Today I’m going to show you how to measure your owned social media traffic and your earned social media traffic. First, some definitions:
Owned Social Traffic - a visitor that comes to your site by clicking on a link that YOU shared on a social platform (Facebook, Twitter, etc.)
Earned Social Traffic – a visitor that comes to your site by clicking on a link that someone else has shared on a social platform
Here’s a quick guide on defining paid, owned and earned media that is applicable to what I’ll be talking about.
Tag, you’re it
To be able to measure owned vs. earned, you need to do something special to the links YOU share. You need to tag them with some extra information. So instead of sharing this:
You add some campaign parameters (in a way that Google Analytics recognizes) to indicate the source, medium and campaign. So you end up with one very long URL like below (which we’ll deal with in a moment):
For the utm_source, this is the social network on which you’re sharing the link. So if you’re sharing the link on Twitter, the source is twitter. It’s important to be consistent. These parameters are case sensative – Google Analytics makes a distinction between Twitter (with a capital T) and twitter (lowercase t). If you’re not consistent, you wont be able to easily roll up all of your data from each source into one report.
For the utm_medium, this should be social.
And for the utm_campaign, indicate either a marketing campaign that the tweet is part of, or the type of content you’re sharing. For example, blog posts are often commonly tweeted or posted to Facebook when they’re published, but it’s not necessarily part of a marketing campaign. For tweets with links to blog posts, consider using blog as the campaign.
Once you’ve added those campaign parameters to the URL, you’ll need to shorten it for 2 reasons:
1. So it doesn’t take up precious characters (in the example above, you’d only have 44 characters left for the actual tweet!)
2. So it doesn’t look so ugly. Purely aesthetic.
Bit.ly is a great tool to shorten links (my favorite) and there are many other options out there, like Google’s URL shortener (goo.gl) and ow.ly.
Of course, if you’re doing a lot of sharing on a lot of networks, you won’t want to manually create a unique shortened link for each piece of content that you’re sharing on each social site. That would get tedious and time consuming. Fortunately, I’ve built a tool to do this for you. All you have to do is enter the URL you’re sharing and it automatically creates the shortened links to share on Twitter, Facebook, Google+ and LinkedIn.
Once you get in the habit of tagging your own shared social links, you’ll be able to segment them in Google Analytics using Custom Segments. In a second, I’ll share the custom segments with you to do this. Then you can apply those custom segments to your Social Sources report, like below:
This graph shows visits to the site from owned social media in orange, and visits that resulted from other people sharing our content – earned social traffic – in blue. Below the graph we get the breakdown of owned vs. earned for each social source:
This is a great way to begin doing some correlative research to see how your sharing impacts not only traffic to your site, but other people’s sharing of your content.
Here are links to the custom segments. Keep in mind, these will only make sense in the context of the Social Reports, and you have to tag your links to make these work.
It’s been about a year since Google Analytics launched Real Time reports. At first, they were cool to look at but provided seemingly limited functionality and actionable insights. One of the few examples of usefulness was measuring the immediate impact to your site from social media activities.
But lately, I’ve been turning to Real Time reports for a different use: research and troubleshooting. There is one area in particular that you can use Real Time reports to help with (more…)
Have you ever needed to make a tiny change to your Google Analytics tracking code, but your IT team told you it would be 6 weeks until the next code refresh? What if you could just log into a tool, make the change, and have it go live immediately – without IT involvement? Now you can.
Google announced the launch of Google Tag Manager this morning at the eMetrics conference in Boston. Google Tag Manager (GTM) is a free tool that lets you manage the marketing and measurement tags for your web site in one place. This means no more scattering scripts across your pages and waiting on IT to track them down.
Sometimes direct is good. For example, I would have much rather been on a direct flight to Denver for this week’s Google Analytics training than having a layover in Charlotte.
But not everything direct is good. Take visits to your website, for instance. Direct visits are the worst visits you can have (at least from the perspective of a web analyst).
Why, you ask? Because direct visits are a mystery. We don’t know why they came to our site, how they found out about us or whether our marketing and advertising worked.
When you have referral or campaign information about the visits to your site, you can start to see which marketing initiatves are working and which ones you should dump.
Here’s the problem: even with rock solid campaign tagging, you’re still going to get Direct visits. Even worse, the number of those visits that aren’t truly Direct is on the rise.
Let me say that again. Not all of the “Direct” visits to your site are Direct. We have a wolf in sheep’s clothing here.
I don’t see a lot of people talking about this issue (at least publicly) but I expect it to become one of the most important issues in the web analytics industry in the next year or two.
First, let’s look at where these wolves are coming from, then talk about ways to rip off their disguises.
Mobile Devices and Apps
Let’s face it, we’re living in a multi-device world, where people connect with your content not just from a computer, but from their phones and tablets too. And they don’t always use a web browser to pull up your site. They click on links within apps for Twitter, Facebook, Pinterest, LinkedIn and many, many more.
Many of these visits show up as Direct.
Clicks on (untagged) links within Outlook and other desktop email clients show up as Direct. That’s always been the case. But what’s become more common is for web-based email clients (Gmail, Yahoo! Mail, etc.) to use secure browsing (https). Consider your referrer lost – “Direct” visits strike again, more wolves.
Tweetdeck and other non-mobile Twitter apps can also show up as Direct, if a link shortener is used (bit.ly or goo.gl, for example).
How often do you send a link to someone in Google Talk or Skype? If they click on it, they are a “Direct” visit.
Aside from things you have little to no control over, there are some causes that you an fix. For example, you may have errors in you code or conflicts with scripts that cause your visitors’ sessions and/or cookies to be reset, resulting in more false Direct visits.
What can you do?
The best way to clean up your data and identify these wolves is to tag them. This means having a stringent campaign tagging plan in place for every marketing effort your company does. Paid search, display ads, email marketing, TV, radio, print, direct mail, you name it – it can (and should!) be tagged. Try this campaign tagging worksheet to get started. It’s a Google Doc spreadsheet – just click File > Make a copy and it’s all yours. If the Make a copy link is grayed out, look at the link in the top right to make sure you’re signed in.
The other important thing is to realize that even that won’t solve 100% of the problem. Although you can use campaign tagging on everything you share, there will always be people who come to your site, like what they see, and copy/paste your URL into Facebook, Twitter, etc. This is especially true for blog posts and other informational content that is more likely to be shared.
One thing that will help is to look for patterns and trends in your data. As Direct visits increase, look for correlations between visits from social media (that you can still identify as such) and direct visits from mobile devices. This will be especially apparent if the direct visits are landing on internal pages with long URLs (that are mot likely not being typed in directly).
What weapons are you using to hunt down these wolves? How has this issue affected your data? The comments are yours!
“You don’t want the next Penguin update,” according to Matt Cutts, head of Google’s webspam team. Those were his words less than a week ago at the keynote of the SES San Francisco conference.
Many SEOs and webmasters have been getting antsy over the impending update, which Cutts has said will be “jarring and jolting.” Although I won’t contribute to the millions of pixels that already dissect his statements, I will show you how to set up an early warning and monitoring station, using Google Analytics, of course.
How to use Google Analytics to monitor for Penguin updates
To monitor for updates from this flightless bird, we’ll be using 4 features of Google Analytics: 1) Custom Reports, 2) Dashboards, 3) Custom Segments and 4) Custom Alerts. Keep in mind, this will only let you know if you’ve been affected (positively or negatively) by Penguin. It won’t tell you when the Penguin update happened if it doesn’t affect your site.
1. Custom Reports
The standard way to check your organic traffic from Google in Google Analytics is this:
Upon Logging into your account, you click Traffic Sources
choose Source as your Primary Dimension
choose keyword as your secondary dimension
use the advanced filter to exclude keyword exactly matching (not provided), click Apply
I don’t know about you, but that is far too many clicks and actions to get to data that I want to see on a regular basis. Fortunately, it’s easy to create a custom report that gives us the same data, but will only require two clicks to get to it.
This report takes those eight steps above and gives it to you in two clicks. It shows you Google organic search traffic (excluding visits from users who are logged in to a Google account – those show up with (not provided) for the keyword).
It may also be helpful to see not just the overall visits from Google organic search, but to segment them by the number of words in the search queries. If Penguin targets specific keywords (as opposed to pages or sitewide adjustments) it may show up better this way.
Now, once you have those custom reports in place, you can package them all together into a one-stop Google organic dashboard. You can check in here daily to see any fluctuations or unusual activity coming from the big G.
I know you’re busy. You don’t have time to check your analytics every single day, let alone obsess over every keyword. That’s where custom alerts come in.
Using a custom alert, you can be notified (by email or text message) whenever visits from Google organic decrease or increase by more than a given percent compared to the same day the previous week.
To do this, you’ll want to create custom advanced segments to match the custom reports above (include only Google organic visits, excluding (not provided) keyword). You could even go so far as to create a custom segment for each keyword length (I’ve done that for you below). This way, you’ll get an email if visits from Google organic where the searcher used between 3-5 words in their query decreases by more than 10%, for example.
Once you have your custom segments, go to your Admin settings (click Admin in the top right of the orange navigation bar). In the Assets tab, click on Custom Alerts, then Create new alert. Give your alert a name (e.g. 20% decrease in 2-word queries), then choose the period to be Day. This will make the comparisons on a daily basis (instead of weekly or monthly) and will alert you to any changes almost immediately.
Select the check box to send an email when the alert triggers. This will send an email to the address you use to log in to Google Analytics. If that’s not one you check regularly, you can also supply additional email addresses to receive the alert. And if that’s not enough, you can even set up your phone to receive a text message with the alert.
Under the Alert Conditions, select one of your custom segments from above for This applies to. For Alert me when, choose Visits, for the condition, and choose % decreases by more than and set a value. You may want to start out with something small, like 10%, then gradually increase if you find you are getting too many alerts that don’t reflect any actual changes. For Compared to, you’ll want to choose Same day in the previous week.
Set up a custom alert for each custom segment, to alert you to both increases and decreases in visits, and you’ll be on top of any changes in organic traffic from Google. This is good to have regardless of Pandas, Penguins, or any other black and white animals.
How important is the Unique Visitors metric to you? What if I told you it’s not as accurate as you thought it was? What if it was off by 50%?
As more people shift toward browsing not just on their computer, but on their phones and tablets too, visitor-based metrics become less accurate. This is because a visitor in analytics isn’t an individual person (that’s what we’d like!) but a unique browser on a unique device. Each browser on each computer/device uses it’s own set of cookies, so if I visit your site from my work computer, then visit again at home on my laptop, I’ll show up as two unique visitors. Or if I go to your site using Firefox, then again with Safari, and a third time using Chrome (all on the same computer), you guessed it – three visitors.
Let’s look at another example. You just launched a new product or service and have a hilarious video on your homepage to get people talking (think Dollar Shave Club). I hear the buzz on Twitter, so I go to your site to check it out on my work computer, using Firefox (1).
Later at lunch, I tell my coworkers about this hysterical video and pull up your site on my phone (2) to show them. It loads, but the screen is too small. So I grab my iPad (3) and go to your site so everyone can see it clearly.
Later, I find myself in the after-lunch sleepy period and need a wake-me-up. So I grab a cup of coffee and go back to your site again, this time using Chrome (4).
When I get home, I tell my wife about the funny video and grab the laptop to show her. I type the address in the browser that’s already open, unfortunately it’s IE6 (5) and the site doesn’t load quite right. So I open Firefox and go back to your site (6).
We have many laughs. But you’re not laughing because I just racked up six unique visitors in your analytics. Six. But I’m just one person.
This isn’t a new problem, but one that is commonly misunderstood. Unfortunately, there’s another twist that is starting to exacerbate this problem. Apps.
Have you ever tapped on a link from within the Twitter app and it opens the page – not in Safari, but still within the app itself? That app uses a browser that uses its own cookies too.
And if you’re like me, you prefer to view the page in Safari, not the Twitter app. So you go to the bottom right corner and tap on the little box and arrow, then tap “Open in Safari.” Oops, now I’m a new visitor again. Bummer.
Here’s the double whammy of app traffic (traffic to your site from apps, not traffic to your apps). Many of your favorite apps show up as Direct visits in your analytics. In the scenario above, tapping on the link in the Twitter app will show up as a direct visit in GA instead of a referral visit from Twitter. Which apps behave this way, you ask? Check the table below:
Referral / m.facebook.com
Pocket (formerly Read It Later)
Note: the test above is not all inclusive – there are hundreds of thousands of apps and I didn’t have time to check them all! Also, I only checked from an iPhone and an iPad (both running iOS 5.1.1). I did not check an Android device – I’ll leave that to you, our faithful readers. Please share in the comments what you find!
This week’s topic is Social Media. In this episode we answer:
- Is it better to have a single Facebook page for your business, or multiple Facebook pages for various locations/divisions/etc.?
LunaTV is a weekly segment where LunaMetrics team members answer your questions from across the web. Got a question you want answered? Tweet at us at @LunaMetrics with #lunaTV and we’ll answer your question!
I know – like we need another Twitter tool, right? In this age of attention deficit, I’ll just cut to the chase. This is a Google Docs tool that helps you quickly identify key influencers to engage with.Here’s your copy.(Once it’s open, go to File > Make a copy so you can edit it).
This is a topic that a lot of you are interested in, judging by the amount of traffic and shares that post got. So I decided build on Robbin’s post by creating a template that you can all use for your own attribution modeling needs. The Attribution Modeling Tool is a Google Doc spreadsheet that has all the formulas you need built right in. It will do the following types of modeling:
The second sheet (MCF Data) is where you paste in the data that you export from the Top Conversion Paths report in GA (refer back to Robbin’s post if you need more details). Specifically, you’ll need to copy the first three columns of data, starting at row 8 (the first row of actual data below the headings Basic Channel Grouping Path, Conversions, and Conversion Value). Don’t copy the last row of data, which is just the totals.
* TIP: Before you export your Top Conversion Paths report from GA, make sure to show 500 rows, or as many rows as you need to get all of the paths and data. The Attribution Modeling Tool can accommodate up to 500 rows of data.
The first sheet (Attribution Models) gives you the numbers and graphs of the value of each channel using each of the above models. The remaining sheets (which are hidden) do all the heavy lifting. There are around 14,000 formulas in this spreadsheet, so it may take a few seconds or longer to fully load or update.
Insights from attribution modeling
Remember, attribution modeling let’s us divvy up the conversion value to various channels that led up to that conversion. For example, look again at the graph above (click to enlarge). This shows us that Direct visits are worth nearly $85,000 with a last touch model, with Organic Search and Paid Search being worth about $26,000. But look at how things change with the first click model. Now Direct,Organic Search and Paid Search are each worth around $44,000.
If you’re only looking at the last touch attribution, you may be tempted to put less money into your search marketing and SEO efforts. You may be foolishly thinking that the people who purchase all come to you directly, so why should you pay? But as you can see above, a lot of people who come to you directly and buy were introduced to your site from a paid or organic search. Otherwise, they may have never come back directly and purchased.
Go get the Attribution Modeling Tool and play around with it. Do you find it useful or a waste of time? Anything surprise you? Bugs or other unusual quirks (Google Docs are know for those)? Let us know in the comments!
Do you have questions about Pinterest? Are you wondering about the Pinterest API? Does the thought of tracking yet another social media platform raise more questions than answers for you?
I’ve curated the best information about Pinterest that I can find, ranging from techie (API details, code, tracking, etc.) to non-techie (marketing, how-to, etc.). Please, do not use this information for evil.
Pinterest used to have a page for developers that documented their API. Then, around the middle of February, that page disappeared and now returns a 404 error. According to this thread on StackOverflow, they “pushed the docs live in an attempt to get feedback but we had to shut it down since we saw activity.” Apparently, their explosive growth caused them to pull back their documentation, possibly to avoid the same problems faced by Twitter in the early days, with the notorious fail whale. Fortunately, you can still access the API documentation at the above link. The endpoint is still active, as long as you are making public calls that don’t require authentication. (more…)