Stop Ghost Spam in Google Analytics with One Filter

Posted by CarloSeo

The spam in Google Analytics (GA) is becoming a serious issue. Due to a deluge of referral spam from social buttons, adult sites, and many, many other sources, people are starting to become overwhelmed by all the filters they are setting up to manage the useless data they are receiving.

The good news is, there is no need to panic. In this post, I’m going to focus on the most common mistakes people make when fighting spam in GA, and explain an efficient way to prevent it.

But first, let’s make sure we understand how spam works. A couple of months ago, Jared Gardner wrote an excellent article explaining what referral spam is, including its intended purpose. He also pointed out some great examples of referral spam.

Types of spam

The spam in Google Analytics can be categorized by two types: ghosts and crawlers.

Ghosts

The vast majority of spam is this type. They are called ghosts because they never access your site. It is important to keep this in mind, as it’s key to creating a more efficient solution for managing spam.

As unusual as it sounds, this type of spam doesn’t have any interaction with your site at all. You may wonder how that is possible since one of the main purposes of GA is to track visits to our sites.

They do it by using the Measurement Protocol, which allows people to send data directly to Google Analytics’ servers. Using this method, and probably randomly generated tracking codes (UA-XXXXX-1) as well, the spammers leave a “visit” with fake data, without even knowing who they are hitting.

Crawlers

This type of spam, the opposite to ghost spam, does access your site. As the name implies, these spam bots crawl your pages, ignoring rules like those found in robots.txt that are supposed to stop them from reading your site. When they exit your site, they leave a record on your reports that appears similar to a legitimate visit.

Crawlers are harder to identify because they know their targets and use real data. But it is also true that new ones seldom appear. So if you detect a referral in your analytics that looks suspicious, researching it on Google or checking it against this list might help you answer the question of whether or not it is spammy.

Most common mistakes made when dealing with spam in GA

I’ve been following this issue closely for the last few months. According to the comments people have made on my articles and conversations I’ve found in discussion forums, there are primarily three mistakes people make when dealing with spam in Google Analytics.

Mistake #1. Blocking ghost spam from the .htaccess file

One of the biggest mistakes people make is trying to block Ghost Spam from the .htaccess file.

For those who are not familiar with this file, one of its main functions is to allow/block access to your site. Now we know that ghosts never reach your site, so adding them here won’t have any effect and will only add useless lines to your .htaccess file.

Ghost spam usually shows up for a few days and then disappears. As a result, sometimes people think that they successfully blocked it from here when really it’s just a coincidence of timing.

Then when the spammers later return, they get worried because the solution is not working anymore, and they think the spammer somehow bypassed the barriers they set up.

The truth is, the .htaccess file can only effectively block crawlers such as buttons-for-website.com and a few others since these access your site. Most of the spam can’t be blocked using this method, so there is no other option than using filters to exclude them.

Mistake #2. Using the referral exclusion list to stop spam

Another error is trying to use the referral exclusion list to stop the spam. The name may confuse you, but this list is not intended to exclude referrals in the way we want to for the spam. It has other purposes.

For example, when a customer buys something, sometimes they get redirected to a third-party page for payment. After making a payment, they’re redirected back to you website, and GA records that as a new referral. It is appropriate to use referral exclusion list to prevent this from happening.

If you try to use the referral exclusion list to manage spam, however, the referral part will be stripped since there is no preexisting record. As a result, a direct visit will be recorded, and you will have a bigger problem than the one you started with since. You will still have spam, and direct visits are harder to track.

Mistake #3. Worrying that bounce rate changes will affect rankings

When people see that the bounce rate changes drastically because of the spam, they start worrying about the impact that it will have on their rankings in the SERPs.

bounce.png

This is another mistake commonly made. With or without spam, Google doesn’t take into consideration Google Analytics metrics as a ranking factor. Here is an explanation about this from Matt Cutts, the former head of Google’s web spam team.

And if you think about it, Cutts’ explanation makes sense; because although many people have GA, not everyone uses it.

Assuming your site has been hacked

Another common concern when people see strange landing pages coming from spam on their reports is that they have been hacked.

landing page

The page that the spam shows on the reports doesn’t exist, and if you try to open it, you will get a 404 page. Your site hasn’t been compromised.

But you have to make sure the page doesn’t exist. Because there are cases (not spam) where some sites have a security breach and get injected with pages full of bad keywords to defame the website.

What should you worry about?

Now that we’ve discarded security issues and their effects on rankings, the only thing left to worry about is your data. The fake trail that the spam leaves behind pollutes your reports.

It might have greater or lesser impact depending on your site traffic, but everyone is susceptible to the spam.

Small and midsize sites are the most easily impacted – not only because a big part of their traffic can be spam, but also because usually these sites are self-managed and sometimes don’t have the support of an analyst or a webmaster.

Big sites with a lot of traffic can also be impacted by spam, and although the impact can be insignificant, invalid traffic means inaccurate reports no matter the size of the website. As an analyst, you should be able to explain what’s going on in even in the most granular reports.

You only need one filter to deal with ghost spam

Usually it is recommended to add the referral to an exclusion filter after it is spotted. Although this is useful for a quick action against the spam, it has three big disadvantages.

  • Making filters every week for every new spam detected is tedious and time-consuming, especially if you manage many sites. Plus, by the time you apply the filter, and it starts working, you already have some affected data.
  • Some of the spammers use direct visits along with the referrals.
  • These direct hits won’t be stopped by the filter so even if you are excluding the referral you will sill be receiving invalid traffic, which explains why some people have seen an unusual spike in direct traffic.

Luckily, there is a good way to prevent all these problems. Most of the spam (ghost) works by hitting GA’s random tracking-IDs, meaning the offender doesn’t really know who is the target, and for that reason either the hostname is not set or it uses a fake one. (See report below)

Ghost-Spam.png

You can see that they use some weird names or don’t even bother to set one. Although there are some known names in the list, these can be easily added by the spammer.

On the other hand, valid traffic will always use a real hostname. In most of the cases, this will be the domain. But it also can also result from paid services, translation services, or any other place where you’ve inserted GA tracking code.

Valid-Referral.png

Based on this, we can make a filter that will include only hits that use real hostnames. This will automatically exclude all hits from ghost spam, whether it shows up as a referral, keyword, or pageview; or even as a direct visit.

To create this filter, you will need to find the report of hostnames. Here’s how:

  1. Go to the Reporting tab in GA
  2. Click on Audience in the lefthand panel
  3. Expand Technology and select Network
  4. At the top of the report, click on Hostname

Valid-list

You will see a list of all hostnames, including the ones that the spam uses. Make a list of all the valid hostnames you find, as follows:

  • yourmaindomain.com
  • blog.yourmaindomain.com
  • es.yourmaindomain.com
  • payingservice.com
  • translatetool.com
  • anotheruseddomain.com

For small to medium sites, this list of hostnames will likely consist of the main domain and a couple of subdomains. After you are sure you got all of them, create a regular expression similar to this one:

yourmaindomain\.com|anotheruseddomain\.com|payingservice\.com|translatetool\.com

You don’t need to put all of your subdomains in the regular expression. The main domain will match all of them. If you don’t have a view set up without filters, create one now.

Then create a Custom Filter.

Make sure you select INCLUDE, then select “Hostname” on the filter field, and copy your expression into the Filter Pattern box.

filter

You might want to verify the filter before saving to check that everything is okay. Once you’re ready, set it to save, and apply the filter to all the views you want (except the view without filters).

This single filter will get rid of future occurrences of ghost spam that use invalid hostnames, and it doesn’t require much maintenance. But it’s important that every time you add your tracking code to any service, you add it to the end of the filter.

Now you should only need to take care of the crawler spam. Since crawlers access your site, you can block them by adding these lines to the .htaccess file:

## STOP REFERRER SPAM 
RewriteCond %{HTTP_REFERER} semalt\.com [NC,OR] 
RewriteCond %{HTTP_REFERER} buttons-for-website\.com [NC] 
RewriteRule .* - [F]

It is important to note that this file is very sensitive, and misplacing a single character it it can bring down your entire site. Therefore, make sure you create a backup copy of your .htaccess file prior to editing it.

If you don’t feel comfortable messing around with your .htaccess file, you can alternatively make an expression with all the crawlers, then and add it to an exclude filter by Campaign Source.

Implement these combined solutions, and you will worry much less about spam contaminating your analytics data. This will have the added benefit of freeing up more time for you to spend actually analyze your valid data.

After stopping spam, you can also get clean reports from the historical data by using the same expressions in an Advance Segment to exclude all the spam.

Bonus resources to help you manage spam

If you still need more information to help you understand and deal with the spam on your GA reports, you can read my main article on the subject here: http://www.ohow.co/what-is-referrer-spam-how-stop-it-guide/.

Additional information on how to stop spam can be found at these URLs:

In closing, I am eager to hear your ideas on this serious issue. Please share them in the comments below.

(Editor’s Note: All images featured in this post were created by the author.)

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Reblogged 4 years ago from tracking.feedpress.it

The Colossus Update: Waking The Giant

Posted by Dr-Pete

Yesterday morning, we woke up to a historically massive temperature spike on MozCast, after an unusually quiet weekend. The 10-day weather looked like this:

That’s 101.8°F, one of the hottest verified days on record, second only to a series of unconfirmed spikes in June of 2013. For reference, the first Penguin update clocked in at 93.1°.

Unfortunately, trying to determine how the algorithm changed from looking at individual keywords (even thousands of them) is more art than science, and even the art is more often Ms. Johnson’s Kindergarten class than Picasso. Sometimes, though, we catch a break and spot something.

The First Clue: HTTPS

When you watch enough SERPs, you start to realize that change is normal. So, the trick is to find the queries that changed a lot on the day in question but are historically quiet. Looking at a few of these, I noticed some apparent shake-ups in HTTP vs. HTTPS (secure) URLs. So, the question becomes: are these anecdotes, or do they represent a pattern?

I dove in and looked at how many URLs for our 10,000 page-1 SERPs were HTTPS over the past few days, and I saw this:

On the morning of June 17, HTTPS URLs on page 1 jumped from 16.9% to 18.4% (a 9.9% day-over-day increase), after trending up for a few days. This represents the total real-estate occupied by HTTPS URLs, but how did rankings fare? Here are the average rankings across all HTTPS results:

HTTPS URLs also seem to have gotten a rankings boost – dropping (with “dropping” being a positive thing) from an average of 2.96 to 2.79 in the space of 24 hours.

Seems pretty convincing, right? Here’s the problem: rankings don’t just change because Google changes the algorithm. We are, collectively, changing the web every minute of the day. Often, those changes are just background noise (and there’s a lot of noise), but sometimes a giant awakens.

The Second Clue: Wikipedia

Anecdotally, I noticed that some Wikipedia URLs seemed to be flipping from HTTP to HTTPS. I ran a quick count, and this wasn’t just a fluke. It turns out that Wikipedia started switching their entire site to HTTPS around June 12 (hat tip to Jan Dunlop). This change is expected to take a couple of weeks.

It’s just one site, though, right? Well, historically, this one site is the #1 largest land-holder across the SERP real-estate we track, with over 5% of the total page-1 URLs in our tracking data (5.19% as of June 17). Wikipedia is a giant, and its movements can shake the entire web.

So, how do we tease this apart? If Wikipedia’s URLs had simply flipped from HTTP to HTTPS, we should see a pretty standard pattern of shake-up. Those URLs would look to have changed, but the SERPS around them would be quiet. So, I ran an analysis of what the temperature would’ve been if we ignored the protocol (treating HTTP/HTTPS as the same). While slightly lower, that temperature was still a scorching 96.6°F.

Is it possible that Wikipedia moving to HTTPS also made the site eligible for a rankings boost from previous algorithm updates, thus disrupting page 1 without any code changes on Google’s end? Yes, it is possible – even a relatively small rankings boost for Wikipedia from the original HTTPS algorithm update could have a broad impact.

The Third Clue: Google?

So far, Google has only said that this was not a Panda update. There have been rumors that the HTTPS update would get a boost, as recently as SMX Advanced earlier this month, but no timeline was given for when that might happen.

Is it possible that Wikipedia’s publicly announced switch finally gave Google the confidence to boost the HTTPS signal? Again, yes, it’s possible, but we can only speculate at this point.

My gut feeling is that this was more than just a waking giant, even as powerful of a SERP force as Wikipedia has become. We should know more as their HTTPS roll-out continues and their index settles down. In the meantime, I think we can expect Google to become increasingly serious about HTTPS, even if what we saw yesterday turns out not to have been an algorithm update.

In the meantime, I’m going to melodramatically name this “The Colossus Update” because, well, it sounds cool. If this indeed was an algorithm update, I’m sure Google would prefer something sensible, like “HTTPS Update 2” or “Securageddon” (sorry, Gary).

Update from Google: Gary Illyes said that he’s not aware of an HTTPS update (via Twitter):

No comment on other updates, or the potential impact of a Wikipedia change. I feel strongly that there is an HTTPS connection in the data, but as I said – that doesn’t necessarily mean the algorithm changed.

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Why We Can’t Do Keyword Research Like It’s 2010 – Whiteboard Friday

Posted by randfish

Keyword Research is a very different field than it was just five years ago, and if we don’t keep up with the times we might end up doing more harm than good. From the research itself to the selection and targeting process, in today’s Whiteboard Friday Rand explains what has changed and what we all need to do to conduct effective keyword research today.

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

What do we need to change to keep up with the changing world of keyword research?

Howdy, Moz fans, and welcome to another edition of Whiteboard Friday. This week we’re going to chat a little bit about keyword research, why it’s changed from the last five, six years and what we need to do differently now that things have changed. So I want to talk about changing up not just the research but also the selection and targeting process.

There are three big areas that I’ll cover here. There’s lots more in-depth stuff, but I think we should start with these three.

1) The Adwords keyword tool hides data!

This is where almost all of us in the SEO world start and oftentimes end with our keyword research. We go to AdWords Keyword Tool, what used to be the external keyword tool and now is inside AdWords Ad Planner. We go inside that tool, and we look at the volume that’s reported and we sort of record that as, well, it’s not good, but it’s the best we’re going to do.

However, I think there are a few things to consider here. First off, that tool is hiding data. What I mean by that is not that they’re not telling the truth, but they’re not telling the whole truth. They’re not telling nothing but the truth, because those rounded off numbers that you always see, you know that those are inaccurate. Anytime you’ve bought keywords, you’ve seen that the impression count never matches the count that you see in the AdWords tool. It’s not usually massively off, but it’s often off by a good degree, and the only thing it’s great for is telling relative volume from one from another.

But because AdWords hides data essentially by saying like, “Hey, you’re going to type in . . .” Let’s say I’m going to type in “college tuition,” and Google knows that a lot of people search for how to reduce college tuition, but that doesn’t come up in the suggestions because it’s not a commercial term, or they don’t think that an advertiser who bids on that is going to do particularly well and so they don’t show it in there. I’m giving an example. They might indeed show that one.

But because that data is hidden, we need to go deeper. We need to go beyond and look at things like Google Suggest and related searches, which are down at the bottom. We need to start conducting customer interviews and staff interviews, which hopefully has always been part of your brainstorming process but really needs to be now. Then you can apply that to AdWords. You can apply that to suggest and related.

The beautiful thing is once you get these tools from places like visiting forums or communities, discussion boards and seeing what terms and phrases people are using, you can collect all this stuff up, plug it back into AdWords, and now they will tell you how much volume they’ve got. So you take that how to lower college tuition term, you plug it into AdWords, they will show you a number, a non-zero number. They were just hiding it in the suggestions because they thought, “Hey, you probably don’t want to bid on that. That won’t bring you a good ROI.” So you’ve got to be careful with that, especially when it comes to SEO kinds of keyword research.

2) Building separate pages for each term or phrase doesn’t make sense

It used to be the case that we built separate pages for every single term and phrase that was in there, because we wanted to have the maximum keyword targeting that we could. So it didn’t matter to us that college scholarship and university scholarships were essentially people looking for exactly the same thing, just using different terminology. We would make one page for one and one page for the other. That’s not the case anymore.

Today, we need to group by the same searcher intent. If two searchers are searching for two different terms or phrases but both of them have exactly the same intent, they want the same information, they’re looking for the same answers, their query is going to be resolved by the same content, we want one page to serve those, and that’s changed up a little bit of how we’ve done keyword research and how we do selection and targeting as well.

3) Build your keyword consideration and prioritization spreadsheet with the right metrics

Everybody’s got an Excel version of this, because I think there’s just no awesome tool out there that everyone loves yet that kind of solves this problem for us, and Excel is very, very flexible. So we go into Excel, we put in our keyword, the volume, and then a lot of times we almost stop there. We did keyword volume and then like value to the business and then we prioritize.

What are all these new columns you’re showing me, Rand? Well, here I think is how sophisticated, modern SEOs that I’m seeing in the more advanced agencies, the more advanced in-house practitioners, this is what I’m seeing them add to the keyword process.

Difficulty

A lot of folks have done this, but difficulty helps us say, “Hey, this has a lot of volume, but it’s going to be tremendously hard to rank.”

The difficulty score that Moz uses and attempts to calculate is a weighted average of the top 10 domain authorities. It also uses page authority, so it’s kind of a weighted stack out of the two. If you’re seeing very, very challenging pages, very challenging domains to get in there, it’s going to be super hard to rank against them. The difficulty is high. For all of these ones it’s going to be high because college and university terms are just incredibly lucrative.

That difficulty can help bias you against chasing after terms and phrases for which you are very unlikely to rank for at least early on. If you feel like, “Hey, I already have a powerful domain. I can rank for everything I want. I am the thousand pound gorilla in my space,” great. Go after the difficulty of your choice, but this helps prioritize.

Opportunity

This is actually very rarely used, but I think sophisticated marketers are using it extremely intelligently. Essentially what they’re saying is, “Hey, if you look at a set of search results, sometimes there are two or three ads at the top instead of just the ones on the sidebar, and that’s biasing some of the click-through rate curve.” Sometimes there’s an instant answer or a Knowledge Graph or a news box or images or video, or all these kinds of things that search results can be marked up with, that are not just the classic 10 web results. Unfortunately, if you’re building a spreadsheet like this and treating every single search result like it’s just 10 blue links, well you’re going to lose out. You’re missing the potential opportunity and the opportunity cost that comes with ads at the top or all of these kinds of features that will bias the click-through rate curve.

So what I’ve seen some really smart marketers do is essentially build some kind of a framework to say, “Hey, you know what? When we see that there’s a top ad and an instant answer, we’re saying the opportunity if I was ranking number 1 is not 10 out of 10. I don’t expect to get whatever the average traffic for the number 1 position is. I expect to get something considerably less than that. Maybe something around 60% of that, because of this instant answer and these top ads.” So I’m going to mark this opportunity as a 6 out of 10.

There are 2 top ads here, so I’m giving this a 7 out of 10. This has two top ads and then it has a news block below the first position. So again, I’m going to reduce that click-through rate. I think that’s going down to a 6 out of 10.

You can get more and less scientific and specific with this. Click-through rate curves are imperfect by nature because we truly can’t measure exactly how those things change. However, I think smart marketers can make some good assumptions from general click-through rate data, which there are several resources out there on that to build a model like this and then include it in their keyword research.

This does mean that you have to run a query for every keyword you’re thinking about, but you should be doing that anyway. You want to get a good look at who’s ranking in those search results and what kind of content they’re building . If you’re running a keyword difficulty tool, you are already getting something like that.

Business value

This is a classic one. Business value is essentially saying, “What’s it worth to us if visitors come through with this search term?” You can get that from bidding through AdWords. That’s the most sort of scientific, mathematically sound way to get it. Then, of course, you can also get it through your own intuition. It’s better to start with your intuition than nothing if you don’t already have AdWords data or you haven’t started bidding, and then you can refine your sort of estimate over time as you see search visitors visit the pages that are ranking, as you potentially buy those ads, and those kinds of things.

You can get more sophisticated around this. I think a 10 point scale is just fine. You could also use a one, two, or three there, that’s also fine.

Requirements or Options

Then I don’t exactly know what to call this column. I can’t remember the person who’ve showed me theirs that had it in there. I think they called it Optional Data or Additional SERPs Data, but I’m going to call it Requirements or Options. Requirements because this is essentially saying, “Hey, if I want to rank in these search results, am I seeing that the top two or three are all video? Oh, they’re all video. They’re all coming from YouTube. If I want to be in there, I’ve got to be video.”

Or something like, “Hey, I’m seeing that most of the top results have been produced or updated in the last six months. Google appears to be biasing to very fresh information here.” So, for example, if I were searching for “university scholarships Cambridge 2015,” well, guess what? Google probably wants to bias to show results that have been either from the official page on Cambridge’s website or articles from this year about getting into that university and the scholarships that are available or offered. I saw those in two of these search results, both the college and university scholarships had a significant number of the SERPs where a fresh bump appeared to be required. You can see that a lot because the date will be shown ahead of the description, and the date will be very fresh, sometime in the last six months or a year.

Prioritization

Then finally I can build my prioritization. So based on all the data I had here, I essentially said, “Hey, you know what? These are not 1 and 2. This is actually 1A and 1B, because these are the same concepts. I’m going to build a single page to target both of those keyword phrases.” I think that makes good sense. Someone who is looking for college scholarships, university scholarships, same intent.

I am giving it a slight prioritization, 1A versus 1B, and the reason I do this is because I always have one keyword phrase that I’m leaning on a little more heavily. Because Google isn’t perfect around this, the search results will be a little different. I want to bias to one versus the other. In this case, my title tag, since I more targeting university over college, I might say something like college and university scholarships so that university and scholarships are nicely together, near the front of the title, that kind of thing. Then 1B, 2, 3.

This is kind of the way that modern SEOs are building a more sophisticated process with better data, more inclusive data that helps them select the right kinds of keywords and prioritize to the right ones. I’m sure you guys have built some awesome stuff. The Moz community is filled with very advanced marketers, probably plenty of you who’ve done even more than this.

I look forward to hearing from you in the comments. I would love to chat more about this topic, and we’ll see you again next week for another edition of Whiteboard Friday. Take care.

Video transcription by Speechpad.com

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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.

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Reblogged 4 years ago from tracking.feedpress.it

I Can’t Drive 155: Meta Descriptions in 2015

Posted by Dr-Pete

For years now, we (and many others) have been recommending keeping your Meta Descriptions shorter than
about 155-160 characters. For months, people have been sending me examples of search snippets that clearly broke that rule, like this one (on a search for “hummingbird food”):

For the record, this one clocks in at 317 characters (counting spaces). So, I set out to discover if these long descriptions were exceptions to the rule, or if we need to change the rules. I collected the search snippets across the MozCast 10K, which resulted in 92,669 snippets. All of the data in this post was collected on April 13, 2015.

The Basic Data

The minimum snippet length was zero characters. There were 69 zero-length snippets, but most of these were the new generation of answer box, that appears organic but doesn’t have a snippet. To put it another way, these were misidentified as organic by my code. The other 0-length snippets were local one-boxes that appeared as organic but had no snippet, such as this one for “chichen itza”:

These zero-length snippets were removed from further analysis, but considering that they only accounted for 0.07% of the total data, they didn’t really impact the conclusions either way. The shortest legitimate, non-zero snippet was 7 characters long, on a search for “geek and sundry”, and appears to have come directly from the site’s meta description:

The maximum snippet length that day (this is a highly dynamic situation) was 372 characters. The winner appeared on a search for “benefits of apple cider vinegar”:

The average length of all of the snippets in our data set (not counting zero-length snippets) was 143.5 characters, and the median length was 152 characters. Of course, this can be misleading, since some snippets are shorter than the limit and others are being artificially truncated by Google. So, let’s dig a bit deeper.

The Bigger Picture

To get a better idea of the big picture, let’s take a look at the display length of all 92,600 snippets (with non-zero length), split into 20-character buckets (0-20, 21-40, etc.):

Most of the snippets (62.1%) cut off as expected, right in the 141-160 character bucket. Of course, some snippets were shorter than that, and didn’t need to be cut off, and some broke the rules. About 1% (1,010) of the snippets in our data set measured 200 or more characters. That’s not a huge number, but it’s enough to take seriously.

That 141-160 character bucket is dwarfing everything else, so let’s zoom in a bit on the cut-off range, and just look at snippets in the 120-200 character range (in this case, by 5-character bins):

Zooming in, the bulk of the snippets are displaying at lengths between about 146-165 characters. There are plenty of exceptions to the 155-160 character guideline, but for the most part, they do seem to be exceptions.

Finally, let’s zoom in on the rule-breakers. This is the distribution of snippets displaying 191+ characters, bucketed in 10-character bins (191-200, 201-210, etc.):

Please note that the Y-axis scale is much smaller than in the previous 2 graphs, but there is a pretty solid spread, with a decent chunk of snippets displaying more than 300 characters.

Without looking at every original meta description tag, it’s very difficult to tell exactly how many snippets have been truncated by Google, but we do have a proxy. Snippets that have been truncated end in an ellipsis (…), which rarely appears at the end of a natural description. In this data set, more than half of all snippets (52.8%) ended in an ellipsis, so we’re still seeing a lot of meta descriptions being cut off.

I should add that, unlike titles/headlines, it isn’t clear whether Google is cutting off snippets by pixel width or character count, since that cut-off is done on the server-side. In most cases, Google will cut before the end of the second line, but sometimes they cut well before this, which could suggest a character-based limit. They also cut off at whole words, which can make the numbers a bit tougher to interpret.

The Cutting Room Floor

There’s another difficulty with telling exactly how many meta descriptions Google has modified – some edits are minor, and some are major. One minor edit is when Google adds some additional information to a snippet, such as a date at the beginning. Here’s an example (from a search for “chicken pox”):

With the date (and minus the ellipsis), this snippet is 164 characters long, which suggests Google isn’t counting the added text against the length limit. What’s interesting is that the rest comes directly from the meta description on the site, except that the site’s description starts with “Chickenpox.” and Google has removed that keyword. As a human, I’d say this matches the meta description, but a bot has a very hard time telling a minor edit from a complete rewrite.

Another minor rewrite occurs in snippets that start with search result counts:

Here, we’re at 172 characters (with spaces and minus the ellipsis), and Google has even let this snippet roll over to a third line. So, again, it seems like the added information at the beginning isn’t counting against the length limit.

All told, 11.6% of the snippets in our data set had some kind of Google-generated data, so this type of minor rewrite is pretty common. Even if Google honors most of your meta description, you may see small edits.

Let’s look at our big winner, the 372-character description. Here’s what we saw in the snippet:

Jan 26, 2015 – Health• Diabetes Prevention: Multiple studies have shown a correlation between apple cider vinegar and lower blood sugar levels. … • Weight Loss: Consuming apple cider vinegar can help you feel more full, which can help you eat less. … • Lower Cholesterol: … • Detox: … • Digestive Aid: … • Itchy or Sunburned Skin: … • Energy Boost:1 more items

So, what about the meta description? Here’s what we actually see in the tag:

Were you aware of all the uses of apple cider vinegar? From cleansing to healing, to preventing diabetes, ACV is a pantry staple you need in your home.

That’s a bit more than just a couple of edits. So, what’s happening here? Well, there’s a clue on that same page, where we see yet another rule-breaking snippet:

You might be wondering why this snippet is any more interesting than the other one. If you could see the top of the SERP, you’d know why, because it looks something like this:

Google is automatically extracting list-style data from these pages to fuel the expansion of the Knowledge Graph. In one case, that data is replacing a snippet
and going directly into an answer box, but they’re performing the same translation even for some other snippets on the page.

So, does every 2nd-generation answer box yield long snippets? After 3 hours of inadvisable mySQL queries, I can tell you that the answer is a resounding “probably not”. You can have 2nd-gen answer boxes without long snippets and you can have long snippets without 2nd-gen answer boxes,
but there does appear to be a connection between long snippets and Knowledge Graph in some cases.

One interesting connection is that Google has begun bolding keywords that seem like answers to the query (and not just synonyms for the query). Below is an example from a search for “mono symptoms”. There’s an answer box for this query, but the snippet below is not from the site in the answer box:

Notice the bolded words – “fatigue”, “sore throat”, “fever”, “headache”, “rash”. These aren’t synonyms for the search phrase; these are actual symptoms of mono. This data isn’t coming from the meta description, but from a bulleted list on the target page. Again, it appears that Google is trying to use the snippet to answer a question, and has gone well beyond just matching keywords.

Just for fun, let’s look at one more, where there’s no clear connection to the Knowledge Graph. Here’s a snippet from a search for “sons of anarchy season 4”:

This page has no answer box, and the information extracted is odd at best. The snippet bears little or no resemblance to the site’s meta description. The number string at the beginning comes out of a rating widget, and some of the text isn’t even clearly available on the page. This seems to be an example of Google acknowledging IMDb as a high-authority site and desperately trying to match any text they can to the query, resulting in a Frankenstein’s snippet.

The Final Verdict

If all of this seems confusing, that’s probably because it is. Google is taking a lot more liberties with snippets these days, both to better match queries, to add details they feel are important, or to help build and support the Knowledge Graph.

So, let’s get back to the original question – is it time to revise the 155(ish) character guideline? My gut feeling is: not yet. To begin with, the vast majority of snippets are still falling in that 145-165 character range. In addition, the exceptions to the rule are not only atypical situations, but in most cases those long snippets don’t seem to represent the original meta description. In other words, even if Google does grant you extra characters, they probably won’t be the extra characters you asked for in the first place.

Many people have asked: “How do I make sure that Google shows my meta description as is?” I’m afraid the answer is: “You don’t.” If this is very important to you, I would recommend keeping your description below the 155-character limit, and making sure that it’s a good match to your target keyword concepts. I suspect Google is going to take more liberties with snippets over time, and we’re going to have to let go of our obsession with having total control over the SERPs.

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How to Create Boring-Industry Content that Gets Shared

Posted by ronell-smith

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

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

(image source)

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

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

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

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

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

Looking at content for boring industries all wrong

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

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

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

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

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

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

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

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

content does not equal amplification

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

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

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

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

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

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

Develop a link earning mindset

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

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

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

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

ranking factors survey results

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

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

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

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

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

Setting the right goals

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

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

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

Know the audience

marketing group discussing personas

(image source)

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

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

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

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

PR and outreach: Your amplification engines

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

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

Putting it all together

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

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

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

Eureka!

lightbulb

(image source)

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

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

Content is only as boring as your outlook

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

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

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

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

The untold truth about creating content for boring industries

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

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

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

big content example

(Amazing piece of interactive content created by BuiltVisible)

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

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

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

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

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

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

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

Create a winning trifecta: Outreach, promotion and amplification

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

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

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

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

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

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

Summary

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

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