;
};
In the [Thin dimensional spine for multi-fact event analysis guide](/guides/patterns/thin-dimension-spine), you learned what a thin dimensional spine is, how it works, and how to implement your own using dbt and Omni.
In this guide, we'll build on the knowledge in that guide to walk you through several approaches to cohort analysis.
## Requirements
To follow along with this guide, you'll need:
* **To have read the [Thin dimensional spine for multi-fact event analysis guide](/guides/patterns/thin-dimension-spine).** This guide describes the basics of thin dimensional spines and how to build and materialize your own.
* **Familiarity with [modeling](/modeling) in Omni**
## Attribute-based cohorts
To build this type of analysis:
1. Maintain an attribute table with per-user first/last timestamps, e.g., `attr__users_event_bounds` with `first_signup_ts`, `first_purchase_ts`.
2. Group and filter users by these milestones across event streams
## Event-to-event cohorts
This type of analysis looks at the timing between events — for example, signup-to-first-login latency, or engagement patterns following a first purchase.
## Dynamic 'time since selected event' cohorting
Using the spine, you can model a dynamic "since basis" and "grain" selector to compute buckets of time since a specific event:
```yaml title="Snippet: dim__user_event_spine_thin.view" expandable theme={null}
dimensions:
selected_basis_ts:
type: timestamp
sql: |
case {{filters.dim__user_event_spine_thin.cohort_basis.value}}
when 'first_ad' then ${attr__users_event_bounds.first_ad_ts}
when 'first_signup' then ${attr__users_event_bounds.first_signup_ts}
else ${attr__users_event_bounds.first_ad_ts}
end
time_since_selected_event:
type: number
label: Time Since Selected Event
sql: |
datediff(
case {{filters.dim__user_event_spine_thin.cohort_grain.value}}
when 'day' then 'day'
else 'day'
end,
${selected_basis_date},
${event_date_day}
)
```