

Both the Apple App Store and Google Play have roughly 1.3 million apps each, and both stores feature a similar breakdown by app category. Apps ranking in the two stores should, theoretically, be on a fairly level playing field in terms of search volume and competition.

Of these apps, nearly two-thirds have not received a single rating and 99% are considered unprofitable. These studies, therefore, single out the rare exceptions to the rule—the top 500 ranked apps in each store.

While neither Apple nor Google have revealed specifics about how they calculate search rankings, it is generally accepted that both app store algorithms factor in:

  • Average app store rating
  • Rating/review volume
  • Download and install counts
  • Uninstalls (what retention and churn look like for the app)
  • App usage statistics (how engaged an app’s users are and how frequently they launch the app)
  • Growth trends weighted toward recency (how daily download counts changed over time and how today’s ratings compare to last week’s)
  • Keyword density of the app’s landing page (Ian did a great job covering this factor in a previous Moz post)

I’ve simplified this formula to a function highlighting the four elements with sufficient data (or at least proxy data) for our analysis:

Ranking = fn(Rating, Rating Count, Installs, Trends)

Of course, right now, this generalized function doesn’t say much. Over the next five studies, however, we’ll revisit this function before ultimately attempting to compare the weights of each of these four variables on app store rankings.

(For the purpose of brevity, I’ll stop here with the assumptions, but I’ve gone into far greater depth into how I’ve reached these conclusions in a 55-page report on app store rankings.)

Now, for the Mad Science.

Study #1: App-les to app-les app store ranking volatility

The first, and most straight forward of the five studies involves tracking daily movement in app store rankings across iOS and Android versions of the same apps to determine any trends of differences between ranking volatility in the two stores.

I went with a small sample of five apps for this study, the only criteria for which were that:

  • They were all apps I actively use (a criterion for coming up with the five apps but not one that influences rank in the U.S. app stores)
  • They were ranked in the top 500 (but not the top 25, as I assumed app store rankings would be stickier at the top—an assumption I’ll test in study #2)
  • They had an almost identical version of the app in both Google Play and the App Store, meaning they should (theoretically) rank similarly
  • They covered a spectrum of app categories

The apps I ultimately chose were Lyft, Venmo, Duolingo, Chase Mobile, and LinkedIn. These five apps represent the travel, finance, education banking, and social networking categories.

Hypothesis

Going into this analysis, I predicted slightly more volatility in Apple App Store rankings, based on two statistics:

Both of these assumptions will be tested in later analysis.

Results

7-Day App Store Ranking Volatility in the App Store and Google Play

Among these five apps, Google Play rankings were, indeed, significantly less volatile than App Store rankings. Among the 35 data points recorded, rankings within Google Play moved by as much as 23 positions/ranks per day while App Store rankings moved up to 89 positions/ranks. The standard deviation of ranking volatility in the App Store was, furthermore, 4.45 times greater than that of Google Play.

Of course, the same apps varied fairly dramatically in their rankings in the two app stores, so I then standardized the ranking volatility in terms of percent change to control for the effect of numeric rank on volatility. When cast in this light, App Store rankings changed by as much as 72% within a 24-hour period while Google Play rankings changed by no more than 9%.

Also of note, daily rankings tended to move in the same direction across the two app stores approximately two-thirds of the time, suggesting that the two stores, and their customers, may have more in common than we think.

Study #2: App store ranking volatility across the top charts

Testing the assumption implicit in standardizing the data in study No. 1, this one was designed to see if app store ranking volatility is correlated with an app’s current rank. The sample for this study consisted of the top 500 ranked apps in both Google Play and the App Store, with special attention given to those on both ends of the spectrum (ranks 1–100 and 401–500).

Hypothesis

I anticipated rankings to be more volatile the higher an app is ranked—meaning an app ranked No. 450 should be able to move more ranks in any given day than an app ranked No. 50. This hypothesis is based on the assumption that higher ranked apps have more installs, active users, and ratings, and that it would take a large margin to produce a noticeable shift in any of these factors.

Results

App Store Ranking Volatility of Top 500 Apps

One look at the chart above shows that apps in both stores have increasingly more volatile rankings (based on how many ranks they moved in the last 24 hours) the lower on the list they’re ranked.

This is particularly true when comparing either end of the spectrum—with a seemingly straight volatility line among Google Play’s Top 100 apps and very few blips within the App Store’s Top 100. Compare this section to the lower end, ranks 401–)500, where both stores experience much more turbulence in their rankings. Across the gamut, I found a 24% correlation between rank and ranking volatility in the Play Store and 28% correlation in the App Store.

To put this into perspective, the average app in Google Play’s 401–)500 ranks moved 12.1 ranks in the last 24 hours while the average app in the Top 100 moved a mere 1.4 ranks. For the App Store, these numbers were 64.28 and 11.26, making slightly lower-ranked apps more than five times as volatile as the highest ranked apps. (I say slightly as these “lower-ranked” apps are still ranked higher than 99.96% of all apps.)

The relationship between rank and volatility is pretty consistent across the App Store charts, while rank has a much greater impact on volatility at the lower end of Google Play charts (ranks 1-100 have a 35% correlation) than it does at the upper end (ranks 401-500 have a 1% correlation).

Study #3: App store rankings across the stars

The next study looks at the relationship between rank and star ratings to determine any trends that set the top chart apps apart from the rest and explore any ties to app store ranking volatility.

Hypothesis

Ranking = fn(Rating, Rating Count, Installs, Trends)

As discussed in the introduction, this study relates directly to one of the factors commonly accepted as influential to app store rankings: average rating.

Getting started, I hypothesized that higher ranks generally correspond to higher ratings, cementing the role of star ratings in the ranking algorithm.

As far as volatility goes, I did not anticipate average rating to play a role in app store ranking volatility, as I saw no reason for higher rated apps to be less volatile than lower rated apps, or vice versa. Instead, I believed volatility to be tied to rating volume (as we’ll explore in our last study).

Results

Average App Store Ratings of Top Apps

The chart above plots the top 100 ranked apps in either store with their average rating (both historic and current, for App Store apps). If it looks a little chaotic, it’s just one indicator of the complexity of ranking algorithm in Google Play and the App Store.

If our hypothesis was correct, we’d see a downward trend in ratings. We’d expect to see the No. 1 ranked app with a significantly higher rating than the No. 100 ranked app. Yet, in neither store is this the case. Instead, we get a seemingly random plot with no obvious trends that jump off the chart.

A closer examination, in tandem with what we already know about the app stores, reveals two other interesting points:

  1. The average star rating of the top 100 apps is significantly higher than that of the average app. Across the top charts, the average rating of a top 100 Android app was 4.319 and the average top iOS app was 3.935. These ratings are 0.32 and 0.27 points, respectively, above the average rating of all rated apps in either store. The averages across apps in the 401–)500 ranks approximately split the difference between the ratings of the top ranked apps and the ratings of the average app.
  2. The rating distribution of top apps in Google Play was considerably more compact than the distribution of top iOS apps. The standard deviation of ratings in the Apple App Store top chart was over 2.5 times greater than that of the Google Play top chart, likely meaning that ratings are more heavily weighted in Google Play’s algorithm.

App Store Ranking Volatility and Average Rating

Looking next at the relationship between ratings and app store ranking volatility reveals a -15% correlation that is consistent across both app stores; meaning the higher an app is rated, the less its rank it likely to move in a 24-hour period. The exception to this rule is the Apple App Store’s calculation of an app’s current rating, for which I did not find a statistically significant correlation.

Study #4: App store rankings across versions

This next study looks at the relationship between the age of an app’s current version, its rank and its ranking volatility.

Hypothesis

Ranking = fn(Rating, Rating Count, Installs, Trends)

In alteration of the above function, I’m using the age of a current app’s version as a proxy (albeit not a very good one) for trends in app store ratings and app quality over time.

Making the assumptions that (a) apps that are updated more frequently are of higher quality and (b) each new update inspires a new wave of installs and ratings, I’m hypothesizing that the older the age of an app’s current version, the lower it will be ranked and the less volatile its rank will be.

Results

How update frequency correlates with app store rank

The first and possibly most important finding is that apps across the top charts in both Google Play and the App Store are updated remarkably often as compared to the average app.

At the time of conducting the study, the current version of the average iOS app on the top chart was only 28 days old; the current version of the average Android app was 38 days old.

As hypothesized, the age of the current version is negatively correlated with the app’s rank, with a 13% correlation in Google Play and a 10% correlation in the App Store.

How update frequency correlates with app store ranking volatility

The next part of the study maps the age of the current app version to its app store ranking volatility, finding that recently updated Android apps have less volatile rankings (correlation: 8.7%) while recently updated iOS apps have more volatile rankings (correlation: -3%).

Study #5: App store rankings across monthly active users

In the final study, I wanted to examine the role of an app’s popularity on its ranking. In an ideal world, popularity would be measured by an app’s monthly active users (MAUs), but since few mobile app developers have released this information, I’ve settled for two publicly available proxies: Rating Count and Installs.

Hypothesis

Ranking = fn(Rating, Rating Count, Installs, Trends)

For the same reasons indicated in the second study, I anticipated that more popular apps (e.g., apps with more ratings and more installs) would be higher ranked and less volatile in rank. This, again, takes into consideration that it takes more of a shift to produce a noticeable impact in average rating or any of the other commonly accepted influencers of an app’s ranking.

Results

Apps with more ratings and reviews typically rank higher

The first finding leaps straight off of the chart above: Android apps have been rated more times than iOS apps, 15.8x more, in fact.

The average app in Google Play’s Top 100 had a whopping 3.1 million ratings while the average app in the Apple App Store’s Top 100 had 196,000 ratings. In contrast, apps in the 401–)500 ranks (still tremendously successful apps in the 99.96 percentile of all apps) tended to have between one-tenth (Android) and one-fifth (iOS) of the ratings count as that of those apps in the top 100 ranks.

Considering that almost two-thirds of apps don’t have a single rating, reaching rating counts this high is a huge feat, and a very strong indicator of the influence of rating count in the app store ranking algorithms.

To even out the playing field a bit and help us visualize any correlation between ratings and rankings (and to give more credit to the still-staggering 196k ratings for the average top ranked iOS app), I’ve applied a logarithmic scale to the chart above:

The relationship between app store ratings and rankings in the top 100 apps

From this chart, we can see a correlation between ratings and rankings, such that apps with more ratings tend to rank higher. This equates to a 29% correlation in the App Store and a 40% correlation in Google Play.

Apps with more ratings typically experience less app store ranking volatility

Next up, I looked at how ratings count influenced app store ranking volatility, finding that apps with more ratings had less volatile rankings in the Apple App Store (correlation: 17%). No conclusive evidence was found within the Top 100 Google Play apps.

Apps with more installs and active users tend to rank higher in the app stores

And last but not least, I looked at install counts as an additional proxy for MAUs. (Sadly, this is a statistic only listed in Google Play. so any resulting conclusions are applicable only to Android apps.)

Among the top 100 Android apps, this last study found that installs were heavily correlated with ranks (correlation: -35.5%), meaning that apps with more installs are likely to rank higher in Google Play. Android apps with more installs also tended to have less volatile app store rankings, with a correlation of -16.5%.

Unfortunately, these numbers are slightly skewed as Google Play only provides install counts in broad ranges (e.g., 500k–)1M). For each app, I took the low end of the range, meaning we can likely expect the correlation to be a little stronger since the low end was further away from the midpoint for apps with more installs.

Summary

To make a long post ever so slightly shorter, here are the nuts and bolts unearthed in these five mad science studies in app store optimization:

  1. Across the top charts, Apple App Store rankings are 4.45x more volatile than those of Google Play
  2. Rankings become increasingly volatile the lower an app is ranked. This is particularly true across the Apple App Store’s top charts.
  3. In both stores, higher ranked apps tend to have an app store ratings count that far exceeds that of the average app.
  4. Ratings appear to matter more to the Google Play algorithm, especially as the Apple App Store top charts experience a much wider ratings distribution than that of Google Play’s top charts.
  5. The higher an app is rated, the less volatile its rankings are.
  6. The 100 highest ranked apps in either store are updated much more frequently than the average app, and apps with older current versions are correlated with lower ratings.
  7. An app’s update frequency is negatively correlated with Google Play’s ranking volatility but positively correlated with ranking volatility in the App Store. This likely due to how Apple weighs an app’s most recent ratings and reviews.
  8. The highest ranked Google Play apps receive, on average, 15.8x more ratings than the highest ranked App Store apps.
  9. In both stores, apps that fall under the 401–500 ranks receive, on average, 10–20% of the rating volume seen by apps in the top 100.
  10. Rating volume and, by extension, installs or MAUs, is perhaps the best indicator of ranks, with a 29–40% correlation between the two.

Revisiting our first (albeit oversimplified) guess at the app stores’ ranking algorithm gives us this loosely defined function:

Ranking = fn(Rating, Rating Count, Installs, Trends)

I’d now re-write the function into a formula by weighing each of these four factors, where a, b, c, & d are unknown multipliers, or weights:

Ranking = (Rating * a) + (Rating Count * b) + (Installs * c) + (Trends * d)

These five studies on ASO shed a little more light on these multipliers, showing Rating Count to have the strongest correlation with rank, followed closely by Installs, in either app store.

It’s with the other two factors—rating and trends—that the two stores show the greatest discrepancy. I’d hazard a guess to say that the App Store prioritizes growth trends over ratings, given the importance it places on an app’s current version and the wide distribution of ratings across the top charts. Google Play, on the other hand, seems to favor ratings, with an unwritten rule that apps just about have to have at least four stars to make the top 100 ranks.

Thus, we conclude our mad science with this final glimpse into what it takes to make the top charts in either store:

Weight of factors in the Apple App Store ranking algorithm

Rating Count > Installs > Trends > Rating

Weight of factors in the Google Play ranking algorithm

Rating Count > Installs > Rating > Trends


Again, we’re oversimplifying for the sake of keeping this post to a mere 3,000 words, but additional factors including keyword density and in-app engagement statistics continue to be strong indicators of ranks. They simply lie outside the scope of these studies.

I hope you found this deep-dive both helpful and interesting. Moving forward, I also hope to see ASOs conducting the same experiments that have brought SEO to the center stage, and encourage you to enhance or refute these findings with your own ASO mad science experiments.

Please share your thoughts in the comments below, and let’s deconstruct the ranking formula together, one experiment at a time.

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Reblogged 3 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 3 years ago from tracking.feedpress.it

Grow Your Own SEOs: Professional Development for Digital Marketers

Posted by RuthBurrReedy

Finding your next SEO hire is hard, but it’s only half the battle. Growing a team isn’t just about hiring—it’s about making your whole team, newbies and experts alike, better marketers.

It’s almost impossible to build a one-size-fits-all training program for digital marketers, since the tasks involved will depend a lot on the role. Even “SEO” can mean a lot of different things. Your role might be highly technical, highly creative, or a mix of both. Tactics like local SEO or conversion rate optimization might be a huge part of an SEO’s job or might be handled by another person entirely. Sometimes an SEO role includes elements like social media or paid search. The skills you teach your trainees will depend on what you need them to do, and more specifically, what you need them to do right now.

Whatever the specifics of the marketing role,
you need to make sure you’re providing a growth plan for your digital marketers (this goes for your more experienced team members, as well as your newbies). A professional growth plan helps you and your team members:

  • Track whether or not they’re making progress in their roles. Taking on a new skill set can be daunting. Having a growth plan can alleviate some of the stress less-experienced employees may feel when learning a new skill, and makes sure more experienced employees aren’t stagnating. 
  • Spot problem areas. Everyone’s talents are different, but you don’t want someone to miss out on growth opportunities because they’re such a superstar in one area and are neglecting everything else. 
  • Have conversations around promotions and raises. Consistently tracking people’s development across a variety of skill sets allows you to compare where someone is now to where they were when you hired them; it also gives you a framework to discuss what additional steps might be needed before a promotion or raise is in order, and help them develop a plan to get there. 
  • Advance their careers. One of your duties as their manager is to make sure you’re giving them what they need to continue on their career path. A professional development plan should be managed with career goals in mind. 
  • Increase employee retention. Smart people like to learn and grow, and if you’re not providing them ways to do so, they’re not going to stick around.

We have technical/on-page SEOs, content marketers, local SEOs and marketing copywriters all working together on the same team at BigWing. We wanted to create a framework for professional development that we could apply to the whole team, so we identified a set of areas that any digital marketer should be growing in, regardless of their focus. This growth plan is part of everyone’s mid-year and year-end reviews.

Here’s what it looks like:

Growth areas for digital marketers

Want your own copy of the Professional Advancement Sheet? Get it here!

Tactical -> strategic

At the beginner level, team members are still learning the basic concepts and tasks associated with their role, and how those translate to the client metrics they’re being measured on. It takes time to encounter and fix enough different kinds of things to know “in x situation, look at a, b and c and then try y or z.”

As someone grows in their role, they will learn more advanced tactics. They should also be more and more able to use critical thinking to figure out how to solve problems and tackle longer-term client goals and projects.
At the senior level, an SEO should be building long-term strategies and be comfortable with unusual campaigns and one-off projects.

Small clients -> big clients

There are plenty of small brochure websites in the world, and these sites are a great testing ground for the fundamentals of SEO: they may still have weird jacked-up problems (so many websites do), but they are a manageable size and don’t usually have the potential for esoteric technical issues that large, complex sites do. Once someone has a handle on SEO, you can start assigning bigger and badder sites and projects (with plenty of mentoring from more experienced team members—more on that later).

We thought about making this one “Easy clients -> difficult clients,” because there’s another dimension to this line of progress: increasingly complex client relationships. Clients with very large or complicated websites (or clients with more than one website) are likely to have higher budgets, bigger internal staff, and more stakeholders. As the number of people involved increases, so does the potential for friction, so a senior-level SEO should be able to handle those complex relationships with aplomb.

Learning -> teaching

At the beginner level, people are learning digital marketing in general and learning about our specific internal processes. As they gain experience, they become a resource for team members still in the “learning” phase, and at the senior level they should be a go-to for tough questions and expert opinions.

Even a beginner digital marketer may have other things to teach the team; skills learned from previous careers, hobbies or side gigs can be valuable additions. For example, we had a brand-new team member with a lot of experience in photography, a valuable skill for content marketers; she was able to start teaching her teammates more about taking good photos while still learning other content marketing fundamentals herself.

learning

I love this stock picture because the chalkboard just says “learning.” Photo via
Pixabay.

Since managers can’t be everywhere at once, more experienced employees must take an active role in teaching.
It’s not enough that they be experts (which is why this scale doesn’t go from “Learning” to “Mastering”); they have to be able to impart that expertise to others. Teaching is more than just being available when people have questions, too: senior team members are expected to be proactive about taking the time to show junior team members the ropes.

Prescribed -> creative

The ability to move from executing a set series of tasks to creating creative, heavily client-focused digital marketing campaigns is, in my opinion,
one of the best predictors of long-term SEO success. When someone is just starting out in SEO, it’s appropriate to have a fairly standard set of tasks they’re carrying out. For a lot of those small sites that SEO trainees start on, that set of SEO fundamentals goes a long way. The challenge comes when the basics aren’t enough.

Creative SEO comes from being able to look at a client’s business, not just their website, and tailor a strategy to their specific needs. Creative SEOs are looking for unique solutions to the unique problems that arise from that particular client’s combination of business model, target market, history and revenue goals. Creativity can also be put to work internally, in the form of suggested process improvements and new revenue-driving projects.

General -> T-shaped

The concept of the T-shaped marketer has been around for a few years (if you’re not familiar with the idea, you can read up on it on
Rand’s blog or the Distilled blog). Basically, it means that in addition to deep knowledge whatever area(s) of inbound marketing we specialize in, digital marketers should also work to develop basic knowledge of a broad set of marketing disciplines, in order to understand more about the craft of marketing as a whole.

t-shaped marketer

Source:
The T-Shaped Marketer

A digital marketer who’s just starting out will naturally be focusing more on the broad part of their T, getting their head around the basic concepts and techniques that make up the digital marketing skill set. Eventually most people naturally find a few specialty areas that they’re really passionate about. Encouraging employees to build deep expertise ultimately results in a whole team full of subject matter experts in a whole team’s worth of subjects.

Beginner -> expert

This one is pretty self-explanatory. The important thing to note is that expertise isn’t something that just happens to you after you do something a lot (although that’s definitely part of it).
Honing expertise means actively pursuing new learning opportunities and testing new ideas and tactics, and we look for the pursuit of expertise as part of evaluating someone’s professional growth.

Observing -> leading

Anyone who is working in inbound marketing should be consistently observing the industry—they should be following search engine news, reading blog posts from industry experts, and attending events and webinars to learn more about their craft. It’s a must-do at all levels, and even someone who’s still learning the ropes can be keeping an eye on industry buzz and sharing items of interest with their co-workers.

Not everyone is crazy about the phrase “thought leadership.” When you’re a digital marketing agency, though,
your people are your product—their depth of knowledge and quality of work is a big part of what you’re selling. As your team gains experience and confidence, it’s appropriate to expect them to start participating more in the digital marketing space, both online and in person. This participation could look like: 

  • Pitching and speaking at marketing conferences 
  • Contributing to blogs, whether on your site or in other marketing communities 
  • Organizing local tech meetups 
  • Regularly participating in online events like #seochat

…or a variety of other activities, depending on the individual’s talents and interests. Not only does this kind of thought-leadership activity promote your agency brand, it also helps your employees build their personal brands—and don’t forget, a professional development plan needs to be as much about helping your people grow in their careers as it is about growing the skill sets you need.

Low output -> high output

I love the idea of meticulous, hand-crafted SEO, but let’s be real: life at an agency means getting stuff done. When people are learning to do stuff, it takes them longer to do (which is BY FAR MY LEAST FAVORITE PART OF LEARNING TO DO THINGS, I HATE IT SO MUCH), so expectations of the number of clients/volume of work they can handle should scale appropriately. It’s okay for people to work at their own pace and in their own way, but at some point you need to be able to rely on your team to turn things around quickly, handle urgent requests, and consistently hit deadlines, or you’re going to lose customers.

You may notice that some of these growth areas overlap, and that’s okay—the idea is to create a nuanced approach that captures all the different ways a digital marketer can move toward excellence.

Like with all other aspects of a performance review, it’s important to be as specific as possible when discussing a professional growth plan. If there’s an area a member of your team needs to make more progress in, don’t just say e.g. “You need to be more strategic.” Come up with specific projects and milestones for your marketer to hit so you’re both clear on when they’re growing and what they need to do to get to the next level.

Sign up for The Moz Top 10, a semimonthly mailer updating you on the top ten hottest pieces of SEO news, tips, and rad links uncovered by the Moz team. Think of it as your exclusive digest of stuff you don’t have time to hunt down but want to read!

Reblogged 3 years ago from tracking.feedpress.it

WordPress London – WordPress Site Structure for SEO – 17 November 2011

London WordPress meetup on 17 November 2011 hosted by Headshift | Dachis Group. This talk about SEO in web sites, including specifics about WordPress was giv…

Reblogged 3 years ago from www.youtube.com

The Future of Link Building

Posted by Paddy_Moogan

Building the types of links that help grow your online business and organic search traffic is getting harder. It used to be fairly straightforward, back before Google worked out how to treat links with different levels of quality and trust. However, the fact that it’s getting harder doesn’t mean that it’s dead.

What does the future hold?

I’m going to talk about links, but the truth is, the future isn’t really about the links. It is far bigger than that.

Quick sidenote: I’m aware that doing a blog post about the future of link building the week of a likely Penguin update could leave me with egg on my face! But we’ll see what happens.

Links will always be a ranking factor in some form or another. I can see the dials being turned down or off on certain aspects of links (more on that below) but I think they will always be there. Google is always looking for more data, more signals, more indicators of whether or not a certain page is a good result for a user at a certain moment in time. They will find them too, as we can see from
patents such as this. A natural consequence is that other signals may be diluted or even replaced as Google becomes smarter and understands the web and users a lot better.

What this means for the future is that the links valued by Google will be the ones you get as a result of having a great product and great marketing. Essentially, links will be symptomatic of amazing marketing. Hat tip to
Jess Champion who I’ve borrowed this term from.

This isn’t easy, but it shouldn’t be. That’s the point.

To go a bit further, I think we also need to think about the bigger picture. In the grand scheme of things, there are so many more signals that Google can use which, as marketers, we need to understand and use to our advantage. Google is changing and we can’t bury our heads in the sand and ignore what is going on.

A quick side note on spammy links

My background is a spammy one so I can’t help but address this quickly. Spam will continue to work for short-term hits and churn and burn websites. I’ve talked before about 
my position on this so I won’t go into too much more detail here. I will say though that those people who are in the top 1% of spammers will continue to make money, but even for them, it will be hard to maintain over a long period of time.

Let’s move onto some more of the detail around my view of the future by first looking at the past and present.

What we’ve seen in the past

Google didn’t understand links.

The fundamental issue that Google had for a long, long time was that they didn’t understand enough about links. They didn’t understand things such as:

  • How much to trust a link
  • Whether a link was truly editorially given or not
  • Whether a link was paid for or not
  • If a link was genuinely high quality (PageRank isn’t perfect)
  • How relevant a link was

Whilst they still have work to do on all of these, they have gotten much better in recent years. At one time, a link was a link and it was pretty much a case of whoever had the most links, won. I think that for a long time, Google was trying very hard to understand links and find which ones were high quality, but there was so much noise that it was very difficult. I think that eventually they realised that they had to attack the problem from a different angle and 
Penguin came along. So instead of focusing on finding the “good” signals of links, they focused on finding the “bad” signals and started to take action on them. This didn’t fix everything, but it did enough to shock our industry into moving away from certain tactics and therefore, has probably helped reduce a lot of the noise that Google was seeing.

What we’re seeing right now

Google is understanding more about language.

Google is getting better at understanding everything.
Hummingbird was just the start of what Google hopes to achieve on this front and it stands to reason that the same kind of technology that helps the following query work, will also help Google understand links better.

Not many people in the search industry said much when
Google hired this guy back in 2012. We can be pretty sure that it’s partly down to his work that we’re seeing the type of understanding of language that we are. His work has only just begun, though, and I think we’ll see more queries like the one above that just shouldn’t work, but they do. I also think we’ll see more instances of Googlers not knowing why something ranks where it does.

Google is understanding more about people.

I talk about this a little more below but to quickly summarise here, Google is learning more about us all the time. It can seem creepy, but the fact is that Google wants as much data as possible from us so that they can serve more relevant search results—and advertising of course. They are understanding more that the keywords we type into Google may not actually be what we want to find, nor are those keywords enough to find what we really want. Google needs more context.

Tom Anthony has
talked about this extensively so I won’t go into loads more detail. But to bring it back to link building, it is important to be aware of this because it means that there are more and more signals that could mean the dial on links gets turned down a bit more.

Some predictions about the future

I want to make a few things more concrete about my view of the future for link building, so let’s look at a few specifics.

1. Anchor text will matter less and less

Anchor text as a ranking signal was always something that works well in theory but not in reality. Even in my early days of link building, I couldn’t understand why Google put so much weight behind this one signal. My main reason for this view was that using exact match keywords in a link was not natural for most webmasters. I’d go as far as to say the only people who used it were SEOs!

I’m don’t think we’re at a point yet where anchor text as a ranking signal is dead and it will take some more time for Google to turn down the dial. But we definitely are at a point where you can get hurt pretty badly if you have too much commercial anchor text in your link profile. It just isn’t natural.

In the future, Google won’t need this signal. They will be much better at understanding the content of a page and importantly, the context of a page.

2. Deep linking will matter less and less

I was on the fence about this one for a long time but the more I think about it, the more I can see this happening. I’ll explain my view here by using an example.

Let’s imagine you’re an eCommerce website and you sell laptops. Obviously each laptop you sell will have its own product page and if you sell different types, you’ll probably have category pages too. With a products like laptops, chances are that other retailers sell the same ones with the same specifications and probably have very similar looking pages to yours. How does Google know which one to rank better than others?

Links to these product pages can work fine but in my opinion, is a bit of a crude way of working it out. I think that Google will get better at understanding the subtle differences in queries from users which will naturally mean that deep links to these laptop pages will be one of many signals they can use.

Take these queries:


“laptop reviews”

Context: I want to buy a laptop but I don’t know which one.


“asus laptop reviews”

Context: I like the sound of Asus, I want to read more about their laptops.


“sony laptop reviews”

Context: I also like the sound of Sony, I want to read more about their laptops.


“sony vs asus laptop”

Context: I’m confused, they both sound the same so I want a direct comparison to help me decide.


“asus laptop”

Context: I want an Asus laptop.

You can see how the mindset of the user has changed over time and we can easily imagine how the search results will have changed to reflect this. Google already understand this. There are other signals coming into play here too though, what about these bits of additional information that Google can gather about us:

  • Location: I’m on a bus in London, I may not want to buy a £1,000 laptop right now but I’ll happily research them.
  • Device: I’m on my iPhone 6, I may not want to input credit card details into it and I worry that the website I’m using won’t work well on a small screen.
  • Search history: I’ve searched for laptops before and visited several retailers, but I keep going back to the same one as I’ve ordered from them before.

These are just a few that are easy for us to imagine Google using. There are loads more that Google could look at, not to mention signals from the retailers themselves such as secure websites, user feedback, 3rd party reviews, trust signals etc.

When you start adding all of these signals together, it’s pretty easy to see why links to a specific product page may not be the strongest signal for Google to use when determining rankings.

Smaller companies will be able to compete more.

One of the things I loved about SEO when I first got into it was the fact that organic search felt like a level playing field. I knew that with the right work, I could beat massive companies in the search results and not have to spend a fortune doing it. Suffice to say, things have changed quite a bit now and there are some industries where you stand pretty much zero chance of competing unless you have a very big budget to spend and a great product.

I think we will see a shift back in the other direction and smaller companies with fewer links will be able to rank for certain types of queries with a certain type of context. As explained above, context is key and allows Google to serve up search results that meet the context of the user. This means that massive brands are not always going to be the right answer for users and Google have to get better at understanding this. Whether a company is classified as a “brand” or not can be subjective. My local craft beer shop in London is the only one in the world and if you were to ask 100 people if they’d heard of it, they’d all probably say no. But it’s a brand to me because I love their products, their staff are knowledgeable and helpful, their marketing is cool and I’d always recommend them.

Sometimes, showing the website of this shop above bigger brands in search results is the right thing to do for a user. Google need lots of additional signals beyond “branding” and links in order to do this but I think they will get them.

What all of this means for us

Predicting the future is hard, knowing what to do about it is pretty hard too! But here are some things that I think we should be doing.

  1. Ask really hard questions
    Marketing is hard. If you or your client wants to compete and win customers, then you need to be prepared to ask really hard questions about the company. Here are just a few that I’ve found difficult when talking to clients:

    • Why does the company exist? (A good answer has nothing to do with making money)
    • Why do you deserve to rank well in Google?
    • What makes you different to your competitors?
    • If you disappeared from Google tomorrow, would anyone notice?
    • Why do you deserve to be linked to?
    • What value do you provide for users?

    The answers to these won’t always give you that silver bullet, but they can provoke conversations that make the client look inwardly and at why they should deserve links and customers. These questions are hard to answer, but again, that’s the point.

  2. Stop looking for scalable link building tactics

    Seriously, just stop. Anything that can be scaled tends to lose quality and anything that scales is likely to be targeted by the Google webspam team at some point. A
    recent piece of content we did at Distilled has so far generated links from over 700 root domains—we did NOT send 700 outreach emails! This piece took on a life of its own and generated those links after some promotion by us, but at no point did we worry about scaling outreach for it.

  3. Start focusing on doing marketing that users love

    I’m not talking necessarily about you doing the next
    Volvo ad or to be the next Old Spice guy. If you can then great, but these are out of reach for most of us.That doesn’t mean you can’t do marketing that people love. I often look at companies like Brewdog and Hawksmoor who do great marketing around their products but in a way that has personality and appeal. They don’t have to spend millions of dollars on celebrities or TV advertising because they have a great product and a fun marketing message. They have value to add which is the key, they don’t need to worry about link building because they get them naturally by doing cool stuff.

    Whilst I know that “doing cool stuff” isn’t particularly actionable, I still think it’s fair to say that marketing needs to be loved. In order to do marketing that people love, you need to have some fun and focus on adding value.

  4. Don’t bury your head in the sand

    The worst thing you can do is ignore the trends and changes taking place. Google is changing, user expectations and behaviours are changing, our industry is changing. As an industry, we’ve adapted very well over the last few years. We have to keep doing this if we’re going to survive.

    Going back to link building, you need to accept that this stuff is really hard and building the types of links that Google value is hard.

In summary

Links aren’t going anywhere. But the world is changing and we have to focus on what truly matters: marketing great products and building a loyal audience. 

Sign up for The Moz Top 10, a semimonthly mailer updating you on the top ten hottest pieces of SEO news, tips, and rad links uncovered by the Moz team. Think of it as your exclusive digest of stuff you don’t have time to hunt down but want to read!

Reblogged 4 years ago from feedproxy.google.com

Your Google Algorithm Cheat Sheet: Panda, Penguin, and Hummingbird

Posted by MarieHaynes

If you’re reading the Moz blog, then you probably have a decent understanding of Google and its algorithm changes. However, there is probably a good percentage of the Moz audience that is still confused about the effects that Panda, Penguin, and Hummingbird can have on your site. I did write a post last year about the main 
differences between Penguin and a Manual Unnautral Links Penalty, and if you haven’t read that, it’ll give you a good primer.

The point of this article is to explain very simply what each of these algorithms are meant to do. It is hopefully a good reference that you can point your clients to if you want to explain an algorithm change and not overwhelm them with technical details about 301s, canonicals, crawl errors, and other confusing SEO terminologies.

What is an algorithm change?

First of all, let’s start by discussing the Google algorithm. It’s immensely complicated and continues to get more complicated as Google tries its best to provide searchers with the information that they need. When search engines were first created, early search marketers were able to easily find ways to make the search engine think that their client’s site was the one that should rank well. In some cases it was as simple as putting in some code on the website called a meta keywords tag. The meta keywords tag would tell search engines what the page was about.

As Google evolved, its engineers, who were primarily focused on making the search engine results as relevant to users as possible, continued to work on ways to stop people from cheating, and looked at other ways to show the most relevant pages at the top of their searches. The algorithm now looks at hundreds of different factors. There are some that we know are significant such as having a good descriptive title (between the <title></title> tags in the code.) And there are many that are the subject of speculation such as 
whether or not Google +1’s contribute to a site’s rankings.

In the past, the Google algorithm would change very infrequently. If your site was sitting at #1 for a certain keyword, it was guaranteed to stay there until the next update which might not happen for weeks or months. Then, they would push out another update and things would change. They would stay that way until the next update happened. If you’re interested in reading about how Google used to push updates out of its index, you may find this 
Webmaster World forum thread from 2002 interesting. (Many thanks to Paul Macnamara  for explaining to me how algo changes used to work on Google in the past and pointing me to the Webmaster World thread.)

This all changed with launch of “Caffeine” in 2010. Since Caffeine launched, the search engine results have been changing several times a day rather than every few weeks. Google makes over 600 changes to its algorithm in a year, and the vast majority of these are not announced. But, when Google makes a really big change, they give it a name, usually make an announcement, and everyone in the SEO world goes crazy trying to figure out how to understand the changes and use them to their advantage.

Three of the biggest changes that have happened in the last few years are the Panda algorithm, the Penguin algorithm and Hummingbird.

What is the Panda algorithm?

Panda first launched on February 23, 2011. It was a big deal. The purpose of Panda was to try to show high-quality sites higher in search results and demote sites that may be of lower quality. This algorithm change was unnamed when it first came out, and many of us called it the “Farmer” update as it seemed to affect content farms. (Content farms are sites that aggregate information from many sources, often stealing that information from other sites, in order to create large numbers of pages with the sole purpose of ranking well in Google for many different keywords.) However, it affected a very large number of sites. The algorithm change was eventually officially named after one of its creators, Navneet Panda.

When Panda first happened, a lot of SEOs in forums thought that this algorithm was targeting sites with unnatural backlink patterns. However, it turns out that links are most likely
not a part of the Panda algorithm. It is all about on-site quality.

In most cases, sites that were affected by Panda were hit quite hard. But, I have also seen sites that have taken a slight loss on the date of a Panda update. Panda tends to be a site-wide issue which means that it doesn’t just demote certain pages of your site in the search engine results, but instead, Google considers the entire site to be of lower quality. In some cases though Panda can affect just a section of a site such as a news blog or one particular subdomain.

Whenever a Google employee is asked about what needs to be done to recover from Panda, they refer to a 
blog post by Google Employee Amit Singhal that gives a checklist that you can use on your site to determine if your site really is high quality or not. Here is the list:

  • Would you trust the information presented in this article?
  • Is this article written by an expert or enthusiast who knows the topic well, or is it more shallow in nature?
  • Does the site have duplicate, overlapping, or redundant articles on the same or similar topics with slightly different keyword variations?
  • Would you be comfortable giving your credit card information to this site?
  • Does this article have spelling, stylistic, or factual errors?
  • Are the topics driven by genuine interests of readers of the site, or does the site generate content by attempting to guess what might rank well in search engines?
  • Does the article provide original content or information, original reporting, original research, or original analysis?
  • Does the page provide substantial value when compared to other pages in search results?
  • How much quality control is done on content?
  • Does the article describe both sides of a story?
  • Is the site a recognized authority on its topic?
  • Is the content mass-produced by or outsourced to a large number of creators, or spread across a large network of sites, so that individual pages or sites don’t get as much attention or care?
  • Was the article edited well, or does it appear sloppy or hastily produced?
  • For a health related query, would you trust information from this site?
  • Would you recognize this site as an authoritative source when mentioned by name?
  • Does this article provide a complete or comprehensive description of the topic?
  • Does this article contain insightful analysis or interesting information that is beyond obvious?
  • Is this the sort of page you’d want to bookmark, share with a friend, or recommend?
  • Does this article have an excessive amount of ads that distract from or interfere with the main content?
  • Would you expect to see this article in a printed magazine, encyclopedia or book?
  • Are the articles short, unsubstantial, or otherwise lacking in helpful specifics?
  • Are the pages produced with great care and attention to detail vs. less attention to detail?
  • Would users complain when they see pages from this site?

Phew! That list is pretty overwhelming! These questions do not necessarily mean that Google tries to algorithmically figure out whether your articles are interesting or whether you have told both sides of a story. Rather, the questions are there because all of these factors can contribute to how real-life users would rate the quality of your site. No one really knows all of the factors that Google uses in determining the quality of your site through the eyes of Panda. Ultimately though, the focus is on creating the best site possible for your users.  It is also important that only your best stuff is given to Google to have in its index. There are a few factors that are widely accepted as important things to look at in regards to Panda:

Thin content

A “thin” page is a page that adds little or no value to someone who is reading it. It doesn’t necessarily mean that a page has to be a certain number of words, but quite often, pages with very few words are not super-helpful. If you have a large number of pages on your site that contain just one or two sentences and those pages are all included in the Google index, then the Panda algorithm may determine that the majority of your indexed pages are of low quality.

Having the odd thin page is not going to cause you to run in to Panda problems. But, if a big enough portion of your site contains pages that are not helpful to users, then that is not good.

Duplicate content

There are several ways that duplicate content can cause your site to be viewed as a low-quality site by the Panda algorithm. The first is when a site has a large amount of content that is copied from other sources on the web. Let’s say that you have a blog on your site and you populate that blog with articles that are taken from other sources. Google is pretty good at figuring out that you are not the creator of this content. If the algorithm can see that a large portion of your site is made up of content that exists on other sites then this can cause Panda to look at you unfavorably.

You can also run into problems with duplicated content on your own site. One example would be for a site that has a large number of products for sale. Perhaps each product has a separate page for each color variation and size. But, all of these pages are essentially the same. If one product comes in 20 different colors and each of those come in 6 different sizes, then that means that you have 120 pages for the same product, all of which are almost identical. Now, imagine that you sell 4,000 products. This means that you’ve got almost half a million pages in the Google index when really 4,000 pages would suffice. In this type of situation, the fix for this problem is to use something called a canonical tag. Moz has got a really good guide on using canonical tags 
here, and Dr. Pete has also written this great article on canonical tag use

Low-quality content

When I write an article and publish it on one of my websites, the only type of information that I want to present to Google is information that is the absolute best of its kind. In the past, many SEOs have given advice to site owners saying that it was important to blog every day and make sure that you are always adding content for Google to index. But, if what you are producing is not high quality content, then you could be doing more harm than good. A lot of Amit Singhal’s questions listed above are asking whether the content on your site is valuable to readers. Let’s say that I have an SEO blog and every day I take a short blurb from each of the interesting SEO articles that I have read online and publish it as a blog post on my site. Is Google going to want to show searchers my summary of these articles, or would they rather show them the actual articles? Of course my summary is not going to be as valuable as the real thing! Now, let’s say that I have done this every day for 4 years. Now my site has over 4,000 pages that contain information that is not unique and not as valuable as other sites on the same topics.

Here is another example. Let’s say that I am a plumber. I’ve been told that I should blog regularly, so several times a week I write a 2-3 paragraph article on things like, “How to fix a leaky faucet” or “How to unclog a toilet.” But, I’m busy and don’t have much time to put into my website so each article I’ve written contains keywords in the title and a few times in the content, but the content is not in depth and is not that helpful to readers. If the majority of the pages on my site contain information that no one is engaging with, then this can be a sign of low quality in the eyes of the Panda algorithm.

There are other factors that probably play a roll in the Panda algorithm.  Glenn Gabe recently wrote an 
excellent article on his evaluation of sites affected by the most recent Panda update.  His bullet point list of things to improve upon when affected by Panda is extremely thorough.

How to recover from a Panda hit

Google refreshes the Panda algorithm approximately monthly. They used to announce whenever they were refreshing the algorithm, but now they only do this if there is a really big change to the Panda algorithm. What happens when the Panda algorithm refreshes is that Google takes a new look at each site on the web and determines whether or not it looks like a quality site in regards to the criteria that the Panda algorithm looks at. If your site was adversely affected by Panda and you have made changes such as removing thin and duplicate content then, when Panda refreshes, you should see that things improve. However, for some sites it can take a couple of Panda refreshes to see the full extent of the improvements. This is because it can sometimes take several months for Google to revisit all of your pages and recognize the changes that you have made.

Every now and then, instead of just
refreshing the algorithm, Google does what they call an update. When an update happens, this means that Google has changed the criteria that they use to determine what is and isn’t considered high quality. On May 20, 2014, Google did a major update which they called Panda 4.0. This caused a lot of sites to see significant changes in regards to Panda:

Not all Panda recoveries are as dramatic as this one. But, if you have been affected by Panda and you work hard to make changes to your site, you really should see some improvement.

What is the Penguin algorithm?

Penguin

The Penguin algorithm initially rolled out on April 24, 2012. The goal of Penguin is to reduce the trust that Google has in sites that have cheated by creating unnatural backlinks in order to gain an advantage in the Google results. While the primary focus of Penguin is on unnatural links, there can be other 
factors that can affect a site in the eyes of Penguin as well. Links, though, are known to be by far the most important thing to look at.

Why are links important?

A link is like a vote for your site. If a well respected site links to your site, then this is a recommendation for your site. If a small, unknown site links to you then this vote is not going to count for as much as a vote from an authoritative site. Still, if you can get a large number of these small votes, they really can make a difference. This is why, in the past, SEOs would try to get as many links as they could from any possible source.

Another thing that is important in the Google algorithms is anchor text. Anchor text is the text that is underlined in a link. So, in this link to a great 
SEO blog, the anchor text would be “SEO blog.” If Moz.com gets a number of sites linking to them using the anchor text “SEO blog,” that is a hint to Google that people searching for “SEO blog” probably want to see sites like Moz in their search results.

It’s not hard to see how people could manipulate this part of the algorithm. Let’s say that I am doing SEO for a landscaping company in Orlando. In the past, one of the ways that I could cheat the algorithm into thinking that my company should be ranked highly would be to create a bunch of self made links and use anchor text in these links that contain phrases like
Orlando Landscaping Company, Landscapers in Orlando and Orlando Landscaping. While an authoritative link from a well respected site is good, what people discovered is that creating a large number of links from low quality sites was quite effective. As such, what SEOs would do is create links from easy to get places like directory listings, self made articles, and links in comments and forum posts.

While we don’t know exactly what factors the Penguin algorithm looks at, what we do know is that this type of low quality, self made link is what the algorithm is trying to detect. In my mind, the Penguin algorithm is sort of like Google putting a “trust factor” on your links. I used to tell people that Penguin could affect a site on a page or even a keyword level, but Google employee John Mueller has said several times now that Penguin is a sitewide algorithm. This means that if the Penguin algorithm determines that a large number of the links to your site are untrustworthy, then this reduces Google’s trust in your entire site. As such, the whole site will see a reduction in rankings.  

While Penguin affected a lot of sites drastically, I have seen many sites that saw a small reduction in rankings.  The difference, of course, depends on the amount of link manipulation that has been done.

How to recover from a Penguin hit?

Penguin is a filter just like Panda. What that means, is that the algorithm is re-run periodically and sites are re-evaluated with each re-run. At this point it is not run very often at all. The last update was October 4, 2013 which means that we have currently been waiting eight months for a new Penguin update. In order to recover from Penguin, you need to identify the unnatural links pointing to your site and either remove them, or if you can’t remove them you can ask Google to no longer count them by using the 
disavow tool. Then, the next time that Penguin refreshes or updates, if you have done a good enough job at cleaning up your unnatural links, you will once again regain trust in Google’s eyes.  In some cases, it can take a couple of refreshes in order for a site to completely escape Penguin because it can take up to 6 months for all of a site’s disavow file to be completely processed.

If you are not certain how to identify which links to your site are unnatural, here are some good resources for you:

The disavow tool is something that you probably should only be using if you really understand how it works. It is potentially possible for you to do more harm than good to your site if you disavow the wrong links. Here is some information on using the disavow tool:

It’s important to note that when sites “recover” from Penguin, they often don’t skyrocket up to top rankings once again as those previously high rankings were probably based on the power of links that are now considered unnatural. Here is some information on 
what to expect when you have recovered from a link based penalty or algorithmic issue.

Also, the Penguin algorithm is not the same thing as a manual unnatural links penalty. You do not need to file a reconsideration request to recover from Penguin. You also do not need to document the work that you have done in order to get links removed as no Google employee will be manually reviewing your work. As mentioned previously, here is more information on the 
difference between the Penguin algorithm and a manual unnatural links penalty.

What is Hummingbird?

Hummingbird is a completely different animal than Penguin or Panda. (Yeah, I know…that was a bad pun.) I will commonly get people emailing me telling me that Hummingbird destroyed their rankings. I would say that in almost every case that I have evalutated, this was not true. Google made their announcement about Hummingbird on September 26, 2013. However, at that time, they announced that Hummingbird had already been live for about a month. If the Hummingbird algorithm was truly responsible for catastrophic ranking fluctuations then we really should have seen an outcry from the SEO world of something drastic happening in August of 2013, and this did not happen. There did seem to be some type of fluctuation that happened around August 21 as reported here on Search Engine Round Table, but there were not many sites that reported huge ranking changes on that day.

If you think that Hummingbird affected you, it’s not a bad idea to look at your traffic to see if you noticed a drop on October 4, 2013 which was actually a refresh of the Penguin algorithm. I believe that a lot of people who thought that they were affected by Hummingbird were actually affected by Penguin which happened just a week after Google made their announcement about Hummingbird.

There are some excellent articles on Hummingbird here and here. Hummingbird was a complete overhaul of the entire Google algorithm. As Danny Sullivan put it, if you consider the Google algorithm as an engine, Panda and Penguin are algorithm changes that were like putting a new part in the engine such as a filter or a fuel pump. But, Hummingbird wasn’t just a new part; it was a completely new engine. That new engine still makes use of many of the old parts (such as Panda and Penguin) but a good amount of the engine is completely original.

The goal of the Hummingbird algorithm is for Google to better understand a user’s query. Bill Slawski who writes about Google patents has a great example of this in his post here. He explains that when someone searches for “What is the best place to find and eat Chicago deep dish style pizza?”, Hummingbird is able to discern that by “place” the user likely would be interested in results that show “restaurants”. There is speculation that these changes were necessary in order for Google’s voice search to be more effective. When we’re typing a search query, we might type, “best Seattle SEO company” but when we’re speaking a query (i.e. via Google Glass or via Google Now) we’re more likely to say something like, “Which firm in Seattle offers the best SEO services?” The point of Hummingbird is to better understand what users mean when they have queries like this.

So how do I recover or improve in the eyes of Hummingbird?

If you read the posts referenced above, the answer to this question is essentially to create content that answers users queries rather than just trying to rank for a particular keyword. But really, this is what you should already be doing!

It appears that Google’s goal with all of these algorithm changes (Panda, Penguin and Hummingbird) is to encourage webmasters to publish content that is the best of its kind. Google’s goal is to deliver answers to people who are searching. If you can produce content that answers people’s questions, then you’re on the right track.

I know that that is a really vague answer when it comes to “recovering” from Hummingbird. Hummingbird really is different than Panda and Penguin. When a site has been demoted by the Panda or Penguin algorithm, it’s because Google has lost some trust in the site’s quality, whether it is on-site quality or the legitimacy of its backlinks. If you fix those quality issues you can regain the algorithm’s trust and subsequently see improvements. But, if your site seems to be doing poorly since the launch of Hummingbird, then there really isn’t a way to recover those keyword rankings that you once held. You can, however, get new traffic by finding ways to be more thorough and complete in what your website offers.

Do you have more questions?

My goal in writing this article was to have a resource to point people to when they had basic questions about Panda, Penguin and Hummingbird. Recently, when I published my penalty newsletter, I had a small business owner comment that it was very interesting but that most of it went over their head. I realized that many people outside of the SEO world are greatly affected by these algorithm changes, but don’t have much information on why they have affected their website.

Do you have more questions about Panda, Penguin or Hummingbird? If so, I’d be happy to address them in the comments. I also would love for those of you who are experienced with dealing with websites affected by these issues to comment as well.

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Reblogged 4 years ago from feedproxy.google.com

Future of Marketing

Future of Marketing – What will marketing look like in 100 years?

Who wouldn’t like a crystal ball to know the answer to this question?  Do you think it would put you ahead of your competition?

If you think about the last five years in particular the number of channels a marketer can use to connect with their target audience has increased immensely – Pininterest, Instagram, Snap chat, – the mind boggles at what new technology will bring in the next 100 years.

The reality is, to be effective it won’t matter what channels/devices/information are available, success will still come down to four fundamental factors.

And while each of these are important is their own right, you will always get the results when they work synergistically.

1   Defining your target market/audience

The first step for any marketer is determining those with a real need for your product or service. Sometimes business owners fall into the trap of trying to be there for every possible need. This wastes money, resources and energy. The more specific you can be, the more likely you will attract those who will buy. Pinpointing specifics will help you identify the tools that will give the best results.

For example, a personal trainer might find concentrating on women 18- 35 years effective – but focusing on women with depression or body image issues may be a better fit for you.

2   Understand their needs

By understanding the needs of your ideal client/customer you can match your product/service features with the benefits they need.

A local takeaway food bar might struggle competing with all the other fast food outlets providing a quick lunch, but focusing on those who love hot chips that are thick, tasty and covered with gravy can change the competitive landscape totally.

3   Have a relevant message

The more your message connects with your customer at any moment, the more likely they are to take action.  As a small business owner you are trying to cut through all the “noise” your potential customer receives daily so they hear that you understand their needs and your product provides the ideal solution.  And you really only have a couple of seconds to do it.

So let’s say you are selling a weight loss program. In January your messaging will focus on New Year’s resolutions while in August it will be about getting ready for getting in your swimmers.

Same product. Different message.

4   Know where to connect with your customers

Despite the influx of new marketing channels, it is still vital you connect with your customers where they make up their minds. This could be once they enter the supermarket, on the internet or after and a demonstration by a technically competent sales person.

Even the most targeted message will be wasted if your audience is not there. So, if your ideal client is men over 70, a social media campaign using Instagram and Facebook will not get the results you would by using other channels frequented by this group.

So what will marketing look like in 100 years? From a technology perspective who knows. But what I do know is these four fundamentals will still be imperative regardless of whether its 2114 or beyond.

Need some clarity around how your marketing approach, send us an Email or call us on 61 2 8061 4556 today for an obligation-free chat.

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Reblogged 4 years ago from onthemark.com.au