From Editorial Calendars to SEO: Setting Yourself Up to Create Fabulous Content

Posted by Isla_McKetta

Quick note: This article is meant to apply to teams of all sizes, from the sole proprietor who spends all night writing their copy (because they’re doing business during the day) to the copy team who occupies an entire floor and produces thousands of pieces of content per week. So if you run into a section that you feel requires more resources than you can devote just now, that’s okay. Bookmark it and revisit when you can, or scale the step down to a more appropriate size for your team. We believe all the information here is important, but that does not mean you have to do everything right now.

If you thought ideation was fun, get ready for content creation. Sure, we’ve all written some things before, but the creation phase of content marketing is where you get to watch that beloved idea start to take shape.

Before you start creating, though, you want to get (at least a little) organized, and an editorial calendar is the perfect first step.

Editorial calendars

Creativity and organization are not mutually exclusive. In fact, they can feed each other. A solid schedule gives you and your writers the time and space to be wild and creative. If you’re just starting out, this document may be sparse, but it’s no less important. Starting early with your editorial calendar also saves you from creating content willy-nilly and then finding out months later that no one ever finished that pesky (but crucial) “About” page.

There’s no wrong way to set up your editorial calendar, as long as it’s meeting your needs. Remember that an editorial calendar is a living document, and it will need to change as a hot topic comes up or an author drops out.

There are a lot of different types of documents that pass for editorial calendars. You get to pick the one that’s right for your team. The simplest version is a straight-up calendar with post titles written out on each day. You could even use a wall calendar and a Sharpie.

Monday Tuesday Wednesday Thursday Friday
Title
The Five Colors of Oscar Fashion 12 Fabrics We’re Watching for Fall Is Charmeuse the New Corduroy? Hot Right Now: Matching Your Handbag to Your Hatpin Tea-length and Other Fab Vocab You Need to Know
Author Ellie James Marta Laila Alex

Teams who are balancing content for different brands at agencies or other more complex content environments will want to add categories, author information, content type, social promo, and more to their calendars.

Truly complex editorial calendars are more like hybrid content creation/editorial calendars, where each of the steps to create and publish the content are indicated and someone has planned for how long all of that takes. These can be very helpful if the content you’re responsible for crosses a lot of teams and can take a long time to complete. It doesn’t matter if you’re using Excel or a Google Doc, as long as the people who need the calendar can easily access it. Gantt charts can be excellent for this. Here’s a favorite template for creating a Gantt chart in Google Docs (and they only get more sophisticated).

Complex calendars can encompass everything from ideation through writing, legal review, and publishing. You might even add content localization if your empire spans more than one continent to make sure you have the currency, date formatting, and even slang right.

Content governance

Governance outlines who is taking responsibility for your content. Who evaluates your content performance? What about freshness? Who decides to update (or kill) an older post? Who designs and optimizes workflows for your team or chooses and manages your CMS?

All these individual concerns fall into two overarching components to governance: daily maintenance and overall strategy. In the long run it helps if one person has oversight of the whole process, but the smaller steps can easily be split among many team members. Read this to take your governance to the next level.

Finding authors

The scale of your writing enterprise doesn’t have to be limited to the number of authors you have on your team. It’s also important to consider the possibility of working with freelancers and guest authors. Here’s a look at the pros and cons of outsourced versus in-house talent.

In-house authors

Guest authors and freelancers

Responsible to

You

Themselves

Paid by

You (as part of their salary)

You (on a per-piece basis)

Subject matter expertise

Broad but shallow

Deep but narrow

Capacity for extra work

As you wish

Show me the Benjamins

Turnaround time

On a dime

Varies

Communication investment

Less

More

Devoted audience

Smaller

Potentially huge

From that table, it might look like in-house authors have a lot more advantages. That’s somewhat true, but do not underestimate the value of occasionally working with a true industry expert who has name recognition and a huge following. Whichever route you take (and there are plenty of hybrid options), it’s always okay to ask that the writers you are working with be professional about communication, payment, and deadlines. In some industries, guest writers will write for links. Consider yourself lucky if that’s true. Remember, though, that the final paycheck can be great leverage for getting a writer to do exactly what you need them to (such as making their deadlines).

Tools to help with content creation

So those are some things you need to have in place before you create content. Now’s the fun part: getting started. One of the beautiful things about the Internet is that new and exciting tools crop up every day to help make our jobs easier and more efficient. Here are a few of our favorites.

Calendars

You can always use Excel or a Google Doc to set up your editorial calendar, but we really like Trello for the ability to gather a lot of information in one card and then drag and drop it into place. Once there are actual dates attached to your content, you might be happier with something like a Google Calendar.

Ideation and research

If you need a quick fix for ideation, turn your keywords into wacky ideas with Portent’s Title Maker. You probably won’t want to write to the exact title you’re given (although “True Facts about Justin Bieber’s Love of Pickles” does sound pretty fascinating…), but it’s a good way to get loose and look at your topic from a new angle.

Once you’ve got that idea solidified, find out what your audience thinks about it by gathering information with Survey Monkey or your favorite survey tool. Or, use Storify to listen to what people are saying about your topic across a wide variety of platforms. You can also use Storify to save those references and turn them into a piece of content or an illustration for one. Don’t forget that a simple social ask can also do wonders.

Format

Content doesn’t have to be all about the words. Screencasts, Google+ Hangouts, and presentations are all interesting ways to approach content. Remember that not everyone’s a reader. Some of your audience will be more interested in visual or interactive content. Make something for everyone.

Illustration

Don’t forget to make your content pretty. It’s not that hard to find free stock images online (just make sure you aren’t violating someone’s copyright). We like Morgue File, Free Images, and Flickr’s Creative Commons. If you aren’t into stock images and don’t have access to in-house graphic design, it’s still relatively easy to add images to your content. Pull a screenshot with Skitch or dress up an existing image with Pixlr. You can also use something like Canva to create custom graphics.

Don’t stop with static graphics, though. There are so many tools out there to help you create gifs, quizzes and polls, maps, and even interactive timelines. Dream it, then search for it. Chances are whatever you’re thinking of is doable.

Quality, not quantity

Mediocre content will hurt your cause

Less is more. That’s not an excuse to pare your blog down to one post per month (check out our publishing cadence experiment), but it is an important reminder that if you’re writing “How to Properly Install a Toilet Seat” two days after publishing “Toilet Seat Installation for Dummies,” you might want to rethink your strategy.

The thing is, and I’m going to use another cliché here to drive home the point, you never get a second chance to make a first impression. Potential customers are roving the Internet right now looking for exactly what you’re selling. And if what they find is an only somewhat informative article stuffed with keywords and awful spelling and grammar mistakes… well, you don’t want that. Oh, and search engines think it’s spammy too…

A word about copyright

We’re not copyright lawyers, so we can’t give you the ins and outs on all the technicalities. What we can tell you (and you already know this) is that it’s not okay to steal someone else’s work. You wouldn’t want them to do it to you. This includes images. So whenever you can, make your own images or find images that you can either purchase the rights to (stock imagery) or license under Creative Commons.

It’s usually okay to quote short portions of text, as long as you attribute the original source (and a link is nice). In general, titles and ideas can’t be copyrighted (though they might be trademarked or patented). When in doubt, asking for permission is smart.

That said, part of the fun of the Internet is the remixing culture which includes using things like memes and gifs. Just know that if you go that route, there is a certain amount of risk involved.

Editing

Your content needs to go through at least one editing cycle by someone other than the original author. There are two types of editing, developmental (which looks at the underlying structure of a piece that happens earlier in the writing cycle) and copy editing (which makes sure all the words are there and spelled right in the final draft).

If you have a very small team or are in a rush (and are working with writers that have some skill), you can often skip the developmental editing phase. But know that an investment in that close read of an early draft is often beneficial to the piece and to the writer’s overall growth.

Many content teams peer-edit work, which can be great. Other organizations prefer to run their work by a dedicated editor. There’s no wrong answer, as long as the work gets edited.

Ensuring proper basic SEO

The good news is that search engines are doing their best to get closer and closer to understanding and processing natural language. So good writing (including the natural use of synonyms rather than repeating those keywords over and over and…) will take you a long way towards SEO mastery.

For that reason (and because it’s easy to get trapped in keyword thinking and veer into keyword stuffing), it’s often nice to think of your SEO check as a further edit of the post rather than something you should think about as you’re writing.

But there are still a few things you can do to help cover those SEO bets. Once you have that draft, do a pass for SEO to make sure you’ve covered the following:

  • Use your keyword in your title
  • Use your keyword (or long-tail keyword phrase) in an H2
  • Make sure the keyword appears at least once (though not more than four times, especially if it’s a phrase) in the body of the post
  • Use image alt text (including the keyword when appropriate)

Finding time to write when you don’t have any

Writing (assuming you’re the one doing the writing) can require a lot of energy—especially if you want to do it well. The best way to find time to write is to break each project down into little tasks. For example, writing a blog post actually breaks down into these steps (though not always in this order):

  • Research
  • Outline
  • Fill in outline
  • Rewrite and finish post
  • Write headline
  • SEO check
  • Final edit
  • Select hero image (optional)

So if you only have random chunks of time, set aside 15-30 minutes one day (when your research is complete) to write a really great outline. Then find an hour the next to fill that outline in. After an additional hour the following day, (unless you’re dealing with a research-heavy post) you should have a solid draft by the end of day three.

The magic of working this way is that you engage your brain and then give it time to work in the background while you accomplish other tasks. Hemingway used to stop mid-sentence at the end of his writing days for the same reason.

Once you have that draft nailed, the rest of the steps are relatively easy (even the headline, which often takes longer to write than any other sentence, is easier after you’ve immersed yourself in the post over a few days).

Working with design/development

Every designer and developer is a little different, so we can’t give you any blanket cure-alls for inter-departmental workarounds (aka “smashing silos”). But here are some suggestions to help you convey your vision while capitalizing on the expertise of your coworkers to make your content truly excellent.

Ask for feedback

From the initial brainstorm to general questions about how to work together, asking your team members what they think and prefer can go a long way. Communicate all the details you have (especially the unspoken expectations) and then listen.

If your designer tells you up front that your color scheme is years out of date, you’re saving time. And if your developer tells you that the interactive version of that timeline will require four times the resources, you have the info you need to fight for more budget (or reassess the project).

Check in

Things change in the design and development process. If you have interim check-ins already set up with everyone who’s working on the project, you’ll avoid the potential for nasty surprises at the end. Like finding out that no one has experience working with that hot new coding language you just read about and they’re trying to do a workaround that isn’t working.

Proofread

Your job isn’t done when you hand over the copy to your designer or developer. Not only might they need help rewriting some of your text so that it fits in certain areas, they will also need you to proofread the final version. Accidents happen in the copy-and-paste process and there’s nothing sadder than a really beautiful (and expensive) piece of content that wraps up with a typo:

Know when to fight for an idea

Conflict isn’t fun, but sometimes it’s necessary. The more people involved in your content, the more watered down the original idea can get and the more roadblocks and conflicting ideas you’ll run into. Some of that is very useful. But sometimes you’ll get pulled off track. Always remember who owns the final product (this may not be you) and be ready to stand up for the idea if it’s starting to get off track.

We’re confident this list will set you on the right path to creating some really awesome content, but is there more you’d like to know? Ask us your questions in the comments.

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

The Meta Referrer Tag: An Advancement for SEO and the Internet

Posted by Cyrus-Shepard

The movement to make the Internet more secure through HTTPS brings several useful advancements for webmasters. In addition to security improvements, HTTPS promises future technological advances and potential SEO benefits for marketers.

HTTPS in search results is rising. Recent MozCast data from Dr. Pete shows nearly 20% of first page Google results are now HTTPS.

Sadly, HTTPS also has its downsides.

Marketers run into their first challenge when they switch regular HTTP sites over to HTTPS. Technically challenging, the switch typically involves routing your site through a series of 301 redirects. Historically, these types of redirects are associated with a loss of link equity (thought to be around 15%) which can lead to a loss in rankings. This can offset any SEO advantage that Google claims switching.

Ross Hudgens perfectly summed it up in this tweet:

Many SEOs have anecdotally shared stories of HTTPS sites performing well in Google search results (and our soon-to-be-published Ranking Factors data seems to support this.) However, the short term effect of a large migration can be hard to take. When Moz recently switched to HTTPS to provide better security to our logged-in users, we saw an 8-9% dip in our organic search traffic.

Problem number two is the subject of this post. It involves the loss of referral data. Typically, when one site sends traffic to another, information is sent that identifies the originating site as the source of traffic. This invaluable data allows people to see where their traffic is coming from, and helps spread the flow of information across the web.

SEOs have long used referrer data for a number of beneficial purposes. Oftentimes, people will link back or check out the site sending traffic when they see the referrer in their analytics data. Spammers know this works, as evidenced by the recent increase in referrer spam:

This process stops when traffic flows from an HTTPS site to a non-secure HTTP site. In this case, no referrer data is sent. Webmasters can’t know where their traffic is coming from.

Here’s how referral data to my personal site looked when Moz switched to HTTPS. I lost all visibility into where my traffic came from.

Its (not provided) all over again!

Enter the meta referrer tag

While we can’t solve the ranking challenges imposed by switching a site to HTTPS, we can solve the loss of referral data, and it’s actually super-simple.

Almost completely unknown to most marketers, the relatively new meta referrer tag (it’s actually been around for a few years) was designed to help out in these situations.

Better yet, the tag allows you to control how your referrer information is passed.

The meta referrer tag works with most browsers to pass referrer information in a manner defined by the user. Traffic remains encrypted and all the benefits of using HTTPS remain in place, but now you can pass referrer data to all websites, even those that use HTTP.

How to use the meta referrer tag

What follows are extremely simplified instructions for using the meta referrer tag. For more in-depth understanding, we highly recommend referring to the W3C working draft of the spec.

The meta referrer tag is placed in the <head> section of your HTML, and references one of five states, which control how browsers send referrer information from your site. The five states are:

  1. None: Never pass referral data
    <meta name="referrer" content="none">
    
  2. None When Downgrade: Sends referrer information to secure HTTPS sites, but not insecure HTTP sites
    <meta name="referrer" content="none-when-downgrade">
    
  3. Origin Only: Sends the scheme, host, and port (basically, the subdomain) stripped of the full URL as a referrer, i.e. https://moz.com/example.html would simply send https://moz.com
    <meta name="referrer" content="origin">
    

  4. Origin When Cross-Origin: Sends the full URL as the referrer when the target has the same scheme, host, and port (i.e. subdomain) regardless if it’s HTTP or HTTPS, while sending origin-only referral information to external sites. (note: There is a typo in the official spec. Future versions should be “origin-when-cross-origin”)
    <meta name="referrer" content="origin-when-crossorigin">
    
  5. Unsafe URL: Always passes the URL string as a referrer. Note if you have any sensitive information contained in your URL, this isn’t the safest option. By default, URL fragments, username, and password are automatically stripped out.
    <meta name="referrer" content="unsafe-url">
    

The meta referrer tag in action

By clicking the link below, you can get a sense of how the meta referrer tag works.

Check Referrer

Boom!

We’ve set the meta referrer tag for Moz to “origin”, which means when we link out to another site, we pass our scheme, host, and port. The end result is you see http://moz.com as the referrer, stripped of the full URL path (/meta-referrer-tag).

My personal site typically receives several visits per day from Moz. Here’s what my analytics data looked like before and after we implemented the meta referrer tag.

For simplicity and security, most sites may want to implement the “origin” state, but there are drawbacks.

One negative side effect was that as soon as we implemented the meta referrer tag, our AdRoll analytics, which we use for retargeting, stopped working. It turns out that AdRoll uses our referrer information for analytics, but the meta referrer tag “origin” state meant that the only URL they ever saw reported was https://moz.com.

Conclusion

We love the meta referrer tag because it keeps information flowing on the Internet. It’s the way the web is supposed to work!

It helps marketers and webmasters see exactly where their traffic is coming from. It encourages engagement, communication, and even linking, which can lead to improvements in SEO.

Useful links:

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

8 Ways Content Marketers Can Hack Facebook Multi-Product Ads

Posted by Alan_Coleman

The trick most content marketers are missing

Creating great content is the first half of success in content marketing. Getting quality content read by, and amplified to, a relevant audience is the oft overlooked second half of success. Facebook can be a content marketer’s best friend for this challenge. For reach, relevance and amplification potential, Facebook is unrivaled.

  1. Reach: 1 in 6 mobile minutes on planet earth is somebody reading something on Facebook.
  2. Relevance: Facebook is a lean mean interest and demo targeting machine. There is no online or offline media that owns as much juicy interest and demographic information on its audience and certainly no media has allowed advertisers to utilise this information as effectively as Facebook has.
  3. Amplification: Facebook is literally built to encourage sharing. Here’s the first 10 words from their mission statement: “Facebook’s mission is to give people the power to share…”, Enough said!

Because of these three digital marketing truths, if a content marketer gets their paid promotion* right on Facebook, the battle for eyeballs and amplification is already won.

For this reason it’s crucial that content marketers keep a close eye on Facebook advertising innovations and seek out ways to use them in new and creative ways.

In this post I will share with you eight ways we’ve hacked a new Facebook ad format to deliver content marketing success.

Multi-Product Ads (MPAs)

In 2014, Facebook unveiled multi-product ads (MPAs) for US advertisers, we got them in Europe earlier this year. They allow retailers to show multiple products in a carousel-type ad unit.

They look like this:

If the user clicks on the featured product, they are guided directly to the landing page for that specific product, from where they can make a purchase.

You could say MPAs are Facebook’s answer to Google Shopping.

Facebook’s mistake is a content marketer’s gain

I believe Facebook has misunderstood how people want to use their social network and the transaction-focused format is OK at best for selling products. People aren’t really on Facebook to hit the “buy now” button. I’m a daily Facebook user and I can’t recall a time this year where I have gone directly from Facebook to an e-commerce website and transacted. Can you remember a recent time when you did?

So, this isn’t an innovation that removes a layer of friction from something that we are all doing online already (as the most effective innovations do). Instead, it’s a bit of a “hit and hope” that, by providing this functionality, Facebook would encourage people to try to buy online in a way they never have before.

The Wolfgang crew felt the MPA format would be much more useful to marketers and users if they were leveraging Facebook for the behaviour we all demonstrate on the platform every day, guiding users to relevant content. We attempted to see if Facebook Ads Manager would accept MPAs promoting content rather than products. We plugged in the images, copy and landing pages, hit “place order”, and lo and behold the ads became active. We’re happy to say that the engagement rates, and more importantly the amplification rates, are fantastic!

Multi-Content Ads

We’ve re-invented the MPA format for multi-advertisers in multi-ways, eight ways to be exact! Here’s eight MPA Hacks that have worked well for us. All eight hacks use the MPA format to promote content rather than promote products.

Hack #1: Multi-Package Ads

Our first variation wasn’t a million miles away from multi-product ads; we were promoting the various packages offered by a travel operator.

By looking at the number of likes, comments, and shares (in blue below the ads) you can see the ads were a hit with Facebook users and they earned lots of free engagement and amplification.

NB: If you have selected “clicks to website” as your advertising objective, all those likes, comments and shares are free!

Independent Travel Multi Product Ad

The ad sparked plenty of conversation amongst Facebook friends in the comments section.

Comments on a Facebook MPA

Hack #2: Multi-Offer Ads

Everybody knows the Internet loves a bargain. So we decided to try another variation moving away from specific packages, focusing instead on deals for a different travel operator.

Here’s how the ads looked:

These ads got valuable amplification beyond the share. In the comments section, you can see people tagging specific friends. This led to the MPAs receiving further amplification, and a very targeted and personalised form of amplification to boot.

Abbey Travel Facebook Ad Comments

Word of mouth referrals have been a trader’s best friend since the stone age. These “personalised” word of mouth referrals en masse are a powerful marketing proposition. It’s worth mentioning again that those engagements are free!

Hack #3: Multi-Locations Ads

Putting the Lo in SOLOMO.

This multi-product feed ad was hacked to promote numerous locations of a waterpark. “Where to go?” is among the first questions somebody asks when researching a holiday. In creating this top of funnel content, we can communicate with our target audience at the very beginning of their research process. A simple truth of digital marketing is: the more interactions you have with your target market on their journey to purchase, the more likely they are to seal the deal with you when it comes time to hit the “buy now” button. Starting your relationship early gives you an advantage over those competitors who are hanging around the bottom of the purchase funnel hoping to make a quick and easy conversion.

Abbey Travel SplashWorld Facebook MPA

What was surprising here, was that because we expected to reach people at the very beginning of their research journey, we expected the booking enquiries to be some time away. What actually happened was these ads sparked an enquiry frenzy as Facebook users could see other people enquiring and the holidays selling out in real time.

Abbey Travel comments and replies

In fact nearly all of the 35 comments on this ad were booking enquiries. This means what we were measuring as an “engagement” was actually a cold hard “conversion”! You don’t need me to tell you a booking enquiry is far closer to the money than a Facebook like.

The three examples outlined so far are for travel companies. Travel is a great fit for Facebook as it sits naturally in the Facebook feed, my Facebook feed is full of envy-inducing friends’ holiday pictures right now. Another interesting reason why travel is a great fit for Facebook ads is because typically there are multiple parties to a travel purchase. What happened here is the comments section actually became a very visible and measurable forum for discussion between friends and family before becoming a stampede inducing medium of enquiry.

So, stepping outside of the travel industry, how do other industries fare with hacked MPAs?

Hack #3a: Multi-Location Ads (combined with location targeting)

Location, location, location. For a property listings website, we applied location targeting and repeated our Multi-Location Ad format to advertise properties for sale to people in and around that location.

Hack #4: Multi-Big Content Ad

“The future of big content is multi platform”

– Cyrus Shepard

The same property website had produced a report and an accompanying infographic to provide their audience with unique and up-to-the-minute market information via their blog. We used the MPA format to promote the report, the infographic and the search rentals page of the website. This brought their big content piece to a larger audience via a new platform.

Rental Report Multi Product Ad

Hack #5: Multi-Episode Ad

This MPA hack was for an online TV player. As you can see we advertised the most recent episodes of a TV show set in a fictional Dublin police station, Red Rock.

Engagement was high, opinion was divided.

TV3s Red Rock viewer feedback

LOL.

Hack #6: Multi-People Ads

In the cosmetic surgery world, past patients’ stories are valuable marketing material. Particularly when the past patients are celebrities. We recycled some previously published stories from celebrity patients using multi-people ads and targeted them to a very specific audience.

Avoca Clinic Multi People Ads

Hack #7: Multi-UGC Ads

Have you witnessed the power of user generated content (UGC) in your marketing yet? We’ve found interaction rates with authentic UGC images can be up to 10 fold of those of the usual stylised images. In order to encourage further UGC, we posted a number of customer’s images in our Multi-UGC Ads.

The CTR on the above ads was 6% (2% is the average CTR for Facebook News feed ads according to our study). Strong CTRs earn you more traffic for your budget. Facebook’s relevancy score lowers your CPC as your CTR increases.

When it comes to the conversion, UGC is a power player, we’ve learned that “customers attracting new customers” is a powerful acquisition tool.

Hack #8: Target past customers for amplification

“Who will support and amplify this content and why?”

– Rand Fishkin

Your happy customers Rand, that’s the who and the why! Check out these Multi-Package Ads targeted to past customers via custom audiences. The Camino walkers have already told all their friends about their great trip, now allow them to share their great experiences on Facebook and connect the tour operator with their Facebook friends via a valuable word of mouth referral. Just look at the ratio of share:likes and shares:comments. Astonishingly sharable ads!

Camino Ways Mulit Product Ads

Targeting past converters in an intelligent manner is a super smart way to find an audience ready to share your content.

How will hacking Multi-Product Ads work for you?

People don’t share ads, but they do share great content. So why not hack MPAs to promote your content and reap the rewards of the world’s greatest content sharing machine: Facebook.

MPAs allow you to tell a richer story by allowing you to promote multiple pieces of content simultaneously. So consider which pieces of content you have that will work well as “content bundles” and who the relevant audience for each “content bundle” is.

As Hack #8 above illustrates, the big wins come when you match a smart use of the format with the clever and relevant targeting Facebook allows. We’re massive fans of custom audiences so if you aren’t sure where to start, I’d suggest starting there.

So ponder your upcoming content pieces, consider your older content you’d like to breathe some new life into and perhaps you could become a Facebook Ads Hacker.

I’d love to hear about your ideas for turning Multi-Product Ads into Multi-Content Ads in the comments section below.

We could even take the conversation offline at Mozcon!

Happy hacking.


*Yes I did say paid promotion, it’s no secret that Facebook’s organic reach continues to dwindle. The cold commercial reality is you need to pay to play on FB. The good news is that if you select ‘website clicks’ as your objective you only pay for website traffic and engagement while amplification by likes, comments, and shares are free! Those website clicks you pay for are typically substantially cheaper than Adwords, Taboola, Outbrain, Twitter or LinkedIn. How does it compare to display? It doesn’t. Paying for clicks is always preferable to paying for impressions. If you are spending money on display advertising I’d urge you to fling a few spondoolas towards Facebook ads and compare results. You will be pleasantly surprised.

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How to Rid Your Website of Six Common Google Analytics Headaches

Posted by amandaecking

I’ve been in and out of Google Analytics (GA) for the past five or so years agency-side. I’ve seen three different code libraries, dozens of new different features and reports roll out, IP addresses stop being reported, and keywords not-so-subtly phased out of the free platform.

Analytics has been a focus of mine for the past year or so—mainly, making sure clients get their data right. Right now, our new focus is closed loop tracking, but that’s a topic for another day. If you’re using Google Analytics, and only Google Analytics for the majority of your website stats, or it’s your primary vehicle for analysis, you need to make sure it’s accurate.

Not having data pulling in or reporting properly is like building a house on a shaky foundation: It doesn’t end well. Usually there are tears.

For some reason, a lot of people, including many of my clients, assume everything is tracking properly in Google Analytics… because Google. But it’s not Google who sets up your analytics. People do that. And people are prone to make mistakes.

I’m going to go through six scenarios where issues are commonly encountered with Google Analytics.

I’ll outline the remedy for each issue, and in the process, show you how to move forward with a diagnosis or resolution.

1. Self-referrals

This is probably one of the areas we’re all familiar with. If you’re seeing a lot of traffic from your own domain, there’s likely a problem somewhere—or you need to extend the default session length in Google Analytics. (For example, if you have a lot of long videos or music clips and don’t use event tracking; a website like TEDx or SoundCloud would be a good equivalent.)

Typically one of the first things I’ll do to help diagnose the problem is include an advanced filter to show the full referrer string. You do this by creating a filter, as shown below:

Filter Type: Custom filter > Advanced
Field A: Hostname
Extract A: (.*)
Field B: Request URI
Extract B: (.*)
Output To: Request URI
Constructor: $A1$B1

You’ll then start seeing the subdomains pulling in. Experience has shown me that if you have a separate subdomain hosted in another location (say, if you work with a separate company and they host and run your mobile site or your shopping cart), it gets treated by Google Analytics as a separate domain. Thus, you ‘ll need to implement cross domain tracking. This way, you can narrow down whether or not it’s one particular subdomain that’s creating the self-referrals.

In this example below, we can see all the revenue is being reported to the booking engine (which ended up being cross domain issues) and their own site is the fourth largest traffic source:

I’ll also a good idea to check the browser and device reports to start narrowing down whether the issue is specific to a particular element. If it’s not, keep digging. Look at pages pulling the self-referrals and go through the code with a fine-tooth comb, drilling down as much as you can.

2. Unusually low bounce rate

If you have a crazy-low bounce rate, it could be too good to be true. Unfortunately. An unusually low bounce rate could (and probably does) mean that at least on some pages of your website have the same Google Analytics tracking code installed twice.

Take a look at your source code, or use Google Tag Assistant (though it does have known bugs) to see if you’ve got GA tracking code installed twice.

While I tell clients having Google Analytics installed on the same page can lead to double the pageviews, I’ve not actually encountered that—I usually just say it to scare them into removing the duplicate implementation more quickly. Don’t tell on me.

3. Iframes anywhere

I’ve heard directly from Google engineers and Google Analytics evangelists that Google Analytics does not play well with iframes, and that it will never will play nice with this dinosaur technology.

If you track the iframe, you inflate your pageviews, plus you still aren’t tracking everything with 100% clarity.

If you don’t track across iframes, you lose the source/medium attribution and everything becomes a self-referral.

Damned if you do; damned if you don’t.

My advice: Stop using iframes. They’re Netscape-era technology anyway, with rainbow marquees and Comic Sans on top. Interestingly, and unfortunately, a number of booking engines (for hotels) and third-party carts (for ecommerce) still use iframes.

If you have any clients in those verticals, or if you’re in the vertical yourself, check with your provider to see if they use iframes. Or you can check for yourself, by right-clicking as close as you can to the actual booking element:

iframe-booking.png

There is no neat and tidy way to address iframes with Google Analytics, and usually iframes are not the only complicated element of setup you’ll encounter. I spent eight months dealing with a website on a subfolder, which used iframes and had a cross domain booking system, and the best visibility I was able to get was about 80% on a good day.

Typically, I’d approach diagnosing iframes (if, for some reason, I had absolutely no access to viewing a website or talking to the techs) similarly to diagnosing self-referrals, as self-referrals are one of the biggest symptoms of iframe use.

4. Massive traffic jumps

Massive jumps in traffic don’t typically just happen. (Unless, maybe, you’re Geraldine.) There’s always an explanation—a new campaign launched, you just turned on paid ads for the first time, you’re using content amplification platforms, you’re getting a ton of referrals from that recent press in The New York Times. And if you think it just happened, it’s probably a technical glitch.

I’ve seen everything from inflated pageviews result from including tracking on iframes and unnecessary implementation of virtual pageviews, to not realizing the tracking code was installed on other microsites for the same property. Oops.

Usually I’ve seen this happen when the tracking code was somewhere it shouldn’t be, so if you’re investigating a situation of this nature, first confirm the Google Analytics code is only in the places it needs to be.Tools like Google Tag Assistant and Screaming Frog can be your BFFs in helping you figure this out.

Also, I suggest bribing the IT department with sugar (or booze) to see if they’ve changed anything lately.

5. Cross-domain tracking

I wish cross-domain tracking with Google Analytics out of the box didn’t require any additional setup. But it does.

If you don’t have it set up properly, things break down quickly, and can be quite difficult to untangle.

The older the GA library you’re using, the harder it is. The easiest setup, by far, is Google Tag Manager with Universal Analytics. Hard-coded universal analytics is a bit more difficult because you have to implement autoLink manually and decorate forms, if you’re using them (and you probably are). Beyond that, rather than try and deal with it, I say update your Google Analytics code. Then we can talk.

Where I’ve seen the most murkiness with tracking is when parts of cross domain tracking are implemented, but not all. For some reason, if allowLinker isn’t included, or you forget to decorate all the forms, the cookies aren’t passed between domains.

The absolute first place I would start with this would be confirming the cookies are all passing properly at all the right points, forms, links, and smoke signals. I’ll usually use a combination of the Real Time report in Google Analytics, Google Tag Assistant, and GA debug to start testing this. Any debug tool you use will mean you’re playing in the console, so get friendly with it.

6. Internal use of UTM strings

I’ve saved the best for last. Internal use of campaign tagging. We may think, oh, I use Google to tag my campaigns externally, and we’ve got this new promotion on site which we’re using a banner ad for. That’s a campaign. Why don’t I tag it with a UTM string?

Step away from the keyboard now. Please.

When you tag internal links with UTM strings, you override the original source/medium. So that visitor who came in through your paid ad and then who clicks on the campaign banner has now been manually tagged. You lose the ability to track that they came through on the ad the moment they click on the tagged internal link. Their source and medium is now your internal campaign, not that paid ad you’re spending gobs of money on and have to justify to your manager. See the problem?

I’ve seen at least three pretty spectacular instances of this in the past year, and a number of smaller instances of it. Annie Cushing also talks about the evils of internal UTM tags and the odd prevalence of it. (Oh, and if you haven’t explored her blog, and the amazing spreadsheets she shares, please do.)

One clothing company I worked with tagged all of their homepage offers with UTM strings, which resulted in the loss of visibility for one-third of their audience: One million visits over the course of a year, and $2.1 million in lost revenue.

Let me say that again. One million visits, and $2.1 million. That couldn’t be attributed to an external source/campaign/spend.

Another client I audited included campaign tagging on nearly every navigational element on their website. It still gives me nightmares.

If you want to see if you have any internal UTM strings, head straight to the Campaigns report in Acquisition in Google Analytics, and look for anything like “home” or “navigation” or any language you may use internally to refer to your website structure.

And if you want to see how users are moving through your website, go to the Flow reports. Or if you really, really, really want to know how many people click on that sidebar link, use event tracking. But please, for the love of all things holy (and to keep us analytics lovers from throwing our computers across the room), stop using UTM tagging on your internal links.

Now breathe and smile

Odds are, your Google Analytics setup is fine. If you are seeing any of these issues, though, you have somewhere to start in diagnosing and addressing the data.

We’ve looked at six of the most common points of friction I’ve encountered with Google Analytics and how to start investigating them: self-referrals, bounce rate, iframes, traffic jumps, cross domain tracking and internal campaign tagging.

What common data integrity issues have you encountered with Google Analytics? What are your favorite tools to investigate?

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Deconstructing the App Store Rankings Formula with a Little Mad Science

Posted by AlexApptentive

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

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

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

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

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

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

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

Until now, that is.

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

But first, a little context

Image credit: Josh Tuininga, Apptentive

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

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

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

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

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

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

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

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

Now, for the Mad Science.

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

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

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

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

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

Hypothesis

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

Both of these assumptions will be tested in later analysis.

Results

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

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

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

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

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

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

Hypothesis

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

Results

App Store Ranking Volatility of Top 500 Apps

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

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

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

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

Study #3: App store rankings across the stars

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

Hypothesis

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

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

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

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

Results

Average App Store Ratings of Top Apps

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

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

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

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

App Store Ranking Volatility and Average Rating

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

Study #4: App store rankings across versions

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

Hypothesis

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

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

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

Results

How update frequency correlates with app store rank

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

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

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

How update frequency correlates with app store ranking volatility

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

Study #5: App store rankings across monthly active users

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

Hypothesis

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

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

Results

Apps with more ratings and reviews typically rank higher

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

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

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

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

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

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

Apps with more ratings typically experience less app store ranking volatility

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

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

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

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

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

Summary

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

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

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

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

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

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

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

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

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

Weight of factors in the Apple App Store ranking algorithm

Rating Count > Installs > Trends > Rating

Weight of factors in the Google Play ranking algorithm

Rating Count > Installs > Rating > Trends


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

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

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

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Reblogged 4 years ago from tracking.feedpress.it

How We Fixed the Internet (Ok, an Answer Box)

Posted by Dr-Pete

Last year, Google expanded the Knowledge Graph to use data extracted (*cough* scraped) from the index to create answer boxes. Back in October, I wrote about a failed experiment. One of my posts, an odd dive
into Google’s revenue, was being answer-fied for the query “How much does Google make?”:

Objectively speaking, even I could concede that this wasn’t a very good answer in 2014. I posted it on Twitter, and
David Iwanow asked the inevitable question:

Enthusiasm may have gotten the best of us, a few more people got involved (like my former Moz colleague
Ruth Burr Reedy), and suddenly we were going to fix this once and for all:

There Was Just One Problem

I updated the post, carefully rewriting the first paragraph to reflect the new reality of Google’s revenue. I did my best to make the change user-friendly, adding valuable information but not disrupting the original post. I did, however, completely replace the old text that Google was scraping.

Within less than a day, Google had re-cached the content, and I just had to wait to see the new answer box. So, I waited, and waited… and waited. Two months later, still no change. Some days, the SERP showed no answer box at all (although I’ve since found these answer boxes are very dynamic), and I was starting to wonder if it was all a mistake.

Then, Something Happened

Last week, months after I had given up, I went to double-check this query for entirely different reasons, and I saw the following:

Google had finally updated the answer box with the new text, and they had even pulled an image from the post. It was a strange choice of images, but in fairness, it was a strange post.

Interestingly, Google also added the publication date of the post, perhaps recognizing that outdated answers aren’t always useful. Unfortunately, this doesn’t reflect the timing of the new content, but that’s understandable – Google doesn’t have easy access to that data.

It’s interesting to note that sometimes Google shows the image, and sometimes they don’t. This seems to be independent of whether the SERP is personalized or incognito. Here’s a capture of the image-free version, along with the #1 organic ranking:

You’ll notice that the #1 result is also my Moz post, and that result has an expanded meta description. So, the same URL is essentially double-dipping this SERP. This isn’t always the case – answers can be extracted from URLs that appear lower on page 1 (although almost always page 1, in my experience). Anecdotally, it’s also not always the case that these organic result ends up getting an expanded meta description.

However, it definitely seems that some of the quality signals driving organic ranking and expanded meta descriptions are also helping Google determine whether a query deserves a direct answer. Put simply, it’s not an accident that this post was chosen to answer this question.

What Does This Mean for You?

Let’s start with the obvious – Yes, the v2 answer boxes (driven by the index, not Freebase/WikiData)
can be updated. However, the update cycle is independent of the index’s refresh cycle. In other words, just because a post is re-cached, it doesn’t mean the answer box will update. Presumably, Google is creating a second Knowledge Graph, based on the index, and this data is only periodically updated.

It’s also entirely possible that updating could cause you to lose an answer box, if the new data weren’t a strong match to the question or the quality of the content came into question. Here’s an interesting question – on a query where a competitor has an answer box, could you change your own content enough to either replace them or knock out the answer box altogether? We are currently testing this question, but it may be a few more months before we have any answers.

Another question is what triggers this style of answer box in the first place? Eric Enge has an
in-depth look at 850,000 queries that’s well worth your time, and in many cases Google is still triggering on obvious questions (“how”, “what”, “where”, etc.). Nouns that could be interpreted as ambiguous also can trigger the new answer boxes. For example, a search for “ruby” is interpreted by Google as roughly meaning “What is Ruby?”:

This answer box also triggers “Related topics” that use content pulled from other sites but drive users to more Google searches. The small, gray links are the source sites. The much more visible, blue links are more Google searches.

Note that these also have to be questions (explicit or implied) that Google can’t answer with their curated Knowledge Graph (based on sources like Freebase and WikiData). So, for example, the question “When is Mother’s Day?” triggers an older-style answer:

Sites offering this data aren’t going to have a chance to get attribution, because Google essentially already owns the answer to this question as part of their core Knowledge Graph.

Do You Want to Be An Answer?

This is where things get tricky. At this point, we have no clear data on how these answer boxes impact CTR, and it’s likely that the impact depends a great deal on the context. I think we’re facing a certain degree of inevitability – if Google is going to list an answer, better it’s your answer then someone else’s, IMO. On the other hand, what if that answer is so complete that it renders your URL irrelevant? Consider, for example, the SERP for “how to make grilled cheese”:

Sorry, Food Network, but making a grilled cheese sandwich isn’t really that hard, and this answer box doesn’t leave much to the imagination. As these answers get more and more thorough, expect CTRs to fall.

For now, I’d argue that it’s better to have your link in the box than someone else’s, but that’s cold comfort in many cases. These new answer boxes represent what I feel is a dramatic shift in the relationship between Google and webmasters, and they may be tipping the balance. For now, we can’t do much but wait, see, and experiment.

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Reblogged 4 years ago from tracking.feedpress.it

Try Your Hand at A/B Testing for a Chance to Win the Email Subject Line Contest

Posted by danielburstein

This blog post ends with an opportunity for you to win a stay at the ARIA in Vegas and a ticket to
Email Summit, but it begins with an essential question for marketers…

How can you improve already successful marketing, advertising, websites and copywriting?

Today’s Moz blog post is unique. Not only are we going to teach you how to address this challenge, we’re going to offer an example that you can dig into to help drive home the lesson.

Give the people what they want

Some copy and design is so bad, the fixes are obvious. Maybe you shouldn’t insult the customer in the headline. Maybe you should update the website that still uses a dot matrix font.

But when you’re already doing well, how can you continue to improve?

I don’t have the answer for you, but I’ll tell you who does – your customers.

There are many tricks, gimmicks and technology you can use in marketing, but when you strip away all the hype and rhetoric, successful marketing is pretty straightforward –
clearly communicate the value your offer provides to people who will pay you for that value.

Easier said than done, of course.

So how do you determine what customers want? And the best way to deliver it to them?

Well, there are many ways to learn from customers, such as focus groups, surveys and social listening. While there is value in asking people what they want, there is also a major challenge in it. “People’s ability to understand the factors that affect their behavior is surprisingly poor,” according to research from Dr. Noah J. Goldstein, Associate Professor of Management and Organizations, UCLA Anderson School of Management.

Or, as Malcolm Gladwell more glibly puts it when referring to coffee choices, “The mind knows not what the tongue wants.”

Not to say that opinion-based customer preference research is bad. It can be helpful. However, it should be the beginning and not the end of your quest.

…by seeing what they actually do

You can use what you learn from opinion-based research to create a hypothesis about what customers want, and then
run an experiment to see how they actually behave in real-world customer interactions with your product, marketing messages, and website.

The technique that powers this kind of research is often known as A/B testing, split testing, landing page optimization, and/or website optimization. If you are testing more than one thing at a time, it may also be referred to as multi-variate testing.

To offer a simple example, you might assume that customers buy your product because it tastes great. Or because it’s less filling. So you could create two landing pages – one with a headline that promotes that taste (treatment A) and another that mentions the low carbs (treatment B). You then send half the traffic that visits that URL to each version and see which performs better.

Here is a simple visual that Joey Taravella, Content Writer, MECLABS create to illustrate the concept…

That’s just one test. To really learn about your customers, you must continue the process and create a testing-optimization cycle in your organization – continue to run A/B tests, record the findings, learn from them, create more hypotheses, and test again based on these hypotheses.

This is true marketing experimentation, and helps you build your theory of the customer.

But you probably know all that already. So here’s your chance to practice while helping us shape an A/B test. You might even win a prize in the process.

The email subject line contest

The Moz Blog and MarketingExperiments Blog have joined forces to run a unique marketing experimentation contest. We’re presenting you with a real challenge from a real organization (VolunteerMatch) and
asking you to write a subject line to test (it’s simple, just leave your subject line as a comment in this blog post).

We’re going to pick three subject lines suggested by readers of The Moz Blog and three from the MarketingExperiments Blog and run a test with this organization’s customers. Whoever writes the best performing subject line will
win a stay at the ARIA Resort in Las Vegas as well as a two-day ticket to MarketingSherpa Email Summit 2015 to help them gain lessons to further improve their marketing.

Sound good? OK, let’s dive in and tell you more about your “client”…

Craft the best-performing subject line to win the prize

Every year at Email Summit, we run a live A/B test where the audience helps craft the experiment. We then run, validate, close the experiment, and share the results during Summit as a way to teach about marketing experimentation. We have typically run the experiment using MarketingSherpa as the “client” website to test (MarketingExperiments and MarketingSherpa are sister publications, both owned by MECLABS Institute).

However, this year we wanted to try something different and interviewed three national non-profits to find a new “client” for our tests.

We chose
VolunteerMatch – a nonprofit organization that uses the power of technology to make it easier for good people and good causes to connect. One of the key reasons we chose VolunteerMatch is because it is an already successful organization looking to further improve. (Here is a case study explaining one of its successful implementations – Lead Management: How a B2B SaaS nonprofit decreased its sales cycle 99%).

Another reason we chose VolunteerMatch for this opportunity is that it has three types of customers, so the lessons from the content we create can help marketers across a wide range of sales models. VolunteerMatch’s customers are:

  • People who want to volunteer (B2C)
  • Non-profit organizations looking for volunteers (non-profit)
  • Businesses looking for corporate volunteering solutions (B2B) to which it offers a Software-as-a-Service product through VolunteerMatch Solutions

Designing the experiment

After we took VolunteerMatch on as the Research Partner “client,” Jon Powell, Senior Executive Research and Development Manager, MECLABS, worked with Shari Tishman, Director of Engagement and Lauren Wagner, Senior Manager of Engagement, VolunteerMatch, to understand their challenges, take a look at their current assets and performance, and craft a design of experiments to determine what further knowledge about its customers would help VolunteerMatch improve performance.

That design of experiments includes a series of split tests – including the live test we’re going to run at Email Summit, as well as the one you have an opportunity to take part in by writing a subject line in the comments section of this blog post. Let’s take a look at that experiment…

The challenge

VolunteerMatch wants to increase the response rate of the corporate email list (B2B) by discovering the best possible messaging to use. In order to find out, MarketingExperiments wants to run an A/B split test to determine the
best messaging.

However the B2B list is relatively smaller than the volunteer/cause list (B2C) which makes it harder to test in (and gain
statistical significance) and determine which messaging is most effective.

So we’re going to run a messaging test to the B2C list. This isn’t without its challenges though, because most individuals on the B2C list are not likely to immediately connect with B2B corporate solutions messaging.

So the question is…

How do we create an email that is relevant (to the B2C list), which doesn’t ask too much, that simultaneously helps us discover the most relevant aspect of the solutions (B2B) product (if any)?

The approach – Here’s where you come in

This is where the Moz and MarketingExperiments community comes in to help.

We would like you to craft subject lines relevant to the B2C list, which highlight various benefits of the corporate solutions tool.

We have broken down the corporate solutions tool into three main categories of benefit for the SaaS product.
In the comments section below, include which category you are writing a subject line for along with what you think is an effective subject line.

The crew at Moz and MarketingExperiments will then choose the top subject line in each category to test. Below you will find the emails that will be sent as part of the test. They are identical, except for the subject lines (which you will write) and the bolded line in the third paragraph (that ties into that category of value).

Category #1: Proof, recognition, credibility


Category #2: Better, more opportunities to choose from


Category #3: Ease-of-use

About VolunteerMatch’s brand

Since we’re asking you to try your hand at crafting messaging for this example “client,” here is some more information about the brand to inform your messaging…


VolunteerMatch’s brand identity


VolunteerMatch’s core values

Ten things VolunteerMatch believes:

  1. People want to do good
  2. Every great cause should be able to find the help it needs
  3. People want to improve their lives and communities through volunteering
  4. You can’t make a difference without making a connection
  5. In putting the power of technology to good use
  6. Businesses are serious about making a difference
  7. In building relationships based on trust and excellent service
  8. In partnering with like-minded organizations to create systems that result in even greater impact
  9. The passion of our employees drives the success of our products, services and mission
  10. In being great at what we do

And now, we test…

To participate, you must leave your comment with your idea for a subject line before midnight on Tuesday, January 13, 2015. The contest is open to all residents of the 50 US states, the District of Columbia, and Canada (excluding Quebec), 18 or older. If you want more info, here are the
official rules.

When you enter your subject line in the comments section, also include which category you’re entering for (and if you have an idea outside these categories, let us know…we just might drop it in the test).

Next, the Moz marketing team will pick the subject lines they think will perform best in each category from all the comments on The Moz Blog, and the MarketingExperiments team will pick the subject lines we think will perform the best in each category from all the comments on the MarketingExperiments Blog.

We’ll give the VolunteerMatch team a chance to approve the subject lines based on their brand standards, then test all six to eight subject lines and report back to you through the Moz and MarketingExperiments blogs which subject lines won and why they won to help you improve your already successful marketing.

So, what have you got? Write your best subject lines in the comments section below. I look forward to seeing what you come up with.

Related resources

If you’re interested in learning more about marketing experimentation and A/B testing, you might find these links helpful…

And here’s a look at a previous subject line writing contest we’ve run to give you some ideas for your entry…


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Reblogged 4 years ago from moz.com