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What is Tag Management?

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Tag Management: What it is and why you should consider it for your organization

Analytics implementations can get pretty technical, and often that side of things lands in the hands of the IT or web development team, with a technical consultant or extended analyst being the link between the business users and technical needs. It is a necessary part of a solid analytics program – proper data collection is the foundation on which everything else is built. Having a surface level understanding of how the technical side of things works is critical for any good analyst, and having some control is desirable for those with more technical acumen. So all analysts should have a good understanding of tag management, and tag management systems.

Tag management is a concept, and an industry, that has grown dramatically in the last several years. In a nutshell: tag management is the use of a container tag, to consolidate and control all of your analytics, site optimization, and advertising tracking codes with a single script. Analytics tracking codes are those bits of JavaScript that need to be put on your site in order to collect data on your visitors and their actions.  Advertising tracking codes are the scripts from Doubleclick, AdWords, Search Alliance, and other advertiser networks, that are used to track conversions, view through conversions, push remarketing, and track ad viewers who visit your site. Most marketers and analysts are at least familiar with the AdWords conversion code that goes on the “Thank You”, order confirmation, or form submission confirmation page of a site. Tag management systems take all of these scripts, analytics, advertising, etc. and remove them from the site, replacing them with a single script. The use of those scripts, and where they are triggered for visitors, is then all controlled through the tag management systems user interface.

Lets look at how this affects a web site and the implementation of tracking code on the site. First, there is now only one snippet of code. This means the pages have “cleaner” code, and there is generally less code on the page. Now, you may need to still add small amounts of code to your site for advertising or analytics data collection, but it is far less. You’re probably figuring out the implementation benefits – far less involvement for your IT and development teams to deal with, on the coding side of things. Now, IT may manage and work with the tag management system, but they no longer need to push code every time a tag needs to be implemented or changed. This saves considerable development time, limits potential bug-related headaches, and reduces the potential for incorrect tracking code.

Tag management is good for IT and web development, but how does it affect the business users, and ultimate need for improved data collection and analysis? First, the benefit of pushing changes through the tag management UI is that it can generally happen faster. Let’s say you want to tag visitors with a custom variable based on whether they click on a featured link on your home page; this can be done through the user interface – no need to send code to your developer! Let’s say you’re testing out a new remarketing network, and they have their own JavaScript snippet you need to put on your site. Add it through the tag management system in minutes, and you’re up and running. Let’s say 3 months later you decide the network doesn’t work out – simply remove the code in minutes from the tag management UI.

This hopefully gives you an idea of the benefits tag management provides to the technical and business users. You’re probably wondering why more organizations aren’t using tag management. Many organizations haven’t had a need, because they’ve had limited scripts to put on their pages – maybe the standard analytics tag, and the AdWords conversion code. But now they’re doing remarketing with AdWords, and they’ve expanded their Bing and Yahoo advertising, and are looking to get that code in place. More importantly, they’re starting to explore advanced analytics tagging. Suddenly they have a lot of code to manage across their sites.

Another reason tag management hasn’t been adopted too quickly, is the cost is somewhat prohibitive for non-enterprise users. But there is more competition now – a number of new vendors have sprung up in the last couple years, and there are about 10 established vendors now. Prices are going down, allowing medium and smaller businesses to enter the game.

Where does this leave the analyst? If you aren’t using it yet, expect prices to continue to drop, and start to consider if tag management isn’t a good option for your organization. Evaluate the amount of time you wait for code to be implemented, and the amount of code that is deployed, or going to be deployed, across your sites. In terms of man-hours, and efficiency in data collection (i.e. quicker analysis turn around) – you may find that there are dramatic financial benefits to be gained for your organization.

Landing Your First Search Marketing or Web Analytics Position

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We’re hiring again at SwellPath, and that means a lot of resume reading and interviewing candidates. Our most recent hire is for someone with “1-2” years of experience, but we know that means we’re getting entry-level people. I thought it’d be good to cover some things that we like and dislike to see in the hiring process. Hopefully aspiring, or novice SEOs, PPC managers, and web analysts can benefit from these tips. This isn’t all inclusive, I’ve left out general tips (e.g. “have a firm handshake”), and just tried to touch on those that relate to SEM and analyst positions.

Pre-Interview

In the past we’ve placed importance on the LinkedIn profile, and asked candidates to submit it with their cover email and resume. Now we hire entirely through LinkedIn. We see knowledge of LinkedIn postings, and the ability to create a decent profile, as an indicator of an applicant’s social media savviness. If they’re not on LinkedIn, how are they going to understand how LinkedIn advertising fits into a client’s overarching digital marketing strategy? Make sure you have a complete and professional looking profile.

Twitter and Facebook accounts. If you haven’t heard of Rapportive, you should check it out. If you have EVER created a Facebook or Twitter account with the email address you’re putting on your resume, Rapportive will find it. Be conscious of the content in those accounts; many of us use tools like Rapportive these days. We like to see active social media accounts, and even better, a blog. Put the links on your resume. It shows you understand the space, and the importance of content. Own the search results for your own name? Even better.

Finally: PDF your resume. It shows you understand how to generate a professional looking document, which you’re going to need to do almost every day in your desired position.

Interview Prep

If you’re applying for an entry-level SEO position, you should read everything SEO related by Rand. For analytics: every Avinash post. Read other blogs about industry news, tactics, best practices (like this one you’re reading now). My SwellPath co-founder and I joke that if you haven’t become an “expert” in two years in search or analytics – you must not be able to read. Not entirely true, but there is a wealth of information out there that you should be sucking up. Know the experts in the space, and read their material. Even if it is over your head, you should spend some time pushing yourself and reading up on advanced tactics. Be prepared to reference it. I don’t care if you can’t explain things perfectly; knowing that you want to learn, and you’ve been able to find the places to learn, shows a lot.

If you don’t know much about code, servers, and the technical side of our business: take some intro courses on web development, web design, and web hosting (for SEO). The web courses that are a few hours on Lynda.com or something similar. They don’t cost much. I can’t emphasize how much of a difference it makes when someone can speak to a certain depth about the technical side of things. We know we can try to teach you this stuff when you come onboard, but if you haven’t learned much of it yet, we almost always feel there is a risk that it might not be something you can learn. You might be the kind of person that just can’t understand that kind of material well. If you aren’t that kind of person; think hard about whether this is the best career option for you.

Be prepared for some tests. We have some of our team interview potential hires, and they all have their “tests” for the candidate. “What would you do to optimize this site?” “How would you begin to analyze the effectiveness of this page?” etc. It isn’t about the answers (well, kind of) but as much about how you work through a problem. Don’t get nervous about the details – we know we’re smarter than you at this stuff.

Post-Interview

Follow up with the standard emails, but then follow us on Twitter, like us on Facebook, etc. Again, it shows you care and know the space. If you don’t get the position, follow our blog and read up on what we’re talking about. If another opportunity comes up, you’ll be happy you kept current with our happenings. Don’t hesitate to ask why we chose someone else also. We’ll be straightforward about things like “we didn’t think you had a good grasp on the technical side of SEO.” Take that feedback and work on your weak points.

Hopefully these tips help those out there looking for their first search or analytics position. When you land that position, and your company sends you off to SES, MozCon, eMetrics, etc. – you can find me there and buy me a drink as thanks.

Attribution Modeling

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Give Your Traffic Sources the Credit they Deserve with Simple Attribution Enhancements

SwellPath is co-hosting a couple of local events (Web Analytics Wednesday and the new Portland Google Analytics User Group) coming up that I needed to prepare some material for. As I started researching potential topics for both, I kept coming back to the subject of attribution and attribution modeling. My first thought was that surely this was a played out topic, as analysts have been talking about moving beyond last-click attribution for years now. But the more I looked at it, the more I realized it is one of the fundamental concepts in web analytics that most marketers still do not know how to tackle. With this post, I’m going to try to give the average reader some quick ideas they can take away, that will hopefully help them understand their traffic sources a bit better, and give credit where credit is due.

First, a quick primer on attribution. When we talk generally about attribution in web analytics, we’re talking about how you attribute the conversions (revenue, leads, etc.) on your site to various traffic sources. You obviously want to know as much detail as you can about this, because you can use that information to decide how to allocate your marketing resources. For example: if you know that your email campaigns are involved in the decision making process for 50% of the visits that convert on your site, you probably want to allocate a very significant amount of your resources towards your email program.

How we handle crediting traffic sources, is referred to as attribution modeling, or an attribution model. The standard old-school way of doing this is referred to as last-click attribution. This means that the last traffic source that a visitor clicks through to our site from, gets 100% of the credit for a conversion. Let’s say I first visit your site by clicking through a bit.ly link in a Tweet of yours. Then I come back the next day through an organic search result. Later that afternoon, I click on one of your paid search ads, visit your site for the third time, and convert. Your paid search ad gets all the credit for the conversion, despite that fact that your Twitter efforts really provided the awareness, and the organic search helped me research your products. Shouldn’t they get some credit? With last-click attribution they don’t.

So why are we using last-click attribution? Because it used to be the only readily available method, and it was so much better than anything pre-web (i.e. traditional media attribution modeling) that marketers were fine settling with it. But times have changed, and analytics platforms now offer other attribution models. If you are not taking advantage of this (and most of you are not), it’s time to start.

To keep this post simple, but effective, I’m going to focus on simply using some of the Multi-Channel Funnels reporting in Google Analytics. These are baby steps towards a sophisticated attribution model, but they are giant leaps if you’re still just doing last-click attribution. In particular, we’re going to look at the Assisted Conversions report. To get to this report, you’re going to want to navigate to Conversions > Multi-Channel Funnels > Assisted Conversions in the Standard Reporting section of Google Analytics. This report is going to show us all of conversions that our traffic sources assisted, meaning they were a touch point for the visitor, but not the last touch point. It can also show us all the first interaction conversions (the traffic source was the first touch point for the visitor), and of course the last interaction also.

Assisted Conversions - Google Analytics

In the screenshot, I’m looking at a week’s worth of data for a ecommerce client. As you can see, looking at the email channel for last interaction, a total of $568 is attributed. But if we look at the assisted conversions, there is $2,746. All in, the conversions that can be attributed to email go from $568, up to $3,314, that is an increase of almost 500%. This supports the notion that although customers may not click through on an email they receive, and purchase during that visit, the visit keeps them engaged with the brand, and many of them eventually come back and purchase through other channels as a result.

This is just touching the tip of the iceberg with the Assisted Conversions report. You can also look at your First Interaction data (meaning conversions where the traffic source was the first touch point), and you can break down the source into more granular segments. For example, you can see the how your organic search visits, through non-branded keyword results, assist conversions. What all of this allows for, is a greater understanding of how your customers move through the purchase decision-making process, and what sources play fundamental roles at the different phases of that funnel. We can back up our assumption that email isn’t the first touch point (obviously), and isn’t often the final touch point before a conversion, but it is often “in the middle”. Meaning, it is key to the research phase of the customers’ decision-making process. Knowing this, we can adjust the messaging and content in our email campaigns, to better nurture customers at this point.

Hopefully this has been helpful, and at least can get you started with moving beyond last-click attribution models, into something that gives more appropriate credit to your traffic sources. Utilize Google Analytics’ help section to navigate the various features and details of this report, and as you explore the other Multi-Channel Funnel reporting.