​The 2015 Online Marketing Industry Survey

Posted by Dr-Pete

It’s been another wild year in search marketing. Mobilegeddon crushed our Twitter streams, but not our dreams, and Matt Cutts stepped out of the spotlight to make way for an uncertain Google future. Pandas and Penguins continue to torment us, but most days, like anyone else, we were just trying to get the job done and earn a living.

This year, over 3,600 brave souls, each one more intelligent and good-looking than the last, completed our survey. While the last survey was technically “2014”, we collected data for it in late 2013, so the 2015 survey reflects about 18 months of industry changes.

A few highlights

Let’s dig in. Almost half (49%) of our 2015 respondents involved in search marketing were in-house marketers. In-house teams still tend to be small – 71% of our in-house marketers reported only 1-3 people in their company being involved in search marketing at least quarter-time. These teams do have substantial influence, though, with 86% reporting that they were involved in purchasing decisions.

Agency search marketers reported larger teams and more diverse responsibilities. More than one-third (36%) of agency marketers in our survey reported working with more than 20 clients in the previous year. Agencies covered a wide range of services, with the top 5 being:

More than four-fifths (81%) of agency respondents reported providing both SEO and SEM services for clients. Please note that respondents could select more than one service/tool/etc., so the charts in this post will not add up to 100%.

The vast majority of respondents (85%) reported being directly involved with content marketing, which was on par with 2014. Nearly two-thirds (66%) of agency content marketers reported “Content for SEO purposes” as their top activity, although “Building Content Strategy” came in a solid second at 44% of respondents.

Top tools

Where do we get such wonderful toys? We marketers love our tools, so let’s take a look at the Top 10 tools across a range of categories. Please note that this survey was conducted here on Moz, and our audience certainly has a pro-Moz slant.

Up first, here are the Top 10 SEO tools in our survey:

Just like last time, Google Webmaster Tools (now “Search Console”) leads the way. Moz Pro and Majestic slipped a little bit, and Firebug fell out of the Top 10. The core players remained fairly stable.

Here are the Top 10 Content tools in our survey:

Even with its uncertain future, Google Alerts continues to be widely used. There are a lot of newcomers to the content tools world, so year-over-year comparisons are tricky. Expect even more players in this market in the coming year.

Following are our respondents’ Top 10 analytics tools:

For an industry that complains about Google so much, we sure do seem to love their stuff. Google Analytics dominates, crushing the enterprise players, at least in the mid-market. KISSmetrics gained solid ground (from the #10 spot last time), while home-brewed tools slipped a bit. CrazyEgg and WordPress Stats remain very popular since our last survey.

Finally, here are the Top 10 social tools used by our respondents:

Facebook Insights and Hootsuite retained the top spots from last year, but newcomer Twitter Analytics rocketed into the #3 position. LinkedIn Insights emerged as a strong contender, too. Overall usage of all social tools increased. Tweetdeck held the #6 spot in 2014, with 19% usage, but dropped to #10 this year, even bumping up slightly to 20%.

Of course, digging into social tools naturally begs the question of which social networks are at the top of our lists.

The Top 6 are unchanged since our last survey, and it’s clear that the barriers to entry to compete with the big social networks are only getting higher. Instagram doubled its usage (from 11% of respondents last time), but this still wasn’t enough to overtake Pinterest. Reddit and Quora saw steady growth, and StumbleUpon slipped out of the Top 10.

Top activities

So, what exactly do we do with these tools and all of our time? Across all online marketers in our survey, the Top 5 activities were:

For in-house marketers, “Site Audits” dropped to the #6 position and “Brand Strategy” jumped up to the #3 spot. Naturally, in-house marketers have more resources to focus on strategy.

For agencies and consultants, “Site Audits” bumped up to #2, and “Managing People” pushed down social media to take the #5 position. Larger agency teams require more traditional people wrangling.

Here’s a much more detailed breakdown of how we spend our time in 2015:

In terms of overall demand for services, the Top 5 winners (calculated by % reporting increase – % reporting decrease were):

Demand for CRO is growing at a steady clip, but analytics still leads the way. Both “Content Creation” (#2) and “Content Curation” (#6) showed solid demand increases.

Some categories reported both gains and losses – 30% of respondents reported increased demand for “Link Building”, while 20% reported decreased demand. Similarly, 20% reported increased demand for “Link Removal”, while almost as many (17%) reported decreased demand. This may be a result of overall demand shifts, or it may represent more specialization by agencies and consultants.

What’s in store for 2016?

It’s clear that our job as online marketers is becoming more diverse, more challenging, and more strategic. We have to have a command of a wide array of tools and tactics, and that’s not going to slow down any time soon. On the bright side, companies are more aware of what we do, and they’re more willing to spend the money to have it done. Our evolution has barely begun as an industry, and you can expect more changes and growth in the coming year.

Raw data download

If you’d like to take a look through the raw results from this year’s survey (we’ve removed identifying information like email addresses from all responses), we’ve got that for you here:

Download the raw results

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Reblogged 3 years ago from tracking.feedpress.it

Moz Local Dashboard Updates

Posted by NoamC

Today, we’re excited to announce some new features and changes to the Moz Local dashboard. We’ve updated your dashboard to make it easier to manage and gauge the performance of your local search listings.

New and improved dashboard

We spent a lot of time listening to customer feedback and finding areas where we weren’t being as clear as we ought to. We’ve made great strides in improving Moz Local’s dashboard (details below) to give you a lot more information at a glance.

Geo Reporting

Our newest reporting view, geo reporting, shows you the relative strength of locations based on geography. The deeper the blue, the stronger the listings in that region. You can look at your scores broken down by state, or zoom in to see the score breakdown by county. Move your mouse over a region to see your average score there.

Scores on the dashboard

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We’re more clearly surfacing the scores for each of your locations right in our dashboard. Now you can see each location’s individual score immediately.

Exporting reports

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Use the new drop-down at the upper-right corner to download Moz Local reports in CSV format, so that you can access your historical listing data offline and use it to generate your own reports and visualizations.

Search cheat sheet

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If you want to take your search game to the next level, why not start with your Moz Local dashboard? A handy link next to the search bar shows you all the ways you can find what you’re looking for.

We’re still actively addressing feedback and making improvements to Moz Local over time, and you can let us know what we’re missing in the comments below.

We hope that our latest updates will make your Moz Local experience better. But you don’t have to take my word for it; head on over to Moz Local to see our new and improved dashboard and reporting experience today!

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.

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Reblogged 3 years ago from tracking.feedpress.it

Exposing The Generational Content Gap: Three Ways to Reach Multiple Generations

Posted by AndreaLehr

With more people of all ages online than ever before, marketers must create content that resonates with multiple generations. Successful marketers realize that each generation has unique expectations, values and experiences that influence consumer behaviors, and that offering your audience content that reflects their shared interests is a powerful way to connect with them and inspire them to take action.

We’re in the midst of a generational shift, with
Millennials expected to surpass Baby Boomers in 2015 as the largest living generation. In order to be competitive, marketers need to realize where key distinctions and similarities lie in terms of how these different generations consume content and share it with with others.

To better understand the habits of each generation,
BuzzStream and Fractl surveyed over 1,200 individuals and segmented their responses into three groups: Millennials (born between 1977–1995), Generation X (born between 1965–1976), and Baby Boomers (born between 1946–1964). [Eds note: The official breakdown for each group is as follows: Millennials (1981-1997), Generation X (1965-1980), and Boomers (1946-1964)]

Our survey asked them to identify their preferences for over 15 different content types while also noting their opinions on long-form versus short-form content and different genres (e.g., politics, technology, and entertainment).

We compared their responses and found similar habits and unique trends among all three generations.

Here’s our breakdown of the three key takeaways you can use to elevate your future campaigns:

1. Baby Boomers are consuming the most content

However, they have a tendency to enjoy it earlier in the day than Gen Xers and Millennials.

Although we found striking similarities between the younger generations, the oldest generation distinguished itself by consuming the most content. Over 25 percent of Baby Boomers consume 20 or more hours of content each week. Additional findings:

  • Baby Boomers also hold a strong lead in the 15–20 hours bracket at 17 percent, edging out Gen Xers and Millennials at 12 and 11 percent, respectively
  • A majority of Gen Xers and Millennials—just over 22 percent each—consume between 5 and 10 hours per week
  • Less than 10 percent of Gen Xers consume less than five hours of content a week—the lowest of all three groups

We also compared the times of day that each generation enjoys consuming content. The results show that most of our respondents—over 30 percent— consume content between 8 p.m. and midnight. However, there are similar trends that distinguish the oldest generation from the younger ones:

  • Baby Boomers consume a majority of their content in the morning. Nearly 40 percent of respondents are online between 5 a.m. and noon.
  • The least popular time for most respondents to engage with content online is late at night, between midnight and 5 a.m., earning less than 10 percent from each generation
  • Gen X is the only generation to dip below 10 percent in the three U.S. time zones: 5 a.m. to 9 a.m., 6 to 8 p.m., and midnight to 5 a.m.

When Do We Consume Content

When it comes to which device each generation uses to consume content, laptops are the most common, followed by desktops. The biggest distinction is in mobile usage: Over 50 percent of respondents who use their mobile as their primary device for content consumption are Millennials. Other results reveal:

  • Not only do Baby Boomers use laptops the most (43 percent), but they also use their tablets the most. (40 percent of all primary tablet users are Baby Boomers).
  • Over 25 percent of Millennials use a mobile device as their primary source for content
  • Gen Xers are the least active tablet users, with less than 8 percent of respondents using it as their primary device

Device To Consume Content2. Preferred content types and lengths span all three generations

One thing every generation agrees on is the type of content they enjoy seeing online. Our results reveal that the top four content types— blog articles, images, comments, and eBooks—are exactly the same for Baby Boomers, Gen Xers, and Millennials. Additional comparisons indicate:

  • The least preferred content types—flipbooks, SlideShares, webinars, and white papers—are the same across generations, too (although not in the exact same order)
  • Surprisingly, Gen Xers and Millennials list quizzes as one of their five least favorite content types

Most Consumed Content Type

All three generations also agree on ideal content length, around 300 words. Further analysis reveals:

  • Baby Boomers have the highest preference for articles under 200 words, at 18 percent
  • Gen Xers have a strong preference for articles over 500 words compared to other generations. Over 20 percent of respondents favor long-form articles, while only 15 percent of Baby Boomers and Millennials share the same sentiment.
  • Gen Xers also prefer short articles the least, with less than 10 percent preferring articles under 200 words

Content Length PreferencesHowever, in regards to verticals or genres, where they consume their content, each generation has their own unique preference:

  • Baby Boomers have a comfortable lead in world news and politics, at 18 percent and 12 percent, respectively
  • Millennials hold a strong lead in technology, at 18 percent, while Baby Boomers come in at 10 percent in the same category
  • Gen Xers fall between Millennials and Baby Boomers in most verticals, although they have slight leads in personal finance, parenting, and healthy living
  • Although entertainment is the top genre for each generation, Millennials and Baby Boomers prefer it slightly more than than Gen Xers do

Favorite Content Genres

3. Facebook is the preferred content sharing platform across all three generations

Facebook remains king in terms of content sharing, and is used by about 60 percent of respondents in each generation studied. Surprisingly, YouTube came in second, followed by Twitter, Google+, and LinkedIn, respectively. Additional findings:

  • Baby Boomers share on Facebook the most, edging out Millennials by only a fraction of a percent
  • Although Gen Xers use Facebook slightly less than other generations, they lead in both YouTube and Twitter, at 15 percent and 10 percent, respectively
  • Google+ is most popular with Baby Boomers, at 8 percent, nearly double that of both Gen Xers and Millennials

Preferred Social PlatformAlthough a majority of each generation is sharing content on Facebook, the type of content they are sharing, especially visuals, varies by each age group. The oldest generation prefers more traditional content, such as images and videos. Millennials prefer newer content types, such as memes and GIFs, while Gen X predictably falls in between the two generations in all categories except SlideShares. Other findings:

  • The most popular content type for Baby Boomers is video, at 27 percent
  • Parallax is the least popular type for every generation, earning 1 percent or less in each age group
  • Millennials share memes the most, while less than 10 percent of Baby Boomers share similar content

Most Shared Visual ContentMarketing to several generations can be challenging, given the different values and ideas that resonate with each group. With the number of online content consumers growing daily, it’s essential for marketers to understand the specific types of content that each of their audiences connect with, and align it with their content marketing strategy accordingly.

Although there is no one-size-fits-all campaign, successful marketers can create content that multiple generations will want to share. If you feel you need more information getting started, you can review this deck of additional insights, which includes the preferred video length and weekend consuming habits of each generation discussed in this post.

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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

Moz’s 2014 Annual Report

Posted by SarahBird

Moz has a tradition of sharing its financials (check out 2012 and 2013 for funzies). It’s an important part of TAGFEE.

Why do we do it? Moz gets its strength from the community of marketers and entrepreneurs that support it. We celebrated 10 years of our community last October. In some ways, the purpose of this report is to give you an inside look into our company. It’s one of many lenses that tell the story of Moz.

Yep. I know. It’s April. I’m not proud. Better late than never, right?

I had a very long and extensive version of this post planned, something closer to last year’s extravaganza. I finally had to admit to myself that I was letting the perfect become the enemy of the good (or at least the done). There was no way I could capture an entire year’s worth of ups and downs—along with supporting data—in a single blog post.

Without further ado, here’s the meat-and-potatoes 2014 Year In Review (and here’s an infographic with more statistics for your viewing pleasure!):

Moz ended 2014 with $31.3 million in revenue. About $30 million was recurring revenue (mostly from subscriptions to Moz Pro and the API).

Here’s a breakdown of all our major revenue sources:

Compared to previous years, 2014 was a much slower growth year. We knew very early that it was going to be a tough year because we started Q1 with negative growth. We worked very hard and successfully shifted the momentum back to increasingly positive quarterly growth rates. I’m proud of what we’ve accomplished so far. We still have a long ways to go to meet our potential, but we’re on the path.

In subscription businesses, If you start the year with negative or even slow growth it is very hard to have meaningful annual growth. All things being equal, you’re better off having a bad quarter in Q4 than Q1. If you get a new customer in Q1, you usually earn revenue from that customer all year. If you get a new customer in Q4, it will barely make a dent in that year, although it should set you up nicely for the following year.

We exited 2014 on a good flight path, which bodes well for 2015. We slammed right into some nasty billing system challenges in Q1 2015, but still managed to grow revenue 6.5%. Mad props to the team for shifting momentum last year and for digging into the billing system challenges we’re experiencing now.

We were very successful in becoming more efficient and managing costs in 2014. Our Cost of Revenue (COR), the cost of producing what we sell, fell by 30% to $8.2 million. These savings drove our gross profit margin up from 63% in 2013 to 74%.

Our operating profit increased by 30%. Here’s a breakdown of our major expenses (both operating expenses and COR):

Total operating expenses (which don’t include COR) clocked in at about $29.9 million this year.

The efficiency gains positively impacted EBITDA (Earnings Before Interest, Taxes, Depreciation, and Amortization) by pushing it up 50% year over year. In 2013, EBITDA was -$4.5 million. We improved it to -$2.1 million in 2014. We’re a VC-backed startup, so this was a planned loss.

One of the most dramatic indicators of our improved efficiency in 2014 is the substantial decline in our consumption of cash.

In 2014, we spent $1.5 million in cash. This was a planned burn, and is actually very impressive for a startup. In fact, we are intentionally increasing our burn, so we don’t expect EBITDA and cash burn to look as good in 2015! Hopefully, though, you will see that revenue growth rate increase.

Let’s check in on some other Moz KPIs:

At the end of 2014, we reported a little over 27,000 Pro users. When billing system issues hit in Q1 2015, we discovered some weird under- and over-reporting, so the number of subscribers was adjusted down by about ~450 after we scrubbed a bunch of inactive accounts out of the database. We expect accounts to stabilize and be more reliable now that we’ve fixed those issues.

We launched Moz Local about a year ago. I’m amazed and thrilled that we were able to end the year managing 27,000 locations for a range of customers. We just recently took our baby steps into the UK, and we’ve got a bunch of great additional features planned. What an incredible launch year!

We published over 300 posts combined on the Moz Blog and YouMoz. Nearly 20,000 people left comments. Well done, team!

Our content and social efforts are paying off with a 26% year-over-year increase in organic search traffic.

We continue to see good growth across many of our off-site communities, too:

The team grew to 149 people last year. We’re at ~37% women, which is nowhere near where I want it to be. We have a long way to go before the team reflects the diversity of the communities around us.

Our paid, paid vacation perk is very popular with Mozzers, and why wouldn’t it be? Everyone gets $3,000/year to use toward their vacations. In 2014, we spent over $420,000 to help our Mozzers take a break and get connected with matters most.

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Also, we’re hiring! You’ll have my undying gratitude if you send me your best software engineers. Help us, help you. 😉

Last, but certainly not least, Mozzers continue to be generous (the ‘G’ in TAGFEE) and donate to the charities of their choice. In 2014, Mozzers donated $48k, and Moz added another $72k to increase the impact of their gifts. Combining those two figures, we donated $120k to causes our team members are passionate about. That’s an average of $805 per employee!

Mozzers are optimists with initiative. I think that’s why they are so generous with their time and money to folks in need. They believe the world can be a better place if we act to change it.

That’s a wrap on 2014! A year with many ups and downs. Fortunately, Mozzers don’t quit when things get hard. They embrace TAGFEE and lean into the challenge.

Revenue is growing again. We’re still operating very efficiently, and TAGFEE is strong. We’re heads-down executing on some big projects that customers have been clamoring for. Thank you for sticking with us, and for inspiring us to make marketing better every day.

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