Tag Archive | "Tracking"

Zebra Tech Tracking Technology Integrating Deep Into Sports and Business

“We’ve learned this past year that the tracking system we have with the NFL is actually considered to be the best by the broadcasters, coaches, and the fans,” says Zebra Technologies CEO Anders Gustafsson. “Our type of technology works particularly well with football but it would also work for basketball, ice hockey, and soccer. With ice hockey, the challenge is the puck. How do you track the puck and put the tag inside the puck? We can do it but it’s more costly. With basketball, they have been more focused on the ball than the players.”

Anders Gustafsson, CEO of Zebra Technologies, discusses how their tracking technology is being integrated deeply within sports and business in an interview with Jim Cramer on CNBC:

Our Tracking Technology Works Particularly Well With Football

We’ve learned now this past year that the tracking system we have with the NFL is actually considered to be the best by the broadcasters, coaches, and the fans. The NFL owns the data so we can’t give (fantasy players) access to the data. I think they give access to some of the data but not all the data. Then you would have all the information you could possibly want to have about every player on all of the teams. 

Our type of technology works particularly well with football but it would also work for basketball, ice hockey, and soccer. With ice hockey, the challenge is the puck. How do you track the puck and put the tag inside the puck? We can do it but it’s more costly. With basketball, they have been more focused on the ball than the players. 

Zebra Tracking Technology Works Particularly Well With Football

We Are Becoming An Essential Part of Retailers’ Strategies

Savannah is our data platform. We can connect all sorts of devices or sensors on the south side and on the north side we can have APIs to all sorts of other applications. We can provide a lot of analytics around what’s happening there. We integrate with a lot of independent software vendors. If you look at large companies like Oracle, SAP, Manhattan, and JDA, they’re all partners of ours. We exchange data with them and we provide data that they use for their operations. We also have our own software capabilities. We bought a company called Profitect. It does any predictive analytics. This is a good example of this but we have other software capabilities also.

We are now becoming an essential part of retailers’ strategies for building omnichannel and ecommerce capabilities. Historically, we were probably viewed a bit more as a tactical device supplier. Today we’re much more of an integral part of enabling them to execute on their strategy. We moved ourselves up the solution stack to be able to deliver more value to them.

Companies are now tracking employees, patients, assets

Today, more and more things are being tracked and there are more and more efficiencies out of this. Companies are now tracking employees, patients, assets, all of these things. We said we provide the performance edge to the front line of business by having every employee, device, and technical thing being connected and optimally utilized and visible to the network. 

Tableau (a company recently bought by Salesforce) would more than likely integrate our data. We could be a source for data insight analytics for them. We aspire to get those kinds of valuations (and the higher multiples that Tableau got when they sold to Salesforce). We also overlap (with Honeywell) in a number of areas but we do quite a few different things also. We have our own strengths and we compete with them but not everywhere.

Zebra Tech Tracking Technology Integrating Deep Into Sports and Business – CEO Anders Gustafsson

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Parallel tracking, more custom parameters coming to Microsoft Advertising for improved tracking

Parallel tracking, currently in beta, will be rolling out soon.



Please visit Search Engine Land for the full article.


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How to Set Up GTM Cookie Tracking (and Better Understand Content Engagement)

Posted by Joel.Mesherghi

The more you understand the behaviour of your users, the better you can market your product or service — which is why Google Tag Manager (GTM) is a marketer’s best friend. With built-in tag templates, such as scroll depth and click tracking, GTM is a powerful tool to measure the engagement and success of your content. 

If you’re only relying on tag templates in GTM, or the occasionally limiting out-of-box Google Analytics, then you could be missing out on insights that go beyond normal engagement metrics. Which means you may be getting an incomplete story from your data.

This post will teach you how to get even more insight by setting up cookies in GTM. You’ll learn how to tag and track multiple page views in a single session, track a specific set number of pages, based on specific on-page content elements, and understand how users are engaging with your content so you can make data-based decisions to better drive conversions.

Example use case

I recently worked with a client that wanted to better understand the behavior of users that landed on their blog content. The main barrier they faced was their URL structure. Their content didn’t live on logical URL structures — they placed their target keyword straight after the root. So, instead of example.com/blog/some-content, their URL structure looked like example.com/some-content.

You can use advanced segments in Google Analytics (GA) to track any number of metrics, but if you don’t have a logically defined URL, then tracking and measuring those metrics becomes a manual and time-consuming practice — especially when there’s a large number of pages to track.

Fortunately, leveraging a custom cookie code, which I provide below, helps you to cut through that time, requires little implementation effort, and can surface powerful insights:

  1. It can indicate that users are engaged with your content and your brand.
  2. The stored data could be used for content scoring — if a page is included in the three pages of an event it may be more valuable than others. You may want to target these pages with more upsell or cross-sell opportunities, if so.
  3. The same scoring logic could apply to authors. If blogs written by certain authors have more page views in a session, then their writing style/topics could be more engaging and you may want to further leverage their content writing skills.
  4. You can build remarketing audience lists to target these seemingly engaged users to align with your business goals — people who are more engaged with your content could be more likely to convert.

So, let’s briefly discuss the anatomy of the custom code that you will need to add to set cookies before we walk through a step by step implementation guide.

Custom cookie code

Cookies, as we all know, are a small text file that is stored in your browser — it helps servers remember who you are and its code is comprised of three elements:

  • a name-value pair containing data
  • an expiry date after which it is no longer valid
  • the domain and path of the server it should be sent to.

You can create a custom code to add to cookies to help you track and store numerous page views in a session across a set of pages.

The code below forms the foundation in setting up your cookies. It defines specific rules, such as the events required to trigger the cookie and the expiration of the cookie. I’ll provide the code, then break it up into two parts to explain each segment.

The code

<script>
function createCookie(name,value,hours) {
    if (hours) {
        var date = new Date();
        date.setTime(date.getTime()+(hours*60*60*1000));
        var expires = "; expires="+date.toGMTString();
    }
    else var expires = "";
    document.cookie = name+"="+value+expires+"; path=/";
}
if (document.querySelectorAll("CSS SELECTOR GOES HERE"").length > 0) {
var y = {{NumberOfBlogPagesVisited}}
if (y == null) {
    createCookie('BlogPagesVisited',1,1);
}
  else if (y == 1) {
    createCookie('BlogPagesVisited',2,1);
  } 
  else if (y == 2) {
    var newCount = Number(y) + 1;
    createCookie('BlogPagesVisited',newCount,12);
  }
 if (newCount == 3) {
 dataLayer.push({
 'event': '3 Blog Pages'
 });
 }
}
</script>

Part 1

<script>
function createCookie(name,value,hours) {
    if (hours) {
        var date = new Date();
        date.setTime(date.getTime()+(hours*60*60*1000));
        var expires = "; expires="+date.toGMTString();
    }
    else var expires = "";
    document.cookie = name+"="+value+expires+"; path=/";
}

Explanation:

This function, as the name implies, will create a cookie if you specify a name, a value, and the time a cookie should be valid for. I’ve specified “hours,” but if you want to specify “days,” you’ll need to iterate variables of the code. Take a peek at this great resource on setting up cookies.

    Part 2

    if (document.querySelectorAll("CSS SELECTOR GOES HERE").length > 0) {
    var y = {{NumberOfBlogPagesVisited}}
    if (y == null) {
    createCookie('BlogPagesVisited',1,1);
    }
    else if (y == 1) {
    createCookie('BlogPagesVisited',2,1);
    }
    else if (y == 2) {
    var newCount = Number(y) + 1;
    createCookie('BlogPagesVisited',newCount,12);
    }
    if (newCount == 3) {
    dataLayer.push({
    'event': '3 Blog Pages'
    });
    }
    </script>

    Explanation:

    The second part of this script will count the number of page views:

    • The “CSS SELECTOR GOES HERE”, which I’ve left blank for now, will be where you add your CSS selector. This will instruct the cookie to fire if the CSS selector matches an element on a page. You can use DevTools to hover over an on-page element, like an author name, and copy the CSS selector.
    • “y” represents the cookie and “NumberOfBlogPagesVisited” is the name I’ve given to the variable. You’ll want to iterate the variable name as you see fit, but the variable name you set up in GTM should be consistent with the variable name in the code (we’ll go through this during the step-by-step guide).
    • “createCookie” is the actual name of your cookie. I’ve called my cookie “BlogPagesVisited.” You can call your cookie whatever you want, but again, it’s imperative that the name you give your cookie in the code is consistent with the cookie name field when you go on to create your variable in GTM. Without consistency, the tag won’t fire correctly.
    • You can also change the hours at which the cookie expires. If a user accumulates three page views in a single session, the code specifies a 12 hour expiration. The reasoning behind this is that if someone comes back after a day or two and views another blog, we won’t consider that to be part of the same “session,” giving us a clearer insight of the user behaviour of people that trigger three page views in a session.
    • This is rather arbitrary, so you can iterate the cookie expiration length to suit your business goals and customers.

    Note: if you want the event to fire after more than three page views (for example, four-page views) then the code would look like the following:

    var y = {{NumberOfBlogPagesVisited}}
    if (y == null) {
    createCookie('BlogPagesVisited',1,1);
    }
    else if (y == 1) {
    createCookie('BlogPagesVisited',2,1);
    }
    }
    else if (y == 2) {
    createCookie('BlogPagesVisited',3,1);
    }
    else if (y == 3) {
    var newCount = Number(y) + 1;
    createCookie('BlogPagesVisited',newCount,12);
    }
      
    if (newCount == 4) {
    dataLayer.push({
    'event': '4 Blog Pages'
    });

    Now that we have a basic understanding of the script, we can use GTM to implement everything.

    First, you’ll need the set up the following “Tags,” “Triggers”, and ”Variables”:

    Tags

    Custom HTML tag: contains the cookie script

    Event tag: fires the event and sends the data to GA after a third pageview is a session.

    Triggers

    Page View trigger: defines the conditions that will fire your Custom HTML Tag.

    Custom Event trigger: defines the conditions that will fire your event.

    Variable

    First Party Cookie variable: This will define a value that a trigger needs to evaluate whether or not your Custom HTML tag should fire.

    Now, let’s walk through the steps of setting this up in GTM.

    Step 1: Create a custom HTML tag

    First, we’ll need to create a Custom HTML Tag that will contain the cookie script. This time, I’ve added the CSS selector, below:

     #content > div.post.type-post.status-publish.format-standard.hentry > div.entry-meta > span > span.author.vcard > a

    This matches authors on Distilled’s blog pages, so you’ll want to add your own unique selector.

    Navigate to Tags > New > Custom HTML Tag > and paste the script into the custom HTML tag box.

    You’ll want to ensure your tag name is descriptive and intuitive. Google recommends the following tag naming convention: Tag Type – Detail – Location. This will allow you to easily identify and sort related tags from the overview tag interface. You can also create separate folders for different projects to keep things more organized.

    Following Google’s example, I’ve called my tag Custom HTML – 3 Page Views Cookie – Blog.

    Once you’ve created your tag, remember to click save.

    Step 2: Create a trigger

    Creating a trigger will define the conditions that will fire your custom HTML tag. If you want to learn more about triggers, you can read up on Simo Ahava’s trigger guide.

    Navigate to Triggers > New > PageView.

    Once you’ve clicked the trigger configuration box, you’ll want to select “Page View” as a trigger type. I’ve also named my trigger Page View – Cookie Trigger – Blog, as I’m going to set up the tag to fire when users land on blog content.

    Next, you’ll want to define the properties of your trigger.

    Since we’re relying on the CSS selector to trigger the cookie across the site, select “All Page Views”.

    Once you’ve defined your trigger, click save.

    Step 3: Create your variable

    Just like how a Custom HTML tag relies on a trigger to fire, a trigger relies on a variable. A variable defines a value that a trigger needs to evaluate whether or not a tag should fire. If you want to learn more about variables, I recommend reading up on Simo Ahava’s variable guide.

    Head over to Variables > User-Defined Variables > Select 1st Party Cookie. You’ll also notice that I’ve named this variable “NumberOfBlogPagesVisited” — you’ll want this variable name to match what is in your cookie code.

    Having selected “1st Party Cookie,” you’ll now need to input your cookie name. Remember: the cookie name needs to replicate the name you’ve given your cookie in the code. I named my cookie BlogPagesVisited, so I’ve replicated that in the Cookie Name field, as seen below.

    Step 4: Create your event tag

    When a user triggers a third-page view, we’ll want to have it recorded and sent to GA. To do this, we need to set up an “Event” tag.

    First, navigate to Tags > New > Select Google Analytics – Universal Analytics:

    Once you’ve made your tag type “Google Analytics – Universal Analytics”, make sure track type is an “Event” and you name your “Category” and “Action” accordingly. You can also fill in a label and value if you wish. I’ve also selected “True” in the “Non-interaction Hit” field, as I still want to track bounce rate metrics.

    Finally, you’ll want to select a GA Setting variable that will pass on stored cookie information to a GA property.

    Step 5: Create your trigger

    This trigger will reference your event.

    Navigate to Trigger > New > Custom Event

    Once you’ve selected Custom Event, you’ll want to ensure the “Event name” field matches the name you have given your event in the code. In my case, I called the event “3 Blog Pages”.

    Step 6: Audit your cookie in preview mode

    After you’ve selected the preview mode, you should conduct an audit of your cookie to ensure everything is firing properly. To do this, navigate to the site you where you’ve set up cookies.

    Within the debugging interface, head on over to Page View > Variables.

    Next, look to a URL that contains the CSS selector. In the case of the client, we used the CSS selector that referenced an on-page author. All their content pages used the same CSS selector for authors. Using the GTM preview tool you’ll see that “NumberOfBlogPagesVisited” variable has been executed.

    And the actual “BlogPagesVisited” cookie has fired at a value of “1” in Chrome DevTools. To see this, click Inspect > Application > Cookies.

    If we skip the second-page view and execute our third-page view on another blog page, you’ll see that both our GA event and our Custom HTML tag fired, as it’s our third-page view.

    You’ll also see the third-page view triggered our cookie value of “3” in Chrome DevTools.

    Step 7: Set up your advanced segment

    Now that you’ve set up your cookie, you’ll want to pull the stored cookie data into GA, which will allow you to manipulate the data as you see fit.

    In GA, go to Behaviour > Events > Overview > Add Segment > New Segment > Sequences > Event Action > and then add the event name you specified in your event tag. I specified “3 Blog Page Views.”

    And there you have it! 

    Conclusion

    Now that you know how to set up a cookie in GTM, you can get heaps of additional insight into the engagement of your content.

    You also know how also to play around with the code snippet and iterate the number of page views required to fire the cookie event as well as the expiration of the cookies at each stage to suit your needs.

    I’d be interested to hear what other use cases you can think of for this cookie, or what other types of cookies you set up in GTM and what data you get from them.

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    How to Implement a National Tracking Strategy

    Posted by TheMozTeam

    Google is all about serving up results based on your precise location, which means there’s no such thing as a “national” SERP anymore. So, if you wanted to get an accurate representation of how you’re performing nationally, you’d have to track every single street corner across the country.

    Not only is this not feasible, it’s also a headache — and the kind of nightmare that keeps your accounting team up at night. Because we’re in the business of making things easy, we devised a happier (and cost-efficient) alternative.

    Follow along and learn how to set up a statistically robust national tracking strategy in STAT, no matter your business or budget. And while we’re at it, we’ll also show you how to calculate your national ranking average.

    Let’s pretend we’re a large athletic retailer. We have 30 stores across the US, a healthy online presence, and the powers-that-be have approved extra SEO spend — money for 20,000 additional keywords is burning a hole in our pocket. Ready to get started?

    Step 1: Pick the cities that matter most to your business

    Google cares a lot about location and so should you. Tracking a country-level SERP isn’t going to cut it anymore — you need to be hyper-local if you want to nab results.

    The first step to getting more granular is deciding which cities you want to track in — and there are lots of ways to do this: The top performers? Ones that could use a boost? Best and worst of the cyber world as well as the physical world?

    When it comes time for you to choose, nobody knows your business, your data, or your strategy better than you do — ain’t nothing to it but to do it.

    A quick note for all our e-commerce peeps: we know it feels strange to pick a physical place when your business lives entirely online. For this, simply go with the locations that your goods and wares are distributed to most often.

    Even though we’re a retail powerhouse, our SEO resources won’t allow us to manage all 30 physical locations — plus our online hotspots — across the US, so we’ll cut that number in half. And because we’re not a real business and we aren’t privy to sales data, we’ll pick at random.

    From east to west, we now have a solid list of 15 US cities, primed, polished, and poised for our next step: surfacing the top performing keywords.

    Step 2: Uncover your money-maker keywords

    Because not all keywords are created equal, we need to determine which of the 4,465 keywords that we’re already tracking are going to be spread across the country and which are going to stay behind. In other words, we want the keywords that bring home the proverbial bacon.

    Typically, we would use some combination of search volume, impressions, clicks, conversion rates, etc., from sources like STAT, Google Search Console, and Google Analytics to distinguish between the money-makers and the non-money-makers. But again, we’re a make-believe business and we don’t have access to this insight, so we’re going to stick with search volume.

    A right-click anywhere in the site-level keywords table will let us export our current keyword set from STAT. We’ll then order everything from highest search volume to lowest search volume. If you have eyeballs on more of that sweet, sweet insight for your business, order your keywords from most to least money-maker.

    Because we don’t want to get too crazy with our list, we’ll cap it at a nice and manageable 1,500 keywords.

    Step 3: Determine the number of times each keyword should be tracked

    We may have narrowed our cities down to 15, but our keywords need to be tracked plenty more times than that — and at a far more local level.

    True facts: A “national” (or market-level) SERP isn’t a true SERP and neither is a city-wide SERP. The closer you can get to a searcher standing on a street corner, the better, and the more of those locations you can track, the more searchers’ SERPs you’ll sample.

    We’re going to get real nitty-gritty and go as granular as ZIP code. Addresses and geo coordinates work just as well though, so if it’s a matter of one over the other, do what the Disney princesses do and follow your heart.

    The ultimate goal here is to track our top performing keywords in more locations than our poor performing ones, so we need to know the number of ZIP codes each keyword will require. To figure this out, we gotta dust off the old desktop calculator and get our math on.

    First, we’ll calculate the total amount of search volume that all of our keywords generate. Then, we’ll find the percentage of said total that each keyword is responsible for.

    For example, our keyword [yeezy shoes] drew 165,000 searches out of a total 28.6 million, making up 0.62 percent of our traffic.

    A quick reminder: Every time a query is tracked in a distinct location, it’s considered a unique keyword. This means that the above percentages also double as the amount of budgeted keywords (and therefore locations) that we’ll award to each of our queries. In (hopefully) less confusing terms, a keyword that drives 0.62 percent of our traffic gets to use 0.62 percent of our 20,000 budgeted keywords, which in turn equals the number of ZIP codes we can track in. Phew.

    But! Because search volume is, to quote our resident data analyst, “an exponential distribution,” (which in everyone else-speak means “gets crazy large”) it’s likely going to produce some unreasonably big numbers. So, while [yeezy shoes] only requires 124 ZIP codes, a keyword with much higher search volume, like [real madrid], might need over 1,000, which is patently bonkers (and statistical overkill).

    To temper this, we highly recommend that you take the log of the search volume — it’ll keep things relative and relational. If you’re working through all of this in Excel, simply type =log(A2) where A2 is the cell containing the search volume. Because we’re extra fancy, we’ll multiply that by four to linearly scale things, so =log(A2)*4.

    So, still running with our Yeezy example, our keyword goes from driving 0.62 percent of our traffic to 0.13 percent. Which then becomes the percent of budgeted keywords: 0.0013 x 20,000 = tracking [yeezy shoes] in 26 zip codes across our 15 cities.

    We then found a list of every ZIP code in each of our cities to dole them out to.

    The end. Sort of. At this point, like us, you may be looking at keywords that need to be spread across 176 different ZIP codes and wondering how you’re going to choose which ZIP codes — so let our magic spreadsheet take the wheel. Add all your locations to it and it’ll pick at random.

    Of course, because we want our keywords to get equal distribution, we attached a weighted metric to our ZIP codes. We took our most searched keyword, [adidas], found its Google Trends score in every city, and then divided it by the number of ZIP codes in those cities. For example, if [adidas] received a score of 71 in Yonkers and there are 10 ZIP codes in the city, Yonkers would get a weight of 7.1.

    We’ll then add everything we have so far — ZIP codes, ZIP code weights, keywords, keyword weights, plus a few extras — to our spreadsheet and watch it randomly assign the appropriate amount of keywords to the appropriate amount of locations.

    And that’s it! If you’ve been following along, you’ve successfully divvied up 20,000 keywords in order to create a statistically robust national tracking strategy!

    Curious how we’ll find our national ranking average? Read on, readers.

    Step 4: Segment, segment, segment!

    20,000 extra keywords makes for a whole lotta new data to keep track of, so being super smart with our segmentation is going to help us make sense of all our findings. We’ll do this by organizing our keywords into meaningful categories before we plug everything back into STAT.

    Obviously, you are free to sort how you please, but we recommend at least tagging your keywords by their city and product category (so [yeezy shoes] might get tagged “Austin” and “shoes”). You can do all of this in our keyword upload template or while you’re in our magic spreadsheet.

    Once you’ve added a tag or two to each keyword, stuff those puppies into STAT. When everything’s snug as a bug, group all your city tags into one data view and all your product category tags into another.

    Step 5: Calculate your national ranking average

    Now that all of our keywords are loaded and tracking in STAT, it’s time to tackle those ranking averages. To do that, we’ll simply pop on over to the Dashboard tab from either of our two data views.

    A quick glimpse of the Average Ranking module in the Daily Snapshot gives us, well, our average rank, and because these data views contain every keyword that we’re tracking across the country, we’re also looking at the national average for our keyword set. Easy-peasy.

    To see how each tag is performing within those data views, a quick jump to the Tags tab breaks everything down and lets us compare the performance of a segment against the group as a whole.

    So, if our national average rank is 29.7 but our Austin keywords have managed an average rank of 27.2, then we might look to them for inspiration as our other cities aren’t doing quite as well — our keywords in Yonkers have an average rank of 35.2, much worse than the national average.

    Similarly, if our clothes keywords are faring infinitely worse than our other product categories, we may want to revamp our content strategy to even things out.

    Go get your national tracking on

    Any business — yes, even an e-commerce business — can leverage a national tracking strategy. You just need to pick the right keywords and locations.

    Once you have access to your sampled population, you’ll be able to hone in on opportunities, up your ROI, and bring more traffic across your welcome mat (physical or digital).

    Got a question you’re dying to ask us about the STAT product? Reach out to clientsuccess@getSTAT.com. Want a detailed walkthrough of STAT? Say hello (don’t be shy) and request a demo.

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    Popular Google, FireFox Extension Is Secretly Tracking User Activity

    A popular browser extension that helps personalize how a website looks has been found to be tracking user activity. The revelation has pushed Google and Mozilla to remove the Stylish browser extension from their app stores. However, the extension’s official website still remains active.

    Software engineer Robert Heaton claimed in a blog post that the Stylish extension tool steals a user’s internet history and sends information about a person’s browsing history and distinct identifiers to SimilarWeb, the extension’s owner. According to Heaton, this will allow the company to “connect all of an individual’s actions into a single profile.”

    Heaton further explained that Stylish account holders typically have a unique identifier that can be linked to a login cookie. This will then provide SimilarWeb with enough information to “theoretically tie these histories to email addresses and real-world identities.”

    Stylish is an open-source browser extension that gives users the capability to change how a website appears on their browser. With it, users can make websites look brighter and campier. They can also go for a brooding, darker theme or choose popular manga or cartoon characters to add to the website.

    SimilarWeb’s 2017 formal policy does indicate that the extension collates anonymous data. But what Heaton is protesting is the identifier that the extension attaches to the said information before it’s sent to the company servers. He said this leaves the account holder vulnerable to hackers.

    SimilarWeb has already denied these allegations and claimed that they are “not aware of and cannot determine the identity of the users from whom the non-personal information is collected.”

    Google and Mozilla have since removed the extension from its Chrome and FireFox browsers. The former has not explained its decision to cut off Stylish while the latter said that they blocked the extension due to violation of data practices.

    Users utilizing Stylish on their web browsers would no longer be able to access its features. However, the extension remains active online.

    [Featured image via Pixabay]

    The post Popular Google, FireFox Extension Is Secretly Tracking User Activity appeared first on WebProNews.


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    Tracking Your Link Prospecting Using Lists in Link Explorer

    Posted by Dr-Pete

    I’m a lazy marketer some days — I’ll admit it. I don’t do a lot of manual link prospecting, because it’s a ton of work, outreach, and follow-up. There are plenty of times, though, where I’ve got a good piece of content (well, at least I hope it’s good) and I want to know if it’s getting attention from specific sites, whether they’re in the search industry or the broader marketing or PR world. Luckily, we’ve made that question a lot easier to answer in Link Explorer, so today’s post is for all of you curious but occasionally lazy marketers. Hop into the tool if you want to follow along:

    Open Link Explorer

    (1) Track your content the lazy way

    When you first visit Link Explorer, you’ll see that it defaults to “root domain”:

    Some days, you don’t want to wade through your entire domain, but just want to target a single piece of content. Just enter or paste that URL, and select “exact page” (once you start typing a full path, we’ll even auto-select that option for you):

    Now I can see just the link data for that page (note: screenshots have been edited for size):

    Good news — my Whiteboard Friday already has a decent link profile. That’s already a fair amount to sort through, and as the link profile grows, it’s only going to get tougher. So, how can I pinpoint just the sites I’m interested in and track those sites over time?

    (2) Make a list of link prospects

    This is the one part we can’t automate for you. Make a list of prospects in whatever tool you please. Here’s an imaginary list I created in Excel:

    Obviously, this list is on the short side, but let’s say I decide to pull a few of the usual suspects from the search marketing world, plus one from the broader marketing world, and a couple of aspirational sites (I’m probably not going to get that New York Times link, but let’s dream big).

    (3) Create a tracking list in Link Explorer

    Obviously, I could individually search for these domains in my full list of inbound links, but even with six prospects, that’s going to take some time. So, let’s do this the lazy way. Back in Link Explorer, look at the very bottom of the left-hand navigation and you’ll see “Link Targeting Lists”:

    Keep scrolling — I promise it’s down there. Click on it, and you’ll see something like this:

    On the far-right, under the main header, click on “[+] Create new list.” You’ll get an overlay with a three-step form like the one below. Just give your list a name, provide a target URL (the page you want to track links to), and copy-and-paste in your list of prospects. Here’s an example:

    Click “Save,” and you should immediately get back some data.

    Alas, no link from the New York Times. The blue icons show me that the prospects are currently linking to Moz.com, but not to my target page. The green icon shows me that I’ve already got a head-start — Search Engine Land is apparently linking to this post (thanks, Barry!).

    Click on any arrow in the “Notes” column, and you can add a note to that entry, like so:

    Don’t forget to hit “Save.” Congratulations, you’ve created your first list! Well, I’ve created your first list for you. Geez, you really are lazy.

    (4) Check in to track your progress

    Of course, the real magic is that the list just keeps working for you. At any time, you can return to “Link Tracking Lists” on the Link Explorer menu, and now you’ll see a master list of all your lists:

    Just click on the list name you’re interested in, and you can see your latest-and-greatest data. We can’t build the links for you, but we can at least make keeping track of them a lot easier.

    Bonus video: Now in electrifying Link-o-Vision!

    Ok, it’s just a regular video, although it does require electricity. If you’re too lazy to read (in which case, let’s be honest, you probably didn’t get this far), I’ve put this whole workflow into an enchanting collection of words and sounds for you:

    I hope you’ll put your newfound powers to good. Let us know how you’re using Tracking Lists (or how you plan to use them) in the comments, and where you’d like to see us take them next!

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