Reactivation after inactivity

Detect when a user returns after a quiet stretch and route them into a welcome-back flow instead of the normal home screen.

Use this when you want a calendar-based signal — days since last open — and a coarse "they drifted" cutoff is good enough to act on. Don't use this when you have behavioral data that can detect slowdowns before the calendar gap opens; reach for Winback at-risk users instead. Also skip for apps with high daily-use cadence where a 7-day gap is noise.

Goal

A user who returns after a 14-day gap is effectively a new user again — they have forgotten where things are, what the value was, and why they were paying. Dropping them on the home screen wastes the moment. This recipe fires a reactivation flow on the first session back: a short recap, a contextual offer, or a reminder of the one feature they used most.

Setup

In the dashboard

• Create a campaign reactivation-inactive-7d.

• Trigger: session start.

• Conditions: days_since_last_session >= 7 and days_since_last_session < 30 and subscription_status = "free".

• Action: fire yourapp://reactivation/welcome-back with a trial extension or a feature recap.

• Create a second campaign reactivation-inactive-30d with days_since_last_session >= 30. Use a stronger offer; these users are closer to uninstall.

• Frequency: once per returning session. A user who comes back, leaves again, and returns a week later should see it fresh.

• Exclude paying users unless you specifically want to retain lapsed payers.

In the app (engineering hand-off)

• On every session start, compute the gap from the last session timestamp and write it before Amply evaluates campaigns:

  amply.setCustomProperty(key: "days_since_last_session", value: gapDays)
  amply.setCustomProperty(key: "last_core_feature", value: lastFeatureUsed)

• Register yourapp://reactivation/welcome-back to present the recap screen. Populate it with last_core_feature so the screen refers to something the user actually did.

• Track the outcome:

  amply.track("ReactivationShown")
  amply.track("ReactivationConverted")

How it runs

1. User installs, uses the app for a week, then stops.

2. Fourteen days later, the user opens the app. SDK starts a session.

3. App reads last_session_at from local storage, computes days_since_last_session = 14, writes it as a user attribute.

4. Amply evaluates campaigns. reactivation-inactive-7d matches.

5. Amply fires yourapp://reactivation/welcome-back. The app shows the recap screen with the user's previous favorite feature and a trial-extension button.

6. User either converts (app tracks ReactivationConverted and the campaign stops matching for this session) or dismisses. Either way, next time the user opens the app within a few days, the gap is small and the campaign no longer matches.

Metrics to watch

• Return-session-to-conversion rate. Compare the cohort that saw the reactivation flow to a hold-out.

• 7-day retention after the reactivation session. The point of the flow is to restart the habit, not just extract one purchase.

• Uninstall rate in the 72 hours after the flow. A pushy offer can accelerate churn it was meant to prevent.

• Feature re-engagement: did the user actually use last_core_feature again in this session?

Related

• Post-trial recovery — reactivation for the trial-specific case

• Winback at-risk users — a finer-grained version triggered by a behavioral signal, not a calendar gap

• Sessions and events — how session boundaries are defined

• Targeting an audience — composing the inactivity condition

• Tracking events — instrumenting the reactivation outcome

• AI-assisted integration — describe this campaign in plain language and have your AI assistant build it