dotties winners revealed!

Is your head hurting this morning? Yep. We thought so. But what a fantastic night. Laughter erupted from the Troxy as Rob Beckett delivered his hilarious stand-up. Our guests savored some mouthwatering street food. We all enjoyed one-too-many proseccos – and some of us danced til we dropped!

A HUGE thank you to everyone who attended the dotties 2018 – we hope you had an awesome time. A big hand to all the brands who made the shortlist – we were massively impressed with every single entry and our judges faced a tough decision.

But last night, we celebrated you – the winners. Congratulations to the brands who are shaping the digital marketing space, delivering first-class experiences to customers, fans and supporters. And for anyone having a little trouble remembering what went down, allow us to refresh your memory:

The winners

Certsure LLP

Inspiring email creative: The BIG Idea

Winning in Business: Best B2B Marketing Campaign

Barbour

Content Excellence: Most Compelling Campaign

 

Tottenham Hotspur

Creative Flair: Best Subject Line

Star of Leisure: Best use of dotmailer to please

Southampton FC

Data Creativity: Best Use of Data

 

icelolly.com

Innovation in Integration: Combining Tech Powers

 

Neal’s Yard Remedies

Excellence in Automation: Most Powerful Program Use

 

Asthma UK

Omnichannel Pioneers: Most Connected Campaign

Star of Non-profit: Best use of dotmailer to help others

 

Jack Wills

Excellence in Ecommerce: Most Compelling Campaign

 

Greene King

Big Impact, Small Bottom Line: Best Use of Budget

 

Inviqa

UK agency of the year

 

Absolunet

US agency of the year

 

Experius

European agency of the year

 

Crimson Consultants

CRM partner of the year

 

XCM

Integrated partner of the year

 

Yuji Isayama – Selco Builders Warehouse

Star Performer: Excellence in Marketing by an Individual

 

T. M. Lewin

Star Disruptor: Energizing Change in the Marketing Team

 

 

 

 

The post dotties winners revealed! appeared first on The Marketing Automation Blog.

Reblogged 1 year ago from blog.dotmailer.com

Deconstructing the App Store Rankings Formula with a Little Mad Science

Posted by AlexApptentive

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

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

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

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

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

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

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

Until now, that is.

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

But first, a little context

Image credit: Josh Tuininga, Apptentive

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

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

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

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

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

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

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

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

Now, for the Mad Science.

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

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

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

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

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

Hypothesis

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

Both of these assumptions will be tested in later analysis.

Results

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

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

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

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

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

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

Hypothesis

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

Results

App Store Ranking Volatility of Top 500 Apps

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

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

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

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

Study #3: App store rankings across the stars

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

Hypothesis

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

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

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

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

Results

Average App Store Ratings of Top Apps

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

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

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

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

App Store Ranking Volatility and Average Rating

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

Study #4: App store rankings across versions

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

Hypothesis

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

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

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

Results

How update frequency correlates with app store rank

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

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

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

How update frequency correlates with app store ranking volatility

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

Study #5: App store rankings across monthly active users

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

Hypothesis

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

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

Results

Apps with more ratings and reviews typically rank higher

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

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

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

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

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

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

Apps with more ratings typically experience less app store ranking volatility

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

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

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

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

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

Summary

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

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

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

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

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

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

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

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

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

Weight of factors in the Apple App Store ranking algorithm

Rating Count > Installs > Trends > Rating

Weight of factors in the Google Play ranking algorithm

Rating Count > Installs > Rating > Trends


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

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

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

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Reblogged 4 years ago from tracking.feedpress.it

How to Create Boring-Industry Content that Gets Shared

Posted by ronell-smith

If you think creating content for boring industries is tough, try creating content for an expensive product that’ll be sold in a so-called boring industry. Such was the problem faced by Mike Jackson, head of sales for a large Denver-based company that was debuting a line of new high-end products for the fishing industry in 2009.

After years of pestering the executives of his traditional, non-flashy company to create a line of products that could be sold to anglers looking to buy premium items, he finally had his wish: a product so expensive only a small percentage of anglers could afford them.

(image source)

What looked like being boxed into a corner was actually part of the plan.

When asked how he could ever put his neck on the line for a product he’d find tough to sell and even tougher to market, he revealed his brilliant plan.

“I don’t need to sell one million of [these products] a year,” he said. “All I need to do is sell a few hundred thousand, which won’t be hard. And as far as marketing, that’s easy: I’m ignoring the folks who’ll buy the items. I’m targeting professional anglers, the folks the buyers are influenced by. If the pros, the influencers, talk about and use the products, people will buy them.”

Such was my first introduction to how it’s often wise to ignore who’ll buy the product in favor of marketing to those who’ll help you market and sell the product.

These influencers are a sweet spot in product marketing and they are largely ignored by many brands

Looking at content for boring industries all wrong

A few months back, I received a message in Google Plus that really piqued my interest: “What’s the best way to create content for my boring business? Just kidding. No one will read it, nor share information from a painter anyway.”

I went from being dismayed to disheartened. Dismayed because the business owner hadn’t yet found a way to connect with his prospects through meaningful content. Disheartened because he seemed to have given up trying.

You can successfully create content for boring industries. Doing so requires nothing out of the ordinary from what you’d normally do to create content for any industry. That’s the good news.

The bad news: Creating successful content for boring industries requires you think beyond content and SEO, focusing heavily on content strategy and outreach.

Successfully creating content for boring industries—or any industry, for that matter—comes down to who’ll share it and who’ll link to it, not who’ll read it, a point nicely summed up in this tweet:

So when businesses struggle with creating content for their respective industries, the culprits are typically easy to find:

  • They lack clarity on who they are creating content for (e.g., content strategy, personas)
  • There are no specific goals (e.g., traffic, links, conversions, etc.) assigned regarding the content, so measuring its effectiveness is impossible
  • They’re stuck in neutral thinking viral content is the only option, while ignoring the value of content amplification (e.g., PR/outreach)

Alone, these three elements are bad; taken together, though, they spell doom for your brand.

content does not equal amplification

If you lack clarity on who you’re creating content for, the best you can hope for is that sometimes you’ll create and share information members of your audience find useful, but you likely won’t be able to reach or engage them with the needed frequency to make content marketing successful.

Goals, or lack thereof, are the real bugaboo of content creation. The problem is even worse for boring industries, where the pressure is on to deliver a content vehicle that meets the threshold of interest to simply gain attention, much less, earn engagement.

For all the hype about viral content, it’s dismaying that so few marketers aren’t being honest on the topic: it’s typically hard to create, impossible to predict and typically has very, very little connection to conversions for most businesses.

What I’ve found is that businesses, regardless of category, struggle to create worthwhile content, leading me to believe there is no boring industry content, only content that’s boring.

“Whenever we label content as ‘boring,’ we’re really admitting we have no idea how to approach marketing something,” says Builtvisible’s Richard Baxter.

Now that we know what the impediments are to producing content for any industry, including boring industries, it’s time to tackle the solution.

Develop a link earning mindset

There are lots of article on the web regarding how to create content for boring industries, some of which have appeared on this very blog.

But, to my mind, the one issue they all suffer from is they all focus on what content should be created, not (a) what content is worthy of promotion, (b) how to identify those who could help with promotion, and (c) how to earn links from boring industry content. (Remember, much of the content that’s read is never shared; much of what’s shared is never read in its entirety; and some of the most linked-to content is neither heavily shared nor heavily read.)

This is why content creators in boring industries should scrap their notions of having the most-read and most-shared content, shifting their focus to creating content that can earn links in addition to generating traffic and social signals to the site.

After all, links and conversions are the main priorities for most businesses sharing content online, including so-called local businesses.

ranking factors survey results

(Image courtesy of the 2014 Moz Local Search Ranking Factors Survey)

If you’re ready to create link-earning, traffic-generating content for your boring-industry business follow the tips from the fictitious example of RZ’s Auto Repair, a Dallas, Texas, automobile shop.

With the Dallas-Forth Worth market being large and competitive, RZ’s has narrowed their speciality to storm repair, mainly hail damage, which is huge in the area. Even with the narrowed focus, however, they still have stiff competition from the major players in the vertical, including MAACO.

What the brand does have in its favor, however, is a solid website and a strong freelance copywriter to help produce content.

Remember, those three problems we mentioned above—lack of goals, lack of clarity and lack of focus on amplification—we’ll now put them to good use to drive our main objectives of traffic, links and conversions.

Setting the right goals

For RZ, this is easy: He needs sales, business (e.g., qualified leads and conversions), but he knows he must be patient since using paid media is not in the cards.

Therefore, he sits down with his partner, and they come up with what seems like the top five workable, important goals:

  1. Increased traffic on the website – He’s noticed that when traffic increases, so does his business.
  2. More phone calls – If they get a customer on the phone, the chances of closing the sale are around 75%.
  3. One blog per week on the site – The more often he blogs, the more web traffic, visits and phone calls increase.
  4. Links from some of the businesses in the area – He’s no dummy. He knows the importance of links, which are that much better when they come from a large company that could send him business.
  5. Develop relationships with small and midsize non-competing businesses in the area for cross promotions, events and the like.

Know the audience

marketing group discussing personas

(image source)

Too many businesses create cute blogs that might generate traffic but do nothing for sales. RZ isn’t falling for this trap. He’s all about identifying the audience who’s likely to do business with him.

Luckily, his secretary is a meticulous record keeper, allowing him to build a reasonable profile of his target persona based on past clients.

  • 21-35 years old
  • Drives a truck that’s less than fours years old
  • Has an income of $45,000-$59,000
  • Employed by a corporation with greater than 500 employees
  • Active on social media, especially Facebook and Twitter
  • Consumes most of their information online
  • Typically referred by a friend or a co-worker

This information will prove invaluable as he goes about creating content. Most important, these nuggets create a clearer picture of how he should go about looking for people and/or businesses to amplify his content.

PR and outreach: Your amplification engines

Armed with his goals and the knowledge of his audience, RZ can now focus on outreach for amplification, thinking along the lines of…

  • Who/what influences his core audience?
  • What could he offer them by way of content to earn their help?
  • What content would they find valuable enough to share and link to?
  • What challenges do they face that he could help them with?
  • How could his brand set itself apart from any other business looking for help from these potential outreach partners?

Putting it all together

Being the savvy businessperson he is, RZ pulls his small staff together and they put their thinking caps on.

Late spring through early fall is prime hail storm season in Dallas. The season accounts for 80 percent of his yearly business. (The other 20% is fender benders.) Also, they realize, many of the storms happen in the late afternoon/early evening, when people are on their way home from work and are stuck in traffic, or when they duck into the grocery store or hit the gym after work.

What’s more, says one of the staffers, often a huge group of clients will come at once, owing to having been parked in the same lot when a storm hits.

Eureka!

lightbulb

(image source)

That’s when RZ bolts out of his chair with the idea that could put his business on the map: Let’s create content for businesses getting a high volume of after-work traffic—sit-down restaurants, gyms, grocery stores, etc.

The businesses would be offering something of value to their customers, who’ll learn about precautions to take in the event of a hail storm, and RZ would have willing amplifiers for his content.

Content is only as boring as your outlook

First—and this is a fatal mistake too many content creators make—RZ visits the handful of local businesses he’d like to partner with. The key here, however, is he smartly makes them aware that he’s done his homework and is eager to help their patrons while making them aware of his service.

This is an integral part of outreach: there must be a clear benefit to the would-be benefactor.

After RZ learns that several of the businesses are amenable to sharing his business’s helpful information, he takes the next step and asks what form the content should take. For now, all he can get them to promote is a glossy one-sheeter, “How To Protect Your Vehicle Against Extensive Hail Damage,” that the biggest gym in the area will promote via a small display at the check-in in return for a 10% coupon for customers.

Three of the five others he talked to also agreed to promote the one-sheeter, though each said they’d be willing to promote other content investments provided they added value for their customers.

The untold truth about creating content for boring industries

When business owners reach out to me about putting together a content strategy for their boring brand, I make two things clear from the start:

  1. There are no boring brands. Those two words are a cop out. No matter what industry you serve, there are hoards of people who use the products or services who are quite smitten.
  2. What they see as boring, I see as an opportunity.

In almost every case, they want to discuss some of another big content piece that’s sure to draw eyes, engagement, and that maybe even leads to a few links. Sure, I say, if you have tons of money to spend.

big content example

(Amazing piece of interactive content created by BuiltVisible)

Assuming you don’t have money to burn, and you want a plan you can replicate easily over time, try what I call the 1-2-1 approach for monthly blog content:

1: A strong piece of local content (goal: organic reach, topical relevance, local SEO)

2: Two pieces of evergreen content (goal: traffic)

1: A link-worthy asset (goal: links)

This plan is not very hard at all to pull off, provided you have your ear to the street in the local market; have done your keyword research, identifying several long-tail keywords you have the ability to rank for; and you’re willing to continue with outreach.

What it does is allow the brand to create content with enough frequency to attain significance with the search engines, while also developing the habit of sharing, promoting and amplifying content as well. For example, all of the posts would be shared on Twitter, Google Plus, and Facebook. (Don’t sleep on paid promotion via Facebook.)

Also, for the link-worthy asset, there would be outreach in advance of its creation, then amplification, and continued promotion from the company and those who’ve agreed to support the content.

Create a winning trifecta: Outreach, promotion and amplification

To RZ’s credit, he didn’t dawdle, getting right to work creating worthwhile content via the 1-2-1 method:

1: “The Worst Places in Dallas to be When a Hail Storm Hits”
2: “Can Hail Damage Cause Structural Damage to Your Car?” and “Should You Buy a Car Damaged by Hail?”
1: “Big as Hail!” contest

This contest idea came from the owner of a large local gym. RZ’s will give $500 to the local homeowner who sends in the largest piece of hail, as judged by Facebook fans, during the season. In return, the gym will promote the contest at its multiple locations, link to the content promotion page on RZ’s website, and share images of its fans holding large pieces of hail via social media.

What does the gym get in return: A catchy slogan (e.g., it’s similar to “big as hell,” popular gym parlance) to market around during the hail season.

It’s a win-win for everyone involved, especially RZ.

He gets a link, but most important he realizes how to create content to nail each one of his goals. You can do the same. All it takes is a change in mindset. Away from content creation. Toward outreach, promote and amplify.

Summary

While the story of RZ’s entirely fictional, it is based on techniques I’ve used with other small and midsize businesses. The keys, I’ve found, are to get away from thinking about your industry/brand as being boring, even if it is, and marshal the resources to find the audience who’ll benefit from from your content and, most important, identify the influencers who’ll promote and amplify it.

What are your thoughts?

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Reblogged 4 years ago from tracking.feedpress.it

E-Commerce KPI Study: There’s (Finally) a Benchmark for That

Posted by ProfAlfonso

Being a digital marketer, I spend my day knee-deep in data. The time I don’t spend analysing it, I spend explaining its significance to a client or junior colleague or arguing its significance with a client or senior colleague.

But after many debates over the importance of bounce rate, time on site, mobile conversion rate and the colour grey for buttons (our designer partook in that last one), we’re never much closer to an agreement on significance.

Our industry is swimming in data (thanks Google Analytics), but at times we’re drowning in it.

Numbers without context mean nothing. Data in the hands of even the savviest marketer is useless without a context to evaluate its performance against competitors or the industry at large.

Which is why we need benchmarks.
Through benchmarking, marketers can contextualise data to identify under-performing elements and amplify what is over-performing. They can focus on the KPIs that are important, and recognise whether they are achievable.

Benchmarks also give context to those who aren’t familiar with data. One pain point that digital marketers face globally is communicating their performance upwards. There are very few ‘digital natives’ sitting in company boardrooms these days but plenty of executives who know their numbers inside out.

Industry benchmark data arms us with perspective and framework when we need to communicate upwards. It ensures we get pats on the back when deserved and additional budget released when required.

Google Analytics Benchmarking Reports

Google, you might argue, have already solved these problems.

The upgrade and roll-out of Google Analytics Benchmarking Reports has been met with plenty of excitement for these reasons. With its large data set and nifty options to chop up the data by geography and website size, for a minute it certainly seemed like the benchmarking of our dreams. And while we recognise its usefulness to benchmark against real-time data (comparing a surge of traffic from a particular location for example, or seasonal demands), it still left us short of the hard data insights we were looking for.

We wanted reliable KPI data that went beyond user behaviour. We wanted average conversion rates and average transaction values as well as ‘softer’ engagement metrics such as bounce rate and time on site.

Most importantly, we wanted to know which engagement metrics actually correlated with the conversion rate, so we could narrow our field of analysis and efforts in pursuit of a healthier bottom line.

Which is why we went out and got our own and generated this e-commerce KPI report.

Data and methodology

We analysed the 56 million visits and approximately $252 million (€214 million) in revenue that flowed through 30 participating websites between August 1, 2013 and July 30, 2014. The websites were in the retail and travel sectors and included both online-only and those with a physical store as well as an e-commerce site.

We averaged stats on a per-website basis, so that websites with high levels of traffic didn’t skew the stats. We had more retail participants than travel participants so the average e-commerce figures are not the midpoint between travel and retail but the average figure across all study participants. Revenue is attributed on a last-click basis.

Results

Here is a highlight of some of our most relevant and interesting findings. For all the data and results, download the full report on
WolfgangDigital.com.

Average KPIs: Bounce rate, time on site, and conversion rate

First, we calculated some averages across engagement KPIs and commercial KPIs. If you are an e-commerce website in the travel or retail business, you can use these numbers to evaluate how your website is performing when set against a broad swath of your industry peers.

Well, remember the conversion measured here is a sale. If your conversion rate is lower than the study average don’t fire your CMO straight away; check if your average transaction value (ATV) is higher. If they balance each other out you are all good – if they don’t, it’s time to start digging deeper. Does the 1.4% conversion rate give you a smug tingly feeling or a stab of panic?

We often break down conversion rate into two parts: website-to-basket and basket-to-checkout. Industry norms tell us expect about 5% CR on website-to-basket and 30% on basket-to-checkout. Check which one of these conversion rates is most out of kilter on your site, then focus your attention there. This exercise will often give greater visibility on where the hole in your bucket is, Dear Liza.

Another factor in this analysis is that online-only retailers tend to enjoy higher conversion rates as the consumer
must transact via the website. If you have an offline presence, a lower conversion rate comes with the physical territory as your site visitors may convert in store.

KPIs by device: Mobile under scrutiny

Next, we segmented the data by device: desktop, tablet and mobile.

We found that although mobile and tablet together accounted for nearly half of website traffic (43%), they contributed to just over a quarter of revenue (26%).

Mobile alone accounted for 26% of traffic but only 10% of revenue. This suggests that while mobile is a favoured device for browsing and researching, it’s the desktop where users are more likely to whip out the credit card.

When we looked at conversion rates by device, this confirmed it.

What data matters: The correlations

We wanted to know which engagement figures had an influence (if any) on commercial ones.

Then we’d know which behavioural metrics were worth trying to improve to lift conversion rate, and which metrics we could finally label insignificant.

We did this by calculating correlations. A correlation ranges from 0 to 1, so 0 indicates on no correlation at all, while 1 signifies a clear correlation. A negative correlation indicates that as one variable increases the other decreases.

Time on site (0.34) and pages viewed (0.35) both had positive correlations with conversion rate, so our advice is to look at how to improve these metrics for your site to benefit from a higher conversion rate.

We delved into the device data and found mobile was the only device with positive traffic (0.29) and revenue (0.45) correlations to overall conversion rate. In fact, that 0.45 correlation rate between mobile revenue % and conversion rate was actually the strongest correlation rate across all factors we measured.

We infer that while the mobile conversion rate is depressingly low, a mobile user is still somebody with purchase intent who is likely to convert later on another device. The lesson we took from this is to make sure your website is mobile-optimised, particularly for ease of research and browsing content.

Finally, the time came to talk about bounce rate. Our Excel wizard had converted the data to an ‘un-bounce rate’ (1 minus the bounce rate) for consistency with positive time on site and pages viewed metrics. We gathered round the spreadsheet.

He revealed
there is actually a negative correlation (-0.12) between un-bounce rate and conversion rate. This correlation signals that it couldn’t be less influential on conversion rate, so for those unable to sleep at night for bounce anxiety, we’re delighted to let you sleep easy.

Increasing your conversion rate may not be as complex a task as it seems.

Our KPI study shows that if you can increase pages viewed and time on site it will push up your conversion rate (content marketing for conversion optimisation anybody?).

We’ve also proved that mobile matters. Don’t be discouraged if your mobile conversion rate pales against desktop’s performance; keep driving mobile traffic and revenue (however minor) and you’ll see the difference in your bottom line.

Read the full results broken down by industry level by downloading from the Wolfgang Digital e-commerce KPI Study.

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Reblogged 5 years ago from moz.com

Local Search Expert Quiz: How Much Do You Know about Local SEO?

Posted by Cyrus-Shepard

How big is local SEO?

Our latest
Industry Survey revealed over 67% of online marketers report spending time on local search. We’ve witnessed demand for local SEO expertise grow as Google’s competitive landscape continues to evolve.

Last year, Moz introduced the
SEO Expert Quiz, which to date over 40,000 people have attempted to conquer. Today, we’re proud to announce the Local Search Expert Quiz. Written by local search expert Miriam Ellis, the quiz contains 40 questions and only takes less than 10 minutes to complete.

Ready to get started? When you are finished, we’ll automatically score your quiz and reveal the correct answers.

<a href=”http://mozbot.polldaddy.com/s/local-search-expert-quiz”>View Survey</a>

Rating your score

Keep in mind the Local Search Expert Quiz is
just for fun. That said, we’ve established the following guidelines to help judge your results.

  • 0-39% Newbie: Time to study up on your citation data!
  • 40-59% Beginner: Good job, but you’re not quite in the 7-pack yet.
  • 60-79% Intermediate: You’re getting close to the centroid!
  • 80-89% Pro: Let’s tackle multi-location!
  • 90-100% Guru: We all bow down to your local awesomeness

Resources to improve your performance

Want to learn more about local search? Here’s a collection of free learning resources to help up your performance (and possibly your income.)

  1. The Moz Local Learning Center
  2. Glossary of Local Search Terms and Definitions
  3. Guidelines for Representing Your Business on Google
  4. Local Search Ranking Factors
  5. Blumenthal’s Blog
  6. Local SEO Guide
  7. Whitespark Blog

You can also learn the latest local search tips and tricks by signing up for the LocalUp Advanced one-day conference or reading
local SEO posts on the Moz Blog.

Embed this Quiz

We created this quiz using
Polldaddy, and we’re making it available to embed on your own site. This isn’t a backlink play – we didn’t even include a link to our own site (but feel free to include one if you feel generous).

Here’s the embed code:

<iframe frameborder="0" width="100%" height="600" scrolling="auto" allowtransparency="true" src="http://mozbot.polldaddy.com/s/local-search-expert-quiz?iframe=1"><a href="http://mozbot.polldaddy.com/s/local-search-expert-quiz">View Survey</a></iframe>

How did you score on the quiz? Let us know in the comments below!

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www.findawineryvictoria.com.au is a local business directory for wineries

Reblogged 5 years ago from moz.com

10 Predictions for the Marketing World in 2015

Posted by randfish

The beginning of the year marks the traditional week for bloggers to prognosticate about the 12 months ahead, and, over the last decade I’ve created a tradition of joining in this festive custom to predict the big trends in SEO and web marketing. However, I divine the future by a strict code: I’m only allowed to make predictions IF my predictions from last year were at least moderately accurate (otherwise, why should you listen to me?). So, before I bring my crystal-ball-gazing, let’s have a look at how I did for 2014.

Yes, we’ll get to that, but not until you prove you’re a real Wizard, mustache-man.

You can find 
my post from January 5th of last year here, but I won’t force you to read through it. Here’s how I do grading:

  • Spot On (+2) – when a prediction hits the nail on the head and the primary criteria are fulfilled
  • Partially Accurate (+1) – predictions that are in the area, but are somewhat different than reality
  • Not Completely Wrong (-1) – those that landed near the truth, but couldn’t be called “correct” in any real sense
  • Off the Mark (-2) – guesses which didn’t come close

If the score is positive, prepare for more predictions, and if it’s negative, I’m clearly losing the pulse of the industry. Let’s tally up the numbers.

In 2014, I made 6 predictions:

#1: Twitter will go Facebook’s route and create insights-style pages for at least some non-advertising accounts

Grade: +2

Twitter rolled out Twitter analytics for all users this year (
starting in July for some accounts, and then in August for everyone), and while it’s not nearly as full-featured as Facebook’s “Insights” pages, it’s definitely in line with the spirit of this prediction.

#2: We will see Google test search results with no external, organic listings

Grade: -2

I’m very happy to be wrong about this one. To my knowledge, Google has yet to go this direction and completely eliminate external-pointing links on search results pages. Let’s hope they never do.

That said, there are plenty of SERPs where Google is taking more and more of the traffic away from everyone but themselves, e.g.:

I think many SERPs that have basic, obvious functions like ”
timer” are going to be less and less valuable as traffic sources over time.

#3: Google will publicly acknowledge algorithmic updates targeting both guest posting and embeddable infographics/badges as manipulative linking practices

Grade: -1

Google most certainly did release an update (possibly several)
targeted at guest posts, but they didn’t publicly talk about something specifically algorithmic targeting emebedded content/badges. It’s very possible this was included in the rolling Penguin updates, but the prediction said “publicly acknowledge” so I’m giving myself a -1.

#4: One of these 5 marketing automation companies will be purchased in the 9-10 figure $ range: Hubspot, Marketo, Act-On, Silverpop, or Sailthru

Grade: +2

Silverpop was 
purchased by IBM in April of 2014. While a price wasn’t revealed, the “sources” quoted by the media estimated the deal in the ~$270mm range. I’m actually surprised there wasn’t another sale, but this one was spot-on, so it gets the full +2.

#5: Resumes listing “content marketing” will grow faster than either SEO or “social media marketing”

Grade: +1

As a percentage, this certainly appears to be the case. Here’s some stats:

  • US profiles with “content marketing”
    • June 2013: 30,145
    • January 2015: 68,580
    • Growth: 227.5%
  • US profiles with “SEO”
    • June 2013: 364,119
    • January 2015: 596,050
    • Growth: 163.7%
  • US profiles with “social media marketing”
    • June 2013: 938,951
    • January 2015: 1,990,677
    • Growth: 212%

Granted, content marketing appears on far fewer profiles than SEO or social media marketing, but it has seen greater growth. I’m only giving myself a +1 rather than a +2 on this because, while the prediction was mathematically correct, the numbers of SEO and social still dwarf content marketing as a term. In fact, in LinkedIn’s 
annual year-end report of which skills got people hired the most, SEO was #5! Clearly, the term and the skillset continue to endure and be in high demand.

#6: There will be more traffic sent by Pinterest than Twitter in Q4 2014 (in the US)

Grade: +1

This is probably accurate, since Pinterest appears to have grown faster in 2014 than Twitter by a good amount AND this was 
already true in most of 2014 according to SharedCount (though I’m not totally sold on the methodology of coverage for their numbers). However, we won’t know the truth for a few months to come, so I’d be presumptuous in giving a full +2. I am a bit surprised that Pinterest continues to grow at such a rapid pace — certainly a very impressive feat for an established social network.


SOURCE: 
Global Web Index

With Twitter’s expected moves into embedded video, it’s my guess that we’ll continue to see a lot more Twitter engagement and activity on Twitter itself, and referring traffic outward won’t be as considerable a focus. Pinterest seems to be one of the only social networks that continues that push (as Facebook, Instagram, LinkedIn, and YouTube all seem to be pursuing a “keep them here” strategy).

——————————–

Final Score: +3

That positive number means I’ve passed my bar and can make another set of predictions for 2015. I’m going to be a little more aggressive this year, even though it risks ruining my sterling record, simply because I think it’s more exciting 🙂

Thus, here are my 10 predictions for what the marketing world will bring us in 2015:

#1: We’ll see the first major not-for-profit University in the US offer a degree in Internet Marketing, including classes on SEO.

There are already some private, for-profit offerings from places like Fullsail and Univ. of Phoenix, but I don’t know that these pedigrees carry much weight. Seeing a Stanford, a Wharton, or a University of Washington offer undergraduate or MBA programs in our field would be a boon to those seeking options and an equal boon to the universities.

The biggest reason I think we’re ripe for this in 2015 is the 
LinkedIn top 25 job skills data showing the immense value of SEO (#5) and digital/online marketing (#16) in a profile when seeking a new job. That should (hopefully) be a direct barometer for what colleges seek to include in their repertoire.

#2: Google will continue the trend of providing instant answers in search results with more interactive tools.

Google has been doing instant answers for a long time, but in addition to queries with immediate and direct responses, they’ve also undercut a number of online tool vendors by building their own versions directly into the SERPs, like they do currently for queries like ”
timer” and “calculator.”

I predict in 2015, we’ll see more partnerships like what’s provided with 
OpenTable and the ability to book reservations directly from the SERPs, possibly with companies like Uber, Flixster (they really need to get back to a better instant answer for movies+city), Zillow, or others that have unique data that could be surfaced directly.

#3: 2015 will be the year Facebook begins including some form of web content (not on Facebook’s site) in their search functionality.

Facebook 
severed their search relationship with Bing in 2014, and I’m going to make a very risky prediction that in 2015, we’ll see Facebook’s new search emerge and use some form of non-Facebook web data. Whether they’ll actually build their own crawler or merely license certain data from outside their properties is another matter, but I think Facebook’s shown an interest in getting more sophisticated with their ad offerings, and any form of search data/history about their users would provide a powerful addition to what they can do today.

#4: Google’s indexation of Twitter will grow dramatically, and a significantly higher percentage of tweets, hashtags, and profiles will be indexed by the year’s end.

Twitter has been 
putting more muscle behind their indexation and SEO efforts, and I’ve seen more and more Twitter URLs creeping into the search results over the last 6 months. I think that trend continues, and in 2015, we see Twitter.com enter the top 5-6 “big domains” in Mozcast.

#5: The EU will take additional regulatory action against Google that will create new, substantive changes to the search results for European searchers.

In 2014, we saw the EU 
enforce the “right to be forgotten” and settle some antitrust issues that require Google to edit what it displays in the SERPs. I don’t think the EU is done with Google. As the press has noted, there are plenty of calls in the European Parliament to break up the company, and while I think the EU will stop short of that measure, I believe we’ll see additional regulatory action that affects search results.

On a personal opinion note, I would add that while I’m not thrilled with how the EU has gone about their regulation of Google, I am impressed by their ability to do so. In the US, with 
Google becoming the second largest lobbying spender in the country and a masterful influencer of politicians, I think it’s extremely unlikely that they suffer any antitrust or regulatory action in their home country — not because they haven’t engaged in monopolistic behavior, but because they were smart enough to spend money to manipulate elected officials before that happened (unlike Microsoft, who, in the 1990’s, assumed they wouldn’t become a target).

Thus, if there is to be any hedge to Google’s power in search, it will probably come from the EU and the EU alone. There’s no competitor with the teeth or market share to have an impact (at least outside of China, Russia, and South Korea), and no other government is likely to take them on.

#6: Mobile search, mobile devices, SSL/HTTPS referrals, and apps will combine to make traffic source data increasingly hard to come by.

I’ll estimate that by year’s end, many major publishers will see 40%+ of their traffic coming from “direct” even though most of that is search and social referrers that fail to pass the proper referral string. Hopefully, we’ll be able to verify that through folks like 
Define Media Group, whose data sharing this year has made them one of the best allies marketers have in understanding the landscape of web traffic patterns.

BTW – I’d already estimate that 30-50% of all “direct” traffic is, in fact, search or social traffic that hasn’t been properly attributed. This is a huge challenge for web marketers — maybe one of the greatest challenges we face, because saying “I brought in a lot more traffic, I just can’t prove it or measure it,” isn’t going to get you nearly the buy-in, raises, or respect that your paid-traffic compatriots can earn by having every last visit they drive perfectly attributed.

#7: The content advertising/recommendation platforms will continue to consolidate, and either Taboola or Outbrain will be acquired or do some heavy acquiring themselves.

We just witnessed the 
surprising shutdown of nRelate, which I suspect had something to do with IAC politics more than just performance and potential for the company. But given that less than 2% of the web’s largest sites use content recommendation/promotion services and yet both Outbrain and Taboola are expected to have pulled in north of $200m in 2014, this is a massive area for future growth.

Yahoo!, Facebook, and Google are all potential acquirers here, and I could even see AOL (who already own Gravity) or Buzzfeed making a play. Likewise, there’s a slew of smaller/other players that Taboola or Outbrain themselves could acquire: Zemanta, Adblade, Zegnet, Nativo, Disqus, Gravity, etc. It’s a marketplace as ripe for acquisition as it is for growth.

#8: Promoted pins will make Pinterest an emerging juggernaut in the social media and social advertising world, particularly for e-commerce.

I’d estimate we’ll see figures north of $50m spent on promoted pins in 2015. This is coming after Pinterest only just 
opened their ad platform beyond a beta group this January. But, thanks to high engagement, lots of traffic, and a consumer base that B2C marketers absolutely love and often struggle to reach, I think Pinterest is going to have a big ad opportunity on their hands.

Note the promoted pin from Mad Hippie on the right

(apologies for very unappetizing recipes featured around it)

#9: Foursquare (and/or Swarm) will be bought, merge with someone, or shut down in 2015 (probably one of the first two).

I used to love Foursquare. I used the service multiple times every day, tracked where I went with it, ran into friends in foreign cities thanks to its notifications, and even used it to see where to go sometimes (in Brazil, for example, I found Foursquare’s business location data far superior to Google Maps’). Then came the split from Swarm. Most of my friends who were using Foursquare stopped, and the few who continued did so less frequently. Swarm itself tried to compete with Yelp, but it looks like 
neither is doing well in the app rankings these days.

I feel a lot of empathy for Dennis and the Foursquare team. I can totally understand the appeal, from a development and product perspective, of splitting up the two apps to let each concentrate on what it’s best at, and not dilute a single product with multiple primary use cases. Heck, we’re trying to learn that lesson at Moz and refocus our products back on SEO, so I’m hardly one to criticize. That said, I think there’s trouble brewing for the company and probably some pressure to sell while their location and check-in data, which is still hugely valuable, is robust enough and unique enough to command a high price.

#10: Amazon will not take considerable search share from Google, nor will mobile search harm Google’s ad revenue substantively.

The “Google’s-in-trouble” pundits are mostly talking about two trends that could hurt Google’s revenue in the year ahead. First, mobile searchers being less valuable to Google because they don’t click on ads as often and advertisers won’t pay as much for them. And, second, Amazon becoming the destination for direct, commercial queries ahead of Google.

In 2015, I don’t see either of these taking a toll on Google. I believe most of Amazon’s impact as a direct navigation destination for e-commerce shoppers has already taken place and while Google would love to get those searchers back, that’s already a lost battle (to the extent it was lost). I also don’t think mobile is a big concern for Google — in fact, I think they’re pivoting it into an opportunity, and taking advantage of their ability to connect mobile to desktop through Google+/Android/Chrome. Desktop search may have flatter growth, and it may even decline 5-10% before reaching a state of equilibrium, but mobile is growing at such a huge clip that Google has plenty of time and even plentier eyeballs and clicks to figure out how to drive more revenue per searcher.

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Reblogged 5 years ago from moz.com

WordPress SEO Secrets Revealed 40 Video Series – Sample 1

WordPress SEO Secrets Revealed 40 Video Series – Sample 2.

Reblogged 5 years ago from www.youtube.com

Two Search Engine Optimization Secrets Revealed

Explore this two search engine optimization secrets that will help you rank better. For more SEO secrets go to http://www.web-searchengineoptimization.com/se…

Reblogged 5 years ago from www.youtube.com