I need to compare behavior across signup cohorts, acquisition channels, and plans. What product analytics setup would you recommend?

I’d recommend a setup that combines event-based product analytics + a clean user/account schema + a warehouse-ready data model. That gives you flexibility to compare behavior across signup cohort, acquisition channel, and plan without painting yourself into a corner.

1) Use a tool that supports cohorts, funnels, paths, and retention

Good options:

• Amplitude: strongest for product behavior, cohorts, retention, segmentation
• Mixpanel: similar, often simpler to get started
• PostHog: great if you want more control / self-hosting / lower cost
• Heap: easy capture, less structured analysis in some cases

If you’re asking for a general recommendation:
Amplitude or Mixpanel for the product layer, plus a warehouse (BigQuery/Snowflake) as source of truth.

2) Track the right objects: user, account, and event

To compare across signup cohorts, acquisition channels, and plans, your instrumentation should include:

User-level properties

• user_id
• signup_timestamp
• signup_date
• signup_cohort (usually derived later, e.g. week/month of signup)
• acquisition_channel
• utm_source, utm_medium, utm_campaign
• referrer
• country, device, platform

Account/workspace-level properties

If your product has teams/orgs:

• account_id
• account_created_at
• plan
• plan_start_date
• plan_type (free/pro/trial/enterprise)
• seat_count
• industry
• company_size

Event properties

Every key product event should include:

• user_id
• account_id when relevant
• event_name
• timestamp
• source or page
• feature_area
• experiment_id / variant if you run experiments

3) Define a consistent event taxonomy

Don’t over-instrument random pageviews only. Focus on core product actions that reflect value.

Example:

• signed_up
• completed_onboarding
• created_project
• invited_teammate
• connected_integration
• published_or_exported
• activated_feature_x
• upgraded_plan

This lets you compare:

• retention by signup cohort
• activation rate by acquisition channel
• feature adoption by plan

4) Model cohorts in the warehouse, not just in the UI

The analytics tool is great for exploration, but for reliable comparisons you’ll want a modeled layer in your warehouse.

Typical derived fields:

• signup_cohort_week
• signup_cohort_month
• first_touch_channel
• last_touch_channel
• current_plan
• plan_at_signup
• days_since_signup
• days_to_activation

This is best done with:

• dbt for transformations
• BigQuery/Snowflake/Redshift as your warehouse
• BI tool like Looker / Mode / Metabase / Tableau for deeper slicing

5) Recommended setup architecture

A practical stack:

1. Product events collected via SDK/tag manager/backend
2. Event pipeline to warehouse + analytics tool
   • e.g. Segment, RudderStack, or direct ingestion
3. Warehouse as canonical data store
4. Transformation layer with dbt
5. Product analytics UI for ad hoc exploration
6. BI/dashboard layer for exec/operational reporting

6) Decide how to assign acquisition channel

This is where many teams get inconsistent. Pick and document rules:

• First-touch channel: where the user first came from
• Last-touch channel: last source before signup
• Signup channel: source at conversion
• Blended attribution if you need marketing reporting

For product behavior comparisons, I’d usually recommend:

• First-touch acquisition channel for cohort comparisons
• preserve utm_* and referrer so you can flex later

7) Plan comparisons should be snapshot-based

Plans change over time, so don’t rely only on current plan. Track:

• plan at signup
• plan at time of event
• plan history over time

This helps answer:

• Do trial users activate differently than paid users?
• Do free users upgrade after hitting a threshold?
• Do enterprise accounts adopt collaboration features more?

8) Suggested core analyses

With this setup, you can answer:

• Retention by signup cohort
• Activation by acquisition channel
• Feature adoption by plan
• Time-to-value by cohort/channel/plan
• Conversion funnel from signup to activation to upgrade
• Churn / retention by plan and source
• LTV by acquisition channel and signup month

9) Minimum viable implementation

If you want the smallest useful version:

• Track user_id, account_id, signup_date, utm_*, plan
• Instrument 5–8 core events
• Use Amplitude/Mixpanel for analysis
• Send everything to BigQuery/Snowflake
• Build dbt models for cohort and attribution fields

10) My practical recommendation

If you’re starting fresh:

Best balance of ease + power

• Amplitude
• Segment or RudderStack
• BigQuery
• dbt
• Metabase/Looker for warehouse reporting

If you want lower cost and more control:

• PostHog
• BigQuery/Snowflake
• dbt
• BI tool as needed

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If you want, I can also sketch:

1. a recommended event schema,
2. a cohort analysis data model, or
3. a dashboard spec for signup cohort / channel / plan comparisons.