The document discusses the Lego Data Layer, which provides a standardized structure for collecting analytics data about user interactions on websites. It involves tracking interactions as events with a subject, verb, object, and context. Entities like products, users, and transactions are identified and standardized attributes are collected for each. The data is structured to link interactions to the relevant business entities and allow attributes of those entities to be tracked across interactions. This provides a consistent way to analyze user behavior and attributes in a structured format.

1. The Lego Data Layer

2. Digital Event Grammar:
subject + verb + object + context
Ecommerce:

3. Files downloaded, buttons and
links clicked, pages seen….
Stuff that users interacted
with directly (the object)
We track it, then classify it.

4. Then came
Enhanced Ecommerce
... enables the measurement of user
interactions with products on ecommerce
websites ...

5. It doesn’t “fit”
Sometimes product is object, other times it’s part of context.

6. Entity = a thing that’s of
particular interest to the business.
The shopper
The product
The product category
The transaction
The shopping visit aka the checkout
The promotion
The campaign

7. It’s not the clicked button that
counts (the object).
But the business entity
that click is related to.

8. Link interactions to the
business entities
they’re related to at
data collection time.
subject + verb + object + context + entity

9. The Entity
Dictionary:
- Standalone attributes
- Gained attributes

10. Interaction -> system
composed of business entities

11. Simply “slot” entities
into the structure of the
new interaction.

12. Especially when you have
parent-child relationships.

13. New attributes
automatically “travel” with
the entity.
They trickle through to the
entities and interactions
they’re a part of.

14. How does it work in practice

15. Interaction recipe for GTM…
"added_product_to_basket": {
"action": {
"category": "shopping",
"timestamp": "1410962241"
},
"user": <user dict>,
"object": {
"type": "product",
"dict": <product dict>
},
"context": {
"notification": <notification dict>,
"checkout": <checkout dict>
}
}

16. …human friendly for us
added_product_to_basket:
action:
category: shopping
timestamp: 1410962241
user: <user dict>
object:
type: product
dict: <product dict>
context:
notification: <notification dict>
checkout: <checkout dict>

17. Get devs to create helpers
who get the dictionaries
ready.

18. Call on the helpers immediately
after the interaction occurs.

19. Meet the entity dictionary workhorse

20. Automatically
highlight changes
+ product.cohort_added
+ product.date_added
!
- product.brand.variation

21. Automatically
create human friendly spec
product
id // server-side / client-side markup
variations
number // server-side
!
brand
name // server-side
!

22. Automatically
create precise JSON spec
{
"product": {
"id": "[id of product]",
"name": "[name of of product]",
"variations": {
"number": "[number of variations available]"
},
"brand": {
"name": "[manufacturer brand sold under]"
}
}
}

23. Automatically
create HTML5 markup spec
<*
data-entity=“product”
data-product-id=“value”
data-product-context-collection-position=“
value”
>

24. Special thanks….
Alex & Yali
snowplowanalytics.com
!
Simo Ahava
simoahava.com

25. Data Layer = like a box of
ready-made Lego characters
(but for analytics).
In-depth blog post series:
http://clearclu.es/LegoLayer
@carmenmardiros