Is Australia the land of opportunity for your retail brand?

Australia has a resident population of more than 24 million and, according to eMarketer, the country’s ecommerce sales are predicted to reach A$32.56 billion by 2017. The country’s remote location in the APAC region means that unlike European countries or the USA, traditionally there have been a lack of global brands sold locally.

Of course, we also know that many expatriates, particularly from inside the Commonwealth, have made Australia their home and are keen to buy products they know and love from their country of origin.

All of these factors present a huge and potentially lucrative opportunity for non-Australian brands wanting to open up their new and innovative products to a fresh market, or compete for market share.

But it’s not just non-Australian retailers who are at an advantage here: Australia was late to the ecommerce party because native, established brands were trading well without it. Subsequently, Australian retailers’ ecommerce technology stacks are much more recent and not burdened by legacy systems. This makes it much easier to extend, or get started with, best-of-breed technologies and cash in on a market that’s booming. To put some of this into perspective, Magento’s innovative ecommerce platform currently takes 42% of Australia’s market share and the world’s first adopter of Magento 2.0 was an Australian brand.

The GST loophole

At the moment, local retailers are campaigning against a rule that exempts foreign websites from being charged a 10% general sales tax (GST) on purchases under A$1,000. And in 2013, Australian consumers made $3.11 billion worth of purchases under A$1,000.[1]

While the current GST break appears to put non-Australian retailers at an advantage, Australian-based brands such as Harvey Norman are using it to their advantage by setting up ecommerce operations in Asia to enjoy the GST benefit.

Australian consumers have also countered the argument by saying that price isn’t always the motivator when it comes to making purchasing decisions.

It’s not a place where no man has gone before

Often, concerns around meeting local compliance and lack of overseas business knowledge prevent outsiders from taking the leap into cross-border trade. However, this ecommerce passport, created by Ecommerce Worldwide and NORA, is designed to support those considering selling in Australia. The guide provides a comprehensive look into everything from the country’s economy and trade status, to logistics and dealing with international payments.

Global expansion success stories are also invaluable sources of information. For instance, it’s not just lower-end retailers that are fitting the bill, with brands like online luxury fashion retailer Net-a-Porter naming Australia as one of its biggest markets.

How tech-savvy are the Aussies?

One of the concerns you might have as a new entrant into the market is how you’ll reach and sell to your new audience, particularly without having a physical presence. The good news is that more than 80% of the country is digitally enabled and 60% of mobile phone users own a smartphone – so online is deeply rooted into the majority of Australians’ lives. [2]

Marketing your brand

Heard the saying “Fire bullets then fire cannonballs”? In any case, you’ll want to test the waters and gauge people’s reactions to your product or service.

It all starts with the website because, without it, you’re not discoverable or searchable, and you’ve nowhere to drive people to when running campaigns. SEO and SEM should definitely be a priority, and an online store that can handle multiple regions and storefronts, like Magento, will make your life easier. A mobile-first mentality and well thought-out UX will also place you in a good position.

Once your new web store is set up, you should be making every effort to collect visitors’ email addresses, perhaps via a popover. Why? Firstly, email is one of the top three priority areas for Australian retailers, because it’s a cost-effective, scalable marketing channel that enables true personalization.

Secondly, email marketing automation empowers you to deliver the customer experience today’s consumer expects, as well as enabling you to communicate with them throughout the lifecycle. Check out our ‘Do customer experience masters really exist?’ whitepaper for some real-life success stories.

Like the Magento platform, dotmailer is set up to handle multiple languages, regions and accounts, and is designed to grow with you.

In summary, there’s great scope for ecommerce success in Australia, whether you’re a native bricks-and-mortar retailer, a start-up or a non-Australian merchant. The barriers to cross-border trade are falling and Australia is one of APAC’s most developed regions in terms of purchasing power and tech savviness.

We recently worked with ecommerce expert Chloe Thomas to produce a whitepaper on cross-border trade, which goes into much more detail on how to market and sell successfully in new territories. You can download a free copy here.

[1] Australian Passport 2015: Cross-Border Trading Report

[2] Australian Passport 2015: Cross-Border Trading Report

Reblogged 2 years ago from blog.dotmailer.com

​​Measure Your Mobile Rankings and Search Visibility in Moz Analytics

Posted by jon.white

We have launched a couple of new things in Moz Pro that we are excited to share with you all: Mobile Rankings and a Search Visibility score. If you want, you can jump right in by heading to a campaign and adding a mobile engine, or keep reading for more details!

Track your mobile vs. desktop rankings in Moz Analytics

Mobilegeddon came and went with slightly less fanfare than expected, somewhat due to the vast ‘Mobile Friendly’ updates we all did at super short notice (nice work everyone!). Nevertheless, mobile rankings visibility is now firmly on everyone’s radar, and will only become more important over time.

Now you can track your campaigns’ mobile rankings for all of the same keywords and locations you are tracking on desktop.

For this campaign my mobile visibility is almost 20% lower than my desktop visibility and falling;
I can drill down to find out why

Clicking on this will take you into a new Engines tab within your Keyword Rankings page where you can find a more detailed version of this chart as well as a tabular view by keyword for both desktop and mobile. Here you can also filter by label and location.

Here I can see Search Visibility across engines including mobile;
in this case, for my branded keywords.

We have given an extra engine to all campaigns

We’ve given customers an extra engine for each campaign, increasing the number from 3 to 4. Use the extra slot to add the mobile engine and unlock your mobile data!

We will begin to track mobile rankings within 24 hours of adding to a campaign. Once you are set up, you will notice a new chart on your dashboard showing visibility for Desktop vs. Mobile Search Visibility.

Measure your Search Visibility score vs. competitors

The overall Search Visibility for my campaign

Along with this change we have also added a Search Visibility score to your rankings data. Use your visibility score to track and report on your overall campaign ranking performance, compare to your competitors, and look for any large shifts that might indicate penalties or algorithm changes. For a deeper drill-down into your data you can also segment your visibility score by keyword labels or locations. Visit the rankings summary page on any campaign to get started.

How is Search Visibility calculated?

Good question!

The Search Visibility score is the percentage of clicks we estimate you receive based on your rankings positions, across all of your keywords.

We take each ranking position for each keyword, multiply by an estimated click-thru-rate, and then take the average of all of your keywords. You can think of it as the percentage of your SERPs that you own. The score is expressed as a percentage, though scores of 100% would be almost impossible unless you are tracking keywords using the “site:” modifier. It is probably more useful to measure yourself vs. your competitors rather than focus on the actual score, but, as a rule of thumb, mid-40s is probably the realistic maximum for non-branded keywords.

Jeremy, our Moz Analytics TPM, came up with this metaphor:

Think of the SERPs for your keywords as villages. Each position on the SERP is a plot of land in SERP-village. The Search Visibility score is the average amount of plots you own in each SERP-village. Prime real estate plots (i.e., better ranking positions, like #1) are worth more. A complete monopoly of real estate in SERP-village would equate to a score of 100%. The Search Visibility score equates to how much total land you own in all SERP-villages.

Some neat ways to use this feature

  • Label and group your keywords, particularly when you add them – As visibility score is an average of all of your keywords, when you add or remove keywords from your campaign you will likely see fluctuations in the score that are unrelated to performance. Solve this by getting in the habit of labeling keywords when you add them. Then segment your data by these labels to track performance of specific keyword groups over time.
  • See how location affects your mobile rankings – Using the Engines tab in Keyword Rankings, use the filters to select just local keywords. Look for big differences between Mobile and Desktop where Google might be assuming local intent for mobile searches but not for desktop. Check out how your competitors perform for these keywords. Can you use this data?

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

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.

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

Simple Steps for Conducting Creative Content Research

Posted by Hannah_Smith

Most frequently, the content we create at Distilled is designed to attract press coverage, social shares, and exposure (and links) on sites our clients’ target audience reads. That’s a tall order.

Over the years we’ve had our hits and misses, and through this we’ve recognised the value of learning about what makes a piece of content successful. Coming up with a great idea is difficult, and it can be tough to figure out where to begin. Today, rather than leaping headlong into brainstorming sessions, we start with creative content research.

What is creative content research?

Creative content research enables you to answer the questions:

“What are websites publishing, and what are people sharing?”

From this, you’ll then have a clearer view on what might be successful for your client.

A few years ago this required quite an amount of work to figure out. Today, happily, it’s much quicker and easier. In this post I’ll share the process and tools we use.

Whoa there… Why do I need to do this?

I think that the value in this sort of activity lies in a couple of directions:

a) You can learn a lot by deconstructing the success of others…

I’ve been taking stuff apart to try to figure out how it works for about as long as I can remember, so applying this process to content research felt pretty natural to me. Perhaps more importantly though, I think that deconstructing content is actually easier when it isn’t your own. You’re not involved, invested, or in love with the piece so viewing it objectively and learning from it is much easier.

b) Your research will give you a clear overview of the competitive landscape…

As soon as a company elects to start creating content, they gain a whole raft of new competitors. In addition to their commercial competitors (i.e. those who offer similar products or services), the company also gains content competitors. For example, if you’re a sports betting company and plan to create content related to the sports events that you’re offering betting markets on; then you’re competing not just with other betting companies, but every other publisher who creates content about these events. That means major news outlets, sports news site, fan sites, etc. To make matters even more complicated, it’s likely that you’ll actually be seeking coverage from those same content competitors. As such, you need to understand what’s already being created in the space before creating content of your own.

c) You’re giving yourself the data to create a more compelling pitch…

At some point you’re going to need to pitch your ideas to your client (or your boss if you’re working in-house). At Distilled, we’ve found that getting ideas signed off can be really tough. Ultimately, a great idea is worthless if we can’t persuade our client to give us the green light. This research can be used to make a more compelling case to your client and get those ideas signed off. (Incidentally, if getting ideas signed off is proving to be an issue you might find this framework for pitching creative ideas useful).

Where to start

Good ideas start with a good brief, however it can be tough to pin clients down to get answers to a long list of questions.

As a minimum you’ll need to know the following:

  • Who are they looking to target?
    • Age, sex, demographic
    • What’s their core focus? What do they care about? What problems are they looking to solve?
    • Who influences them?
    • What else are they interested in?
    • Where do they shop and which brands do they buy?
    • What do they read?
    • What do they watch on TV?
    • Where do they spend their time online?
  • Where do they want to get coverage?
    • We typically ask our clients to give us a wishlist of 10 or so sites they’d love to get coverage on
  • Which topics are they comfortable covering?
    • This question is often the toughest, particularly if a client hasn’t created content specifically for links and shares before. Often clients are uncomfortable about drifting too far away from their core business—for example, if they sell insurance, they’ll typically say that they really want to create a piece of content about insurance. Whilst this is understandable from the clients’ perspective it can severely limit their chances of success. It’s definitely worth offering up a gentle challenge at this stage—I’ll often cite Red Bull, who are a great example of a company who create content based on what their consumers love, not what they sell (i.e. Red Bull sell soft drinks, but create content about extreme sports because that’s the sort of content their audience love to consume). It’s worth planting this idea early, but don’t get dragged into a fierce debate at this stage—you’ll be able to make a far more compelling argument once you’ve done your research and are pitching concrete ideas.

Processes, useful tools and sites

Now you have your brief, it’s time to begin your research.

Given that we’re looking to uncover “what websites are publishing and what’s being shared,” It won’t surprise you to learn that I pay particular attention to pieces of content and the coverage they receive. For each piece that I think is interesting I’ll note down the following:

  • The title/headline
  • A link to the coverage (and to the original piece if applicable)
  • How many social shares the coverage earned (and the original piece earned)
  • The number of linking root domains the original piece earned
  • Some notes about the piece itself: why it’s interesting, why I think it got shares/coverage
  • Any gaps in the content, whether or not it’s been executed well
  • How we might do something similar (if applicable)

Whilst I’m doing this I’ll also make a note of specific sites I see being frequently shared (I tend to check these out separately later on), any interesting bits of research (particularly if I think there might be an opportunity to do something different with the data), interesting threads on forums etc.

When it comes to kicking off your research, you can start wherever you like, but I’d recommend that you cover off each of the areas below:

What does your target audience share?

Whilst this activity might not uncover specific pieces of successful content, it’s a great way of getting a clearer understanding of your target audience, and getting a handle on the sites they read and the topics which interest them.

  • Review social profiles / feeds
    • If the company you’re working for has a Facebook page, it shouldn’t be too difficult to find some people who’ve liked the company page and have a public profile. It’s even easier on Twitter where most profiles are public. Whilst this won’t give you quantitative data, it does put a human face to your audience data and gives you a feel for what these people care about and share. In addition to uncovering specific pieces of content, this can also provide inspiration in terms of other sites you might want to investigate further and ideas for topics you might want to explore.
  • Demographics Pro
    • This service infers demographic data from your clients’ Twitter followers. I find it particularly useful if the client doesn’t know too much about their audience. In addition to demographic data, you get a breakdown of professions, interests, brand affiliations, and the other Twitter accounts they follow and who they’re most influenced by. This is a paid-for service, but there are pay-as-you-go options in addition to pay monthly plans.

Finding successful pieces of content on specific sites

If you’ve a list of sites you know your target audience read, and/or you know your client wants to get coverage on, there are a bunch of ways you can uncover interesting content:

  • Using your link research tool of choice (e.g. Open Site Explorer, Majestic, ahrefs) you can run a domain level report to see which pages have attracted the most links. This can also be useful if you want to check out commercial competitors to see which pieces of content they’ve created have attracted the most links.
  • There are also tools which enable you to uncover the most shared content on individual sites. You can use Buzzsumo to run content analysis reports on individual domains which provide data on average social shares per post, social shares by network, and social shares by content type.
  • If you just want to see the most shared content for a given domain you can run a simple search on Buzzsumo using the domain; and there’s also the option to refine by topic. For example a search like [guardian.com big data] will return the most shared content on the Guardian related to big data. You can also run similar reports using ahrefs’ Content Explorer tool.

Both Buzzsumo and ahrefs are paid tools, but both offer free trials. If you need to explore the most shared content without using a paid tool, there are other alternatives. Check out Social Crawlytics which will crawl domains and return social share data, or alternatively, you can crawl a site (or section of a site) and then run the URLs through SharedCount‘s bulk upload feature.

Finding successful pieces of content by topic

When searching by topic, I find it best to begin with a broad search and then drill down into more specific areas. For example, if I had a client in the financial services space, I’d start out looking at a broad topic like “money” rather than shooting straight to topics like loans or credit cards.

As mentioned above, both Buzzsumo and ahrefs allow you to search for the most shared content by topic and both offer advanced search options.

Further inspiration

There are also several sites I like to look at for inspiration. Whilst these sites don’t give you a great steer on whether or not a particular piece of content was actually successful, with a little digging you can quickly find the original source and pull link and social share data:

  • Visually has a community area where users can upload creative content. You can search by topic to uncover examples.
  • TrendHunter have a searchable archive of creative ideas, they feature products, creative campaigns, marketing campaigns, advertising and more. It’s best to keep your searches broad if you’re looking at this site.
  • Check out Niice (a moodboard app) which also has a searchable archive of handpicked design inspiration.
  • Searching Pinterest can allow you to unearth some interesting bits and pieces as can Google image searches and regular Google searches around particular topics.
  • Reviewing relevant sections of discussion sites like Quora can provide insight into what people are asking about particular topics which may spark a creative idea.

Moving from data to insight

By this point you’ve (hopefully) got a long list of content examples. Whilst this is a great start, effectively what you’ve got here is just data, now you need to convert this to insight.

Remember, we’re trying to answer the questions: “What are websites publishing, and what are people sharing?”

Ordinarily as I go through the creative content research process, I start to see patterns or themes emerge. For example, across a variety of topics areas you’ll see that the most shared content tends to be news. Whilst this is good to know, it’s not necessarily something that’s going to be particularly actionable. You’ll need to dig a little deeper—what else (aside from news) is given coverage? Can you split those things into categories or themes?

This is tough to explain in the abstract, so let me give you an example. We’d identified a set of music sites (e.g. Rolling Stone, NME, CoS, Stereogum, Pitchfork) as target publishers for a client.

Here’s a summary of what I concluded following my research:

The most-shared content on these music publications is news: album launches, new singles, videos of performances etc. As such, if we can work a news hook into whatever we create, this could positively influence our chances of gaining coverage.

Aside from news, the content which gains traction tends to fall into one of the following categories:

Earlier in this post I mentioned that it can be particularly tough to create content which attracts coverage and shares if clients feel strongly that they want to do something directly related to their product or service. The example I gave at the outset was a client who sold insurance and was really keen to create something about insurance. You’re now in a great position to win an argument with data, as thanks to your research you’ll be able to cite several pieces of insurance-related content which have struggled to gain traction. But it’s not all bad news as you’ll also be able to cite other topics which are relevant to the client’s target audience and stand a better chance of gaining coverage and shares.

Avoiding the pitfalls

There are potential pitfalls when it comes to creative content research in that it’s easy to leap to erroneous conclusions. Here’s some things to watch out for:

Make sure you’re identifying outliers…

When seeking out successful pieces of content you need to be certain that what you’re looking at is actually an outlier. For example, the average post on BuzzFeed gets over 30k social shares. As such, that post you found with just 10k shares is not an outlier. It’s done significantly worse than average. It’s therefore not the best post to be holding up as a fabulous example of what to create to get shares.

Don’t get distracted by formats…

Pay more attention to the idea than the format. For example, the folks at Mashable, kindly covered an infographic about Instagram which we created for a client. However, the takeaway here is not that Instagram infographics get coverage on Mashable. Mashable didn’t cover this because we created an infographic. They covered the piece because it told a story in a compelling and unusual way.

You probably shouldn’t create a listicle…

This point is related to the point above. In my experience, unless you’re a publisher with a huge, engaged social following, that listicle of yours is unlikely to gain traction. Listicles on huge publisher sites get shares, listicles on client sites typically don’t. This is doubly important if you’re also seeking coverage, as listicles on clients sites don’t typically get links or coverage on other sites.

How we use the research to inform our ideation process

At Distilled, we typically take a creative brief and complete creative content research and then move into the ideation process. A summary of the research is included within the creative brief, and this, along with a copy of the full creative content research is shared with the team.

The research acts as inspiration and direction and is particularly useful in terms of identifying potential topics to explore but doesn’t mean team members don’t still do further research of their own.

This process by no means acts as a silver bullet, but it definitely helps us come up with ideas.


Thanks for sticking with me to the end!

I’d love to hear more about your creative content research processes and any tips you have for finding inspirational content. Do let me know via the comments.

Image credits: Research, typing, audience, inspiration, kitteh.

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Reblogged 3 years ago from tracking.feedpress.it

Kasper Szymanski, ex-Google Spam Fighter Guests at #MajesticMemo

Kasper is a former Web Spam Fighter at Google. He founded SEARCHBROTHERS with fellow spam fighter, Fili Wiese where he specialises in backlink analysis, reconsideration requests and site recovery. Here’s a summary of the #MajesticMemo tweetchat: [View the story “Kaspar Szymanski guests at #MajesticMemo” on Storify]  

The post Kasper Szymanski, ex-Google Spam Fighter Guests at #MajesticMemo appeared first on Majestic Blog.

Reblogged 4 years ago from blog.majestic.com

How Much Has Link Building Changed in Recent Years?

Posted by Paddy_Moogan

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

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

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

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

My personal view

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

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

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

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

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

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

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

The typical mindset for building links has changed

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

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

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

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

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

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

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

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

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

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

A few other opinions…

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

Dr. Pete

 

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

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

Carl Hendy

 

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

Danny Denhard

Signals that surround link building have changed

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

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

Domain level link metrics

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

Phil Nottingham

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

Anchor text

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

SEOs. 🙂

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

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

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

Rand Fishkin

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

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

Paul Rogers

New signals have been introduced

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

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

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

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

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

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

– Rand Fishkin

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

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

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

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

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

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

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

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

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

– Dr. Pete

The core principles have not changed

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

You need an asset

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

You need to promote that asset to the right audience

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

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

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

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

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

You need consistency

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

Anything scalable is at risk

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

The part Google plays in this

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

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

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

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

Dan Barker

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

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

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

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

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

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

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

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

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

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Reblogged 4 years ago from tracking.feedpress.it