Tag Archive | "Data"

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

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Everyone knows that if you want to be a savvy modern marketer, you need data. Agencies tout their expertise in data-driven marketing, big brands herald a new age driven by big data trends, and it’s standard practice to have Google Analytics set up on your website. But let’s get real. You might have Google Analytics
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The post The Practical Steps that Help More Marketers Use Data appeared first on Copyblogger.


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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 xxpressions.

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 give you 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:

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How Much Data Is Missing from Analytics? And Other Analytics Black Holes

Posted by Tom.Capper

If you’ve ever compared two analytics implementations on the same site, or compared your analytics with what your business is reporting in sales, you’ve probably noticed that things don’t always match up. In this post, I’ll explain why data is missing from your web analytics platforms and how large the impact could be. Some of the issues I cover are actually quite easily addressed, and have a decent impact on traffic — there’s never been an easier way to hit your quarterly targets. ;)

I’m going to focus on GA (Google Analytics), as it’s the most commonly used provider, but most on-page analytics platforms have the same issues. Platforms that rely on server logs do avoid some issues but are fairly rare, so I won’t cover them in any depth.

Side note: Our test setup (multiple trackers & customized GA)

On Distilled.net, we have a standard Google Analytics property running from an HTML tag in GTM (Google Tag Manager). In addition, for the last two years, I’ve been running three extra concurrent Google Analytics implementations, designed to measure discrepancies between different configurations.

(If you’re just interested in my findings, you can skip this section, but if you want to hear more about the methodology, continue reading. Similarly, don’t worry if you don’t understand some of the detail here — the results are easier to follow.)

Two of these extra implementations — one in Google Tag Manager and one on page — run locally hosted, renamed copies of the Google Analytics JavaScript file (e.g. www.distilled.net/static/js/au3.js, instead of www.google-analytics.com/analytics.js) to make them harder to spot for ad blockers. I also used renamed JavaScript functions (“tcap” and “Buffoon,” rather than the standard “ga”) and renamed trackers (“FredTheUnblockable” and “AlbertTheImmutable”) to avoid having duplicate trackers (which can often cause issues).

This was originally inspired by 2016-era best practice on how to get your Google Analytics setup past ad blockers. I can’t find the original article now, but you can see a very similar one from 2017 here.

Lastly, we have (“DianaTheIndefatigable”), which just has a renamed tracker, but uses the standard code otherwise and is implemented on-page. This is to complete the set of all combinations of modified and unmodified GTM and on-page trackers.

Two of Distilled’s modified on-page trackers, as seen on https://www.distilled.net/

Overall, this table summarizes our setups:

Tracker

Renamed function?

GTM or on-page?

Locally hosted JavaScript file?

Default

No

GTM HTML tag

No

FredTheUnblockable

Yes – “tcap”

GTM HTML tag

Yes

AlbertTheImmutable

Yes – “buffoon”

On page

Yes

DianaTheIndefatigable

No

On page

No

I tested their functionality in various browser/ad-block environments by watching for the pageviews appearing in browser developer tools:

Reason 1: Ad Blockers

Ad blockers, primarily as browser extensions, have been growing in popularity for some time now. Primarily this has been to do with users looking for better performance and UX on ad-laden sites, but in recent years an increased emphasis on privacy has also crept in, hence the possibility of analytics blocking.

Effect of ad blockers

Some ad blockers block web analytics platforms by default, others can be configured to do so. I tested Distilled’s site with Adblock Plus and uBlock Origin, two of the most popular ad-blocking desktop browser addons, but it’s worth noting that ad blockers are increasingly prevalent on smartphones, too.

Here’s how Distilled’s setups fared:

(All numbers shown are from April 2018)

Setup

Vs. Adblock

Vs. Adblock with “EasyPrivacy” enabled

Vs. uBlock Origin

GTM

Pass

Fail

Fail

On page

Pass

Fail

Fail

GTM + renamed script & function

Pass

Fail

Fail

On page + renamed script & function

Pass

Fail

Fail

Seems like those tweaked setups didn’t do much!

Lost data due to ad blockers: ~10%

Ad blocker usage can be in the 15–25% range depending on region, but many of these installs will be default setups of AdBlock Plus, which as we’ve seen above, does not block tracking. Estimates of AdBlock Plus’s market share among ad blockers vary from 50–70%, with more recent reports tending more towards the former. So, if we assume that at most 50% of installed ad blockers block analytics, that leaves your exposure at around 10%.

Reason 2: Browser “do not track”

This is another privacy motivated feature, this time of browsers themselves. You can enable it in the settings of most current browsers. It’s not compulsory for sites or platforms to obey the “do not track” request, but Firefox offers a stronger feature under the same set of options, which I decided to test as well.

Effect of “do not track”

Most browsers now offer the option to send a “Do not track” message. I tested the latest releases of Firefox & Chrome for Windows 10.

Setup

Chrome “do not track”

Firefox “do not track”

Firefox “tracking protection”

GTM

Pass

Pass

Fail

On page

Pass

Pass

Fail

GTM + renamed script & function

Pass

Pass

Fail

On page + renamed script & function

Pass

Pass

Fail

Again, it doesn’t seem that the tweaked setups are doing much work for us here.

Lost data due to “do not track”: <1%

Only Firefox Quantum’s “Tracking Protection,” introduced in February, had any effect on our trackers. Firefox has a 5% market share, but Tracking Protection is not enabled by default. The launch of this feature had no effect on the trend for Firefox traffic on Distilled.net.

Reason 3: Filters

It’s a bit of an obvious one, but filters you’ve set up in your analytics might intentionally or unintentionally reduce your reported traffic levels.

For example, a filter excluding certain niche screen resolutions that you believe to be mostly bots, or internal traffic, will obviously cause your setup to underreport slightly.

Lost data due to filters: ???

Impact is hard to estimate, as setup will obviously vary on a site-by site-basis. I do recommend having a duplicate, unfiltered “master” view in case you realize too late you’ve lost something you didn’t intend to.

Reason 4: GTM vs. on-page vs. misplaced on-page

Google Tag Manager has become an increasingly popular way of implementing analytics in recent years, due to its increased flexibility and the ease of making changes. However, I’ve long noticed that it can tend to underreport vs. on-page setups.

I was also curious about what would happen if you didn’t follow Google’s guidelines in setting up on-page code.

By combining my numbers with numbers from my colleague Dom Woodman’s site (you’re welcome for the link, Dom), which happens to use a Drupal analytics add-on as well as GTM, I was able to see the difference between Google Tag Manager and misplaced on-page code (right at the bottom of the <body> tag) I then weighted this against my own Google Tag Manager data to get an overall picture of all 5 setups.

Effect of GTM and misplaced on-page code

Traffic as a percentage of baseline (standard Google Tag Manager implementation):

Google Tag Manager

Modified & Google Tag Manager

On-Page Code In <head>

Modified & On-Page Code In <head>

On-Page Code Misplaced In <Body>

Chrome

100.00%

98.75%

100.77%

99.80%

94.75%

Safari

100.00%

99.42%

100.55%

102.08%

82.69%

Firefox

100.00%

99.71%

101.16%

101.45%

90.68%

Internet Explorer

100.00%

80.06%

112.31%

113.37%

77.18%

There are a few main takeaways here:

  • On-page code generally reports more traffic than GTM
  • Modified code is generally within a margin of error, apart from modified GTM code on Internet Explorer (see note below)
  • Misplaced analytics code will cost you up to a third of your traffic vs. properly implemented on-page code, depending on browser (!)
  • The customized setups, which are designed to get more traffic by evading ad blockers, are doing nothing of the sort.

It’s worth noting also that the customized implementations actually got less traffic than the standard ones. For the on-page code, this is within the margin of error, but for Google Tag Manager, there’s another reason — because I used unfiltered profiles for the comparison, there’s a lot of bot spam in the main profile, which primarily masquerades as Internet Explorer. Our main profile is by far the most spammed, and also acting as the baseline here, so the difference between on-page code and Google Tag Manager is probably somewhat larger than what I’m reporting.

I also split the data by mobile, out of curiosity:

Traffic as a percentage of baseline (standard Google Tag Manager implementation):

Google Tag Manager

Modified & Google Tag Manager

On-Page Code In <head>

Modified & On-Page Code In <head>

On-Page Code Misplaced In <Body>

Desktop

100.00%

98.31%

100.97%

100.89%

93.47%

Mobile

100.00%

97.00%

103.78%

100.42%

89.87%

Tablet

100.00%

97.68%

104.20%

102.43%

88.13%

The further takeaway here seems to be that mobile browsers, like Internet Explorer, can struggle with Google Tag Manager.

Lost data due to GTM: 1–5%

Google Tag Manager seems to cost you a varying amount depending on what make-up of browsers and devices use your site. On Distilled.net, the difference is around 1.7%; however, we have an unusually desktop-heavy and tech-savvy audience (not much Internet Explorer!). Depending on vertical, this could easily swell to the 5% range.

Lost data due to misplaced on-page code: ~10%

On Teflsearch.com, the impact of misplaced on-page code was around 7.5%, vs Google Tag Manager. Keeping in mind that Google Tag Manager itself underreports, the total loss could easily be in the 10% range.

Bonus round: Missing data from channels

I’ve focused above on areas where you might be missing data altogether. However, there are also lots of ways in which data can be misrepresented, or detail can be missing. I’ll cover these more briefly, but the main issues are dark traffic and attribution.

Dark traffic

Dark traffic is direct traffic that didn’t really come via direct — which is generally becoming more and more common. Typical causes are:

  • Untagged campaigns in email
  • Untagged campaigns in apps (especially Facebook, Twitter, etc.)
  • Misrepresented organic
  • Data sent from botched tracking implementations (which can also appear as self-referrals)

It’s also worth noting the trend towards genuinely direct traffic that would historically have been organic. For example, due to increasingly sophisticated browser autocompletes, cross-device history, and so on, people end up “typing” a URL that they’d have searched for historically.

Attribution

I’ve written about this in more detail here, but in general, a session in Google Analytics (and any other platform) is a fairly arbitrary construct — you might think it’s obvious how a group of hits should be grouped into one or more sessions, but in fact, the process relies on a number of fairly questionable assumptions. In particular, it’s worth noting that Google Analytics generally attributes direct traffic (including dark traffic) to the previous non-direct source, if one exists.

Discussion

I was quite surprised by some of my own findings when researching this post, but I’m sure I didn’t get everything. Can you think of any other ways in which data can end up missing from analytics?

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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

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What Google’s GDPR Compliance Efforts Mean for Your Data: Two Urgent Actions

Posted by willcritchlow

It should be quite obvious for anyone that knows me that I’m not a lawyer, and therefore that what follows is not legal advice. For anyone who doesn’t know me: I’m not a lawyer, I’m certainly not your lawyer, and what follows is definitely not legal advice.

With that out of the way, I wanted to give you some bits of information that might feed into your GDPR planning, as they come up more from the marketing side than the pure legal interpretation of your obligations and responsibilities under this new legislation. While most legal departments will be considering the direct impacts of the GDPR on their own operations, many might miss the impacts that other companies’ (namely, in this case, Google’s) compliance actions have on your data.

But I might be getting a bit ahead of myself: it’s quite possible that not all of you know what the GDPR is, and why or whether you should care. If you do know what it is, and you just want to get to my opinions, go ahead and skip down the page.

What is the GDPR?

The tweet-length version is that the GDPR (General Data Protection Regulation) is new EU legislation covering data protection and privacy for EU citizens, and it applies to all companies offering goods or services to people in the EU.

Even if you aren’t based in the EU, it applies to your company if you have customers who are, and it has teeth (fines of up to the greater of 4% of global revenue or EUR20m). It comes into force on May 25. You have probably heard about it through the myriad organizations who put you on their email list without asking and are now emailing you to “opt back in.”

In most companies, it will not fall to the marketing team to research everything that has to change and achieve compliance, though it is worth getting up to speed with at least the high-level outline and in particular its requirements around informed consent, which is:

“…any freely given, specific, informed, and unambiguous indication of the data subject’s wishes by which he or she, by a statement or by a clear affirmative action, signifies agreement to the processing of personal data relating to him or her.”

As always, when laws are made about new technology, there are many questions to be resolved, and indeed, jokes to be made:

But my post today isn’t about what you should do to get compliant — that’s specific to your circumstances — and a ton has been written about this already:

My intention is not to write a general guide, but rather to warn you about two specific things you should be doing with analytics (Google Analytics in particular) as a result of changes Google is making because of GDPR.

Unexpected consequences of GDPR

When you deal directly with a person in the EU, and they give you personally identifiable information (PII) about themselves, you are typically in what is called the “data controller” role. The GDPR also identifies another role, which it calls “data processor,” which is any other company your company uses as a supplier and which handles that PII. When you use a product like Google Analytics on your website, Google is taking the role of data processor. While most of the restrictions of the GDPR apply to you as the controller, the processor must also comply, and it’s here that we see some potentially unintended (but possibly predictable) consequences of the legislation.

Google is unsurprisingly seeking to minimize their risk (I say it’s unsurprising because those GDPR fines could be as large as $ 4.4 billion based on last year’s revenue if they get it wrong). They are doing this firstly by pushing as much of the obligation onto you (the data controller) as possible, and secondly, by going further by default than the GDPR requires and being more aggressive than the regulation requires in shutting down accounts that infringe their terms (regardless of whether the infringement also infringes the GDPR).

This is entirely rational — with GA being in most cases a product offered for free, and the value coming to Google entirely in the aggregate, it makes perfect sense to limit their risks in ways that don’t degrade their value, and to just kick risky setups off the platform rather than taking on extreme financial risk for individual free accounts.

It’s not only Google, by the way. There are other suppliers doing similar things which will no doubt require similar actions, but I am focusing on Google here simply because GA is pervasive throughout the web marketing world. Some companies are even going as far as shutting down entirely for EU citizens (like unroll.me). See this Twitter thread of others.

Consequence 1: Default data retention settings for GA will delete your data

Starting on May 25, Google will be changing the default for data retention, meaning that if you don’t take action, certain data older than the cutoff will be automatically deleted.

You can read more about the details of the change on Krista Seiden’s personal blog (Krista works at Google, but this post is written in her personal capacity).

The reason I say that this isn’t strictly a GDPR thing is that it is related to changes Google is making on their end to ensure that they comply with their obligations as a data processor. It gives you tools you might need but isn’t strictly related to your GDPR compliance. There is no particular “right” answer to the question of how long you need to/should be/are allowed to keep this data stored in GA under the GDPR, but by my reading, given that it shouldn’t be PII anyway (see below) it isn’t really a GDPR question for most organizations. In particular, there is no particular reason to think that Google’s default is the correct/mandated/only setting you can choose under the GDPR.

Action: Review the promises being made by your legal team and your new privacy policy to understand the correct timeline setting for your org. In the absence of explicit promises to your users, my understanding is that you can retain any of this data you were allowed to capture in the first place unless you receive a deletion request against it. So while most orgs will have at least some changes to make to privacy policies at a minimum, most GA users can change back to retain this data indefinitely.

Consequence 2: Google is deleting GA accounts for capturing PII

It has long been against the Terms of Service to store any personally identifiable information (PII) in Google Analytics. Recently, though, it appears that Google has become far more diligent in checking for the presence of PII and robust in their handling of accounts found to contain any. Put more simply, Google will delete your account if they find PII.

It’s impossible to know for sure that this is GDPR-related, but being able if necessary to demonstrate to regulators that they are taking strict actions against anyone violating their PII-related terms is an obvious move for Google to reduce the risk they face as a Data Processor. It makes particular sense in an area where the vast majority of accounts are free accounts. Much like the previous point, and the reason I say that this is related to Google’s response to the GDPR coming into force, is that it would be perfectly possible to get your users’ permission to record their data in third-party services like GA, and fully comply with the regulations. Regardless of the permissions your users give you, Google’s GDPR-related crackdown (and heavier enforcement of the related terms that have been present for some time) means that it’s a new and greater risk than it was before.

Action: Audit your GA profile and implementation for PII risks:

  • There are various ways you can search within GA itself to find data that could be personally identifying in places like page titles, URLs, custom data, etc. (see these two excellent guides)
  • You can also audit your implementation by reviewing rules in tag manager and/or reviewing the code present on key pages. The most likely suspects are the places where people log in, take key actions on your site, give you additional personal information, or check out

Don’t take your EU law advice from big US tech companies

The internal effort and coordination required at Google to do their bit to comply even “just” as data processor is significant. Unfortunately, there are strong arguments that this kind of ostensibly user-friendly regulation which incurs outsize compliance burdens on smaller companies will cement the duopoly and dominance of Google and Facebook and enables them to pass the costs and burdens of compliance onto sectors that are already struggling.

Regardless of the intended or unintended consequences of the regulation, it seems clear to me that we shouldn’t be basing our own businesses’ (and our clients’) compliance on self-interested advice and actions from the tech giants. No matter how impressive their own compliance, I’ve been hugely underwhelmed by guidance content they’ve put out. See, for example, Google’s GDPR “checklist” — not exactly what I’d hope for:

Client Checklist: As a marketer we know you need to select products that are compliant and use personal data in ways that are compliant. We are committed to complying with the GDPR and would encourage you to check in on compliance plans within your own organisation. Key areas to think about:  How does your organisation ensure user transparency and control around data use? Do you explain to your users the types of data you collect and for what purposes? Are you sure that your organisation has the right consents in place where these are needed under the GDPR? Do you have all of the relevant consents across your ad supply chain? Does your organisation have the right systems to record user preferences and consents? How will you show to regulators and partners that you meet the principles of the GDPR and are an accountable organisation?

So, while I’m not a lawyer, definitely not your lawyer, and this is not legal advice, if you haven’t already received any advice, I can say that you probably can’t just follow Google’s checklist to get compliant. But you should, as outlined above, take the specific actions you need to take to protect yourself and your business from their compliance activities.

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Yext adds TripAdvisor to listings network, conversational UI for local data updates

Messaging-based updates especially useful for SMBs and local store managers.
Please visit Search Engine Land for the full article.



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