Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.

The Triangle - A universal method of working with digital analytics and marketing


Published on

The triangular shape is a stable in communicating, simplifying and modelling complex information.
In digital analytics and marketing is used in everything from conversion funnels, user management and abstract modelling - maybe due to its inherent aspects of "action".
This presentation showcases some examples and should be seen as a base for further discussions.

Session held at MeasureCamp Milan, October 12. 2018.

Published in: Data & Analytics
  • Be the first to comment

  • Be the first to like this

The Triangle - A universal method of working with digital analytics and marketing

  1. 1. The Triangle △ A universal model for working with digital analytics and marketing MeasureCamp Milan October 13, 2018
  2. 2. Today’s Session # The obligatory 40 seconds “about me” # What’s the deal with this triangle? # Six examples: 1. Marketing funnel and attribution 2. Understanding analytics 3. Creating governance 4. Building dashboard structures 5. “Everyone one loves funnel reports” 6. Lean analytics # Discussion – Examples of other models?
  3. 3. Senior Analytics Strategist at IIH Nordic A Copenhagen based digital marketing agency 4 days work week (!) Enterprise Analytics Data visualization Courses and teaching MeasureCamp Copenhagen Robert Børlum-Bach
  4. 4. △?
  5. 5. We understand more easily, when things are: • Structured • Simplified • Recognizable • Systematised
  6. 6. Example #1 – Marketing funnel Exposure/ads Discovery Consideration Conversion Relationship Retention How is the content found? Organic/ paid, links, emails, social etc. The site experience, discovery of content, research. Buying consideration: Does the product/service match the user’s need. The conversion of an action that makes the user a costumer. The post buying process. Did the product meet the expectation. Customer service. If the customer had a good experience, maybe she/he will return.
  7. 7. This is also how Google Analytics is structured Who visits the site (Audience)? Where are the visitors coming from (Acquisition) ? How are the users interacting with the site (Behaviour)? Why are they converting (Conversions)?
  8. 8. Who visits the site (Audience)? Where are the visitors coming from (Acquisition) ? How are the users interacting with the site (Behaviour)? Why are they converting (Conversions)?
  9. 9. Example #2 – Understanding analytics User/Visitor Session/Visit Hit/pageview/event/transaction
  10. 10. Example #2 – Understanding analytics User, session, and hit scopes essential in creating a robust implementation • Setting the correct persistence and event correlation (Adobe Analytics) • Working with the appropriate scopes in custom dimensions and segmentations (Google Analytics) • Create a solution design reference (SDR) where the different scopes and use cases are evident • Naming conventions and preset in segments and reports are important – as confusions will occur (especially in Adobe Analytics)
  11. 11. Example #2 – Understanding analytics Custom Dimension Custom dimension name Scope Module Datalayer 1Author Hit page 2Brand Hit page page.brand 3Type Hit page page.type 4Breadcrumb Hit page page.breadcrumb 5Title Hit page page.title 6ID Hit page 7CMS Hit page page.cms 8Environment Hit page page.sysEnv 9Platform Hit page page.platform 10Tags Hit page page.tags 21Conversion action Hit conversion conversion.action 22Conversion action Session conversion conversion.action 23Conversion action User conversion conversion.action 24Conversion type Hit conversion conversion.type 25Conversion type Session conversion conversion.type 26Conversion type User conversion conversion.type 27Conversion flow Hit conversion conversion.flow 28Conversion step Hit conversion conversion.step 29Conversion value Hit conversion conversion.value 31Global ID User user user.idGlobal 32Logged In User user user.isLoggedIn 33Last Login Hit user user.lastLogin 34Something User user user.something
  12. 12. Example #3 – Creating governance Administrator (very few) (Can publish, manage users, setup goals, etc) Editor (few) (Can customize, create tags, reports, comment etc) Users (many) (Can read, share, analyse etc (democratizing data)
  13. 13. Example #3 – Creating governance Often it is enough with a spreadsheet or similar, where each user (both internal and external) is specified which access level they have for each analytics/tag management/data visualization tool, and if applicable; what they are responsible for
  14. 14. Example #3 – Creating governance
  15. 15. Example #4 – Building dashboard structures Excutive Level Managerial Level Interested in few overall KPIs Few details, high level, fast, comparisons Scorecards, easy to digest Engine Room Level Interested in why More details, more data, but not too nerdy Graphs, trend lines, context Interested in the data analysis Most detail and interpreation Tables, breakdowns
  16. 16. This is how Google Data Studio is structured Report Level Page Level Widget Level
  17. 17. Example #5 – Everyone loves funnel reports Start % Some Step % Another Step % Conversion % Dropoff % Dropoff % Dropoff %
  18. 18. Example #5 – Everyone loves funnel reports
  19. 19. Example #6 – Lean analytics Data Ideas Code Measure Learn Build
  20. 20. Other examples of modeling, simplifying, communicating using triangles?
  21. 21. Thank you twerob