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 3 years ago from blog.dotmailer.com

The Magento Xcelerate program: A positive sum game

As an open source ecommerce platform, Magento is flexible and accessible for developers to work with and as a result, an active community of developers emerged on online forums and at offline meetups all over the world. Many of these were happily plugging away independently of Magento until the split from eBay in early 2015.

Free from the reins of eBay, Magento has decisively been reaching out to, promoting and rewarding the individuals, agencies and technology providers that make up its ecosystem. Last February they announced the Magento Masters Program, empowering the top platform advocates, frequent forum contributors and the innovative solution implementers. Then at April‘s Magento Imagine conference (the largest yet) the theme emerged as ‘We are Magento”, in celebration of the community.

The new Xcelerate Technology Partner Program focuses not on individuals but on business partnerships formed with the technology companies that offer tools for Magento merchants to implement.

 Sharing ideas, opportunities and successes:

This is the Xcelerate Program tagline, which acts as a sort of mission statement to get the technology partners involved moving with regards to continuously considering Magento in their own technology roadmap and jointly communicating successes and learnings from working on implementations with merchants.

“In turn, the program offers members the tools to get moving, through events, resources and contacts. Our goal is to enable you to be an integral part of the Magento ecosystem” Jon Carmody, Head of Technology Partners

The program in practice:

The new program is accompanied by the new Marketplace from which the extensions can be purchased and downloaded. The program splits the extensions into 3 partnership levels:

Registered Partners – these are technology extensions that the new Magento Marketplace team test for code quality. Extensions must now pass this initial level to be eligible for the Marketplace. With each merchant having on average 15 extensions for their site, this is a win for merchants when it comes to extension trustworthiness.

Select Partners – extensions can enter this second tier if the technology falls into one of the strategic categories identified by Magento and if they pass an in-depth technical review. These will be marked as being ‘Select’ in the Marketplace.

Premier Partners – this level is by invitation only, chosen as providing crucial technology to Magento merchants (such as payments, marketing, tax software). The Magento team’s Extension Quality Program looks at coding structure, performance, scalability, security and compatibility but influence in the Community is also a consideration. dotmailer is proud to be the first Premier Technology Partner in the marketing space for Magento.

All in all, the latest move from Magento in illuminating its ecosystem should be positive for all; the merchants who can now choose from a vetted list of extensions and know when to expect tight integration, the technology partners building extensions now with clearer merchant needs/extension gaps in mind and guidance from Magento, and of course the solution implementers recommending the best extension for the merchant now knowing it will be maintained.

Reblogged 3 years ago from blog.dotmailer.com

Becoming Better SEO Scientists – Whiteboard Friday

Posted by MarkTraphagen

Editor’s note: Today we’re featuring back-to-back episodes of Whiteboard Friday from our friends at Stone Temple Consulting. Make sure to also check out the second episode, “UX, Content Quality, and SEO” from Eric Enge.

Like many other areas of marketing, SEO incorporates elements of science. It becomes problematic for everyone, though, when theories that haven’t been the subject of real scientific rigor are passed off as proven facts. In today’s Whiteboard Friday, Stone Temple Consulting’s Mark Traphagen is here to teach us a thing or two about the scientific method and how it can be applied to our day-to-day work.

For reference, here’s a still of this week’s whiteboard.
Click on it to open a high resolution image in a new tab!

Video transcription

Howdy, Mozzers. Mark Traphagen from Stone Temple Consulting here today to share with you how to become a better SEO scientist. We know that SEO is a science in a lot of ways, and everything I’m going to say today applies not only to SEO, but testing things like your AdWords, how does that work, quality scores. There’s a lot of different applications you can make in marketing, but we’ll focus on the SEO world because that’s where we do a lot of testing. What I want to talk to you about today is how that really is a science and how we need to bring better science in it to get better results.

The reason is in astrophysics, things like that we know there’s something that they’re talking about these days called dark matter, and dark matter is something that we know it’s there. It’s pretty much accepted that it’s there. We can’t see it. We can’t measure it directly. We don’t even know what it is. We can’t even imagine what it is yet, and yet we know it’s there because we see its effect on things like gravity and mass. Its effects are everywhere. And that’s a lot like search engines, isn’t it? It’s like Google or Bing. We see the effects, but we don’t see inside the machine. We don’t know exactly what’s happening in there.

An artist’s depiction of how search engines work.

So what do we do? We do experiments. We do tests to try to figure that out, to see the effects, and from the effects outside we can make better guesses about what’s going on inside and do a better job of giving those search engines what they need to connect us with our customers and prospects. That’s the goal in the end.

Now, the problem is there’s a lot of testing going on out there, a lot of experiments that maybe aren’t being run very well. They’re not being run according to scientific principles that have been proven over centuries to get the best possible results.

Basic data science in 10 steps

So today I want to give you just very quickly 10 basic things that a real scientist goes through on their way to trying to give you better data. Let’s see what we can do with those in our SEO testing in the future.

So let’s start with number one. You’ve got to start with a hypothesis. Your hypothesis is the question that you want to solve. You always start with that, a good question in mind, and it’s got to be relatively narrow. You’ve got to narrow it down to something very specific. Something like how does time on page effect rankings, that’s pretty narrow. That’s very specific. That’s a good question. Might be able to test that. But something like how do social signals effect rankings, that’s too broad. You’ve got to narrow it down. Get it down to one simple question.

Then you choose a variable that you’re going to test. Out of all the things that you could do, that you could play with or you could tweak, you should choose one thing or at least a very few things that you’re going to tweak and say, “When we tweak this, when we change this, when we do this one thing, what happens? Does it change anything out there in the world that we are looking at?” That’s the variable.

The next step is to set a sample group. Where are you going to gather the data from? Where is it going to come from? That’s the world that you’re working in here. Out of all the possible data that’s out there, where are you going to gather your data and how much? That’s the small circle within the big circle. Now even though it’s smaller, you’re probably not going to get all the data in the world. You’re not going to scrape every search ranking that’s possible or visit every URL.

You’ve got to ask yourself, “Is it large enough that we’re at least going to get some validity?” If I wanted to find out what is the typical person in Seattle and I might walk through just one part of the Moz offices here, I’d get some kind of view. But is that a typical, average person from Seattle? I’ve been around here at Moz. Probably not. But this was large enough.

Also, it should be randomized as much as possible. Again, going back to that example, if I just stayed here within the walls of Moz and do research about Mozzers, I’d learn a lot about what Mozzers do, what Mozzers think, how they behave. But that may or may not be applicable to the larger world outside, so you randomized.

We want to control. So we’ve got our sample group. If possible, it’s always good to have another sample group that you don’t do anything to. You do not manipulate the variable in that group. Now, why do you have that? You have that so that you can say, to some extent, if we saw a change when we manipulated our variable and we did not see it in the control group, the same thing didn’t happen, more likely it’s not just part of the natural things that happen in the world or in the search engine.

If possible, even better you want to make that what scientists call double blind, which means that even you the experimenter don’t know who that control group is out of all the SERPs that you’re looking at or whatever it is. As careful as you might be and honest as you might be, you can end up manipulating the results if you know who is who within the test group? It’s not going to apply to every test that we do in SEO, but a good thing to have in mind as you work on that.

Next, very quickly, duration. How long does it have to be? Is there sufficient time? If you’re just testing like if I share a URL to Google +, how quickly does it get indexed in the SERPs, you might only need a day on that because typically it takes less than a day in that case. But if you’re looking at seasonality effects, you might need to go over several years to get a good test on that.

Let’s move to the second group here. The sixth thing keep a clean lab. Now what that means is try as much as possible to keep anything that might be dirtying your results, any kind of variables creeping in that you didn’t want to have in the test. Hard to do, especially in what we’re testing, but do the best you can to keep out the dirt.

Manipulate only one variable. Out of all the things that you could tweak or change choose one thing or a very small set of things. That will give more accuracy to your test. The more variables that you change, the more other effects and inner effects that are going to happen that you may not be accounting for and are going to muddy your results.

Make sure you have statistical validity when you go to analyze those results. Now that’s beyond the scope of this little talk, but you can read up on that. Or even better, if you are able to, hire somebody or work with somebody who is a trained data scientist or has training in statistics so they can look at your evaluation and say the correlations or whatever you’re seeing, “Does it have a statistical significance?” Very important.

Transparency. As much as possible, share with the world your data set, your full results, your methodology. What did you do? How did you set up the study? That’s going to be important to our last step here, which is replication and falsification, one of the most important parts of any scientific process.

So what you want to invite is, hey we did this study. We did this test. Here’s what we found. Here’s how we did it. Here’s the data. If other people ask the same question again and run the same kind of test, do they get the same results? Somebody runs it again, do they get the same results? Even better, if you have some people out there who say, “I don’t think you’re right about that because I think you missed this, and I’m going to throw this in and see what happens,” aha they falsify. That might make you feel like you failed, but it’s success because in the end what are we after? We’re after the truth about what really works.

Think about your next test, your next experiment that you do. How can you apply these 10 principles to do better testing, get better results, and have better marketing? Thanks.

Video transcription by Speechpad.com

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

Reblogged 4 years ago from 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 4 years ago from tracking.feedpress.it

Deconstructing the App Store Rankings Formula with a Little Mad Science

Posted by AlexApptentive

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

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

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

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

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

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

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

Until now, that is.

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

But first, a little context

Image credit: Josh Tuininga, Apptentive

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

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

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

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

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

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

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

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

Now, for the Mad Science.

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

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

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

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

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

Hypothesis

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

Both of these assumptions will be tested in later analysis.

Results

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

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

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

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

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

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

Hypothesis

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

Results

App Store Ranking Volatility of Top 500 Apps

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

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

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

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

Study #3: App store rankings across the stars

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

Hypothesis

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

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

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

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

Results

Average App Store Ratings of Top Apps

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

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

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

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

App Store Ranking Volatility and Average Rating

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

Study #4: App store rankings across versions

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

Hypothesis

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

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

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

Results

How update frequency correlates with app store rank

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

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

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

How update frequency correlates with app store ranking volatility

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

Study #5: App store rankings across monthly active users

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

Hypothesis

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

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

Results

Apps with more ratings and reviews typically rank higher

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

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

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

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

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

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

Apps with more ratings typically experience less app store ranking volatility

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

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

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

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

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

Summary

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

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

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

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

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

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

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

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

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

Weight of factors in the Apple App Store ranking algorithm

Rating Count > Installs > Trends > Rating

Weight of factors in the Google Play ranking algorithm

Rating Count > Installs > Rating > Trends


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

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

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

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

Reblogged 4 years ago from tracking.feedpress.it

Give It Up for Our MozCon 2015 Community Speakers

Posted by EricaMcGillivray

Super thrilled that we’re able to announce this year’s community speakers for MozCon, July 13-15th in Seattle!

Wow. Each year I feel that I say the pool keeps getting more and more talented, but it’s the truth! We had more quality pitches this year than in the past, and quantity-wise, there were 241, around 100 more entries than years previously. Let me tell you, many of the review committee members filled our email thread with amazement at this.

And even though we had an unprecedented six slots, the choices seemed even tougher!

241 pitches
Let that number sink in for a little while.

Because we get numerous questions about what makes a great pitch, I wanted to share both information about the speakers and their great pitches—with some details removed for spoilers. (We’re still working with each speaker to polish and finalize their topic.) I’ve also included my or Matt Roney‘s own notes on each one from when we read them without knowing who the authors were.

Please congratulate our MozCon 2015 community speakers!

Adrian Vender

Adrian is the Director of Analytics at IMI and a general enthusiast of coding and digital marketing. He’s also a life-long drummer and lover of music. Follow him at @adrianvender.

Adrian’s pitch:

Content Tracking with Google Tag Manager

While marketers have matured in the use of web analytics tools, our ability to measure how users interact with our sites’ content needs improvement. Users are interacting with dynamic content that just aren’t captured in a pageview. While there are JavaScript tricks to help track these details, working with IT to place new code is usually the major hurdle that stops us.

Finally, Google Tag Manager is that bridge to advanced content analysis. GTM may appear technical, but it can easily be used by any digital marketer to track almost any action on a site. My goal is to make ALL attendees users of GTM.

My talk will cover the following GTM concepts:

[Adrian lists 8 highly-actionable tactics he’ll cover.]

I’ll share a client example of tracking content interaction in GA. I’ll also share a link to a GTM container file that can help people pre-load the above tag templates into their own GTM.

Matt’s notes: Could be good. I know a lot of people have questions about Tag Manager, and the ubiquity of GA should help it be pretty well-received.


Chris DayleyChris Dayley

Chris is a digital marketing expert and owner of Dayley Conversion. His company provides full-service A/B testing for businesses, including design, development, and test execution. Follow him at @chrisdayley.

Chris’ pitch:

I would like to present a super actionable 15 minute presentation focused on the first two major steps businesses should take to start A/B testing:

1. Radical Redesign Testing

2. Iterative Testing (Test EVERYTHING)

I am one of the few CROs out there that recommends businesses to start with a radical redesign test. My reasoning for doing so is that most businesses have done absolutely no testing on their current website, so the current landing page/website really isn’t a “best practice” design yet.

I will show several case studies where clients saw more than a 50% lift in conversion rates just from this first step of radical redesign testing, and will offer several tips for how to create a radical redesign test. Some of the tips include:

[Chris lists three direct and interesting tips he’ll share.]

Next I suggest moving into the iterative phase.

I will show several case studies of how to move through iterative testing so you eventually test every element on your page.

Erica’s notes: Direct, interesting, and with promise of multiple case studies.


Duane BrownDuane Brown

Duane is a digital marketer with 10 years’ experience having lived and worked in five cities across three continents. He’s currently at Unbounce. When not working, you can find Duane traveling to some far-flung location around the world to eat food and soak up the culture. Follow him at @DuaneBrown.

Duane’s pitch:

What Is Delightful Remarketing & How You Can Do It Too

A lot of people find remarketing creepy and weird. They don’t get why they are seeing those ads around the internet…. let alone how to make them stop showing.

This talk will focus on the different between remarketing & creating delightful remarketing that can help grow the revenue & profit at a company and not piss customers off. 50% of US marketers don’t use remarketing according to eMarketer (2013).

– [Duane’s direct how-to for e-commerce customers.] Over 60% of customers abandon a shopping cart each year: http://baymard.com/lists/cart-abandonment-rate (3 minute)

– Cover a SaaS company using retargeting to [Duane’s actionable item]. This remarketing helps show your products sticky features while showing off your benefits (3 minute)

– The Dos: [Duane’s actionable tip], a variety of creative & a dedicated landing page creates delightful remarketing that grows revenue (3 minute)

– Wrap up and review main points. (2 minutes)

Matt’s notes: Well-detailed, an area in which there’s a lot of room for improvement.


Gianluca FiorelliGianluca Fiorelli

Moz Associate, official blogger for StateofDigital.com and known international SEO and inbound strategist, Gianluca works in the digital marketing industry, but he still believes that he just know that he knows nothing. Follow him at @gfiorelli1.

Gianluca’s pitch:

Unusual Sources for Keyword and Topical Research

A big percentage of SEOs equal Keyword and Topical Research to using Keyword Planner and Google Suggest.

However, using only them, we cannot achieve a real deep knowledge of the interests, psychology and language of our target.

In this talk, I will present unusual sources and unnoticed features of very well-known tools, and offer a final example based on a true story.

Arguments touched in the speech (not necessarily in this order):

[Gianluca lists seven how-tos and one unique case study.]

Erica’s notes: Theme of Google not giving good keyword info. Lots of unique actionable points and resources. Will work in 15 minute time limit.


Ruth Burr ReedyRuth Burr Reedy

Ruth is the head of on-site SEO for BigWing Interactive, a full-service digital marketing agency in Oklahoma City, OK. At BigWing, she manages a team doing on-site, technical, and local SEO. Ruth has been working in SEO since 2006. Follow her at @ruthburr.

Ruth’s pitch:

Get Hired to Do SEO

This talk will go way beyond “just build your own website” and talk about specific ways SEOs can build evidence of their skills across the web, including:

[Ruth lists 7 how-tos with actionable examples.]

All in a funny, actionable, beautiful, easy-to-understand get-hired masterpiece.

Erica’s notes: Great takeaways. Wanted to do a session about building your resume as a marketer for a while.


Stephanie WallaceStephanie Wallace

Stephanie is director of SEO at Nebo, a digital agency in Atlanta. She helps clients navigate the ever-changing world of SEO by understanding their audience and helping them create a digital experience that both the user and Google appreciates. Follow her at @SWallaceSEO.

Stephanie’s pitch:

Everyone knows PPC and SEO complement one another – increased visibility in search results help increase perceived authority and drive more clickthroughs to your site overall. But are you actively leveraging the wealth of PPC data available to build on your existing SEO strategy? The key to effectively using this information lies in understanding how to test SEO tactics and how to apply the results to your on-page strategies. This session will delve into actionable strategies for using PPC campaign insights to influence on-page SEO and content strategies. Key takeaways include:

[Stephanie lists four how-tos.]

Erica’s notes: Nice and actionable. Like this a lot.


As mentioned, we had 241 entries, and many of them were stage quality. Notable runners up included AJ Wilcox, Ed Reese, and Daylan Pearce, and a big pat on the back to all those who tossed their hat in.

Also, a huge thank you to my fellow selection committee members for 2015: Charlene Inoncillo, Cyrus Shepard, Danie Launders, Jen Lopez, Matt Roney, Rand Fishkin, Renea Nielsen, and Trevor Klein.

Buy your ticket now

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

Reblogged 4 years ago from tracking.feedpress.it