Start with the server-side / no app release path. Most teams see their first payback report within 48 hours of connecting their subscription source and MMP.
For first-client launches, monetization starts in evidence/import mode: connect an
existing subscription source or provide approved manual evidence first. FloKit
does not require Paddle checkout execution to produce the first payback report.
Path 1: Server-side / no app release (recommended)
Connect App Store Connect, Google Play, Stripe, RevenueCat, Adapty, Superwall, or manual subscription evidence + AppsFlyer or Adjust. No mobile SDK changes required.
What it is
FloKit pulls subscription state from App Store Connect, Google Play, Stripe, or
subscription platforms via API, webhook import, or approved evidence, and reads
install attribution from AppsFlyer or Adjust via API pull. Your app code is not
touched. No release is gated on this path.
Time to first report
24–72 hours after connecting sources.
What you get
- Subscription events: trial starts, activations, renewals, cancellations, refunds
- Install attribution by campaign, ad set, creative, and country
- CAC by campaign and creative
- Payback curves and cohort LTV
- Action queue populated with budget and creative recommendations
Best for
- Teams selling through the app stores or Stripe today
- Any MMP (AppsFlyer or Adjust) in place
- Teams that want to validate payback before making any code changes
- Growth teams who need a report this week, not next quarter
Setup
Path 2: Warehouse-first
Connect BigQuery, Snowflake, or Redshift with a cohort table or event export.
What it is
FloKit connects to your warehouse and reads a cohort table or event export you define. FloKit maps your schema to its internal model (install date, revenue events, subscription state, attribution fields) and builds payback curves from your historical data.
Time to first report
2–4 hours after schema mapping is complete.
What you get
- Historical payback analysis on existing cohorts going back months or years
- LTV models based on observed renewal behavior
- CAC curves by channel, if attribution data is in the warehouse
- Source-of-truth payback reporting backed by finance-approved revenue figures
Best for
- Data teams with clean warehouse exports and reliable attribution joins already built
- Teams that want to validate historical payback before connecting live subscription sources
- Organizations where finance owns the revenue number and it lives in the warehouse
Setup
Path 3: Server-side events (enhanced)
Send lifecycle events via the FloKit Events API.
What it is
Your backend sends lifecycle events to FloKit’s Events API: paywall views, trial starts, offer interactions, subscription activations, cancellations, and any custom funnel steps. FloKit uses these events to build the payback model, either standalone or layered on top of a subscription platform integration.
Time to first report
7–14 days of event collection, since FloKit needs enough subscription lifecycle data to calculate renewal rates and payback curves.
What you get
- Richer funnel signals: onboarding steps, paywall variant exposure, offer views, feature adoption
- Custom event properties for segmenting by any dimension you define
- Real-time identity stitching as users move from anonymous → authenticated → subscribed
- Full payback analysis when combined with attribution data
Best for
- Teams with custom subscription logic not fully covered by their subscription platform
- Hybrid web/app flows where subscription state lives in your own backend
- Teams that want paywall variant and offer data FloKit can’t pull from a subscription platform
Setup
Comparison
Can I combine paths?
Yes. Most production deployments layer all three:
- Start with server-side / no app release for a live payback signal within 48 hours.
- Add a warehouse connection to backfill historical cohorts and enable finance-reconciled reporting.
- Add server-side events incrementally to enrich the payback model with funnel and paywall signals.
You do not need to choose one path permanently. FloKit merges data from all active sources into a single identity-stitched cohort model.
