Meet Dan Morris, Executive Vice President, North America

  1. Why did you decide to come to dotmailer?

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

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

      1. Tell us a bit about your new role

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Reblogged 2 years ago from blog.dotmailer.com

Help Us Improve the Moz Blog: 2015 Reader Survey

Posted by Trevor-Klein

In late 2013, we asked you all about your experience with the Moz Blog. It was the first time we’d collected direct feedback from our readers in more than three years—an eternity in the marketing industry. With the pace of change in our line of work (not to mention your schedules and reading habits) we didn’t want to wait that long again, so we’re taking this opportunity to ask you how well we’re keeping up.

Our mission is to help you all become better marketers, and to do that, we need to know more about you. What challenges do you all face? What are your pain points? Your day-to-day frustrations? If you could learn more about one or two (or three) topics, what would those be?

If you’ll help us out by taking this five-minute survey, we can make sure we’re offering the most useful and valuable content we possibly can. When we’re done looking through the responses, we’ll follow up with a post about what we learned.

Thanks, everyone; we’re excited to see what you have to say!

(function(){var qs,js,q,s,d=document,gi=d.getElementById,ce=d.createElement,gt=d.getElementsByTagName,id=’typef_orm’,b=’https://s3-eu-west-1.amazonaws.com/share.typeform.com/’;if(!gi.call(d,id)){js=ce.call(d,’script’);js.id=id;js.src=b+’widget.js’;q=gt.call(d,’script’)[0];q.parentNode.insertBefore(js,q)}})()

Can’t see the survey? Click here to take it in a new tab.

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Reblogged 3 years ago from tracking.feedpress.it

5 Spreadsheet Tips for Manual Link Audits

Posted by MarieHaynes

Link auditing is the part of my job that I love the most. I have audited a LOT of links over the last few years. While there are some programs out there that can be quite helpful to the avid link auditor, I still prefer to create a spreadsheet of my links in Excel and then to audit those links one-by-one from within Google Spreadsheets. Over the years I have learned a few tricks and formulas that have helped me in this process. In this article, I will share several of these with you.

Please know that while I am quite comfortable being labelled a link auditing expert, I am not an Excel wizard. I am betting that some of the things that I am doing could be improved upon if you’re an advanced user. As such, if you have any suggestions or tips of your own I’d love to hear them in the comments section!

1. Extract the domain or subdomain from a URL

OK. You’ve downloaded links from as many sources as possible and now you want to manually visit and evaluate one link from every domain. But, holy moly, some of these domains can have THOUSANDS of links pointing to the site. So, let’s break these down so that you are just seeing one link from each domain. The first step is to extract the domain or subdomain from each url.

I am going to show you examples from a Google spreadsheet as I find that these display nicer for demonstration purposes. However, if you’ve got a fairly large site, you’ll find that the spreadsheets are easier to create in Excel. If you’re confused about any of these steps, check out the animated gif at the end of each step to see the process in action.

Here is how you extract a domain or subdomain from a url:

  • Create a new column to the left of your url column.
  • Use this formula:

    =LEFT(B1,FIND(“/”,B1,9)-1)

    What this will do is remove everything after the trailing slash following the domain name. http://www.example.com/article.html will now become http://www.example.com and http://www.subdomain.example.com/article.html will now become http://www.subdomain.example.com.

  • Copy our new column A and paste it right back where it was using the “paste as values” function. If you don’t do this, you won’t be able to use the Find and Replace feature.
  • Use Find and Replace to replace each of the following with a blank (i.e. nothing):
    http://
    https://
    www.

And BOOM! We are left with a column that contains just domain names and subdomain names. This animated gif shows each of the steps we just outlined:

2. Just show one link from each domain

The next step is to filter this list so that we are just seeing one link from each domain. If you are manually reviewing links, there’s usually no point in reviewing every single link from every domain. I will throw in a word of caution here though. Sometimes a domain can have both a good link and a bad link pointing to you. Or in some cases, you may find that links from one page are followed and from another page on the same site they are nofollowed. You can miss some of these by just looking at one link from each domain. Personally, I have some checks built in to my process where I use Scrapebox and some internal tools that I have created to make sure that I’m not missing the odd link by just looking at one link from each domain. For most link audits, however, you are not going to miss very much by assessing one link from each domain.

Here’s how we do it:

  • Highlight our domains column and sort the column in alphabetical order.
  • Create a column to the left of our domains, so that the domains are in column B.
  • Use this formula:

    =IF(B1=B2,”duplicate”,”unique”)

  • Copy that formula down the column.
  • Use the filter function so that you are just seeing the duplicates.
  • Delete those rows. Note: If you have tens of thousands of rows to delete, the spreadsheet may crash. A workaround here is to use “Clear Rows” instead of “Delete Rows” and then sort your domains column from A-Z once you are finished.

We’ve now got a list of one link from every domain linking to us.

Here’s the gif that shows each of these steps:

You may wonder why I didn’t use Excel’s dedupe function to simply deduplicate these entries. I have found that it doesn’t take much deduplication to crash Excel, which is why I do this step manually.

3. Finding patterns FTW!

Sometimes when you are auditing links, you’ll find that unnatural links have patterns. I LOVE when I see these, because sometimes I can quickly go through hundreds of links without having to check each one manually. Here is an example. Let’s say that your website has a bunch of spammy directory links. As you’re auditing you notice patterns such as one of these:

  • All of these directory links come from a url that contains …/computers/internet/item40682/
  • A whole bunch of spammy links that all come from a particular free subdomain like blogspot, wordpress, weebly, etc.
  • A lot of links that all contain a particular keyword for anchor text (this is assuming you’ve included anchor text in your spreadsheet when making it.)

You can quickly find all of these links and mark them as “disavow” or “keep” by doing the following:

  • Create a new column. In my example, I am going to create a new column in Column C and look for patterns in urls that are in Column B.
  • Use this formula:

    =FIND(“/item40682”,B1)
    (You would replace “item40682” with the phrase that you are looking for.)

  • Copy this formula down the column.
  • Filter your new column so that you are seeing any rows that have a number in this column. If the phrase doesn’t exist in that url, you’ll see “N/A”, and we can ignore those.
  • Now you can mark these all as disavow

4. Check your disavow file

This next tip is one that you can use to check your disavow file across your list of domains that you want to audit. The goal here is to see which links you have disavowed so that you don’t waste time reassessing them. This particular tip only works for checking links that you have disavowed on the domain level.

The first thing you’ll want to do is download your current disavow file from Google. For some strange reason, Google gives you the disavow file in CSV format. I have never understood this because they want you to upload the file in .txt. Still, I guess this is what works best for Google. All of your entries will be in column A of the CSV:

What we are going to do now is add these to a new sheet on our current spreadsheet and use a VLOOKUP function to mark which of our domains we have disavowed.

Here are the steps:

  • Create a new sheet on your current spreadsheet workbook.
  • Copy and paste column A from your disavow spreadsheet onto this new sheet. Or, alternatively, use the import function to import the entire CSV onto this sheet.
  • In B1, write “previously disavowed” and copy this down the entire column.
  • Remove the “domain:” from each of the entries by doing a Find and Replace to replace domain: with a blank.
  • Now go back to your link audit spreadsheet. If your domains are in column A and if you had, say, 1500 domains in your disavow file, your formula would look like this:

    =VLOOKUP(A1,Sheet2!$A$1:$B$1500,2,FALSE)

When you copy this formula down the spreadsheet, it will check each of your domains, and if it finds the domain in Sheet 2, it will write “previously disavowed” on our link audit spreadsheet.

Here is a gif that shows the process:

5. Make monthly or quarterly disavow work easier

That same formula described above is a great one to use if you are doing regular repeated link audits. In this case, your second sheet on your spreadsheet would contain domains that you have previously audited, and column B of this spreadsheet would say, “previously audited” rather than “previously disavowed“.

Your tips?

These are just a few of the formulas that you can use to help make link auditing work easier. But there are lots of other things you can do with Excel or Google Sheets to help speed up the process as well. If you have some tips to add, leave a comment below. Also, if you need clarification on any of these tips, I’m happy to answer questions in the comments section.

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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The Long Click and the Quality of Search Success

Posted by billslawski

“On the most basic level, Google could see how satisfied users were. To paraphrase Tolstoy, happy users were all the same. The best sign of their happiness was the “Long Click” — This occurred when someone went to a search result, ideally the top one, and did not return. That meant Google has successfully fulfilled the query.”

~ Steven Levy. In the Plex: How Google Thinks, Works, and Shapes our Lives

I often explore and read patents and papers from the search engines to try to get a sense of how they may approach different issues, and learn about the assumptions they make about search, searchers, and the Web. Lately, I’ve been keeping an eye open for papers and patents from the search engines where they talk about a metric known as the “long click.”

A recently granted Google patent uses the metric of a “Long Click” as the center of a process Google may use to track results for queries that were selected by searchers for long visits in a set of search results.

This concept isn’t new. In 2011, I wrote about a Yahoo patent in How a Search Engine May Measure the Quality of Its Search Results, where they discussed a metric that they refer to as a “target page success metric.” It included “dwell time” upon a result as a sign of search success (Yes, search engines have goals, too).

5543947f5bb408.24541747.jpg

Another Google patent described assigning web pages “reachability scores” based upon the quality of pages linked to from those initially visited pages. In the post Does Google Use Reachability Scores in Ranking Resources? I described how a Google patent that might view a long click metric as a sign to see if visitors to that page are engaged by the links to content they find those links pointing to, including links to videos. Google tells us in that patent that it might consider a “long click” to have been made on a video if someone watches at least half the video or 30 seconds of it. The patent suggests that a high reachability score on a page may mean that page could be boosted in Google search results.

554394a877e8c8.30299132.jpg

But the patent I’m writing about today is focused primarily upon looking at and tracking a search success metric like a long click or long dwell time. Here’s the abstract:

Modifying ranking data based on document changes

Invented by Henele I. Adams, and Hyung-Jin Kim

Assigned to Google

US Patent 9,002,867

Granted April 7, 2015

Filed: December 30, 2010

Abstract

Methods, systems, and apparatus, including computer programs encoded on computer storage media for determining a weighted overall quality of result statistic for a document.

One method includes receiving quality of result data for a query and a plurality of versions of a document, determining a weighted overall quality of result statistic for the document with respect to the query including weighting each version specific quality of result statistic and combining the weighted version-specific quality of result statistics, wherein each quality of result statistic is weighted by a weight determined from at least a difference between content of a reference version of the document and content of the version of the document corresponding to the version specific quality of result statistic, and storing the weighted overall quality of result statistic and data associating the query and the document with the weighted overall quality of result statistic.

This patent tells us that search results may be be ranked in an order, according to scores assigned to the search results by a scoring function or process that would be based upon things such as:

  • Where, and how often, query terms appear in the given document,
  • How common the query terms are in the documents indexed by the search engine, or
  • A query-independent measure of quality of the document itself.

Last September, I wrote about how Google might identify a category associated with a query term base upon clicks, in the post Using Query User Data To Classify Queries. In a query for “Lincoln.” the results that appear in response might be about the former US President, the town of Lincoln, Nebraska, and the model of automobile. When someone searches for [Lincoln], Google returning all three of those responses as a top result could be said to be reasonable. The patent I wrote about in that post told us that Google might collect information about “Lincoln” as a search entity, and track which category of results people clicked upon most when they performed that search, to determine what categories of pages to show other searchers. Again, that’s another “search success” based upon a past search history.

There likely is some value in working to find ways to increase the amount of dwell time someone spends upon the pages of your site, if you are already having some success in crafting page titles and snippets that persuade people to click on your pages when they those appear in search results. These approaches can include such things as:

  1. Making visiting your page a positive experience in terms of things like site speed, readability, and scannability.
  2. Making visiting your page a positive experience in terms of things like the quality of the content published on your pages including spelling, grammar, writing style, interest, quality of images, and the links you share to other resources.
  3. Providing a positive experience by offering ideas worth sharing with others, and offering opportunities for commenting and interacting with others, and by being responsive to people who do leave comments.

Here are some resources I found that discuss this long click metric in terms of “dwell time”:

Your ability to create pages that can end up in a “long click” from someone who has come to your site in response to a query, is also a “search success” metric on the search engine’s part, and you both succeed. Just be warned that as the most recent patent from Google on Long Clicks shows us, Google will be watching to make sure that the content of your page doesn’t change too much, and that people are continuing to click upon it in search results, and spend a fair amount to time upon it.

(Images for this post are from my Go Fish Digital Design Lead Devin Holmes @DevinGoFish. Thank you, Devin!)

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Understand and Harness the Power of Archetypes in Marketing

Posted by gfiorelli1

Roger Dooley, neuromarketing expert, reminds us in his book Brainfluence that in 80% of cases we make a decision before being rationally aware of it.

Although Dooley explains this effect in terms of how our brain works, in my opinion, distinctly separating neuroscience and the theory of archetypes would be incorrect. On the contrary, I believe that these two aspects of the study of the human mind are complementary.

According to
Jung, archetypes are “[…] forms or images of a collective nature which occur practically all over the Earth as constituents of myths and—at the same time—as individual products of unconscious”. He then, added something that interests us greatly: “The [forms and images] are imprinted and hardwired into out psyches”.

Being able to design a brand personality around an archetype that connects unconsciously with our audience is a big first step for: brand loyalty, community creation, engagement, conversions.

The Slender Man is the “Internet age” version of the archetype figure of the Shadow

Archetypes can be also used for differentiating our brand and its messaging from others in our same market niche and to give that brand a unique voice.

If we put users at the center of our marketing strategy, then
we cannot limit ourselves in knowing how they search, how they talk on social media, what they like to share or what their demographics are.

No,
we should also understand the deep psychological reasons why they desire something they search for, talk the way they talk, share what they share, and their psychological relation with the environment and society they live in.

Knowing that,
we can use archetypes to create a deep emotional connection with our audience and earn their strong positive attitude toward us thanks to the empathy that is created between them and us.

Narrative modes, then, help us in shaping in a structured way a brand storytelling able to guide and engage the users, and not simply selling or creating content narrative doomed to fail.

The 12 archetypes




graph by Emily Bennet

The chart above presents the 12 Jungian archetypes (i.e: Hero), to what principal human desire (i.e.: leave a mark on the world) they correspond and what is the main behavior each one uses for achieving that desire (i.e.: mastery).


Remember: if the audience instinctively recognizes the archetypal figure of the brand and its symbolism and instinctively connect with it, then your audience is more ready to like and trust what your brand proposes
.

On the other hand, it is also a good exercise to experiment with archetypes that we would not think are our brand’s one, expanding the practice of A/B tests to make sure we’re working with the correct archetype. 

The Creator

In my last post I used Lego as example of a brand that is winning Internet marketing thanks to its holistic and synergistic use of offline and online marketing channels.

I explained also how part of its success is due to the fact Lego was able to shape its messages and brand personality around the Creator archetype (sometimes called the “Builder”) which is embodied by their tagline, “let’s build”.

Creators tend to be nonconformist and to enjoy self expression.
A Creator brand, then, will empower and prize its audience as much as it is able to express itself using its products.

The Ruler

The Ruler is the leader, the one setting the rules others will follow, even competitors. Usually it’s paired with an
idea of exclusiveness and status growth.

A brand that presents itself as a Ruler is suggesting to their audience that they can be rulers too.

A classic example of Ruler brand is Mercedes:

The Caregiver

Altruism, compassion, generosity.
Caregiver brands present themselves as someone to trust, because they care and empathize with their audience.

The Caregiver is one of the most positive archetypes, and it is obviously used by nonprofit organizations or governmental institutions like UNICEF, but brands like Johnson & Johnson have also shaped their personality and messages around this figure.

The Innocent

The Innocent finds positive sides in everyone and everything

It sees beauty even in things that others will not even consider, and feels in peace with its inner beauty.

Dove, obviously, is a good representation of the Innocent archetype.

The Sage

The Sages wants to know and understand things. 


The Sage is deeply humanist and believe in the power of humankind to shape a better world through knowledge
.

However, the Sage also has a shadowed side: intolerance to ideas others than their own.

Google, in both cases, is a good example a Sage brand.

The Explorer

The Explorer is adventurous, brave, and loves challenges. He tends to be an individualist too, and loves to challenge himself so as to find his real self.


Explorer brands prompt their audience to challenge themselves and to discover the Explorer within

Red Bull is a classic example of these kinds of brands, but REI and Patagonia are even better representations.

The Hero

In many aspects, the Hero archetype is similar to the Explorer and Outlaw ones, with the difference that the Hero many times never wanted to be the hero, but injustice and external events obliged him to find the courage, braveness, and the honor to become one.

Nike, and also its competitor Adidas, shapes its brand voice around this archetypal figure.

The Magician

The Magician is clever, intelligent, and sometimes his ability can be considered supernatural. 


The Magician is able to make the impossible possible
. Because of that some of the best known technology brands use this archetype as their own to showcase their innovation and how they use their advanced knowledge creatively.

Apple—even if you are not an Apple fan—created a powerful brand by shaping it around this archetype. 

The Outlaw


The Outlaw is the rebel, the one who breaks the rules in order to free his true self
.

The Outlaw goes against the canon and is very aware of the constrictions society creates.

A great example of a brand that very well represents the Outlaw archetype is Betabrand.

The Everyman

It is perfectly fine to be “normal,” and happiness can come from simply sharing things with people we love.


Brands targeting the Everyman audience (and painting themselves as such) craft their messages about the beauty of simple things and daily real life
.

Ikea is probably the brand that’s achieved mastery in the use of this archetype over the past few years.

The Jester 

Fun, irreverent, energetic, impulsive and against the established rules at the same time, the Jester is also the only one who is able to tell the truth with a joke. 

Jesters can be revolutionary too, and their motto could be “a laugh will bury you all.”


A brand that presents itself as the Jester is a brand that wants to make our lives easier and more bearable, providing us joy.

The Lover


Sensuality is the main characteristic of the Lover archetype
, as well as strong physicality, passion, and a need for deep and strong sensations.

But the Lover can be also the idealist, the romantic longing for the perfect love.

Archetypes and brand storytelling

Our brain, as many neuroscientists have proved, is
hard-wired for stories (I suggest you to watch this TEDx too).

Therefore, once we have decided what archetype figure best responds both to our audience and our values as a brand,
we must translate the psychology we created for our brand into
brand storytelling.
That storytelling must then be attuned to the psychology of our audience based on our psychographic analysis of them.

Good (brand) storytelling is very hard to achieve, and most of the time we see brands that miserably fail when trying to tell branded stories.

Introducing the Theory of Literary (or Narrative) Modes

In order to help my clients find the correct narrative, I rely on something that usually is not considered by marketers: the
Theory of Literary Modes.

I use this theory, presented first by
Northrop Frye in it essay Anatomy of Criticism, because it is close to our “technical marketer” mindset.

In fact:

  1. The theory is based on a objective and “scientific” analysis of data (the literary corpus produced by humans);
  2. It refuses “personal taste” as a metric, which in web marketing would be the same as creating a campaign with tactics you like but you don’t really know if your public is interested in. Even worse, it would be like saying “create great content” without defining what that means.

Moreover, the
Theory of Literary Modes is deeply structured and strongly relies on semiotics, which is going to be the natural evolution of how search engines like Google will comprehend the content published in the Internet. Semantic thinking is just the first step as well explained 
Isla McKetta here on Moz few months ago.

Finally, Northrop Fryed
considers also archetypes this theory because of the psychological and semiotic value of the symbolism attached to the archetypal figure.

Therefore, my election to use the Theory of Literary Modes responds 

  1. To the need to translate ideal brand storytelling into something real that can instinctively connect with the brand’s audience;
  2. To make the content based on that storytelling process understandable also by search engines.

The Theory of Literary Modes in marketing

To understand how this works in marketing, we need to dig a little deeper into the theory.

A literary work can be classified in two different but complementary ways:

1) Considering only the
relation between the nature of the main character (the Hero) and the ambient (or environment) where he acts.

2) Considering also
if the Hero is refused or accepted by society (Tragedy and Comedy).

In the
first case, as represented in the schema above, if the Hero:
  1. Is higher by nature than the readers and acts in a completely different ambient than theirs, we have a Romance;
  2. Is higher by nature than the readers, but acts in their same ambient, we have an Epic;
  3. Is someone like the reader and acts in the reader’s own ambient, we are in field of Realism;
  4. Is someone lower by nature than the readers and acts in a different or identical ambient, we are in the realm of Irony, which is meant as “distance.”
A fifth situation exists too, the
Myth, when the nature of the Hero is different than ours and acts in an ambient different than ours. The Hero, in this case, is the God.

If we consider also if society refuses or accepts the hero, we can discover the different versions of Tragedy and Comedy.

I will not enter in the details of Tragedy, because
we will not use its modes for brand storytelling (this is only common in specific cases of political marketing or propaganda, classic examples are the mythology of Nazism or Communism).

On the contrary,
the most common modes used in brand storytelling are related to Comedy, where the Hero, who usually is the target audience, is eventually accepted by society (the archetypal world designed by the brand).

In
Comedy we have several sub modes of storytelling:

  1. “The God Accepted.” The Hero is a god or god-like kind of person who must pass through trials in order to be accepted by the society;
  2. The Idyll, where the Hero uses his skills to explore (or conquer) an ideal world and/or become part of an ideal society. Far West and its heir, Space Opera (think of Interstellar) are classic examples. 
  3. Comedy sees the hero trying to impose his own view of the world, fighting for it and finally being awarded with acceptance of his worldview. A good example of this is every well ending biopic of an entrepreneur, and Comedy is the exact contrary of melodrama. 
  4. On a lower level we can find the Picaresque Comedy, where the hero is by nature inferior to the society, but – thanks to his cleverness – is able to elevate himself to society’s level. Some technology business companies use this narrative mode for telling their users that they can “conquer” their market niche despite not having the same economic possibilities as the big brands (this conquering usually involves the brand’s tools).
  5. Finally we have the Irony Mode of Comedy which is quite complex to define. 
    1. It can represent stories where the hero is actually an antihero, who finally fails in his integration into the society. 
    2. It can also be about inflicting pain on helpless victims, as in mystery novels. 
    3. It can also be Parody.

Some examples

The Magician, gamification, and the Idyllic mode

Consider this brand plot:

The user (the Hero) can become part of a community of users only if he or she passes through a series of tasks, which will award prizes and more capabilities. If the user is able to pass through all the tasks, he will not only be accepted but also may have the opportunity to be among the leaders of the community itself.

And now
consider sites, which are strongly centered on communities like GitHub and Code Academy. Consider also SAAS companies that present the freemium model like Moz or mobile games like Boom Beach, where you can unlock new weapons only if you pass a given trial (or you buy them).

The Magician is usually the archetype of reference for these kinds of brands. The Hero (the user) will be able to dominate a complex art thanks to the help of a Master (the brand), which will offer him instruments (i.e.: tools/courses/weapons). 

Trials are not necessarily tests. A trial can be doing something that will be awarded, for instance, with points (like commenting on a Moz blog post), and the more the points the more the recognition, with all the advantages that it may offer. 

Gamification, then, assumes an even stronger meaning and narrative function when tied to an archetype and literary mode.

Ikea, the Everyman, and the Comedic mode

Another
example is Ikea, which we cited before when talking of the Everyman archetype.

In this case, the Hero is someone like me or you who is not an interior designer or decorator or, maybe, who does not have the money for hiring those professionals or buying very expensive furniture and decoration.

But, faithful to its mission statements (“design for all”, “design your own life”…), Ikea is there to help Everyman kind of people like me and you in every way as we decorate our own houses.

On the practical side, this narrative is delivered in all the possible channels used by Ikea: web site, mobile app, social media (look at its
Twitter profile) and YouTube channel.

Betabrand, the Outlaw, and Picaresque Comedy

A third and last example can be
Betabrand.

In this case both the brand and the audience is portrayed using the
Outlaw archetype, and the brand narrative tend to use the Picaresque mode.

The Heroes is the Betabrand community who does not care what the mainstream concept of fashion is and designs and crowdfounds “its fashion.”

How to use archetypes and narrative modes in your brand storytelling

The first thing you must understand is what archetype best responds to your company tenets and mission. 

Usually this is not something an SEO can decide by him- or herself, but it is something that founders, CEOs, and directors of a company can inform.

Oftentimes a small to medium business company can achieve this with a long talk among those company figures and where they are asked to directly define the idealistic “why?” of their company.

In case of bigger companies, defining an archetype can seem almost impossible to do, but the same history of the company and hidden treasure pages like “About Us” can offer clear inspiration.

Look at REI:

Clearly the archetype figure that bests fits REI is the Explorer.

Then, using the information we retrieve when creating the
psychographic of our audience and buyer personas, matching with the characteristics each archetype has, and comparing it with the same brand core values, we can start to understand the archetype and narrative mode. If we look at REI’s audience, then we will see how it also has a certain affinity with the Everyman archetypal figure (and that also explains why REI also dedicates great attention to family as audience).

Once we have defined the best archetype commonly shared by our company and our audience, we must translate this figure and its symbolism into brand storytelling, which in web site includes design, especially the following:

  • Color pattern, because colors have a direct relation with psychological reaction (see this article, especially all the sources it links to)
  • Images, considering that in user-centric marketing the ideal is always to represent our targeted audience (or a credible approximation) as their main characters. I am talking of the so called “hero-shots”, about which Angie Shoetmuller brilliantly discussed in the deck I embed here below:

If you want to dig deeper in discovering the meaning and value of symbols worldwide, I suggest you become member of
Aras.org or to buy the Book of Symbols curated by Aras.

  • Define the best narrative mode to use. REI, again, does this well, using the Idyllic mode where the Hero explores and become part of an ideal society (the REI community, which literally means becoming a member of REI). 

We should, then:

  1. Continue investigating the archetypal nature of our audience conducting surveys
  2. Analyzing the demographic data Google Analytics offers us about our users 
  3. Using GA insights in combination with the data and demographic information offered by social networks’ ad platforms in order to create not only the interest graph of our audience but also to understand the psychology behind those interests 
  4. Doing A/B tests so to see whether symbols, images, and copywriting based on the targeted archetypes work better and if we have the correct archetype.

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Reblogged 3 years ago from tracking.feedpress.it

Everything You Need to Know About Mobile App Search

Posted by Justin_Briggs

Mobile isn’t the future. It’s the present. Mobile apps are not only changing how we interact with devices and websites, they’re changing the way we search. Companies are creating meaningful experiences on mobile-friendly websites and apps, which in turn create new opportunities to get in front of users.

I’d like to explore the growth of mobile app search and its current opportunities to gain visibility and drive engagement.

Rise of mobile app search

The growth of mobile device usage has driven a significant lift in app-related searches. This is giving rise to mobile app search as a vertical within traditional universal search.

While it has been clear for some time that mobile search is important, that importance has been more heavily emphasized by Google recently, as they continue to push
mobile-friendly labels in SERPs, and are likely increasing mobile-friendliness’s weight as a ranking factor.

The future of search marketing involves mobile, and it will not be limited to optimizing HTML webpages, creating responsive designs, and optimizing UX. Mobile SEO is a world where apps, knowledge graph, and conversational search are front and center.

For the
top 10 leading properties online, 34% of visitors are mobile-only (comScore data), and, anecdotally, we’re seeing similar numbers with our clients, if not more.

Mobile device and app growth

It’s also worth noting that
72% of mobile engagement relies on apps vs. on browsers. Looking at teen usage, apps are increasingly dominant. Additionally,
55% of teens use voice search more than once per day

If you haven’t read it, grab some coffee and read
A Teenagers View on Social Media, which is written by a 19-year old who gives his perspective of online behavior. Reading between the lines shows a number of subtle shifts in behavior. I noticed that every time I expected him say website, he said application. In fact, he referenced application 15 times, and it is the primary way he describes social networks.

This means that one of the fasting growing segments of mobile users cannot be marketed to by optimizing HTML webpages alone, requiring search marketers to expand their skills into app optimization.

The mobile app pack

This shift is giving rise to the mobile app pack and app search results, which are triggered on searches from mobile devices in instances of high mobile app intent. Think of these as being similar to local search results. Considering
mobile searcher behavior, these listings dominate user attention.

Mobile app search results and mobile app pack

As with local search, mobile app search can reorder traditional results, completely push them down, or integrate app listings with traditional web results.

You can test on your desktop using a
user-agent switcher, or by searching on your iOS or Android device. 

There are slight differences between iPhone and Android mobile app results:

iOS and Android mobile search result listing

From what I’ve seen, mobile app listings trigger more frequently, and with more results, on Android search results when compared to iOS. Additionally, iOS mobile app listings are represented as a traditional website result listing, while mobile app listings on Android are more integrated.

Some of the differences also come from the differences in app submission guidelines on the two major stores, the Apple App Store and Google Play.

Overview of differences in mobile app results

  1. Title – Google uses the app listing page’s HTML title (which is the app’s title). iOS app titles can exceed 55-62 characters, which causes wrapping and title truncation like a traditional result. Android app title requirements are shorter, so titles are typically shorter on Android mobile app listings.
  2. URL – iOS mobile app listings display the iTunes URL to the App Store as part of the search result.
  3. Icon – iOS icons are square and Android icons have rounded corners.
  4. Design – Android results stand out more, with an “Apps” headline above the pack and a link to Google Play at the end.
  5. App store content – The other differences show up in the copy, ratings, and reviews on each app store.

Ranking in mobile app search results

Ranking in mobile app search results is a
combination of App Store Optimization (ASO) and traditional SEO. The on-page factors are dependent upon your app listing, so optimization starts with having solid ASO. If you’re not familiar with ASO, it’s the process of optimizing your app listing for internal app store search.

Basics of ASO

Ranking in the Apple App Store and in Google Play is driven by two primary factors: keyword alignment and app performance. Text fields in the app store listing, such as title, description, and keyword list, align the app with a particular set of keywords. Performance metrics including download velocity, app ratings, and reviews determine how well the app will rank for each of those keywords. (Additionally, the Google Play algorithm may include external, web-based performance metrics like citations and links as ranking factors.)

App store ranking factors

Mobile app listing optimization

While I won’t explore ASO in-depth here, as it’s very similar to traditional SEO,
optimizing app listings is primarily a function of keyword targeting.

Tools like
Sensor Tower, MobileDevHQ, and App Annie can help you with mobile app keyword research. However, keep in mind that mobile app search listings show up in universal search, so it’s important to leverage traditional keyword research tools like the AdWords Tool or Google Trends.

While there are similarities with ASO, optimizing for these mobile app search listings on the web has some slight differences.

Differences between ASO & mobile app SEO targeting

  1. Titles – While the Apple App Store allows relatively long titles, they are limited to the preview length in organic search. Titles should be optimized with Google search in mind, in addition to optimizing for the app store. Additionally, several apps aggressively target keywords in their app title, but caution should be used as spamming keywords could influence app performance in Google.
  2. Description – The app description on the App Store may not be a factor in internal search, but it will impact external app search results. Leverage keyword targeting best practices when writing your iOS app description, as well as your Android app description.
  3. Device and platform keywords – When targeting for app store search, it is not as important to target terms related to the OS or device. However, these terms can help visibility in external search. Include device and OS terms, such as Android, Samsung Note, iOS, iPad, and iPhone.

App performance optimization

Outside of content optimization, Google looks at the performance of the app. On the Android side, they have access to the data, but for iOS they have to rely on publicly available information.

App performance factors

  • Number of ratings
  • Average rating score
  • Content and sentiment analysis of reviews
  • Downloads / installs
  • Engagement and retention
  • Internal links on app store

For iOS, the primary public metrics are ratings and reviews. However, app performance can be inferred using the App Store’s ranking charts and search results, which can be leveraged as proxies of these performance metrics.


The following objectives will have the greatest influence on your mobile app search ranking:

  1. Increase your average rating number
  2. Increase your number of ratings
  3. Increase downloads

For app ratings and reviews, leverage platforms like
Apptentive to improve your ratings. They are very effective at driving positive ratings. Additionally, paid tactics are a great way to drive install volume and are one area where paid budget capacity could directly influence organic results in Google. Anecdotally, both app stores use rating numbers (typically above or below 4 stars) to make decisions around promoting an app, either through merchandising spots or co-branded campaigns. I suspect this is being used as a general cut-off for what is displayed in universal results. Increasing your rating above 4 stars should improve the likelihood you’ll appear in mobile app search results.

Lastly, think of merchandising and rankings in terms of 
internal linking structures. The more visible you are inside of the app store, the more visibility you have in external search.

App web performance optimization

Lastly, we’re talking Google rankings, so factors like links, citations, and social shares matter. You should be
conducting content marketing, PR, and outreach for your app. Focus on merchandising your app on your own site, as well as increasing coverage of your app (linking to the app store page). The basics of link optimization apply here.

App indexation – drive app engagement

Application search is not limited to driving installs via app search results. With app indexing, you can leverage your desktop/mobile website visibility in organic search to drive engagement with those who have your app installed. Google can discover and expose content deep inside your app directly in search results. This means that when a user clicks on your website in organic search, it can open your app directly, taking them to that exact piece of content in your app, instead of opening your website.

App indexation fundamentally changes technical SEO, extending SEO from server and webpage setup to the setup and optimization of applications.

App indexation on Google

This also fundamentally changes search. Your most avid and engaged user may choose to no longer visit your website. For example, on my Note 4, when I click a link to a site of a brand that I have an app installed for, Google gives me the option not only to open in the app, but to set opening the app as a default behavior.

If a user chooses to open your site in your app, they may never visit your site from organic search again.

App indexation is currently limited to Android devices, but there is evidence to suggest that it’s already in the works and is
soon to be released on iOS devices. There have been hints for some time, but markup is showing up in the wild suggesting that Google is actively working with Apple and select brands to develop iOS app indexing.

URI optimization for apps

The first step in creating an indexable app is to set up your app to support deep links. Deep links are URIs that are understood by your app and will open up a specific piece of content. They are effectively URLs for applications.

Once this URI is supported, a user can be sent to deep content in the app. These can be discovered as alternates to your desktop site’s URLs, similar to how
separate-site mobile sites are defined as alternate URLs for the desktop site. In instances of proper context (on an Android device with the app installed), Google can direct a user to the app instead of the website.

Setting this up requires working with your app developer to implement changes inside the app as well as working with your website developers to add references on your desktop site.

Adding intent filters

Android has
documented the technical setup of deep links in detail, but it starts with setting up intent filters in an app’s Android manifest file. This is done with the following code.

<activity android:name="com.example.android.GizmosActivity"
android:label="@string/title_gizmos" >
<intent-filter android:label="@string/filter_title_viewgizmos">
<action android:name="android.intent.action.VIEW" />
<data android:scheme="http"
android:host="example.com"
android:pathPrefix="/gizmos" />
<category android:name="android.intent.category.DEFAULT" />
<category android:name="android.intent.category.BROWSABLE" />
</intent-filter>
</activity>

This dictates the technical optimization of your app URIs for app indexation and defines the elements used in the URI example above.

  • The <intent-filter> element should be added for activities that should be launchable from search results.
  • The <action> element specifies the ACTION_VIEW intent action so that the intent filter can be reached from Google Search.
  • The <data> tag represents a URI format that resolves to the activity. At minimum, the <data> tag must include the android:scheme attribute.
  • Include the BROWSABLE category. The BROWSABLE category is required in order for the intent filter to be accessible from a web browser. Without it, clicking a link in a browser cannot resolve to your app. The DEFAULT category is optional, but recommended. Without this category, the activity can be started only with an explicit intent, using your app component name.

Testing deep links

Google has created tools to help test your deep link setup. You can use
Google’s Deep Link Test Tool to test your app behavior with deep links on your phone. Additionally, you can create an HTML page with an intent:// link in it.

For example
:

<a href="intent://example.com/page-1#Intent;scheme=http;package=com.example.android;end;"> <a href="http://example.com/page-1">http://example.com/page-1></a>

This link would open up deep content inside the app from the HTML page.

App URI crawl and discovery

Once an app has deep link functionality, the next step is to
ensure that Google can discover these URIs as part of its traditional desktop crawling.

Ways to get apps crawled

  1. Rel=”alternate” in HTML head
  2. ViewAction with Schema.org
  3. Rel=”alternate” in XML Sitemap

Implementing all three will create clear signals, but at minimum you should add the rel=”alternate” tag to the HTML head of your webpages.

Effectively, think of the app URI as being similar to a mobile site URL when
setting up a separate-site mobile site for SEO. The mobile deep link is an alternative way to view a webpage on your site. You map a piece of content on your site to a corresponding piece of content inside the app.

Before you get started, be sure to
verify your website and app following the guidelines here. This will verify your app in Google Play Developer Console and Google Webmaster Tools.

#1: Rel=”alternate” in HTML head

On an example page, such as example.com/page-1, you would add the following code to the head of the document. Again, very similar to separate-site mobile optimization.

<html>
<head> 
... 
<link rel="alternate" href="android-app://com.example.android/http/example.com/page-1" /> 
...
</head>
<body>
</body>
#2: ViewAction with Schema.org

Additionally, you can reference the deep link using Schema.org and JSON by using a 
ViewAction.

<script type="application/ld+json"> 
{ 
"@context": "http://schema.org", 
"@type": "WebPage", 
"@id": "http://example.com/gizmos", 
"potentialAction": { 
"@type": "ViewAction", 
"target": "android-app://com.example.android/http/example.com/gizmos" 
} 
} 
</script>
#3 Rel=”alternate” in XML sitemap

Lastly, you can reference the alternate URL in your XML Sitemaps, similar to using the rel=”alternate” for mobile sites.

<?xml version="1.0" encoding="UTF-8" ?>
<urlset xmlns="http://www.sitemaps.org/schemas/sitemap/0.9" xmlns:xhtml="http://www.w3.org/1999/xhtml"> 
<url> 
<loc>http://example.com/page-1</loc> 
<xhtml:link rel="alternate" href="android-app://com.example.android/http/example.com/page-1" /> 
</url> 
... 
</urlset>

Once these are in place, Google can discover the app URI and provide your app as an alternative way to view content found in search.

Bot control and robots noindex for apps

There may be instances where there is content within your app that you do not want indexed in Google. A good example of this might be content or functionality that is built out on your site, but has not yet been developed in your app. This would create an inferior experience for users. The good news is that we can block indexation with a few updates to the app.

First, add the following to your app resource directory (res/xml/noindex.xml).

<?xml version="1.0" encoding="utf-8"?> 
<search-engine xmlns:android="http://schemas.android.com/apk/res/android"> 
<noindex uri="http://example.com/gizmos/hidden_uri"/> 
<noindex uriPrefix="http://example.com/gizmos/hidden_prefix"/> 
<noindex uri="gizmos://hidden_path"/> 
<noindex uriPrefix="gizmos://hidden_prefix"/> 
</search-engine>

As you can see above, you can block an individual URI or define a URI prefix to block entire folders.

Once this has been added, you need to update the AndroidManifest.xml file to denote that you’re using noindex.html to block indexation.

<manifest xmlns:android="http://schemas.android.com/apk/res/android" package="com.example.android.Gizmos"> 
<application> 
<activity android:name="com.example.android.GizmosActivity" android:label="@string/title_gizmos" > 
<intent-filter android:label="@string/filter_title_viewgizmos"> 
<action android:name="android.intent.action.VIEW"/> 
... 
</activity> 
<meta-data android:name="search-engine" android:resource="@xml/noindex"/> 
</application> 
<uses-permission android:name="android.permission.INTERNET"/> 
</manifest>

App indexing API to drive re-engagement

In addition to URI discovery via desktop crawl, your mobile app can integrate
Google’s App Indexing API, which communicates with Google when users take actions inside your app. This sends information to Google about what users are viewing in the app. This is an additional method for deep link discovery and has some benefits.

The primary benefit is the ability to appear in
autocomplete. This can drive re-engagement through Google Search query autocompletions, providing access to inner pages in apps.

App auto suggest

Again, be sure to
verify your website and app following the guidelines here. This will verify your app in Google Play Developer Console and Google Webmaster Tools.

App actions with knowledge graph

The next, and most exciting, evolution of search is leveraging actions. These will be powerful when
combined with voice search, allowing search engines to take action on behalf of users, turning spoken language into executed actions.

App indexing allows you to take advantage of actions by allowing Google to not only launch an app, but execute actions inside of the app. Order me a pizza? Schedule my meeting? Drive my car? Ok, Google.

App actions work via entity detection and the application of the knowledge graph, allowing search engines to understand actions, words, ideas and objects. With that understanding, they can build an action graph that allows them to define common actions by entity type.

Here is a list of actions currently supported by Schema.org

For example, the PlayAction could be used to play a song in a music app. This can be achieve with the following markup.

<script type="application/ld+json">
{
"@context": "http://schema.org",
"@type": "MusicGroup",
"name": "Weezer", "potentialAction": {
"@type": "ListenAction",
"target": "android-app://com.spotify.music/http/we.../listen"
}
}
</script>
Once this is implemented, these app actions can begin to appear in search results and knowledge graph.

deep links in app search results

Overview of mobile app search opportunities

In summary, there are five primary ways to increase visibility and engagement for your mobile app in traditional organic search efforts.

Mobile apps in search results

The growth of mobile search is transforming how we define technical SEO, moving beyond front-end and back-end optimization of websites into the realm of structured data and application development. As app indexing expands to include iOS, I suspect the possibilities and opportunities associated with indexing applications, and their corresponding actions, to grow extensively. 

For those with Android apps, app indexing is a potential leapfrog style opportunity to get ahead of competitors who are dominant in traditional desktop search. Those with iOS devices should start by optimizing their app listings, while preparing to implement indexation, as I suspect it’ll be released for iOS this year.

Have you been leveraging traditional organic search to drive visibility and engagement for apps? Share your experiences in the comments below.

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