Tag Archive | "Data"

How to Create a Local Marketing Results Dashboard in Google Data Studio – Whiteboard Friday

Posted by DiTomaso

Showing clients that you’re making them money is one of the most important things you can communicate to them, but it’s tough to know how to present your results in a way they can easily understand. That’s where Google Data Studio comes in. In this week’s edition of Whiteboard Friday, our friend Dana DiTomaso shares how to create a client-friendly local marketing results dashboard in Google Data Studio from start to finish.

Click on the whiteboard image above to open a high-resolution version in a new tab!

Video Transcription

Hi, Moz fans. My name is Dana DiTomaso. I’m President and partner of Kick Point. We’re a digital marketing agency way up in the frozen north of Edmonton, Alberta. We work with a lot of local businesses, both in Edmonton and around the world, and small local businesses usually have the same questions when it comes to reporting.

Are we making money?

What I’m going to share with you today is our local marketing dashboard that we share with clients. We build this in Google Data Studio because we love Google Data Studio. If you haven’t watched my Whiteboard Friday yet on how to do formulas in Google Data Studio, I recommend you hit Pause right now, go back and watch that, and then come back to this because I am going to talk about what happened there a little bit in this video.

The Google Data Studio dashboard

This is a Google Data Studio dashboard which I’ve tried to represent in the medium of whiteboard as best as I could. Picture it being a little bit better design than my left-handedness can represent on a whiteboard, but you get the idea. Every local business wants to know, “Are we making money?” This is the big thing that people care about, and really every business cares about making money. Even charities, for example: money is important obviously because that’s what keeps the lights on, but there’s also perhaps a mission that they have.

But they still want to know: Are people filling out our donation form? Are people contacting us? These are important things for every business, organization, not-for-profit, whatever to understand and know. What we’ve tried to do in this dashboard is really boil it down to the absolute basics, one thing you can look at, see a couple of data points, know whether things are good or things are bad.

Are people contacting you?

Let’s start with this up here. The first thing is: Are people contacting you? Now you can break this out into separate columns. You can do phone calls and emails for example. Some of our clients prefer that. Some clients just want one mashed up number. So we’ll take the number of calls that people are getting.

If you’re using a call tracking tool, such as CallRail, you can import this in here. Emails, for example, or forms, just add it all together and then you have one single number of the number of times people contacted you. Usually this is a way bigger number than people think it is, which is also kind of cool.

Are people taking the action you want them to take?

The next thing is: Are people doing the thing that you want them to do? This is really going to decide on what’s meaningful to the client.

For example, if you have a client, again thinking about a charity, how many people filled out your donation form, your online donation form? For a psychologist client of ours, how many people booked an appointment? For a client of ours who offers property management, how many people booked a viewing of a property? What is the thing you want them to do? If they have online e-commerce, for example, then maybe this is how many sales did you have.

Maybe this will be two different things — people walking into the store versus sales. We’ve also represented in this field if a person has a people counter in their store, then we would pull that people counter data into here. Usually we can get the people counter data in a Google sheet and then we can pull it into Data Studio. It’s not the prettiest thing in the world, but it certainly represents all their data in one place, which is really the whole point of why we do these dashboards.

Where did visitors com from, and where are your customers coming from?

People contacting you, people doing the thing you want them to do, those are the two major metrics. Then we do have a little bit deeper further down. On this side here we start with: Where did visitors come from, and where are your customers coming from? Because they’re really two different things, right? Not every visitor to the website is going to become a customer. We all know that. No one has a 100% conversion rate, and if you do, you should just retire.

Filling out the dashboard

We really need to differentiate between the two. In this case we’re looking at channel, and there probably is a better word for channel. We’re always trying to think about, “What would clients call this?” But I feel like clients are kind of aware of the word “channel” and that’s how they’re getting there. But then the next column, by default this would be called users or sessions. Both of those are kind of cruddy. You can rename fields in Data Studio, and we can call this the number of people, for example, because that’s what it is.

Then you would use the users as the metric, and you would just call it number of people instead of users, because personally I hate the word “users.” It really boils down the humanity of a person to a user metric. Users are terrible. Call them people or visitors at least. Then unfortunately, in Data Studio, when you do a comparison field, you cannot rename and call it comparison. It does this nice percentage delta, which I hate.

It’s just like a programmer clearly came up with this. But for now, we have to deal with it. Although by the time this video comes out, maybe it will be something better, and then I can go back and correct myself in the comments. But for now it’s percentage delta. Then goal percentage and then again delta. They can sort by any of these columns in Data Studio, and it’s real live data.

Put a time period on this, and people can pick whatever time period they want and then they can look at this data as much as they want, which is delightful. If you’re not delivering great results, it may be a little terrifying for you, but really you shouldn’t be hiding that anyway, right? Like if things aren’t going well, be honest about it. That’s another talk for another time. But start with this kind of chart. Then on the other side, are you showing up on Google Maps?

We use the Supermetrics Google My Business plug-in to grab this kind of information. We hook it into the customer’s Google Maps account. Then we’re looking at branded searches and unbranded searches and how many times they came up in the map pack. Usually we’ll have a little explanation here. This is how many times you came up in the map pack and search results as well as Google Maps searches, because it’s all mashed in together.

Then what happens when they find you? So number of direction requests, number of website visits, number of phone calls. Now the tricky thing is phone calls here may be captured in phone calls here. You may not want to add these two pieces of data or just keep this off on its own separately, depending upon how your setup is. You could be using a tracking number, for example, in your Google My Business listing and that therefore would be captured up here.

Really just try to be honest about where that data comes from instead of double counting. You don’t want to have that happen. The last thing is if a client has messages set up, then you can pull that message information as well.

Tell your clients what to do

Then at the very bottom of the report we have a couple of columns, and usually this is a longer chart and this is shorter, so we have room down here to do this. Obviously, my drawing skills are not as good as as aligning things in Data Studio, so forgive me.

But we tell them what to do. Usually when we work with local clients, they can’t necessarily afford a monthly retainer to do stuff for clients forever. Instead, we tell them, “Here’s what you have to do this month.Here’s what you have to do next month. Hey, did you remember you’re supposed to be blogging?” That sort of thing. Just put it in here, because clients are looking at results, but they often forget the things that may get them those results. This is a really nice reminder of if you’re not happy with these numbers, maybe you should do these things.

Tell your clients how to use the report

Then the next thing is how to use. This is a good reference because if they only open it say once every couple months, they probably have forgotten how to do the stuff in this report or even things like up at the top make sure to set the time period for example. This is a good reminder of how to do that as well.

Because the report is totally editable by you at any time, you can always go in and change stuff later, and because the client can view the report at any time, they have a dashboard that is extremely useful to them and they don’t need to bug you every single time they want to see a report. It saves you time and money. It saves them time and money. Everybody is happy. Everybody is saving money. I really recommend setting up a really simple dashboard like this for your clients, and I bet you they’ll be impressed.

Thanks so much.

Video transcription by Speechpad.com

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


Moz Blog

Posted in IM NewsComments Off

New report from MarTech Today: Enterprise Customer Data Platforms: A Marketer’s Guide

Learn everything you need to know about enterprise customer data platforms.



Please visit Search Engine Land for the full article.


Search Engine Land: News & Info About SEO, PPC, SEM, Search Engines & Search Marketing

Posted in IM NewsComments Off

Google Collaborates With Newsgroups to Show Tabular Data in Search Results

Google just announced that it will be using a new type of formatting for how newsgroups present their data in Search results. This will make it easier for users to find the information they need. The move to make data more accessible is part of the Google News Initiative. The project allows the company to work together with the journalism and news industry to build a stronger and more dynamic future for news.

So far, Google has collaborated with 30 expert data journalists to develop ways to better present key data. The partnership has resulted in a format well-suited for search results. Users will now see structured data in a tabular form displayed directly in the top search result. The format makes it more readable and easier for users to locate the information they need. It has also made it simpler to add structured data to a website’s existing code.

Google has used indexed data in Search before, but it’s only now that news organizations are included.

 

ProPublica has already utilized this improved method of showing search results. The news organization’s Deputy Managing Editor, Scott Klein, commented on the enhanced presentation and said the real-world impact news orgs have make it vital that people are given the information they need when they need it.

“If we can make the data we’ve worked hard to collect and prepare available to people at the very moment when they’re researching a big life decision, and thereby help them make the best decision they can, it’s an absolute no-brainer for us,” Klein said. He also added that adding extra code is trivial at best.

Google has been on a mission to improve Chrome, with reports of a makeover or an improved image search function floating around for months now. But changing the format for how data is presented in search results so that it’s comprehensive and easier to understand is the right step to take.

[Featured image via Pixabay]

The post Google Collaborates With Newsgroups to Show Tabular Data in Search Results appeared first on WebProNews.


WebProNews

Posted in IM NewsComments Off

Calculated Fields in Google Data Studio – Whiteboard Friday

Posted by DiTomaso

Google Data Studio is a powerful tool to have in your SEO kit. Knowing how to get the most out of its power begins with understanding how to use calculated fields to apply good old-fashioned math to your data. In this week’s Whiteboard Friday, we’re delighted to welcome guest host Dana DiTomaso as she takes us through how to use calculated fields in Google Data Studio to uncover more value in your data and improve your reports.

Calculated Fields in Google Data Studio

Click on the whiteboard image above to open a high-resolution version in a new tab!

Video Transcription

Hi, Moz fans. I’m Dana DiTomaso, President and partner at Kick Point, and we love Google Data Studio at Kick Point. You may not love Google Data Studio yet, but after you watch this I think you probably will.

One of the first things that you think about Google Data Studio is: Why would I use this? It’s just charts. It’s the same thing I can get in Analytics or a billion other dashboarding tools out there. But one of the things that I really like about Google Data Studio is math. You can do lots of different stuff in Data Studio, and I’m going to go through four of the basic types in Data Studio and then how you can use that to improve your reports, just as you sort of dip your toes into the Google Data Studio pool. What I’ve done here is I have written out a lot of the formulas that you’re going to be using.

The types

It’s a lot of obviously written out formulas, but when you get into Data Studio, you should be able to type these in and they’ll work. Let’s start at the beginning with the types.

  1. Basic math. This is pretty obvious. 1 + 1 = 2. Phone calls plus emails equals this, for example. You can add together different fields.
  2. Transforms. Let’s say people are really bad at writing some things upper case and some things lower case. You have a problem with URLs being written a couple of different ways. You can use a transform to transform upper case into lower case. That’s pretty nice.
  3. Formulas. Formulas is where you’re saying only show this subset of the data. Or how often does this happen? That could be things like the Count function, so count how many times this occurs, for example, and present that as a totally separate metric, which can be really useful for things like when you want to count the number of times an event occurs and then compare that against something else. It can just pull out that kind of data.
  4. Logic. This is the more complex one. If X, then Y. If this happens, then that’s going to happen. There’s a lot of really complex stuff in there. But if you’re just getting started, start with this, and then look at the Google Data Studio documentation. You’ll find some cooler stuff in there.

1. Basic math

Here are some examples of how we use this in our Google Data Studio dashboards. So basic math, one of the things that a lot of people care about is: Are people getting in touch with me?

This is the basics of the reason why we do marketing. Are people getting in touch? So, for example, you can do some basic math and say, “All right. So I know on our website in Google Tag Manager, we have a trigger that fires whenever somebody taps or clicks a MailTo link on the site.” In addition to that, we’re tracking how many people submit a form, as you should.

Instead of reporting these separately, really they’re kind of the same thing. They’re emailing one way or the other. Why don’t we just submit them as one metric? So in that case, you can say grab all the mail to form completions and then grab all the form goal completions, and now you have a total email requests or total requests or whatever you might want to call it. You can do the same thing where it’s like, well, phone calls and emails, does it really matter if they’re in separate buckets?

Just put them all in one. The same thing with the basic math. Just add it all together and then you’ve got one total metric you can present to the client. Here’s how much money we made for you. Boom. That’s a nice one. The next thing — I’m just going to flip over here — is formulas.

2. Formulas

Okay, so formulas, one of the things that I really like doing is looking at your Google Search Console data. This is in Data Studio. You’re going to use Search Console for this, which is a nice data source. We all know Search Console data is not necessarily 100% accurate, but there’s always lots of keyword treasure in there to be found if it’s easy to find, which the Search Console interface isn’t super great.

So you can make a report in Data Studio and say regex match, and so don’t be afraid of regex. I think everyone should learn it. But if you’re not super familiar with it, this is a really easy way to do it. Say, okay, every time a keyword contains why, how, can, what, for example, then those are question searches. You may change it to whatever makes sense for you.

But this is just pulling out that subset of data. Then you can see, so if these are question searches, do we have content that answers that question? No. Maybe this is something we need to think about. Or we’re getting impressions for this. You could filter it and say only show questions searches where our average rank is below 20. Maybe if we improve this content, this is a featured snippet opportunity for us, for example. That’s a real gold mine of data you can play around with.

3. Transforms

The third one is transforms. As I mentioned earlier, this is a really nice way to take Facebook, for example. We had a client who had Facebook in all upper case and Facebook in title case and Facebook in lower case in their sources and mediums, because they were very casual with how they used their UTM codes. We just standardized them all to go to lower, and those are nice text transforms that you can do.

It just makes things look a little bit nicer. I do recommend doing some of this, especially if you have messy data.

4. Logic

Then the big one here. This is logic, and I’m just going to toss over here for a second. Now logic has a lot of different components. What I’m showing you right now is a case when else end transform or logic. We use this to tidy up bad channel data.

So that client that I mentioned, who was just super casual with their UTM tags and they would just put in any old stuff, I think they had retargeting ads as a medium. You can set up channels and whatnot in Google Analytics. But I mean, really, when it comes down to it, not everybody is great at following the rules for UTMs that you’ve set up. Stuff happens.

It’s okay. You can fix it in Data Studio. Especially if you open up Google Analytics and you see that you have this other channel, which I’m sure when we’ve inherited an Analytics account, we take a look at it, and there’s this channel, and it’s just a big bag of crap.

You can go in there and turn that into real, useful, actual channel data that matches up with where it should go. What I’ve got here is a really simple example. This could go on for lines and line and lines. I’ve just included two lines because this whiteboard is only so big.

So you start off by saying case. It is the case when, is the idea when, and then the first line here is source equals direct and medium equals not set or medium none, then direct. So I’m saying, okay, so this is the basics of how direct traffic happens.

If the source is direct and the medium is not set or the medium is none, like if I have no data whatsoever, now it’s direct traffic. Great, that’s basically what Google Analytics does. Nothing fancy is going on here. Now here’s the next thing. In this case, I’m saying now I’m combining a regex match, which we talked about up here, with the case, and so now what I’m saying is when regex match medium, and then I’ve got this here.

Don’t be scared of this. I know it’s regex and maybe you’re not super comfortable with it, but this is pretty elementary stuff, and once you do this, you will feel like a data wizard, I guarantee. The first time I did this I stood up from my computer and said “Yes” the first time it worked. Just play with it. It’s going to be awesome. So you’ve got a little … what’s the thing called? You’ve got a little up arrow thingy there, very bad mediums dollar sign.

What this is saying is that if you’ve got anything in there that’s sort of a weird medium, just write out all the crud that people have put in there over the years, all the weird mediums that totally don’t make any sense at all. Just put it all in there and then you can toss it in a bucket say called paid social. You can do the same thing with referral traffic. Or, for example, this is really useful if a client is saying, “Well, I want to know how this set of affiliate traffic compares to say this set of affiliate traffic,” then you can separate these out into different buckets.

This isn’t just for channel data. I’ve done this, for example, where we were looking at social data and we were comparing NFL teams as an example for another tool, Rival IQ. What I said was, okay, so these teams here are in the AFC East, and these teams are in the AFC West. If I’ve screwed up and I said AFC East and West, please don’t get mad at me in the comments. I promise I play fantasy football. I just don’t remember right now.

But you can combine different areas. This is great for things like sales regions, for example. So North America equals Canada plus the USA plus Mexico, if you’re feeling generous. This is NAFTA politics. It really depends on what you want to do with those sales regions and how your data, what is meaningful for you. That’s the most important thing about this is that you can change this data to be whatever you need it to be to make that reporting so much easier for you.

I mean, Else then, we don’t know if this might actually output. I haven’t tried this myself. If it does, please leave a comment and let me know.

Then you end up with an End. When you’re in Data Studio, when you’re making these calculated formulas, you’ll see right away whether or not it works or not. Just keep trying until you see it happen.

One of the great things about Data Studio is that if it’s right, you’ll see these types of colors, and I’ve used different color whiteboard markers to indicate how it should look. If you see red where you should be seeing black or green where you should be seeing black, for example, then you know you’ve typed in something wrong in your formula. For me, typically I find it’s a misplaced bracket. Just keep an eye on that.

Have fun with Data Studio. One of the great things too is that you can’t mess up your original data when doing calculated fields, so you can go hog wild and it’s not going to mess with the original data. I hope you have a great time in Data Studio. Tell me what you’ve done in the comments, please. Thank you.

Video transcription by Speechpad.com

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


Moz Blog

Posted in IM NewsComments Off

Trust Your Data: How to Efficiently Filter Spam, Bots, & Other Junk Traffic in Google Analytics

Posted by Carlosesal

There is no doubt that Google Analytics is one of the most important tools you could use to understand your users’ behavior and measure the performance of your site. There’s a reason it’s used by millions across the world.

But despite being such an essential part of the decision-making process for many businesses and blogs, I often find sites (of all sizes) that do little or no data filtering after installing the tracking code, which is a huge mistake.

Think of a Google Analytics property without filtered data as one of those styrofoam cakes with edible parts. It may seem genuine from the top, and it may even feel right when you cut a slice, but as you go deeper and deeper you find that much of it is artificial.

If you’re one of those that haven’t properly configured their Google Analytics and you only pay attention to the summary reports, you probably won’t notice that there’s all sorts of bogus information mixed in with your real user data.

And as a consequence, you won’t realize that your efforts are being wasted on analyzing data that doesn’t represent the actual performance of your site.

To make sure you’re getting only the real ingredients and prevent you from eating that slice of styrofoam, I’ll show you how to use the tools that GA provides to eliminate all the artificial excess that inflates your reports and corrupts your data.

Common Google Analytics threats

As most of the people I’ve worked with know, I’ve always been obsessed with the accuracy of data, mainly because as a marketer/analyst there’s nothing worse than realizing that you’ve made a wrong decision because your data wasn’t accurate. That’s why I’m continually exploring new ways of improving it.

As a result of that research, I wrote my first Moz post about the importance of filtering in Analytics, specifically about ghost spam, which was a significant problem at that time and still is (although to a lesser extent).

While the methods described there are still quite useful, I’ve since been researching solutions for other types of Google Analytics spam and a few other threats that might not be as annoying, but that are equally or even more harmful to your Analytics.

Let’s review, one by one.

Ghosts, crawlers, and other types of spam

The GA team has done a pretty good job handling ghost spam. The amount of it has been dramatically reduced over the last year, compared to the outbreak in 2015/2017.

However, the millions of current users and the thousands of new, unaware users that join every day, plus the majority’s curiosity to discover why someone is linking to their site, make Google Analytics too attractive a target for the spammers to just leave it alone.

The same logic can be applied to any widely used tool: no matter what security measures it has, there will always be people trying to abuse its reach for their own interest. Thus, it’s wise to add an extra security layer.

Take, for example, the most popular CMS: WordPress. Despite having some built-in security measures, if you don’t take additional steps to protect it (like setting a strong username and password or installing a security plugin), you run the risk of being hacked.

The same happens to Google Analytics, but instead of plugins, you use filters to protect it.

In which reports can you look for spam?

Spam traffic will usually show as a Referral, but it can appear in any part of your reports, even in unsuspecting places like a language or page title.

Sometimes spammers will try to fool by using misleading URLs that are very similar to known websites, or they may try to get your attention by using unusual characters and emojis in the source name.

Independently of the type of spam, there are 3 things you always should do when you think you found one in your reports:

  1. Never visit the suspicious URL. Most of the time they’ll try to sell you something or promote their service, but some spammers might have some malicious scripts on their site.
  2. This goes without saying, but never install scripts from unknown sites; if for some reason you did, remove it immediately and scan your site for malware.
  3. Filter out the spam in your Google Analytics to keep your data clean (more on that below).

If you’re not sure whether an entry on your report is real, try searching for the URL in quotes (“example.com”). Your browser won’t open the site, but instead will show you the search results; if it is spam, you’ll usually see posts or forums complaining about it.

If you still can’t find information about that particular entry, give me a shout — I might have some knowledge for you.

Bot traffic

A bot is a piece of software that runs automated scripts over the Internet for different purposes.

There are all kinds of bots. Some have good intentions, like the bots used to check copyrighted content or the ones that index your site for search engines, and others not so much, like the ones scraping your content to clone it.

2016 bot traffic report. Source: Incapsula

In either case, this type of traffic is not useful for your reporting and might be even more damaging than spam both because of the amount and because it’s harder to identify (and therefore to filter it out).

It’s worth mentioning that bots can be blocked from your server to stop them from accessing your site completely, but this usually involves editing sensible files that require high technical knowledge, and as I said before, there are good bots too.

So, unless you’re receiving a direct attack that’s skewing your resources, I recommend you just filter them in Google Analytics.

In which reports can you look for bot traffic?

Bots will usually show as Direct traffic in Google Analytics, so you’ll need to look for patterns in other dimensions to be able to filter it out. For example, large companies that use bots to navigate the Internet will usually have a unique service provider.

I’ll go into more detail on this below.

Internal traffic

Most users get worried and anxious about spam, which is normal — nobody likes weird URLs showing up in their reports. However, spam isn’t the biggest threat to your Google Analytics.

You are!

The traffic generated by people (and bots) working on the site is often overlooked despite the huge negative impact it has. The main reason it’s so damaging is that in contrast to spam, internal traffic is difficult to identify once it hits your Analytics, and it can easily get mixed in with your real user data.

There are different types of internal traffic and different ways of dealing with it.

Direct internal traffic

Testers, developers, marketing team, support, outsourcing… the list goes on. Any member of the team that visits the company website or blog for any purpose could be contributing.

In which reports can you look for direct internal traffic?

Unless your company uses a private ISP domain, this traffic is tough to identify once it hits you, and will usually show as Direct in Google Analytics.

Third-party sites/tools

This type of internal traffic includes traffic generated directly by you or your team when using tools to work on the site; for example, management tools like Trello or Asana,

It also considers traffic coming from bots doing automatic work for you; for example, services used to monitor the performance of your site, like Pingdom or GTmetrix.

Some types of tools you should consider:

  • Project management
  • Social media management
  • Performance/uptime monitoring services
  • SEO tools
In which reports can you look for internal third-party tools traffic?

This traffic will usually show as Referral in Google Analytics.

Development/staging environments

Some websites use a test environment to make changes before applying them to the main site. Normally, these staging environments have the same tracking code as the production site, so if you don’t filter it out, all the testing will be recorded in Google Analytics.

In which reports can you look for development/staging environments?

This traffic will usually show as Direct in Google Analytics, but you can find it under its own hostname (more on this later).

Web archive sites and cache services

Archive sites like the Wayback Machine offer historical views of websites. The reason you can see those visits on your Analytics — even if they are not hosted on your site — is that the tracking code was installed on your site when the Wayback Machine bot copied your content to its archive.

One thing is for certain: when someone goes to check how your site looked in 2015, they don’t have any intention of buying anything from your site — they’re simply doing it out of curiosity, so this traffic is not useful.

In which reports can you look for traffic from web archive sites and cache services?

You can also identify this traffic on the hostname report.

A basic understanding of filters

The solutions described below use Google Analytics filters, so to avoid problems and confusion, you’ll need some basic understanding of how they work and check some prerequisites.

Things to consider before using filters:

1. Create an unfiltered view.

Before you do anything, it’s highly recommendable to make an unfiltered view; it will help you track the efficacy of your filters. Plus, it works as a backup in case something goes wrong.

2. Make sure you have the correct permissions.

You will need edit permissions at the account level to create filters; edit permissions at view or property level won’t work.

3. Filters don’t work retroactively.

In GA, aggregated historical data can’t be deleted, at least not permanently. That’s why the sooner you apply the filters to your data, the better.

4. The changes made by filters are permanent!

If your filter is not correctly configured because you didn’t enter the correct expression (missing relevant entries, a typo, an extra space, etc.), you run the risk of losing valuable data FOREVER; there is no way of recovering filtered data.

But don’t worry — if you follow the recommendations below, you shouldn’t have a problem.

5. Wait for it.

Most of the time you can see the effect of the filter within minutes or even seconds after applying it; however, officially it can take up to twenty-four hours, so be patient.

Types of filters

There are two main types of filters: predefined and custom.

Predefined filters are very limited, so I rarely use them. I prefer to use the custom ones because they allow regular expressions, which makes them a lot more flexible.

Within the custom filters, there are five types: exclude, include, lowercase/uppercase, search and replace, and advanced.

Here we will use the first two: exclude and include. We’ll save the rest for another occasion.

Essentials of regular expressions

If you already know how to work with regular expressions, you can jump to the next section.

REGEX (short for regular expressions) are text strings prepared to match patterns with the use of some special characters. These characters help match multiple entries in a single filter.

Don’t worry if you don’t know anything about them. We will use only the basics, and for some filters, you will just have to COPY-PASTE the expressions I pre-built.

REGEX special characters

There are many special characters in REGEX, but for basic GA expressions we can focus on three:

  • ^ The caret: used to indicate the beginning of a pattern,
  • $ The dollar sign: used to indicate the end of a pattern,
  • | The pipe or bar: means “OR,” and it is used to indicate that you are starting a new pattern.

When using the pipe character, you should never ever:

  • Put it at the beginning of the expression,
  • Put it at the end of the expression,
  • Put 2 or more together.

Any of those will mess up your filter and probably your Analytics.

A simple example of REGEX usage

Let’s say I go to a restaurant that has an automatic machine that makes fruit salad, and to choose the fruit, you should use regular expressions.

This super machine has the following fruits to choose from: strawberry, orange, blueberry, apple, pineapple, and watermelon.

To make a salad with my favorite fruits (strawberry, blueberry, apple, and watermelon), I have to create a REGEX that matches all of them. Easy! Since the pipe character “|” means OR I could do this:

  • REGEX 1: strawberry|blueberry|apple|watermelon

The problem with that expression is that REGEX also considers partial matches, and since pineapple also contains “apple,” it would be selected as well… and I don’t like pineapple!

To avoid that, I can use the other two special characters I mentioned before to make an exact match for apple. The caret “^” (begins here) and the dollar sign “$ ” (ends here). It will look like this:

  • REGEX 2: strawberry|blueberry|^apple$ |watermelon

The expression will select precisely the fruits I want.

But let’s say for demonstration’s sake that the fewer characters you use, the cheaper the salad will be. To optimize the expression, I can use the ability for partial matches in REGEX.

Since strawberry and blueberry both contain “berry,” and no other fruit in the list does, I can rewrite my expression like this:

  • Optimized REGEX: berry|^apple$ |watermelon

That’s it — now I can get my fruit salad with the right ingredients, and at a lower price.

3 ways of testing your filter expression

As I mentioned before, filter changes are permanent, so you have to make sure your filters and REGEX are correct. There are 3 ways of testing them:

  • Right from the filter window; just click on “Verify this filter,” quick and easy. However, it’s not the most accurate since it only takes a small sample of data.

  • Using an online REGEX tester; very accurate and colorful, you can also learn a lot from these, since they show you exactly the matching parts and give you a brief explanation of why.

  • Using an in-table temporary filter in GA; you can test your filter against all your historical data. This is the most precise way of making sure you don’t miss anything.

If you’re doing a simple filter or you have plenty of experience, you can use the built-in filter verification. However, if you want to be 100% sure that your REGEX is ok, I recommend you build the expression on the online tester and then recheck it using an in-table filter.

Quick REGEX challenge

Here’s a small exercise to get you started. Go to this premade example with the optimized expression from the fruit salad case and test the first 2 REGEX I made. You’ll see live how the expressions impact the list.

Now make your own expression to pay as little as possible for the salad.

Remember:

  • We only want strawberry, blueberry, apple, and watermelon;
  • The fewer characters you use, the less you pay;
  • You can do small partial matches, as long as they don’t include the forbidden fruits.

Tip: You can do it with as few as 6 characters.

Now that you know the basics of REGEX, we can continue with the filters below. But I encourage you to put “learn more about REGEX” on your to-do list — they can be incredibly useful not only for GA, but for many tools that allow them.

How to create filters to stop spam, bots, and internal traffic in Google Analytics

Back to our main event: the filters!

Where to start: To avoid being repetitive when describing the filters below, here are the standard steps you need to follow to create them:

  1. Go to the admin section in your Google Analytics (the gear icon at the bottom left corner),
  2. Under the View column (master view), click the button “Filters” (don’t click on “All filters“ in the Account column):
  3. Click the red button “+Add Filter” (if you don’t see it or you can only apply/remove already created filters, then you don’t have edit permissions at the account level. Ask your admin to create them or give you the permissions.):
  4. Then follow the specific configuration for each of the filters below.

The filter window is your best partner for improving the quality of your Analytics data, so it will be a good idea to get familiar with it.

Valid hostname filter (ghost spam, dev environments)

Prevents traffic from:

  • Ghost spam
  • Development hostnames
  • Scraping sites
  • Cache and archive sites

This filter may be the single most effective solution against spam. In contrast with other commonly shared solutions, the hostname filter is preventative, and it rarely needs to be updated.

Ghost spam earns its name because it never really visits your site. It’s sent directly to the Google Analytics servers using a feature called Measurement Protocol, a tool that under normal circumstances allows tracking from devices that you wouldn’t imagine that could be traced, like coffee machines or refrigerators.

Real users pass through your server, then the data is sent to GA; hence it leaves valid information. Ghost spam is sent directly to GA servers, without knowing your site URL; therefore all data left is fake. Source: carloseo.com

The spammer abuses this feature to simulate visits to your site, most likely using automated scripts to send traffic to randomly generated tracking codes (UA-0000000-1).

Since these hits are random, the spammers don’t know who they’re hitting; for that reason ghost spam will always leave a fake or (not set) host. Using that logic, by creating a filter that only includes valid hostnames all ghost spam will be left out.

Where to find your hostnames

Now here comes the “tricky” part. To create this filter, you will need, to make a list of your valid hostnames.

A list of what!?

Essentially, a hostname is any place where your GA tracking code is present. You can get this information from the hostname report:

  • Go to Audience > Select Network > At the top of the table change the primary dimension to Hostname.

If your Analytics is active, you should see at least one: your domain name. If you see more, scan through them and make a list of all the ones that are valid for you.

Types of hostname you can find

The good ones:

Type

Example

Your domain and subdomains

yourdomain.com

Tools connected to your Analytics

YouTube, MailChimp

Payment gateways

Shopify, booking systems

Translation services

Google Translate

Mobile speed-up services

Google weblight

The bad ones (by bad, I mean not useful for your reports):

Type

Example/Description

Staging/development environments

staging.yourdomain.com

Internet archive sites

web.archive.org

Scraping sites that don’t bother to trim the content

The URL of the scraper

Spam

Most of the time they will show their URL, but sometimes they may use the name of a known website to try to fool you. If you see a URL that you don’t recognize, just think, “do I manage it?” If the answer is no, then it isn’t your hostname.

(not set) hostname

It usually comes from spam. On rare occasions it’s related to tracking code issues.

Below is an example of my hostname report. From the unfiltered view, of course, the master view is squeaky clean.

Now with the list of your good hostnames, make a regular expression. If you only have your domain, then that is your expression; if you have more, create an expression with all of them as we did in the fruit salad example:

Hostname REGEX (example)


yourdomain.com|hostname2|hostname3|hostname4

Important! You cannot create more than one “Include hostname filter”; if you do, you will exclude all data. So try to fit all your hostnames into one expression (you have 255 characters).

The “valid hostname filter” configuration:

  • Filter Name: Include valid hostnames
  • Filter Type: Custom > Include
  • Filter Field: Hostname
  • Filter Pattern: [hostname REGEX you created]

Campaign source filter (Crawler spam, internal sources)

Prevents traffic from:

  • Crawler spam
  • Internal third-party tools (Trello, Asana, Pingdom)

Important note: Even if these hits are shown as a referral, the field you should use in the filter is “Campaign source” — the field “Referral” won’t work.

Filter for crawler spam

The second most common type of spam is crawler. They also pretend to be a valid visit by leaving a fake source URL, but in contrast with ghost spam, these do access your site. Therefore, they leave a correct hostname.

You will need to create an expression the same way as the hostname filter, but this time, you will put together the source/URLs of the spammy traffic. The difference is that you can create multiple exclude filters.

Crawler REGEX (example)


spam1|spam2|spam3|spam4

Crawler REGEX (pre-built)


As I promised, here are latest pre-built crawler expressions that you just need to copy/paste.

The “crawler spam filter” configuration:

  • Filter Name: Exclude crawler spam 1
  • Filter Type: Custom > Exclude
  • Filter Field: Campaign source
  • Filter Pattern: [crawler REGEX]

Filter for internal third-party tools

Although you can combine your crawler spam filter with internal third-party tools, I like to have them separated, to keep them organized and more accessible for updates.

The “internal tools filter” configuration:

  • Filter Name: Exclude internal tool sources
  • Filter Pattern: [tool source REGEX]

Internal Tools REGEX (example)


trello|asana|redmine

In case, that one of the tools that you use internally also sends you traffic from real visitors, don’t filter it. Instead, use the “Exclude Internal URL Query” below.

For example, I use Trello, but since I share analytics guides on my site, some people link them from their Trello accounts.

Filters for language spam and other types of spam

The previous two filters will stop most of the spam; however, some spammers use different methods to bypass the previous solutions.

For example, they try to confuse you by showing one of your valid hostnames combined with a well-known source like Apple, Google, or Moz. Even my site has been a target (not saying that everyone knows my site; it just looks like the spammers don’t agree with my guides).

However, even if the source and host look fine, the spammer injects their message in another part of your reports like the keyword, page title, and even as a language.

In those cases, you will have to take the dimension/report where you find the spam and choose that name in the filter. It’s important to consider that the name of the report doesn’t always match the name in the filter field:

Report name

Filter field

Language

Language settings

Referral

Campaign source

Organic Keyword

Search term

Service Provider

ISP Organization

Network Domain

ISP Domain

Here are a couple of examples.

The “language spam/bot filter” configuration:

  • Filter Name: Exclude language spam
  • Filter Type: Custom > Exclude
  • Filter Field: Language settings
  • Filter Pattern: [Language REGEX]

Language Spam REGEX (Prebuilt)


\s[^\s]*\s|.{15,}|\.|,|^c$

The expression above excludes fake languages that don’t meet the required format. For example, take these weird messages appearing instead of regular languages like en-us or es-es:

Examples of language spam

The organic/keyword spam filter configuration:

  • Filter Name: Exclude organic spam
  • Filter Type: Custom > Exclude
  • Filter Field: Search term
  • Filter Pattern: [keyword REGEX]

Filters for direct bot traffic

Bot traffic is a little trickier to filter because it doesn’t leave a source like spam, but it can still be filtered with a bit of patience.

The first thing you should do is enable bot filtering. In my opinion, it should be enabled by default.

Go to the Admin section of your Analytics and click on View Settings. You will find the option “Exclude all hits from known bots and spiders” below the currency selector:

It would be wonderful if this would take care of every bot — a dream come true. However, there’s a catch: the key here is the word “known.” This option only takes care of known bots included in the “IAB known bots and spiders list.” That’s a good start, but far from enough.

There are a lot of “unknown” bots out there that are not included in that list, so you’ll have to play detective and search for patterns of direct bot traffic through different reports until you find something that can be safely filtered without risking your real user data.

To start your bot trail search, click on the Segment box at the top of any report, and select the “Direct traffic” segment.

Then navigate through different reports to see if you find anything suspicious.

Some reports to start with:

  • Service provider
  • Browser version
  • Network domain
  • Screen resolution
  • Flash version
  • Country/City

Signs of bot traffic

Although bots are hard to detect, there are some signals you can follow:

  • An unnatural increase of direct traffic
  • Old versions (browsers, OS, Flash)
  • They visit the home page only (usually represented by a slash “/” in GA)
  • Extreme metrics:
    • Bounce rate close to 100%,
    • Session time close to 0 seconds,
    • 1 page per session,
    • 100% new users.

Important! If you find traffic that checks off many of these signals, it is likely bot traffic. However, not all entries with these characteristics are bots, and not all bots match these patterns, so be cautious.

Perhaps the most useful report that has helped me identify bot traffic is the “Service Provider” report. Large corporations frequently use their own Internet service provider name.

I also have a pre-built expression for ISP bots, similar to the crawler expressions.

The bot ISP filter configuration:

  • Filter Name: Exclude bots by ISP
  • Filter Type: Custom > Exclude
  • Filter Field: ISP organization
  • Filter Pattern: [ISP provider REGEX]

ISP provider bots REGEX (prebuilt)


hubspot|^google\sllc$ |^google\sinc\.$ |alibaba\.com\sllc|ovh\shosting\sinc\.

Latest ISP bot expression

IP filter for internal traffic

We already covered different types of internal traffic, the one from test sites (with the hostname filter), and the one from third-party tools (with the campaign source filter).

Now it’s time to look at the most common and damaging of all: the traffic generated directly by you or any member of your team while working on any task for the site.

To deal with this, the standard solution is to create a filter that excludes the public IP (not private) of all locations used to work on the site.

Examples of places/people that should be filtered

  • Office
  • Support
  • Home
  • Developers
  • Hotel
  • Coffee shop
  • Bar
  • Mall
  • Any place that is regularly used to work on your site

To find the public IP of the location you are working at, simply search for “my IP” in Google. You will see one of these versions:

IP version

Example

Short IPv4

1.23.45.678

Long IPv6

2001:0db8:85a3:0000:0000:8a2e:0370:7334

No matter which version you see, make a list with the IP of each place and put them together with a REGEX, the same way we did with other filters.

  • IP address expression: IP1|IP2|IP3|IP4 and so on.

The static IP filter configuration:

  • Filter Name: Exclude internal traffic (IP)
  • Filter Type: Custom > Exclude
  • Filter Field: IP Address
  • Filter Pattern: [The IP expression]

Cases when this filter won’t be optimal:

There are some cases in which the IP filter won’t be as efficient as it used to be:

  • You use IP anonymization (required by the GDPR regulation). When you anonymize the IP in GA, the last part of the IP is changed to 0. This means that if you have 1.23.45.678, GA will pass it as 1.23.45.0, so you need to put it like that in your filter. The problem is that you might be excluding other IPs that are not yours.
  • Your Internet provider changes your IP frequently (Dynamic IP). This has become a common issue lately, especially if you have the long version (IPv6).
  • Your team works from multiple locations. The way of working is changing — now, not all companies operate from a central office. It’s often the case that some will work from home, others from the train, in a coffee shop, etc. You can still filter those places; however, maintaining the list of IPs to exclude can be a nightmare,
  • You or your team travel frequently. Similar to the previous scenario, if you or your team travels constantly, there’s no way you can keep up with the IP filters.

If you check one or more of these scenarios, then this filter is not optimal for you; I recommend you to try the “Advanced internal URL query filter” below.

URL query filter for internal traffic

If there are dozens or hundreds of employees in the company, it’s extremely difficult to exclude them when they’re traveling, accessing the site from their personal locations, or mobile networks.

Here’s where the URL query comes to the rescue. To use this filter you just need to add a query parameter. I add “?internal” to any link your team uses to access your site:

  • Internal newsletters
  • Management tools (Trello, Redmine)
  • Emails to colleagues
  • Also works by directly adding it in the browser address bar

Basic internal URL query filter

The basic version of this solution is to create a filter to exclude any URL that contains the query “?internal”.

  • Filter Name: Exclude Internal Traffic (URL Query)
  • Filter Type: Custom > Exclude
  • Filter Field: Request URI
  • Filter Pattern: \?internal

This solution is perfect for instances were the user will most likely stay on the landing page, for example, when sending a newsletter to all employees to check a new post.

If the user will likely visit more than the landing page, then the subsequent pages will be recorded.

Advanced internal URL query filter

This solution is the champion of all internal traffic filters!

It’s a more comprehensive version of the previous solution and works by filtering internal traffic dynamically using Google Tag Manager, a GA custom dimension, and cookies.

Although this solution is a bit more complicated to set up, once it’s in place:

  • It doesn’t need maintenance
  • Any team member can use it, no need to explain techy stuff
  • Can be used from any location
  • Can be used from any device, and any browser

To activate the filter, you just have to add the text “?internal” to any URL of the website.

That will insert a small cookie in the browser that will tell GA not to record the visits from that browser.

And the best of it is that the cookie will stay there for a year (unless it is manually removed), so the user doesn’t have to add “?internal” every time.

Bonus filter: Include only internal traffic

In some occasions, it’s interesting to know the traffic generated internally by employees — maybe because you want to measure the success of an internal campaign or just because you’re a curious person.

In that case, you should create an additional view, call it “Internal Traffic Only,” and use one of the internal filters above. Just one! Because if you have multiple include filters, the hit will need to match all of them to be counted.

If you configured the “Advanced internal URL query” filter, use that one. If not, choose one of the others.

The configuration is exactly the same — you only need to change “Exclude” for “Include.”

Cleaning historical data

The filters will prevent future hits from junk traffic.

But what about past affected data?

I know I told you that deleting aggregated historical data is not possible in GA. However, there’s still a way to temporarily clean up at least some of the nasty traffic that has already polluted your reports.

For this, we’ll use an advanced segment (a subset of your Analytics data). There are built-in segments like “Organic” or “Mobile,” but you can also build one using your own set of rules.

To clean our historical data, we will build a segment using all the expressions from the filters above as conditions (except the ones from the IP filter, because IPs are not stored in GA; hence, they can’t be segmented).

To help you get started, you can import this segment template.

You just need to follow the instructions on that page and replace the placeholders. Here is how it looks:

In the actual template, all text is black; the colors are just to help you visualize the conditions.

After importing it, to select the segment:

  1. Click on the box that says “All users” at the top of any of your reports
  2. From your list of segments, check the one that says “0. All Users – Clean”
  3. Lastly, uncheck the “All Users”

Now you can navigate through your reaports and all the junk traffic included in the segment will be removed.

A few things to consider when using this segment:

  • Segments have to be selected each time. A way of having it selected by default is by adding a bookmark when the segment is selected.
  • You can remove or add conditions if you need to.
  • You can edit the segment at any time to update it or add conditions (open the list of segments, then click “Actions” then “Edit”).

  • The hostname expression and third-party tools expression are different for each site.
  • If your site has a large volume of traffic, segments may sample your data when selected, so if you see the little shield icon at the top of your reports go yellow (normally is green), try choosing a shorter period (i.e. 1 year, 6 months, one month).

Conclusion: Which cake would you eat?

Having real and accurate data is essential for your Google Analytics to report as you would expect.

But if you haven’t filtered it properly, it’s almost certain that it will be filled with all sorts of junk and artificial information.

And the worst part is that if don’t realize that your reports contain bogus data, you will likely make wrong or poor decisions when deciding on the next steps for your site or business.

The filters I share above will help you prevent the three most harmful threats that are polluting your Google Analytics and don’t let you get a clear view of the actual performance of your site: spam, bots, and internal traffic.

Once these filters are in place, you can rest assured that your efforts (and money!) won’t be wasted on analyzing deceptive Google Analytics data, and your decisions will be based on solid information.

And the benefits don’t stop there. If you’re using other tools that import data from GA, for example, WordPress plugins like GADWP, excel add-ins like AnalyticsEdge, or SEO suites like Moz Pro, the benefits will trickle down to all of them as well.

Besides highlighting the importance of the filters in GA (which I hope I made clear by now), I would also love for the preparation of these filters to inspire the curiosity and basis to create others that will allow you to do all sorts of remarkable things with your data.

Remember, filters not only allow you to keep away junk, you can also use them to rearrange your real user information — but more on that on another occasion.


That’s it! I hope these tips help you make more sense of your data and make accurate decisions.

Have any questions, feedback, experiences? Let me know in the comments, or reach me on Twitter @carlosesal.

Complementary resources:

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!


Moz Blog

Posted in IM NewsComments Off

Google Chrome, Mozilla Firefox Leaked Facebook User Data Caused by Browser Vulnerability

Google Chrome and Mozilla Firefox might have inadvertently leaked the Facebook usernames, profile pictures and even the likes of their users because of a side-channel vulnerability.

A side-channel vulnerability was discovered in a CSS3 feature dubbed the “mix-blend-mode.” This allowed a hacker to discover the identity of a Facebook account holder using Chrome or Firefox by getting them to visit a specially-designed website.

This critical flaw was discovered in 2017 by security researchers Dario Weißer and Ruslan Habalov and also by independent researcher Max May.

The researchers created a proof-of-concept (POC) exploit to show how the vulnerability could be misused. Weißer and Habalov’s concept showed how they were able to visually harvest data like username, profile picture, and “like” status of a user. What’s more, this insidious hack could be accomplished in the background when the user visits a malicious website.

The visual leak could happen on sites using iFrames that connect to Facebook in via login buttons and social plugins. Due to a security feature called the “same-origin policy,” sites can’t directly access iFrame content. But the researchers were able to get the information by developing an overlay on the cross-origin iFrame in order to work with the underlying pixels.

It took Habalov and Weißer’s POC about 20 seconds to get the username and about five minutes to create a vague copy of the profile picture. The program also took about 500 milliseconds to check the “like” status. Keep in mind, however, that for this vulnerability to work, the user should be logged into their Facebook account.

Habalov and Weißer privately notified both Google and Mozilla and steps were taken to contain the threat. Google was able to fix the flaw on their end when version 63 was released last December. On Firefox’s end, a patch was made available 14 days ago with the release of the browser’s version 60. The delay was due to the researchers’ late disclosure of their findings to Mozilla.

IE and Edge browsers weren’t exposed to the side-channel exploit as they don’t support the needed feature. Safari was also safe from the flaw.

[Featured image via Pixabay]

The post Google Chrome, Mozilla Firefox Leaked Facebook User Data Caused by Browser Vulnerability appeared first on WebProNews.


WebProNews

Posted in IM NewsComments Off

PICA Protocol: A Visualization Prescription for Impactful Data Storytelling – Whiteboard Friday

Posted by Lea-Pica

If you find your presentations are often met with a lukewarm reception, it’s a sure sign it’s time for you to invest in your data storytelling. By following a few smart rules, a structured approach to data visualization could make all the difference in how stakeholders receive and act upon your insights. In this edition of Whiteboard Friday, we’re thrilled to welcome data viz expert Lea Pica to share her strategic methodology for creating highly effective charts.

A Visualization Prescription for Impactful Storytelling

Click on the whiteboard image above to open a high-resolution version in a new tab!

Video Transcription

Hello, Moz fans. Welcome to another edition of Whiteboard Friday. I’m here to talk to you this week about a very hot topic in the digital marketing space. So my name is Lea Pica, and I am a data storytelling trainer, coach, speaker, blogger, and podcaster at LeaPica.com.

I want to tell you a little story. So as 12 years I spent as a digital analyst and SEM, I used to present insights a lot, but nothing ever happened as a result of it. People fell asleep or never responded. No action was being taken. So I decided to figure out what was happening, and I learned all these great tricks for doing it.

What I learned in my journey is that effective data visualization communicates a story quickly, clearly, accurately, and ethically, and it had really four main goals — to inform decisions, to inspire action, to galvanize people, and most importantly to communicate the value of the work that you do.

Now, there are lots of things you can do, but I was struggling to find one specific process that was going to help me get from what I was trying to communicate to getting people to act on it. So I developed my own methodology. It’s called the PICA Protocol, and it’s a visualization prescription for impactful data storytelling. What I like about this protocol is that it’s practical, approachable. It’s not complicated. It’s prescriptive, and it’s repeatable. I believe it’s going to get you where you need to go every time.

So let’s say one of your managers, clients, stakeholders is asking you for something like, “What are our most successful keyword groups?” Something delightfully vague like that. Now, before you jump into your data visualization platform and start dropping charts like it’s hot, I want you to take a step back and start with the first step in the process, which is P for purpose.

P for Purpose

So I found that every great data visualization started with a very focused question or questions.

  • Why do you exist? Get philosophical with it.
  • What need of my audience are you meeting?
  • What decisions are you going to inform?

These questions help you get really focused about what you’re going to present and avoid the sort of needle in a haystack approach to seeing what might stick.

So the answers to these questions are going to help you make an important decision, to choose an appropriate chart type for the message that you’re trying to convey. Some of the ways you want to do that — I hear you guys are like into keywords a little bit — you want to listen for the keywords of what people are asking you for. So in this case, we have “most successful.” Okay, that indicates a comparison. Different types or campaigns or groups, those are categories. So it sounds like what we’re going for is a categorical comparison. There are other kinds of keywords you can look for, like changing over time, how this affects that. Answers or opinions. All of those are going to help you determine your most appropriate visual.

Now, in this case, we have a categorical comparison, so I always go back to basics. It’s an oldie but goodie, but we’re going to do the tried-and-true bar chart. It’s universally understood and doesn’t have a learning curve. What I would not recommend are pie charts. No, no, no. Unless you only have two segments in your visual and one is unmistakably larger than the other, pie charts are not your best choice for communicating categorical comparison, composition, or ranking.

I for Insight

So we have our choice. We’re now going to move on to the next step in the methodology, which is I for insight. So an insight is something that gives a person a capacity to understand something quickly, accurately, and intuitively. Think of those criteria.

So here, does my display surface the story and answer these questions intuitively? That’s our criteria. The components of that are:

  • Layout and orientation. So how is the chart configured? Very often we’ll use vertical bar charts for categorical comparison, but that will end up having diagonal labels if they’re really long, and unless your audience walks around like this all the time, it’s going to be confusing because that would be weird. So you want to make sure it’s oriented well.
  • Labeling. In the case of bars, I always prefer to label each bar directly rather than relying on just an axis, because then their eyes aren’t jumping from bar to axis to bar to axis and they’re paying more attention to you. That’s also for line charts. Very often I’ll label a line with a maximum, a minimum, and maybe the most important data point.
  • Interpretation of the data and where we’re placing it, the location.
    • So our interpretation, is it objective or is it subjective? So subjective words are like better or worse or stupid or awesome. Those are opinions. But objective words are higher, lower, most efficient, least efficient. So you really want your observations to be objective.
    • Have you presented it ethically? Or have you manipulated the view in a way that isn’t telling a really ethical picture, like adjusting a bar axis above zero, which is a no-no? But you can do that with a line graph in certain cases. So look for those nuances. You want to basically ask yourself, “Would I be able to uphold this visual in a court of law or sleep at night?”
    • Location of that insight. So very often we’ll put our insights, our interpretation down here or in really tiny letters up here. Then up here we’ll put big letters saying this is sales, my keyword category. No. What we want to do is we want to put our interpretation up here. This top area is the most important real estate on your visual. That’s where their eyes are going to look first. So think of this like a BuzzFeed headline for your visual. What do you want them to take away? You can always put what the chart is here in a little subtitle.
  • Make recommendations. Because that’s what a really powerful visual is going to do.
    • I always suggest having two recommendations at least, because this way you’re empowering your audience with a choice. This way you can actually be subjective. That is okay in this case, because that’s your unique subject matter expertise.
    • Are your recommendations accountable to specific people? Are they feasible?
    • What’s the cost of not acting on your recommendations? Put some urgency behind it. So I like to put my recommendations in a little box or callout on the side here so it’s really clear after I’ve presented my facts.

C for Context

The next step in the methodology is C for context. What this is saying is, “Do I have all the data points I need to paint a complete picture, or is there more to this story?” So some additional lenses you might find useful are past period comparison, targets or benchmarks are useful, segmentation, things like geography, mobile device. Or what are the typical questions or arguments that your audience has when you present data? They can be super value contextual points.

In this case, I might decide that while they care about the number of sales, because that’s most successful to them, I care about the keywords “conversion rates.” So I’m going to add a second bar chart here like this, and I’m going to see there’s a different story that’s popping out here now.

Now, this is where your data storytelling really comes into play. This particular strategy is called a table lens or a side-by-side bar chart. It’s what I recommend if you want to combine two categorical metrics together.

A for Aesthetics

Now, the last step in the methodology is A for aesthetics. Aesthetics are how things look. So it’s not about making it look pretty. No, it’s asking, “Does my viz comply with brain best practices of how we absorb information?”

1. Decrease visual noise

So the first step in doing that is we want to decrease visual noise, because that creates a lot of tension. So decreasing noise will increase the chance of a happy brain.

Now, I’m a crunchy granola hippie, so I love to detox every day. I’ve developed a data visualization detox that entails removing things like grid lines, borders, axis lines, line markers, and backgrounds. Get all of that junk out of there, really clean up. You can align everything to the left to make sure that the brain is following things properly down. Don’t center everything.

2. Use uniform colors (plus one standout color for emphasis)

Now, you’ll notice that most of my bars here have a uniform color — simple black. I like to color everything one color, because then I’ll use a separate, standout color, like this blue, to strategically emphasize my key message. You might notice that I did that throughout this step for the words that I want you to pick out. That’s why I colored these particular bars, because this feels like the story to me, because that is the storytelling part of this message.

Notice that I also colored the category in my observation to create a connective tissue between these two items. So using color intentionally means things like using green for good and red for bad, not arbitrarily, and then maybe blue for what’s important.

3. Source your data

Then finally, you always want to source your data. That increases the trust. So you want to put your platform and your date range. Really simple.

So this is the anatomy of an awesome data viz. I’ve adapted it from a great book called “Good Charts” by my friend, Scott Berinato. What I have found that by using this protocol, you’re going to end up with these wonderful, raving fans who are going to love your work and understand your value. I included a little kitty fan because I can. It’s my Whiteboard Friday.

So that is the protocol. I actually have included a free gift for you today. If you click the link at the end of this post, you’ll be able to sign up for a Chart Detox Checklist, a full printable PICA Protocol prescription and a Chart Choosing Guide.

Get the PICA Protocol prescription

I would actually love to hear from you. What are the kinds of struggles that you have in presenting your insights to stakeholders, where you just feel like they’re not getting the value of what you’re doing? I’d love to hear any questions you have about the methodology as well.

So thank you for watching this edition of Whiteboard Friday. I hope you enjoyed it. We’ll see you next week, and please remember to viz responsibly, my friends. Namaste.

Video transcription by Speechpad.com

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


Moz Blog

Posted in IM NewsComments Off

Facebook ‘Weaponized’ User Data, Says Bikini Photo-Finding App Developer

Facebook is facing accusations of gathering more user data than disclosed. According to court filings, former start-up Six4Three claimed that the social media company conducted mass surveillance on its users and their friends alike.

Based on the lawsuit documents, Facebook reportedly had access to its users’ text messages, photos, and microphones. It can even track their locations by remotely activating the Bluetooth on mobile devices without permission. All of these accusations were detailed in Six4Three’s fifth version of the complaint, initially filed in 2015.

The court document read, in part:

“Facebook continued to explore and implement ways to track users’ location, to track and read their texts, to access and record their microphones on their phones, to track and monitor their usage of competitive apps on their phones, and to track and monitor their calls.”

In response, Facebook refuted the claims by saying that these “have no merit and we will continue to defend ourselves vigorously.” The company clarified rumors back in March that it was monitoring calls and messages of its users. Rather, they only collected call and text message history as part of its opt-in feature under Facebook Lite and Messenger on Android.

The former start-up also contended that Facebook had access to several photos on iPhones. But the social media company pointed out that users can opt-in to the photo syncing feature of the app for easier uploading.

Allegations of breaching user privacy and data collection remain touchy subjects for Facebook,  following its involvement in the Cambridge Analytica fiasco. Prior to the scandal, the social media giant has removed the access of third-party developers to personal information. This policy change reportedly led to the failure of Six4Three’s controversial paid app Pikinis, where users can find their Facebook friends’ swimsuit photos.

Along with accusations of causing its financial ruin, Six4Three claimed that Facebook ‘weaponized’ its ability to access user data, sometimes without explicit consent, to earn billions of dollars. There was also a mass surveillance scheme, details of which were redacted from the latest filings per Facebook’s request. These documents, such as email correspondence among senior executives, contain confidential business matters and were sealed from public view until further notice. 

Facebook has continued to deny the purported claims, filing a motion to have the case dismissed by invoking the free speech defense under the law in California. Six4Three, on the other hand, is trying to stop the social media giant from getting the case thrown out. As the legal battle wages on, Facebook still faces continued scrutiny over its users’ paranoia on weak data privacy and protection controls.

The post Facebook 'Weaponized' User Data, Says Bikini Photo-Finding App Developer appeared first on WebProNews.


WebProNews

Posted in IM NewsComments Off

Moz’s Link Data Used to Suck… But Not Anymore! The New Link Explorer is Here – Whiteboard Friday

Posted by randfish

Earlier this week we launched our brand-new link building tool, and we’re happy to say that Link Explorer addresses and improves upon a lot of the big problems that have plagued our legacy link tool, Open Site Explorer. In today’s Whiteboard Friday, Rand transparently lists out many of the biggest complaints we’ve heard about OSE over the years and explains the vast improvements Link Explorer provides, from DA scores updated daily to historic link data to a huge index of almost five trillion URLs.

Moz's Link Data Used to Suck... But Not Anymore! The New Link Explorer is Here - Whiteboard Friday

Click on the whiteboard image above to open a high-resolution version in a new tab!


Video Transcription

Howdy, Moz fans, and welcome to another edition of Whiteboard Friday. This week I’m very excited to say that Moz’s Open Site Explorer product, which had a lot of challenges with it, is finally being retired, and we have a new product, Link Explorer, that’s taking its place. So let me walk you through why and how Moz’s link data for the last few years has really kind of sucked. There’s no two ways about it.

If you heard me here on Whiteboard Friday, if you watched me at conferences, if you saw me blogging, you’d probably see me saying, “Hey, I personally use Ahrefs, or I use Majestic for my link research.” Moz has a lot of other good tools. The crawler is excellent. Moz Pro is good. But Open Site Explorer was really lagging, and today, that’s not the case. Let me walk you through this.

The big complaints about OSE/Mozscape

1. The index was just too small

Moz's Link Data Used to Suck... But Not Anymore! The New Link Explorer is Here - Whiteboard Friday

Mozscape was probably about a fifth to a tenth the size of its competitors. While it got a lot of the quality good links of the web, it just didn’t get enough. As SEOs, we need to know all of the links, the good ones and the bad ones.

2. The data was just too old

Moz's Link Data Used to Suck... But Not Anymore! The New Link Explorer is Here - Whiteboard Friday

So, in Mozscape, a link that you built on November 1st, you got a link added to a website, you’re very proud of yourself. That’s excellent. You should expect that a link tool should pick that up within maybe a couple weeks, maybe three weeks at the outside. Google is probably picking it up within just a few days, sometimes hours.

Yet, when Mozscape would crawl that, it would often be a month or more later, and by the time Mozscape processed its index, it could be another 40 days after that, meaning that you could see a 60- to 80-day delay, sometimes even longer, between when your link was built and when Mozscape actually found it. That sucks.

3. PA/DA scores took forever to update

Moz's Link Data Used to Suck... But Not Anymore! The New Link Explorer is Here - Whiteboard Friday

PA/DA scores, likewise, took forever to update because of this link problem. So the index would say, oh, your DA is over here. You’re at 25, and now maybe you’re at 30. But in reality, you’re probably far ahead of that, because you’ve been building a lot of links that Mozscape just hasn’t picked up yet. So this is this lagging indicator. Sometimes there would be links that it just didn’t even know about. So PA and DA just wouldn’t be as accurate or precise as you’d want them to be.

4. Some scores were really confusing and out of date

Moz's Link Data Used to Suck... But Not Anymore! The New Link Explorer is Here - Whiteboard Friday

MozRank and MozTrust relied on essentially the original Google PageRank paper from 1997, which there’s no way that’s what’s being used today. Google certainly uses some view of link equity that’s passed between links that is similar to PageRank, and I think they probably internally call that PageRank, but it looks nothing like what MozRank was called.

Likewise, MozTrust, way out of date, from a paper in I think 2002 or 2003. Much more advancements in search have happened since then.

Spam score was also out of date. It used a system that was correlated with what spam looked like three, four years ago, so much more up to date than these two, but really not nearly as sophisticated as what Google is doing today. So we needed to toss those out and find their replacements as well.

5. There was no way to see links gained and lost over time

Moz's Link Data Used to Suck... But Not Anymore! The New Link Explorer is Here - Whiteboard Friday

Mozscape had no way to see gained and lost links over time, and folks thought, “Gosh, these other tools in the SEO space give me this ability to show me links that their index has discovered or links they’ve seen that we’ve lost. I really want that.”

6. DA didn’t correlate as well as it should have

Moz's Link Data Used to Suck... But Not Anymore! The New Link Explorer is Here - Whiteboard Friday

So over time, DA became a less and less indicative measure of how well you were performing in Google’s rankings. That needed to change as well. The new DA, by the way, much, much better on this front.

7. Bulk metrics checking and link reporting was too hard and manual

Moz's Link Data Used to Suck... But Not Anymore! The New Link Explorer is Here - Whiteboard Friday

So folks would say, “Hey, I have this giant spreadsheet with all my link data. I want to upload that. I want you guys to crawl it. I want to go fetch all your metrics. I want to get DA scores for these hundreds or thousands of websites that I’ve got. How do I do that?” We didn’t provide a good way for you to do that either unless you were willing to write code and loop in our API.

8. People wanted distribution of their links by DA

Moz's Link Data Used to Suck... But Not Anymore! The New Link Explorer is Here - Whiteboard Friday

They wanted distributions of their links by domain authority. Show me where my links come from, yes, but also what sorts of buckets of DA do I have versus my competition? That was also missing.

So, let me show you what the new Link Explorer has.

Moz's new Link Explorer

Click on the whiteboard image above to open a high-resolution version in a new tab!

Wow, look at that magical board change, and it only took a fraction of a second. Amazing.

What Link Explorer has done, as compared to the old Open Site Explorer, is pretty exciting. I’m actually very proud of the team. If you know me, you know I am a picky SOB. I usually don’t even like most of the stuff that we put out here, but oh my god, this is quite an incredible product.

1. Link Explorer has a GIANT index

Moz's Link Data Used to Suck... But Not Anymore! The New Link Explorer is Here - Whiteboard Friday

So I mentioned index size was a big problem. Link Explorer has got a giant index. Frankly, it’s about 20 times larger than what Open Site Explorer had and, as you can see, very, very competitive with the other services out there. Majestic Fresh says they have about a trillion URLs from their I think it’s the last 60 days. Ahrefs, about 3 trillion. Majestic’s historic, which goes all time, has about 7 trillion, and Moz, just in the last 90 days, which I think is our index — maybe it’s a little shorter than that, 60 days — 4.7 trillion, so almost 5 trillion URLs. Just really, really big. It covers a huge swath of the web, which is great.

2. All data updates every 24 hours

Moz's Link Data Used to Suck... But Not Anymore! The New Link Explorer is Here - Whiteboard Friday

So, unlike the old index, it is very fresh. Every time it finds a new link, it updates PA scores and DA scores. The whole interface can show you all the links that it found just yesterday every morning.

3. DA and PA are tracked daily for every site

Moz's Link Data Used to Suck... But Not Anymore! The New Link Explorer is Here - Whiteboard Friday

You don’t have to track them yourself. You don’t have to put them into your campaigns. Every time you go and visit a domain, you will see this graph showing you domain authority over time, which has been awesome.

For my new company, I’ve been tracking all the links that come in to SparkToro, and I can see my DA rising. It’s really exciting. I put out a good blog post, I get a bunch of links, and my DA goes up the next day. How cool is that?

4. Old scores are gone, and new scores are polished and high quality

Moz's Link Data Used to Suck... But Not Anymore! The New Link Explorer is Here - Whiteboard Friday

So we got rid of MozRank and MozTrust, which were very old metrics and, frankly, very few people were using them, and most folks who were using them didn’t really know how to use them. PA basically takes care of both of them. It includes the weight of links that come to you and the trustworthiness. So that makes more sense as a metric.

Spam score is now on a 0 to 100% risk model instead of the old 0 to 17 flags and the flags correlate to some percentage. So 0 to 100 risk model. Spam score is basically just a machine learning built model against sites that Google penalized or banned.

So we took a huge amount of domains. We ran their names through Google. If they couldn’t rank for their own name, we said they were penalized. If we did a site: the domain.com and Google had de-indexed them, we said they were banned. Then we built this risk model. So in the 90% that means 90% of sites that had these qualities were penalized or banned. 2% means only 2% did. If you have a 30% spam score, that’s not too bad. If you have a 75% spam score, it’s getting a little sketchy.

5. Discovered and lost links are available for every site, every day

Moz's Link Data Used to Suck... But Not Anymore! The New Link Explorer is Here - Whiteboard Friday

So again, for this new startup that I’m doing, I’ve been watching as I get new links and I see where they come from, and then sometimes I’ll reach out on Twitter and say thank you to those folks who are linking to my blog posts and stuff. But it’s very, very cool to see links that I gain and links that I lose every single day. This is a feature that Ahrefs and Majestic have had for a long time, and frankly Moz was behind on this. So I’m very glad that we have it now.

6. DA is back as a high-quality leading indicator of ranking ability

Moz's Link Data Used to Suck... But Not Anymore! The New Link Explorer is Here - Whiteboard Friday

So, a note that is important: everyone’s DA has changed. Your DA has changed. My DA has changed. Moz’s DA changed. Google’s DA changed. I think it went from a 98 to a 97. My advice is take a look at yourself versus all your competitors that you’re trying to rank against and use that to benchmark yourself. The old DA was an old model on old data on an old, tiny index. The new one is based on this 4.7 trillion size index. It is much bigger. It is much fresher. It is much more accurate. You can see that in the correlations.

7. Building link lists, tracking links that you want to acquire, and bulk metrics checking is now easy

Moz's Link Data Used to Suck... But Not Anymore! The New Link Explorer is Here - Whiteboard Friday

Building link lists, tracking links that you want to acquire, and bulk metrics checking, which we never had before and, in fact, not a lot of the other tools have this link tracking ability, is now available through possibly my favorite feature in the tool called Link Tracking Lists. If you’ve used Keyword Explorer and you’ve set up your keywords to watch those over time and to build a keyword research set, very, very similar. If you have links you want to acquire, you add them to this list. If you have links that you want to check on, you add them to this list. It will give you all the metrics, and it will tell you: Does this link to your website that you can associate with a list, or does it not? Or does it link to some page on the domain, but maybe not exactly the page that you want? It will tell that too. Pretty cool.

8. Link distribution by DA

Moz's Link Data Used to Suck... But Not Anymore! The New Link Explorer is Here - Whiteboard Friday

Finally, we do now have link distribution by DA. You can find that right on the Overview page at the bottom.

Look, I’m not saying Link Explorer is the absolute perfect, best product out there, but it’s really, really damn good. I’m incredibly proud of the team. I’m very proud to have this product out there.

If you’d like, I’ll be writing some more about how we went about building this product and a bunch of agency folks that we spent time with to develop this, and I would like to thank all of them of course. A huge thank you to the Moz team.

I hope you’ll do me a favor. Check out Link Explorer. I think, very frankly, this team has earned 30 seconds of your time to go check it out.

Try out Link Explorer!

All right. Thanks, everyone. We’ll see you again for another edition of Whiteboard Friday. Take care.

Video transcription by Speechpad.com

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


Moz Blog

Posted in IM NewsComments Off

Google Confirms Chrome Usage Data Used to Measure Site Speed

Posted by Tom-Anthony

During a discussion with Google’s John Mueller at SMX Munich in March, he told me an interesting bit of data about how Google evaluates site speed nowadays. It has gotten a bit of interest from people when I mentioned it at SearchLove San Diego the week after, so I followed up with John to clarify my understanding.

The short version is that Google is now using performance data aggregated from Chrome users who have opted in as a datapoint in the evaluation of site speed (and as a signal with regards to rankings). This is a positive move (IMHO) as it means we don’t need to treat optimizing site speed for Google as a separate task from optimizing for users.

Previously, it has not been clear how Google evaluates site speed, and it was generally believed to be measured by Googlebot during its visits — a belief enhanced by the presence of speed charts in Search Console. However, the onset of JavaScript-enabled crawling made it less clear what Google is doing — they obviously want the most realistic data possible, but it’s a hard problem to solve. Googlebot is not built to replicate how actual visitors experience a site, and so as the task of crawling became more complex, it makes sense that Googlebot may not be the best mechanism for this (if it ever was the mechanism).

In this post, I want to recap the pertinent data around this news quickly and try to understand what this may mean for users.

Google Search Console

Firstly, we should clarify our understand of what the “time spent downloading a page” metric in Google Search Console is telling us. Most of us will recognize graphs like this one:

Until recently, I was unclear about exactly what this graph was telling me. But handily, John Mueller comes to the rescue again with a detailed answer [login required] (hat tip to James Baddiley from Chillisauce.com for bringing this to my attention):

John clarified what this graph is showing:

It’s technically not “downloading the page” but rather “receiving data in response to requesting a URL” – it’s not based on rendering the page, it includes all requests made.

And that it is:

this is the average over all requests for that day

Because Google may be fetching a very different set of resources every day when it’s crawling your site, and because this graph does not account for anything to do with page rendering, it is not useful as a measure of the real performance of your site.

For that reason, John points out that:

Focusing blindly on that number doesn’t make sense.

With which I quite agree. The graph can be useful for identifying certain classes of backend issues, but there are also probably better ways for you to do that (e.g. WebPageTest.org, of which I’m a big fan).

Okay, so now we understand that graph and what it represents, let’s look at the next option: the Google WRS.

Googlebot & the Web Rendering Service

Google’s WRS is their headless browser mechanism based on Chrome 41, which is used for things like “Fetch as Googlebot” in Search Console, and is increasingly what Googlebot is using when it crawls pages.

However, we know that this isn’t how Google evaluates pages because of a Twitter conversation between Aymen Loukil and Google’s Gary Illyes. Aymen wrote up a blog post detailing it at the time, but the important takeaway was that Gary confirmed that WRS is not responsible for evaluating site speed:

Twitter conversation with Gary Ilyes

At the time, Gary was unable to clarify what was being used to evaluate site performance (perhaps because the Chrome User Experience Report hadn’t been announced yet). It seems as though things have progressed since then, however. Google is now able to tell us a little more, which takes us on to the Chrome User Experience Report.

Chrome User Experience Report

Introduced in October last year, the Chrome User Experience Report “is a public dataset of key user experience metrics for top origins on the web,” whereby “performance data included in the report is from real-world conditions, aggregated from Chrome users who have opted-in to syncing their browsing history and have usage statistic reporting enabled.”

Essentially, certain Chrome users allow their browser to report back load time metrics to Google. The report currently has a public dataset for the top 1 million+ origins, though I imagine they have data for many more domains than are included in the public data set.

In March I was at SMX Munich (amazing conference!), where along with a small group of SEOs I had a chat with John Mueller. I asked John about how Google evaluates site speed, given that Gary had clarified it was not the WRS. John was kind enough to shed some light on the situation, but at that point, nothing was published anywhere.

However, since then, John has confirmed this information in a Google Webmaster Central Hangout [15m30s, in German], where he explains they’re using this data along with some other data sources (he doesn’t say which, though notes that it is in part because the data set does not cover all domains).

At SMX John also pointed out how Google’s PageSpeed Insights tool now includes data from the Chrome User Experience Report:

The public dataset of performance data for the top million domains is also available in a public BigQuery project, if you’re into that sort of thing!

We can’t be sure what all the other factors Google is using are, but we now know they are certainly using this data. As I mentioned above, I also imagine they are using data on more sites than are perhaps provided in the public dataset, but this is not confirmed.

Pay attention to users

Importantly, this means that there are changes you can make to your site that Googlebot is not capable of detecting, which are still detected by Google and used as a ranking signal. For example, we know that Googlebot does not support HTTP/2 crawling, but now we know that Google will be able to detect the speed improvements you would get from deploying HTTP/2 for your users.

The same is true if you were to use service workers for advanced caching behaviors — Googlebot wouldn’t be aware, but users would. There are certainly other such examples.

Essentially, this means that there’s no longer a reason to worry about pagespeed for Googlebot, and you should instead just focus on improving things for your users. You still need to pay attention to Googlebot for crawling purposes, which is a separate task.

If you are unsure where to look for site speed advice, then you should look at:

That’s all for now! If you have questions, please comment here and I’ll do my best! Thanks!

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!


Moz Blog

Posted in IM NewsComments Off

Advert