LTV and cohorts
How to use Axalot cohort-style Tracker analysis to understand delayed value, repeat events, source quality, and destination quality over time.
What cohort analysis answers
Cohort analysis groups traffic by the time or source context where it started, then compares later outcomes. Use it when first-day performance does not explain the real value of a campaign.
Delayed value
Find campaigns that convert or earn later.
Repeat events
Inspect redeposits or later qualified events.
Source quality
Compare creative, campaign, keyword, and sub values over time.
Destination quality
Compare stream, rotation, PWA, landing, and offer link outcomes.
Console path
Console / Tracker / Reports
Useful cohort views
| Question | Group by | Metrics |
|---|---|---|
| Which launch day aged best? | day, campaign_id | Revenue, EPC, Sales, Leads |
| Which creative produces later revenue? | creative_id, day | Revenue, Revenue / Visit, Unique EPC |
| Which stream is higher quality? | stream_id, offer_link_id | Sales, Revenue, Conversion rates |
| Which PWA flow retains value? | pwa_id, platform | PWA Installs, Sales, Revenue |
| Which partner status changes later? | affiliate_network_id, offer_id | Sales, Leads, Registrations, Revenue |
Workflow
Confirm click attribution and postback matching first.
Start with a time grouping such as day or week.
Add campaign or source fields to identify the acquisition cohort.
Add conversion and revenue metrics, not only traffic metrics.
Compare cohorts only after enough time has passed for delayed events.
Cohorts expose tracking mistakes
If click ids, postback statuses, or revenue macros are wrong, cohort conclusions will be wrong. Fix attribution before judging LTV.