Universal Analytics and Google Tag Manager - Superweek 2014
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Universal Analytics and Google Tag Manager - Superweek 2014

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  • Having worked with businesses in a lot of different industry verticals, I can confidently say that analytics is important to everyone and needs to be tailored to each business.
  • Visitor Stitching-> opinion based on GA Developers Talk at Google I/O broadcast on youtube.
  • The fact that UA is still in beta and is missing a number of these features which I find invaluable to marketers, led me to co-deploy all of my Universal implementations with the “standard” version of GA.
  • This led me to Google Tag Manager as a quick way to update a client’s code base on their site and deploy two versions of Google Analytics at the same time.I quickly noticed that it was so much more.Super PowerfulGreat, Intuitive InterfaceFree
  • General Introduction to Google Tag Manager
  • Want to re-target NON Branded Organic Google Search, have the referrer be Google, URL not equal the homepage or have any tracking parameters on it.
  • For a co-deployment between Universal and GA, we simply set the same rule to fire both UA and GA Tags.
  • Doug had a better naming convention in his tags that I did. Don’t pay too close of attention here.
  • As you can see, for some reason the GTM custom dimensions are not sorted in any sort of usable order. Certainly not the order I created them in.
  • The above funnel shows users who did all the steps in a particular funnel and can be created completely on the fly (similar to goal flow report, but not based on users not sessions).Oneof the things that I like about KissMetrics is that the data is reprocessed and that the visitor stitching is also retroactive. So as soon as you alias a login to the clientID, all reports will reprocess within a day or so to represent the new data set.
  • Basic JSON Object of Name / Value combinations that provides a simple structured of exposing information you want to pass into a some tool, like a Tag Management System.
  • Publish date – is newer content or older content driving bring users to site. How long does it take newer content to begin ranking and bringing in SEO traffic.Published hour = good for determining optimal publish timesCount of publish hour in excel = # of published articles per author
  • Using motion charts to find periods of ‘stickiness’ and overall growth or decline in visitor loyalty. For example, if I was a newspaper that had a revenue model based upon pageviews, I’d definitely be interested in the trends surrounding how many times users are visiting the site.
  • Retention Report.
  • These documents were PDFs so we had to grab meta data about them on click. Normally, PDF tracking is as simple as grabbing the file name onclick. In this case, the documents were served in a different manner by the server so their was no PDF filetype to track. The multiple meta data points around the content was also important to the marketing department. This took a number of months for IT to achieve. In the meantime, using GTM, we were able to deploy a decent working solution which then improved once the development team was able to complete there task.
  • In this image I super imposed the CV over a visits column
  • One of the most important aspects of determining the values that are passed to the data layer, are the companies business objective themselves. This business…. (explain what they do).
  • Clear next step is to focus on acquiring traffic targeted the courses that have a higher propensity to lead to conversion.Creating different funnel visualizations between Course Page > Registration Form, or course page to registration completion.Web Dev and project management had a lot of people make it to the REG form but not complete the form.
  • In the above example, A/B test to emphasize internal search engine as a pathway towards purchase.

Universal Analytics and Google Tag Manager - Superweek 2014 Universal Analytics and Google Tag Manager - Superweek 2014 Presentation Transcript

  • About @analyticsninja Loves working with fun businesses
  • Goals of this presentation • Discuss the benefits of Universal Analytics and Google Tag Manager • Provide a general overview and training for how to use GTM, especially for UA implementations • Tactical implementation examples and how to use the resulting data
  • THANK YOU
  • Caleb Whitmore Sam Briesemeister
  • Benefits Of Universal Analytics • Custom Dimensions and Custom Metrics – Much better reporting that is more accessible across organizations, 20 vs 5 CVs for GA Standard. • Measurement Protocol – Offline conversions FTW! • GA’s First Attempt at Visitor Stitching – From what I can ascertain, still lots of room for improvement. Also, still not out of closed beta. • Many Settings Configured on the Backend – Less likely to cause problems due to coding fails
  • Still Missing… • Demographics • Remarketing • Most 3rd party plugins are stuck in _gaq land • Content Experiments – Not a huge loss
  • Use Case: Teams in US West Coast, Europe, Australia, Israel
  • Surprise Client with Reason to Personalize
  • Surprise Client with Reason to Personalize
  • http://www.simoahava.com/webdevelopment/universal-analyticsweather-custom-dimension/
  • Basic Intro to GTM • Tags  pixels or javascript • Rules  cause tags to fire – URLs / hostnames / referrers – Values or Conditions present in Macro • Macros  values • Events  trigger rules to execute if conditions are not already present to fire tag when GTM loads.
  • Rules
  • Rules
  • Rules
  • Sample Universal Analytics Tags
  • Sample Universal Analytics Macros
  • Sample Universal Analytics Macros
  • Inside the Universal Analytics Tag
  • Tagging using helper file vs. multiple tags / rules
  • Quickly extend implementation
  • Data Layer
  • Sample Data Layer for Publishers Content Level • Article publish date • Article publish hour • Author • Topics / Tags • Article Category – Sub Category • Free or Restricted Content User Level • User Logged In State • Newsletter Subscriber • Registration Date • First Visit Date • # of Weekly Visits
  • # Of Weekly Visits dev.analyticsninja.co/periodic_visit.js
  • # Of Weekly Visits dev.analyticsninja.co/periodic_visit.js
  • Date of First Visit Tableau viz via @calebwhitmore
  • Accessing Restricted Content
  • Create Segments to compare Conversion Rates of users who took specific action
  • Smart Data Layer => Smart Decisions
  • Smart Data Layer => Smart Decisions
  • Smart Data Layer => Smart Decisions
  • Course Technology > Course Name
  • Smart Data Layer => Smart Decisions
  • Sample Data Layer for Ecommerce Product Level • Page Type • Product Category • Product Sub Category (etc) • Product Brand • Product Name • Product SKU • Product Price • Product Gender (if relevant) • Product Promo / Discount User Level • • • • • • • • • Registered User First Visit Date First Purchase Date Count of Purchase Days Since Previous Purchase User registration date User Gender Business Name (B2B) Business Vertical (B2B)
  • All custom dimensions require admin setup
  • Smart Data Layer => Smart Decisions Page Category Page value, assuming properly configured ecommerce and goal values, is an excellent index to use when looking to analyze page level dimensions .
  • Smart Data Layer => Smart Decisions Product Category
  • Smart Data Layer => Smart Decisions Product Category
  • Smart Data Layer => Smart Decisions Product Category
  • Smart Data Layer => Smart Decisions Product Name
  • Smart Data Layer => Smart Decisions Product Name  Product Promotion
  • Smart Data Layer => Smart Decisions “Real” Page Value
  • Divide Unique Purchases by Unique Purchases
  • Explore Profit Metrics in GA
  • Smart Data Layer => Smart Decisions “Real” Page Value = Profit per Unique PV
  • Google Tag Manager Transaction Tags • GTM does not support custom dimensions for item hits (yet). You should still push all of the additional meta data into a transaction_products array. • Use a custom html ecommerce tag if you want to be able to look at secondary dimensions within the commerce reports. • Alternatively, just use event tracking and custom dimensions to rebuild the commerce data model.
  • Universal Analytics for CRM Integrations and B2B Lead Gen
  • Universal Analytics for CRM Integrations and B2B Lead Gen
  • Summary • Universal Analytics offers powerful new features (Custom Dimensions, Measurement Protocol, etc). You should deploy it if you haven’t do so yet. • Google Tag Manager is a free and powerful TMS. Requires someone who knows what they’re doing, but will make implementations more flexible, extendible, and manageable. • Strategic consideration of a business’s objectives and underlying business questions is the foundation upon which a Smart Data Layer is built, which will lead to Smart Decisions.
  • Summary • GTM allows one to navigate the balance between doing things that “right way” (i.e. proper on-page markup, fully defined CMS driven Data Layer) versus bootstrap approaches to get data quickly when IT may take months or more to complete tasks • Page value, assuming properly configured ecommerce and goal values, is an excellent index to use when looking to analyze page level dimensions such as product attributes or service offerings.
  • Summary • Dividing unique purchases of products by unique pageviews of those product pages yields a “look to book” ratio that should have a direct impact on decisions regarding product placement and ad spend. (Propensity to buy). • Use Server-Side hits to capture PROFIT metrics in GA. Profit is far more important than conversion rate or per visit value.