Tag Management Systems

15 Mar The Challenge and Promise of Digital Campaign Tracking

2017-03-08_12-00-47Have a look at this image. Sound familiar? Web analytics has held out the elusive promise of being a set-it-and-forget-it kind of thing. “Set up your reports, and the data will fill itself in.” That promise has largely held true -- for every part of web analytics except Marketing. That’s because with marketing, the web page you have today isn’t the one you had yesterday. There’s constant change: new information, new deals, new parameters. What everyone wants is a system that runs itself. Otherwise, as the figure shows, you spend all your time making sure the reporting is right. Spending time on data correction takes time away from the analysis that will really help the company. It’s a necessary evil. Wouldn’t it be great if we could get marketing data to the same set-and-forget kind of place as the rest of our web analytics?
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23 Feb Test-driven Development in Analytics

checklist-1622517_960_720In the 2017 world of IT and systems engineering, Test-driven development (TDD) is quickly becoming the new mantra. No one writes a line of code these days without the intent to have that code check/test itself. If there is bug in that code, it gets caught and fixed before it goes live, reducing any risk of breakage. This kind of system has never been deployed on the analytics side. By convention, analytics work has relied on hacks; quick and dirty patches that frequently go awry, and are just as likely to backfire and cut down the analyst, as to cut down her obstacles. If the analyst is winging it, to fill in a little gap in the proverbial data wall, he can unwittingly create a huge chasm with a single stroke. Bringing a TDD approach to analytics would go some way in changing that. It would require that whenever you make any change to your analytics, you make sure the change is fully tested before it’s deployed. This method takes more time -- and may frustrate management -- but will result in better quality control.
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04 Jan Campaign Analytics Tracking – How did we get here?

Increasing ComplexityLooking at the world of campaign analytics in 2017, it can be challenging for anyone who didn’t grow up in the industry to make sense of its complexity. Seasoned analysts and marketers have a history with the technology, but they've often witnessed so much change so quickly that it can feel at times like someone snuck up and piled a bunch of new challenges on top of old ones, before solutions to the old problems were fully worked out. When it comes to URL tracking and campaign analytics, the tools at our fingertips are impressively precise. Not that long ago, the only data you could meaningfully derive from a referring URL was how much traffic you’re getting from various websites. At a high level it allowed companies to see which partnerships and publishing platforms were bringing eyes to their sites, but that’s about it.
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02 Nov #AnalyticsSummit Presents: Closing the Loop on Data Validation

I’m pleased to be one of the featured speakers at the upcoming Observepoint Analytics Summit. It’s a free, virtual event, and I hope you’ll sign up for my session. To get you excited about it, here’s a sneak peek at what I’ll be talking about: Closing the Loop on Data Validation. Everybody knows the secret to delivering quality data. You check it. You check it right before release. You check it every time a change is made to the campaign or the website, either through a dev release or a Tag Management release. You check it once it’s pushed to production. Then you put it on the list of things to check again periodically.
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25 Oct Google Analytics vs. Adobe

sitecatalyst-vs-googleanalytics1So many people have written about the pros and cons of Adobe versus Google Analytics (GA). A quick search on the comparison will bring in a huge number of opinions. As a tracking code solutions provider with a considerable interest in the debate, what fresh perspective can we offer? Let’s start with a bit of background. Origin of GA--launched in November 2005
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06 Sep A look under the hood, Pt. 4: Data Capture

Task 4: Ensure that Codes are being Captured Correctly The last task is the most important. Pristine codes, perfectly classified and placed within links, will never do anything if they are never received by the reporting system. A single typo in any URL will most likely send your link and its attendant tracking code into oblivion. But even a flawless link will not ensure capture. Think of it as the baton in a relay race; for the handoff to be successful, another runner must be waiting in the right place to receive it. That receiver is already in place wherever your analytics vendor’s machinery has been included. Unfortunately, the requirement to include this machinery (usually a Javascript library), is often forgotten when a marketing team creates a special landing page, or an agency creates a new microsite. The runner yells “Stick!” yet no outstretched hand receives the tracking code to take the victory lap home.
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01 Sep A look under the hood, Pt. 3: Link Assembly

Task 3: Embed the Tracking Code within Landing Page Links Tracking codes are typically appended to landing pages in the query-string parameter section of the URL. Any time you click on a sponsored ad anywhere on the internet, you’ll see not just your destination’s domain in the URL, but usually a question mark, and a long, unintelligible character string. Somewhere in that morass you’ll find the tracking code, but its placement is something that companies configure independently of one another.
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29 Aug A look under the hood, Pt. 2: Code Classification

Task 2: Classify Codes According to your Own Organization’s Standards  Classifications assemble your tracking codes into channels, groups, families, etc. They let you report on the high-level successes, failures, or fall-out of your campaigns, without sacrificing your ability to monitor the individual initiatives when you need to.
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25 Aug A look under the hood, Pt. 1: Code Composition

Task 1: Generate a Unique Tracking Code for New Campaign Initiatives This part looks easy on the surface, but it carries some hidden complexities. Generating a random string for every new initiative is surely no challenge for a computer, and robust tracking systems such as Adobe Analytics would have no difficulty marshalling all of those random codes on the reporting side. But very rarely will you see companies using that method. Companies generally come up with their own custom recipe for assembling tracking codes, which emerge as a daisy-chained string of descriptors, like these:
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15 Aug Keeping the Trains running on time: A Case Study in Data Capture

Right at the end of the process of preparing its first campaign codes through Tracking First, Amtrak ran into a serious problem. The email pattern had been identified and selected with no issues, and the new codes and classifications followed suit. But when the landing page was entered for each of these email links, Tracking First’s interface lit up with errors. My phone rang, and the users asked me what they had done wrong.
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