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LTV Engine
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Payments
Retention
Features
Apples
Dashboard
📱 Mobile
Apples to Apples
isolated comparison — same calendar window, or equal cohort maturity. No age artifacts, no base-size distortion.
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Same window
Equal maturity
Age matrix
Trend @age
⧉ Age window
Spin
Reasons
Token freq
⚡ Velocity
⚖ Pre/Post
🧭 Journeys
⏱ Pace
💰 LTV
❄ Snowball
▦ Scorecard
days 1–
at day
cohorts
Monthly
Weekly (exact)
Daily (exact)
ages (any numbers)
metric
cohorts
Weekly (exact)
Daily (exact)
Monthly
metric
Rebilled % (r1)
Rev / user
LTV $ / user (= cum. rev/user)
Token $ / user
Token buyers %
Rebill % of rev
Rebilled 2x % (r2)
Rebilled 3x % (r3)
Rebilled 4x % (r4)
Rebilled 5x % (r5)
Cohort size
Msgs / user
% who messaged
Msgs / messenger
Media msgs / user
Sessions / user
Minutes / user
Chats / user
Tokens spent / user
% spending tokens
Features used (breadth)
% with 10+ msgs
% with 50+ msgs
% with 200+ msgs
% spending 1k+ tokens
% with 5+ sessions
% with 1h+ in app
Fresh rev / user
Rebill rev / user
Blended ROAS
Fresh ROAS
CAC
Profit / user
PnL $ (rev − spend)
PnL % (margin)
r1 -> r2 persistence %
r2 -> r3 persistence %
r3 -> r4 persistence %
Cancelled % by age
Failed-rebill % by age
3+ failed rebills %
Involuntary share of churn
Comeback %
Expired % by age
Chargeback % by age
Chargeback $ / user
Avg 1st rebill ticket (monthly)
Avg 2nd rebill ticket (monthly)
Avg 3rd rebill ticket (monthly)
Active % on exact day
% ever used feature (monthly)
Feature events / user (monthly)
Tokens burned / user
Tokens bought / user
Tokens granted / user (2026-04+)
Net token balance / user (2026-04+)
Tokens burned / user / day
split by
All users
Source
Country
Campaign
Creative
Plan (monthly)
at age
ages (any numbers)
7·30·45
30·60·90
cycles
deep
days
hours (0-335)
cohorts
Weekly (exact)
Monthly
metric
split by
All users
Source
Country
Campaign
Creative
Plan (monthly)
age window (any numbers)
→
days
hours
or dates
ref cohort
Compare same age window
min first spinners
split by
All users
Source
metric
max age
smooth
3-day
7-day
metric
cohorts
Weekly
Monthly
event date
or search the timeline
read at age
buckets a side
top sequences
metric
cohorts
Weekly
Monthly
read at d
targets
unit
days
hours
trail
split by
All users
Source
Country
Campaign
Creative
quick metric
rpu
r1
canPct
msgPU
tok
ROAS
PnL%
Go
cohorts
Daily
Weekly
Monthly
ages (any numbers)
∞ infinite
classic
first 30d
deep
last N cohorts
split by
All users
Source
Country
Campaign
Creative
Plan (monthly)
Go
cohorts
Weekly
Monthly
ages (any numbers)
table @ d
Latest
A vs B
Grid
cohort A
vs B
feature
⚖ Compare
basis
vs previous bucket
vs pinned baseline
vs same age window
pinned: — ✕
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Trend
Comparison