Skip to content
Aqar Data Analysis

Monthly vs Yearly Subscription Analysis

Monthly vs Yearly Subscription Analysis

Date: 2026-01-26 Analysis Period: Full history (Feb 2020 - Jan 2026) Data Source: sadb_subs

Executive Summary

This analysis examines the shift from yearly to monthly subscriptions since the launch of monthly billing in July 2024, and tracks conversion patterns between subscription types.

Important Context: When a subscription lapses (is not renewed), users lose their active listings. This creates strong renewal pressure but also makes monthly subscriptions higher-risk for users who may forget to renew.

Key Metrics

MetricValue
Total Subscriptions58,653
Yearly Subscriptions40,848 (70%)
Monthly Subscriptions17,805 (30%)
Active Yearly8,332 (20% of yearly)
Active Monthly1,964 (11% of monthly)
Monthly Launch DateJuly 2024

Note: Active status determined by expire_time > now(), not the status field.

Top Insights

FindingImpact
~50K net revenue units lostConverters are main loss; monthly-only mostly incremental
75.8% waited 4.8 yearsMonthly-only users wouldn’t have subscribed yearly
Converters: 72% revenue loss7,448 users pay avg 2.39 months vs 8.7 yearly
Monthly-only: 60% have 1 listingSmall operators, not comparable to yearly users

1. Historical Overview

Subscription Type Distribution (All Time)

TypeTotal SubsActive SubsActive RateUnique UsersFirst Sub
Yearly40,8488,33220.4%18,383Feb 2020
Monthly17,8051,96411.0%7,448July 2024

Key Finding: Yearly subscriptions have been available since February 2020. Monthly subscriptions officially launched in July 2024 (with limited beta in May-June) and have since captured 30% of all subscriptions in just 18 months.

The low active rate (20% yearly, 11% monthly) reflects that most subscriptions are historical and have expired. Monthly has a lower active rate because users must renew more frequently.

SELECT
    is_monthly_sub,
    count() as total_subscriptions,
    countIf(expire_time > now()) as active_subscriptions,
    round(countIf(expire_time > now()) * 100.0 / count(), 1) as active_rate,
    uniq(mgr_user_id) as unique_users
FROM sadb_subs
WHERE _peerdb_is_deleted = 0
GROUP BY is_monthly_sub
ORDER BY is_monthly_sub

2. Timeline: Monthly Subscription Adoption

Launch Period Detail

MonthMonthly SubsUnique UsersPhase
May 20247171Beta/soft launch
Jun 20243434Beta
Jul 2024152145Official launch
Aug 2024634620Rapid adoption

Annual Trend

YearYearly SubsMonthly SubsMonthly %Notes
20204,11000%Yearly only
20215,82800%Yearly only
20227,61200%Peak yearly growth
20237,56500%Yearly plateau
20246,8684,31038.6%Monthly launched July
20258,36312,05759.0%Monthly overtakes
20265021,43874.1%Monthly dominant

Key Finding: Yearly subscriptions peaked in 2022-2023 (~7,600/year). After monthly launched, yearly dropped to 6,868 in 2024 but recovered to 8,363 in 2025 (highest ever). However, monthly subscriptions grew faster, capturing 59% market share in 2025.

Quarterly Trend

QuarterYearlyMonthlyMonthly %Phase
Q1 20241,24100%Pre-launch
Q2 20241,8671055.3%Beta (May-Jun)
Q3 20242,4461,53038.5%Launch + rapid adoption
Q4 20241,3142,67567.1%Monthly overtakes yearly
Q1 20251,9392,71258.3%Stabilization
Q2 20251,9102,42255.9%Equilibrium
Q3 20252,4423,24457.1%Steady state
Q4 20252,0723,67964.0%Monthly growing
Q1 20265021,43874.1%Monthly dominant
SELECT
    toStartOfQuarter(createdAt) as quarter,
    countIf(is_monthly_sub = false) as yearly_subs,
    countIf(is_monthly_sub = true) as monthly_subs,
    round(countIf(is_monthly_sub = true) * 100.0 / count(), 1) as monthly_pct
FROM sadb_subs
WHERE _peerdb_is_deleted = 0
GROUP BY quarter
ORDER BY quarter

3. Conversion Analysis: Yearly ↔ Monthly

All Subscription Transitions (Full History)

Transition TypeCountUnique Users
Yearly → Yearly (renewal)40,42918,347
Monthly → Monthly (renewal)10,3452,908
Yearly → Monthly7,4607,448
Monthly → Yearly419416

Conversion Ratio: 18:1 — For every user who switches from monthly to yearly, 18 users switch from yearly to monthly.

Key Finding: The near 1:1 ratio between yearly→monthly transitions (7,460) and unique users (7,448) indicates most users make this switch only once. It’s a permanent migration, not an oscillation.

WITH user_subs_ordered AS (
    SELECT
        mgr_user_id,
        is_monthly_sub,
        createdAt,
        lag(is_monthly_sub) OVER (PARTITION BY mgr_user_id ORDER BY createdAt) as prev_sub_monthly
    FROM sadb_subs
    WHERE _peerdb_is_deleted = 0
)
SELECT
    CASE
        WHEN prev_sub_monthly = false AND is_monthly_sub = true THEN 'Yearly → Monthly'
        WHEN prev_sub_monthly = true AND is_monthly_sub = false THEN 'Monthly → Yearly'
        WHEN prev_sub_monthly = false AND is_monthly_sub = false THEN 'Yearly → Yearly (renewal)'
        WHEN prev_sub_monthly = true AND is_monthly_sub = true THEN 'Monthly → Monthly (renewal)'
    END as transition_type,
    count() as transitions,
    uniq(mgr_user_id) as unique_users
FROM user_subs_ordered
WHERE prev_sub_monthly IS NOT NULL
GROUP BY transition_type
ORDER BY transitions DESC

4. Retention After Yearly → Monthly Conversion

Context: When subscriptions lapse, users lose their listings. This means users who don’t renew experience immediate business impact, which may drive some to return after a gap.

Reframed Retention Analysis

Retention StatusUsers% of Converters
Churned (1 month, inactive 60+ days)4,34258%
Short-term (2-3 months)1,63822%
Medium-term (4-6 months)6879%
Long-term (7-12 months)4366%
Recent convert (too early to tell)1963%
Very loyal (13+ months)1492%

Key Finding: 58% of yearly→monthly converters truly churned (only 1 month, then inactive 60+ days). This is better than the raw “61% had only 1 subscription” because it accounts for recent converts.

Retention breakdown:

  • 58% churned — Converted, paid one month, lost listings, never returned
  • 42% retained — Stayed for 2+ months (some with gaps)
WITH yearly_to_monthly_users AS (
    SELECT DISTINCT mgr_user_id
    FROM (
        SELECT
            mgr_user_id,
            is_monthly_sub,
            lag(is_monthly_sub) OVER (PARTITION BY mgr_user_id ORDER BY createdAt) as prev_monthly
        FROM sadb_subs
        WHERE _peerdb_is_deleted = 0
    )
    WHERE prev_monthly = false AND is_monthly_sub = true
),
converter_monthly_history AS (
    SELECT
        s.mgr_user_id,
        countIf(s.is_monthly_sub = true) as monthly_sub_count,
        max(if(s.is_monthly_sub, s.createdAt, null)) as last_monthly
    FROM sadb_subs s
    JOIN yearly_to_monthly_users u ON s.mgr_user_id = u.mgr_user_id
    WHERE s._peerdb_is_deleted = 0
    GROUP BY s.mgr_user_id
)
SELECT
    CASE
        WHEN monthly_sub_count = 1 AND last_monthly < now() - INTERVAL 60 DAY THEN 'Churned (1 month, inactive 60+ days)'
        WHEN monthly_sub_count = 1 THEN 'Recent convert (1 month so far)'
        WHEN monthly_sub_count BETWEEN 2 AND 3 THEN 'Short-term (2-3 months)'
        WHEN monthly_sub_count BETWEEN 4 AND 6 THEN 'Medium-term (4-6 months)'
        WHEN monthly_sub_count BETWEEN 7 AND 12 THEN 'Long-term (7-12 months)'
        ELSE 'Very loyal (13+ months)'
    END as retention_status,
    count() as users
FROM converter_monthly_history
GROUP BY retention_status
ORDER BY users DESC

5. Renewal Gap Analysis

Context: Since listings are lost when subscriptions lapse, gaps between renewals indicate periods where users lost their listings and then decided to re-subscribe.

Gap Between Monthly Renewals

Gap CategoryOccurrencesUnique Users% of Renewals
No gap (renewed on time)13,4847,44876%
1-7 days gap1,4868698%
8-30 days gap1,2178207%
1-3 months gap9327435%
3+ months gap6866384%

Key Finding: 24% of monthly renewals have gaps, meaning users let their subscription (and listings) lapse before returning. This suggests:

  1. Some users forget to renew and lose listings temporarily
  2. Some users intentionally pause and return when needed
  3. The listing loss consequence isn’t preventing gaps entirely
WITH monthly_subs AS (
    SELECT
        mgr_user_id,
        createdAt,
        expire_time,
        lead(createdAt) OVER (PARTITION BY mgr_user_id ORDER BY createdAt) as next_sub_date
    FROM sadb_subs
    WHERE _peerdb_is_deleted = 0
      AND is_monthly_sub = true
),
gaps AS (
    SELECT
        mgr_user_id,
        dateDiff('day', expire_time, next_sub_date) as gap_days
    FROM monthly_subs
    WHERE next_sub_date IS NOT NULL
)
SELECT
    CASE
        WHEN gap_days <= 0 THEN 'No gap (renewed on time)'
        WHEN gap_days BETWEEN 1 AND 7 THEN '1-7 days gap'
        WHEN gap_days BETWEEN 8 AND 30 THEN '8-30 days gap'
        WHEN gap_days BETWEEN 31 AND 90 THEN '1-3 months gap'
        ELSE '3+ months gap'
    END as gap_category,
    count() as occurrences,
    uniq(mgr_user_id) as unique_users
FROM gaps
GROUP BY gap_category
ORDER BY gap_category

Subscription Continuity (Users with 2+ Monthly Subs)

Continuity PatternUsersAvg Monthly SubsAvg Span (Months)
Continuous (no gaps)1,5794.83.7
Mostly continuous2317.69.1
Intermittent (some gaps)4204.77.9
Sparse (large gaps)4103.210.4

Key Finding: Users with gaps tend to have longer spans but fewer subscriptions, indicating they use the service intermittently — subscribing when they need listings, letting them lapse when not actively selling/renting.

WITH user_monthly_history AS (
    SELECT
        mgr_user_id,
        count() as total_monthly_subs,
        dateDiff('month', min(createdAt), max(createdAt)) as span_months
    FROM sadb_subs
    WHERE _peerdb_is_deleted = 0
      AND is_monthly_sub = true
    GROUP BY mgr_user_id
    HAVING total_monthly_subs > 1
)
SELECT
    CASE
        WHEN total_monthly_subs >= span_months THEN 'Continuous (no gaps)'
        WHEN total_monthly_subs >= span_months * 0.75 THEN 'Mostly continuous'
        WHEN total_monthly_subs >= span_months * 0.5 THEN 'Intermittent (some gaps)'
        ELSE 'Sparse (large gaps)'
    END as continuity,
    count() as users,
    round(avg(total_monthly_subs), 1) as avg_subs,
    round(avg(span_months), 1) as avg_span_months
FROM user_monthly_history
WHERE span_months > 0
GROUP BY continuity
ORDER BY users DESC

6. User Journey: First vs Latest Subscription Type

Users with Multiple Subscriptions (Full History)

First TypeLatest TypeUser Count% of Multi-Sub Users
YearlyYearly8,27570.4%
YearlyMonthly1,1389.7%
MonthlyYearly2372.0%
MonthlyMonthly2,10217.9%

Key Finding: Among the ~11,750 users with 2+ subscriptions:

  • 70% who started yearly stayed yearly (loyal yearly base)
  • 10% who started yearly ended on monthly (converted)
  • 2% who started monthly ended on yearly (rare reverse)
  • 18% who started monthly stayed monthly
WITH user_subs_ordered AS (
    SELECT
        mgr_user_id,
        is_monthly_sub,
        createdAt,
        row_number() OVER (PARTITION BY mgr_user_id ORDER BY createdAt) as sub_order
    FROM sadb_subs
    WHERE _peerdb_is_deleted = 0
),
first_and_later AS (
    SELECT
        mgr_user_id,
        argMin(is_monthly_sub, sub_order) as first_sub_monthly,
        argMax(is_monthly_sub, sub_order) as latest_sub_monthly,
        max(sub_order) as total_subs
    FROM user_subs_ordered
    GROUP BY mgr_user_id
    HAVING total_subs > 1
)
SELECT
    first_sub_monthly,
    latest_sub_monthly,
    count() as user_count
FROM first_and_later
GROUP BY first_sub_monthly, latest_sub_monthly
ORDER BY first_sub_monthly, latest_sub_monthly

7. New vs Returning Subscribers

Historical Trend

YearNew SubscribersReturningReturning %
20203,9471634%
20213,3772,45142%
20223,3114,30157%
20233,5883,97753%
20245,2535,92553%
20255,64114,77972%
20263521,58882%

Key Finding: The proportion of returning subscribers jumped significantly in 2025 (72%) and 2026 (82%). This is driven by monthly renewals — monthly subscribers renew 12x per year vs 1x for yearly.

WITH first_sub AS (
    SELECT
        mgr_user_id,
        min(createdAt) as first_sub_date
    FROM sadb_subs
    WHERE _peerdb_is_deleted = 0
    GROUP BY mgr_user_id
)
SELECT
    toYear(s.createdAt) as year,
    countIf(s.createdAt = f.first_sub_date) as new_subscribers,
    countIf(s.createdAt > f.first_sub_date) as returning_subscribers,
    round(countIf(s.createdAt > f.first_sub_date) * 100.0 / count(), 1) as returning_pct
FROM sadb_subs s
JOIN first_sub f ON s.mgr_user_id = f.mgr_user_id
WHERE s._peerdb_is_deleted = 0
GROUP BY year
ORDER BY year

8. Package Distribution

Subscriptions by Package (Full History)

Package IDTypeSubscriptionsUnique Users
1Yearly30,18116,379
3Yearly6,5713,265
2Yearly3,6432,742
4Yearly453220
5Monthly16,8207,106
7Monthly985426

Key Finding: Monthly subscriptions use different packages (5, 7) than yearly (1, 2, 3, 4). Package 5 accounts for 94% of all monthly subscriptions.

SELECT
    is_monthly_sub,
    sub_pkg_id,
    count() as subscriptions,
    uniq(mgr_user_id) as unique_users
FROM sadb_subs
WHERE _peerdb_is_deleted = 0
GROUP BY is_monthly_sub, sub_pkg_id
ORDER BY is_monthly_sub, subscriptions DESC

9. Sample User Journeys

Users with Both Subscription Types

Example journeys (Y = Yearly, M = Monthly):

User IDJourneyPattern
158068YYYYY→MMMM→YYY→MMMMM→YOscillator (rare)
3022093YY→MMMMMMMMMMMMMMMMMMMConverted, stayed monthly
330206YY→MMMMMMMMMMMMMMMMMConverted, stayed monthly
591508MMMMMMMMMMMMMMMMM→YYStarted monthly, went yearly
3831991MMMMMMMMMMMMMMMMM→YYYStarted monthly, went yearly

Key Finding: The dominant pattern is Y...Y→MMMM... (long-term yearly subscriber converts to monthly and stays). True oscillators (switching back and forth) are rare.

10. Revenue Impact Analysis

Pricing: Yearly subscription costs 8.7x the monthly subscription price. This means a user must stay on monthly for ~9 months to equal the yearly revenue.

Converter Revenue Analysis (Per User)

MetricValue
Unique users who converted yearly→monthly7,448
Average monthly subscriptions per user2.39
Users who reached breakeven (9+ months)363 (4.9%)

Revenue Comparison Per User

ScenarioRevenue per User (monthly units)
If user paid 1 yearly8.7
Actual avg monthly paid2.39
Revenue gap per user-6.31 (-72%)

For the full cohort of 7,448 converters:

ScenarioCalculationTotal Revenue (monthly units)
If all paid 1 yearly7,448 × 8.764,798
Actual monthly revenue7,448 × 2.3917,801
Revenue loss-46,997 (-72%)

Critical Finding: Each converter generates on average only 27% of what a single yearly subscription would generate.

User Distribution by Months Paid

Months PaidUsers% of UsersRevenue vs 1 Yearly
1 month4,53860.9%11.5%
2 months1,12315.1%23%
3-4 months81310.9%34-46%
5-8 months6118.2%57-92%
9+ months (breakeven)3634.9%≥100%

Only 4.9% of users paid enough months to match a single yearly subscription.

Monthly-Only Users (Never Subscribed Yearly)

A key question: Is monthly attracting new users who wouldn’t have subscribed otherwise?

User Segmentation

SegmentUsers% of All Users
Yearly only16,83569.3%
Monthly only (never yearly)5,90024.3%
Both types (converters)1,5486.4%

5,900 users have only ever had monthly subscriptions — they never subscribed yearly.

Monthly-Only User Behavior

MetricValue
Unique users5,900
Avg months subscribed2.2
Users with only 1 month3,801 (64.4%)
Users with 9+ months231 (3.9%)
Currently active749 (12.7%)

Key Finding: Monthly-only users have worse retention than yearly→monthly converters:

  • 64.4% churn after 1 month (vs 60.9% for converters)
  • Only 3.9% reach 9+ months (vs 4.9% for converters)

Distribution by Months Subscribed

MonthsUsers%
1 month3,80164.4%
2 months85014.4%
3-4 months60010.2%
5-8 months4187.1%
9+ months2313.9%

Revenue Comparison by Segment

SegmentUsersRevenue per User (monthly units)
Yearly only16,83519.3
Converters (both types)1,54823.0
Monthly only5,9002.2

Monthly-only users generate only 11% of the revenue per user compared to yearly-only users.

Would Monthly-Only Users Have Subscribed Yearly?

Several signals suggest most monthly-only users would NOT have subscribed yearly:

Signal 1: Account Timing

Account CreatedUsers%Avg Days to First Sub
Before monthly launch (Jul 2024)4,47575.8%1,759 days (4.8 years)
After monthly launch1,42524.2%77 days

75.8% of monthly-only users had accounts for years but never subscribed yearly. They waited an average of 4.8 years before subscribing monthly — strong evidence they wouldn’t have subscribed yearly.

Signal 2: Prior Platform Activity

Activity Before SubscribingUsers%
Had listings before subscribing4,49876.2%
No listings before subscribing1,40223.8%

76% were already active on the platform (had listings) but chose not to subscribe yearly.

Signal 3: Operation Size (Listings per User)

SegmentAvg ListingsMedian ListingsUsers with 10+ Listings
Yearly-only213.65513,933 (83%)
Monthly-only19.572,389 (40%)

Monthly-only users have much smaller operations — median 7 listings vs 55 for yearly.

Signal 4: Listings per Subscription

Subscription TypeAvg Listings/SubMedian Listings/Sub
Yearly48.16
Monthly6.02

Monthly subscriptions are associated with far fewer listings.

Signal 5: Monthly-Only User Listing Distribution

Listings Tied to SubscriptionUsers%
1 listing3,52359.7%
2-5 listings1,29421.9%
6-10 listings4417.5%
10+ listings64210.9%

60% of monthly-only users have just 1 listing — likely individual sellers, not professional brokers who would commit to yearly.

Signal 6: New Yearly Acquisition Trend

QuarterNew Yearly Subscribers
Q1 2024 (pre-monthly)583
Q2 2024 (pre-monthly)995
Q3 2024 (monthly launched)757
Q4 2024502
Q1 2025390
Q2 2025286
Q3 2025325
Q4 2025252

New yearly acquisitions dropped ~50% after monthly launched — some cannibalization, but baseline suggests many monthly users are incremental.

Estimation Summary

User GroupCountLikely Would Subscribe Yearly?
Accounts before Jul 2024, waited years4,475No — waited 4.8 years on average
New accounts after Jul 20241,425Unknown — could be either
10+ listings (larger operators)642Maybe — similar scale to yearly users

Conservative estimate: At most 1,400-2,000 of the 5,900 monthly-only users (~25-35%) might have subscribed yearly. The rest are likely incremental users who would not have committed to yearly subscriptions.

-- Account timing analysis
WITH monthly_only_users AS (
    SELECT mgr_user_id
    FROM sadb_subs
    WHERE _peerdb_is_deleted = 0
    GROUP BY mgr_user_id
    HAVING countIf(is_monthly_sub = false) = 0 AND countIf(is_monthly_sub = true) > 0
),
user_timing AS (
    SELECT
        m.mgr_user_id,
        toDateTime(u.create_time) as account_created,
        min(s.createdAt) as first_sub_date
    FROM monthly_only_users m
    JOIN sadb_users u ON m.mgr_user_id = u.user_id
    JOIN sadb_subs s ON m.mgr_user_id = s.mgr_user_id
    WHERE u._peerdb_is_deleted = 0 AND s._peerdb_is_deleted = 0
    GROUP BY m.mgr_user_id, account_created
)
SELECT
    CASE
        WHEN account_created < toDateTime('2024-07-01') THEN 'Before monthly launch'
        ELSE 'After monthly launch'
    END as account_timing,
    count() as users,
    round(avg(dateDiff('day', account_created, first_sub_date)), 0) as avg_days_to_first_sub
FROM user_timing
GROUP BY account_timing
-- User segmentation query
WITH user_segments AS (
    SELECT
        mgr_user_id,
        countIf(is_monthly_sub = false) as yearly_count,
        countIf(is_monthly_sub = true) as monthly_count
    FROM sadb_subs
    WHERE _peerdb_is_deleted = 0
    GROUP BY mgr_user_id
)
SELECT
    CASE
        WHEN yearly_count > 0 AND monthly_count > 0 THEN 'Converters'
        WHEN yearly_count > 0 AND monthly_count = 0 THEN 'Yearly only'
        WHEN yearly_count = 0 AND monthly_count > 0 THEN 'Monthly only'
    END as segment,
    count() as users,
    round(avg(monthly_count), 2) as avg_monthly_subs,
    round(avg(yearly_count), 2) as avg_yearly_subs
FROM user_segments
GROUP BY segment
WITH yearly_to_monthly_users AS (
    SELECT DISTINCT mgr_user_id
    FROM (
        SELECT
            mgr_user_id,
            is_monthly_sub,
            lag(is_monthly_sub) OVER (PARTITION BY mgr_user_id ORDER BY createdAt) as prev_monthly
        FROM sadb_subs
        WHERE _peerdb_is_deleted = 0
    )
    WHERE prev_monthly = false AND is_monthly_sub = true
),
user_monthly_count AS (
    SELECT
        u.mgr_user_id,
        countIf(s.is_monthly_sub = true) as monthly_count
    FROM yearly_to_monthly_users u
    JOIN sadb_subs s ON u.mgr_user_id = s.mgr_user_id
    WHERE s._peerdb_is_deleted = 0
    GROUP BY u.mgr_user_id
)
SELECT
    count() as unique_users,
    round(avg(monthly_count), 2) as avg_monthly_per_user,
    countIf(monthly_count >= 9) as users_reached_breakeven,
    round(countIf(monthly_count >= 9) * 100.0 / count(), 1) as pct_reached_breakeven
FROM user_monthly_count

11. What Happens to Listings When Subscriptions Expire?

Listing Visibility by Subscription Status

Subscription StatusUsersTotal ListingsPublished & Visible% Visible
Active5,0451,506,316798,80153%
Expired18,0012,525,147267,95110.6%

When subscriptions expire, listing visibility drops from 53% to 10.6% — a ~5x reduction. Users don’t lose their listings entirely, but they become unpublished/hidden.

Expired Subscribers: Listings Breakdown

MetricValue
Users with expired subs19,130
Total listings2,526,269
Currently active/visible33,047 (1.3%)
Inactive/unpublished2,258,321 (89%)

The 267,951 “published” listings from expired users may be from free tiers, legacy accounts, or data inconsistencies.

Recent Churners (Expired <90 Days)

Churn TypeUsersTotal ListingsAvg ListingsActive Listings
Yearly churners658163,18324812,079
Monthly churners1,15155,988496,126

Monthly churners have smaller portfolios (49 vs 248 listings) — consistent with smaller operators who don’t need yearly commitment.

Churned Monthly Users: Listings Tied to Subscription

MetricValue
Churned monthly users5,992
Listings tied to subscription (sub_id > 0)88,024
Listings without sub tie122,431
Avg listings per user (with sub)14.7
-- Listing visibility by subscription status
WITH user_status AS (
    SELECT
        mgr_user_id,
        max(expire_time) as last_expire,
        CASE WHEN max(expire_time) > now() THEN 'Active' ELSE 'Expired' END as sub_status
    FROM sadb_subs
    WHERE _peerdb_is_deleted = 0
    GROUP BY mgr_user_id
)
SELECT
    sub_status,
    uniq(u.mgr_user_id) as users,
    count(l.id) as total_listings,
    countIf(l.published = 1 AND l.hidden = 0) as published_visible,
    round(countIf(l.published = 1 AND l.hidden = 0) * 100.0 / count(l.id), 1) as pct_visible
FROM user_status u
JOIN sadb_listings l ON u.mgr_user_id = l.user_id AND l._peerdb_is_deleted = 0
GROUP BY sub_status

Do Users Create NEW Listings Without an Active Subscription?

Yes — 7,614 users have created 41,580 listings when they had NO active subscription covering that time.

Important Correction: This analysis verifies that each listing was created during a period with no active subscription — not just “after expiry” (which could include users who resubscribed).

MetricValue
Users who created listings without ANY active subscription7,614
Listings created without subscription coverage41,580
Average listings per user5.5
Currently published & visible8,302 (20%)
REGA licensed33,854 (81.4%)

Do These Users Later Resubscribe?

MetricValue
Users who created listings without subscription7,614
Later resubscribed3,526 (46.3%)
Never resubscribed4,088 (53.7%)

Nearly half (46.3%) of users who create listings without a subscription eventually resubscribe. This suggests:

  • Creating listings demonstrates ongoing intent to use the platform
  • These users are good reactivation targets
  • The other 54% remain unmonetized demand

Status of Unsubscribed Listings

StatusListings%
Published & Visible8,30220%
Unpublished/Hidden33,27880%

Only 20% of unsubscribed listings are published — much lower than the 53% visibility rate for active subscribers. This confirms listings lose visibility without an active subscription.

-- Listings created when user had NO active subscription
WITH churned_users AS (
    SELECT mgr_user_id, max(expire_time) as last_expire
    FROM sadb_subs
    WHERE _peerdb_is_deleted = 0
    GROUP BY mgr_user_id
    HAVING max(expire_time) < now()
),
listings_by_churned AS (
    SELECT
        l.id as listing_id,
        l.user_id,
        toDateTime(l.create_time) as listing_created,
        l.status,
        l.published,
        l.hidden,
        l.rega_licensed as is_rega
    FROM sadb_listings l
    INNER JOIN churned_users c ON l.user_id = c.mgr_user_id
    WHERE l._peerdb_is_deleted = 0
      AND toDate(l.create_time) >= '2024-07-01'
),
listings_with_coverage AS (
    SELECT
        lc.listing_id,
        lc.user_id,
        lc.listing_created,
        lc.status,
        lc.published,
        lc.hidden,
        lc.is_rega,
        max(CASE
            WHEN s.start_time <= lc.listing_created AND s.expire_time >= lc.listing_created
            THEN 1 ELSE 0
        END) as has_covering_sub
    FROM listings_by_churned lc
    LEFT JOIN sadb_subs s ON lc.user_id = s.mgr_user_id AND s._peerdb_is_deleted = 0
    GROUP BY lc.listing_id, lc.user_id, lc.listing_created, lc.status, lc.published, lc.hidden, lc.is_rega
)
SELECT
    countIf(has_covering_sub = 0) as truly_unsubscribed_listings,
    uniqIf(user_id, has_covering_sub = 0) as users_creating_without_sub,
    countIf(has_covering_sub = 0 AND status IN (0, 4) AND published = 1 AND hidden = 0) as truly_unsubscribed_published
FROM listings_with_coverage

Reactivation Opportunity

There are 2.5M listings from users with expired subscriptions, representing potential reactivation opportunities:

Expiry WindowUsersListingsAvg Listings/User
<30 days671149,228222
30-90 days1,14670,26361
90-180 days1,751113,14565
180-365 days3,315300,31091
1+ year12,2471,893,323155

Key Finding: Users who create listings without a subscription have a 46.3% resubscription rate — significantly higher than typical reactivation rates. These users demonstrate clear intent to continue using the platform.

Opportunity:

  • Target recently expired users (especially <90 days) with reactivation campaigns
  • Specifically target the 7,614 users who created listings without subscriptions
  • Their listings are dormant but could be quickly republished with a new subscription

Business Implications

The Listing Loss Factor

Since users lose listing visibility when subscriptions lapse:

BehaviorImplication
76% renew on timeStrong motivation to avoid listing loss
24% have gapsSome users accept temporary listing loss
58% converter churnMany willing to lose listings rather than continue

This suggests the listing loss consequence:

  • Works for committed users (76% renew on time)
  • Doesn’t prevent intermittent usage (24% have gaps)
  • Doesn’t retain users who weren’t planning to stay (58% churn)

Positive Signals

SignalInterpretation
~65% of monthly-only are incrementalWould NOT have subscribed yearly (waited 4.8 years)
5,900 monthly-only usersBrought in users with smaller operations
Total subs growing2025 had more total subs than any previous year
Yearly still growing8,363 yearly subs in 2025 (highest ever)
8,332 active yearly usersStrong base of committed annual subscribers

Warning Signs

SignalRisk
72% revenue loss from converters7,448 converters pay avg 2.39 months vs 8.7 yearly
Only 4.9% converters reach breakeven95% pay less than 1 yearly subscription
~50K net monthly units lostConverter losses far exceed incremental gains
Converter churn: 60.9%Majority leave after just 1 month
11% monthly active rateOnly 1,964 of 17,805 monthly subs currently active

Recommendations

High Priority

  1. Address the 72% revenue leakage from converters

    • Converters pay only 27% of what they would on yearly
    • Consider: Should yearly→monthly conversion require yearly expiration first?
    • Test: Offer prorated yearly renewals instead of monthly conversion
    • Implement: Stronger yearly retention campaigns before expiration

  2. Increase monthly pricing or reduce yearly discount

    • Current 8.7x ratio strongly favors yearly for committed users
    • Monthly breakeven at 9 months is rarely reached (4.9%)
    • Consider: Adjust ratio to 10x or 11x to improve monthly economics
    • Or: Introduce 6-month option at 5x monthly

  3. Investigate the 58% true churn

    • Why do 4,342 users convert from yearly and then churn?
    • Did they achieve their goal (sell/rent property)?
    • Or did they have a bad experience?
    • These users represent ~37,000 “missing” monthly units of revenue

Medium Priority

  1. Understand intermittent users

    • 830+ users have gaps but returned (lost listings, came back)
    • Are these seasonal brokers? Occasional sellers?
    • Could a “pause” feature serve them better than full lapse?

  2. A/B test commitment incentives

    • Offer meaningful yearly discounts
    • Test quarterly billing as middle-ground
    • Compare retention by default billing option

To Investigate

  1. Why do 2% switch from monthly to yearly?

    • 416 users made this rare switch
    • What motivated them? Could be testimonial material
    • Could this behavior be encouraged?

  2. Revenue impact analysis

    • Compare revenue before/after monthly launch (July 2024)
    • Are we growing total revenue or just changing billing?

Summary

The introduction of monthly subscriptions in July 2024 triggered a massive shift in subscriber behavior:

  • Monthly now represents 74% of new subscriptions (2026)
  • 7,448 users switched from yearly to monthly (vs 419 the other direction)
  • 5,900 users subscribed monthly without ever having yearly
  • Monthly users (both converters and monthly-only) have high churn: 60-65% leave after 1 month

User Segmentation

SegmentUsersAvg Revenue/UserNotes
Yearly only16,83519.3Loyal, high-value base
Monthly only5,9002.2New users, very low value
Converters1,54823.0Highest value (yearly + monthly)

The Revenue Impact

With yearly costing 8.7x monthly, the revenue math is stark:

Converters (7,448 users who switched yearly→monthly):

MetricValue
If each paid 1 yearly64,798 monthly units
Actual monthly revenue17,801 monthly units
Revenue loss-46,997 (-72%)

Monthly-only users (5,900 users who never had yearly):

MetricValue
If each paid 1 yearly51,330 monthly units
Actual monthly revenue12,989 monthly units
Revenue loss-38,341 (-75%)

Combined revenue gap: ~85,000 monthly units

Key Question Answered

Is monthly flexibility increasing total value?

No. Monthly subscriptions are:

  1. Cannibalizing yearly — Converters pay only 27% of yearly value
  2. Not attracting high-value new users — Monthly-only users have worse retention than converters (64.4% vs 60.9% churn)
  3. Generating minimal incremental revenue — 5,900 new users × 2.2 months = 12,989 monthly units

Are Monthly-Only Users Truly Incremental?

Evidence suggests YES — most monthly-only users would NOT have subscribed yearly:

SignalFinding
Account timing75.8% had accounts 4.8 years before subscribing
Prior activity76.2% had listings but never subscribed yearly
Operation sizeMedian 7 listings (vs 55 for yearly users)
Listing distribution60% have just 1 listing

Estimate: Only ~25-35% (1,400-2,000 users) of monthly-only users might have subscribed yearly. The rest are likely truly incremental.

Revised Revenue Impact

If only 1,700 monthly-only users (midpoint) would have subscribed yearly:

SourceRevenue Loss
Converters (7,448 users)-47,000 monthly units
Monthly-only who might have gone yearly (~1,700 users)-12,000 monthly units
Total actual loss~59,000 monthly units
Incremental from monthly-only (~4,200 users × 2.2)+9,240 monthly units
Net revenue impact~-50,000 monthly units

The converter revenue loss remains the primary concern. Monthly-only users are mostly incremental but low-value.

Pre vs Post Monthly Launch Comparison

MetricPre-Launch (2020-Q1 2024)Post-Launch (Q3 2024-Q1 2026)
Avg quarterly subs1,660 yearly2,010 yearly + 2,110 monthly
Total quarterly1,6604,120 (+148%)
Unique users/quarter~1,200~2,800

While total subscription volume has increased significantly (+148%), the revenue implications depend on pricing and the churn/gap dynamics among monthly subscribers.