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.

1. The Triangle △
A universal model for working with digital
analytics and marketing
MeasureCamp Milan
October 13, 2018

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. 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. △?

5. We understand more easily, when things are:
• Structured
• Simplified
• Recognizable
• Systematised

7. 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.

8. 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)?

9. 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)?

10. Example #2 – Understanding analytics
User/Visitor
Session/Visit
Hit/pageview/event/transaction

11. 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)

12. Example #2 – Understanding analytics
Custom Dimension Custom dimension name Scope Module Datalayer
1Author Hit page page.author
2Brand Hit page page.brand
3Type Hit page page.type
4Breadcrumb Hit page page.breadcrumb
5Title Hit page page.title
6ID Hit page page.id
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

13. 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)

14. 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

15. Example #3 – Creating governance

16. 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

17. This is how Google Data Studio is structured
Report Level
Page Level
Widget Level

18. Example #5 – Everyone loves funnel reports
Start %
Some Step %
Another Step %
Conversion %
Dropoff %
Dropoff %
Dropoff %

19. Example #5 – Everyone loves funnel reports

20. Example #6 – Lean analytics
Data
Ideas
Code
Measure
Learn Build

21. Other examples of modeling, simplifying,
communicating using triangles?

22. Thank you
twerob
robert.b@iihnordic.com