Scheduled Data Exports

Data deliveries of all of your apps' transaction data (formerly ETL Exports)

Ask AI

👍Pro Integration

Scheduled data exports are available to all users signed up after September '23, the legacy Grow and Pro plans, and Enterprise plans. If you're on a legacy Free or Starter plan and want to access this integration, migrate to our new pricing via your billing settings.

RevenueCat can automatically send data deliveries of all of your apps' transaction data to various cloud storage providers. These are in the form of gzip compressed .csv files delivered daily. The exports use a comma delimiter, so if they do not appear to be formatted correctly when viewed as spreadsheets, you may need to change your delimiter settings in the spreadsheet software.

Setup Instructions

• Amazon S3 Setup
• Google Cloud Storage Setup
• Azure Blob Storage Setup

Customers on our Enterprise plan have the option to receive data exports more frequently than once per day when receiving new and updated transactions only. Contact your Customer Success Manager with questions, or visit our Pricing Page to learn more.

Version Change Log

• Data Export Version 5 (Latest)
• Data Export Version 4
• Data Export Version 3

Transaction Format

Applicable to the latest version

All dates and times are provided in UTC.

Header	Description	Type	Example value	Can be null
rc_original_app_user_id	Can be used as a unique user identifier to find all of a user's transactions.	string	$RCAnonymousID:87c6049c58069238dce29853916d624c
rc_last_seen_app_user_id_alias	Can be used together with rc_original_app_user_id to match transactions with user identifiers in your systems.	string	$RCAnonymousID:87c6049c58069238dce29853916d624c
country	Store country of a transaction when known, or an IP-based estimate of a subscriber's country when not known.	string	GB	✅
country_source	from_sdk when the store country of a transaction is known, or estimated when country is sourced from an IP-based estimate.	string	from_sdk	✅
product_identifier	The product identifier that was purchased.	string	rc_subscription_monthly
product_display_name	The display name of the product identifier if one has been set	string	Monthly $9.99	✅
product_duration	The standard duration of the product if one is known by RevenueCat. May be null if RevenueCat does not know the authoritative duration. product_duration does not represent the trial or introductory period length of a transaction, it only represents the standard duration of the product that's been subscribed to.	string	P1M	✅
start_time	Purchase time of transaction.	datetime	2023-01-01 08:27:06
end_time	Expected expiration time of subscription. Null when is_auto_renewable = false For Google Play, end_time can be before start_time to indicate an invalid transaction (e.g. billing issue).	datetime	2023-02-01 08:27:06	✅
grace_period_end_time	Expiration time of a grace period (if applicable) for a subscription. Will remain set while a subscription is in its grace period, or if it exited its grace period without renewing. Null when a subscription is not in a grace period or expiration was not due to a grace period.	datetime	2023-02-17 08:27:06	✅
effective_end_time	Single reference point of a subscriber’s expiration and entitlement revocation; inclusive of each store’s logic for refunds, grace periods, etc.	datetime	2023-02-17 08:27:06	✅
store	The source of the transaction. Can be app_store, play_store, stripe, or promotional.	string	play_store
is_auto_renewable	true for auto-renewable subscriptions, false otherwise.	boolean	true
is_trial_period	true if the transaction was a trial.	boolean	false
is_in_intro_offer_period	true if the transaction is in an introductory offer period.	boolean	false
is_sandbox	true for transactions made in a sandbox environment.	boolean	false
price_in_usd	The revenue (converted to USD) generated from the transaction after accounting for full and partial refunds. Can be null if product prices haven't been collected from the user's device.	float	0	✅
purchase_price_in_usd	The gross revenue (converted to USD) generated from the transaction. Remains set for refunded transactions. Can be null if product prices haven't been collected from the user's device.	float	9.99	✅
takehome_percentage	[DEPRECATED] The estimated percentage of the transaction price that will be paid out to developers after commissions, but before VAT and DST taxes are taken into account. (will be either 0.7 or 0.85) We recommend using tax_percentage and commission_percentage to calculate proceeds instead. Learn more here.	float	0.7
tax_percentage	The portion of a transaction’s price that will be deducted by the store for taxes. VAT & Digital Services Taxes may be withheld by stores depending on the store and country. To learn more about how RevenueCat estimates taxes, click here.	float	0.1442
commission_percentage	The portion of a transaction’s price that will be detected by the store for commission. In stores where taxes are deducted before commission, this value will not equal the published commission from a store, because that commission is calculated on the post-tax revenue.	float	0.15
store_transaction_id	orderId or transaction_identifier.	string	123456789012345
original_store_transaction_id	orderId of first purchase or original_transaction_id. Can be used to find all related transactions for a single subscription.	string	011223344556677
refunded_at	When a refund was detected, null if none was detected. Is not set in the case of upgraded transactions for which the App Store issues a partial refund.	datetime	2023-02-20 05:47:55	✅
unsubscribe_detected_at	When we detected an unsubscribe (opt-out of auto renew).	datetime	2023-02-16 14:17:10	✅
billing_issues_detected_at	When we detected billing issues, null if none was detected.	datetime	2023-02-01 08:27:15	✅
purchased_currency	The currency that was used for the transaction.	string	GBP	✅
price_in_purchased_currency	The revenue (in the purchased currency) generated from the transaction after accounting for full and partial refunds. Can be null if product prices haven't been collected from the user's device.	float	0	✅
purchase_price_in_purchased_currency	The gross revenue (in the purchased currency) generated from the transaction. Remains set for refunded transactions. Can be null if product prices haven't been collected from the user's device.	float	3.99	✅
entitlement_identifiers	An array of entitlements that the transaction unlocked or null if it didn't unlock any entitlements.	string array	["membership", "full_access"]	✅
renewal_number	Always starts at 1. Trial conversions are counted as renewals. is_trial_conversion is used to signify whether a transaction was a trial conversion.	integer	2
is_trial_conversion	If true, this transaction is a trial conversion.	boolean	true
presented_offering	The offering presented to users.	string	Default Offering	✅
ownership_type	Will be PURCHASED when a recorded transaction results from the subscriber’s direct purchase of it, or FAMILY_SHARED when a recorded transaction results from the subscriber having received it through Family Sharing. NOTE: The FAMILY_SHARED designation is only supported on App Store transactions.	string	PURCHASED	✅
reserved_subscriber_attributes	The reserved attributes set for the Customer (subscriber). Keys begin with $.	string JSON	{"$ip": {"value": "203.78.120.117", "updated_at_ms": 1672549200}, "$gpsAdId": {"value": "80480bdc-06e0-11ee-be56-0242ac120002", "updated_at_ms": 1672549200}, "$androidId": {"value": "12345a9876b4c123", "updated_at_ms": 1673097132390}}	✅
custom_subscriber_attributes	The custom attributes set for the Customer (subscriber).	string JSON	{"feature_setting": {"value": "1", "updated_at_ms": 1672549200}, "survey_response": {"value": "2", "updated_at_ms": 1599112814785}}	✅
platform	Last seen platform of the subscriber.	string	android	✅
experiment_id	The unique ID of the Experiment that the subscriber is or was enrolled in. Will be null if the subscriber has not been enrolled in an experiment. Learn more about Experiments here.	string	prexp3a8a234abc	✅
experiment_variant	The value of the Experiment variant that the subscriber is or was enrolled in. a represents the Control, and b represents the Treatment. Will be null if the subscriber has not been enrolled in an experiment. Learn more about Experiments here.	string	a	✅
updated_at	The last time an attribute of the transaction was modified.	datetime	2023-02-20 05:47:55
offer*	The offer that was used for a transaction (if applicable).	string	black_friday_discount	✅
offer_type*	The type of offer that was used for a transaction (if applicable).	string	offer_code	✅
first_seen_time*	The time the customer was first seen by RevenueCat.	datetime	2023-01-01 03:00:00
auto_resume_time*	The time when a Play Store subscription would resume after being paused.	datetime	2023-03-20 03:00:00	✅

*Available only on our most recent export version

A note on transaction data

All transaction data is based on the store receipts that RevenueCat has received. Receipts often have inconsistencies and quirks which may need to be considered. For example:

• The expiration date of a purchase can be before the purchase date. This is Google's way of invalidating a transaction, for example when Google is unable to bill a user some time after a subscription renews. This doesn’t occur on iOS.
• If you migrated to RevenueCat, Google subscriptions that were expired for more then 60 days before being migrated will not have transaction histories in export files.
• Apple and Google do not always provide the transaction price directly, so we rely on historical data & store APIs. This may result in inaccuracies if receipts were imported, or if a product price was increased before your App Store Connect API Key was added.
• Renewal numbers start at 1, even for trials. Trial conversions increase the renewal number.
• Data is pulled from a snapshot of the current receipt state, this means that the same transaction can be different from one delivery to another if something changed (e.g. due to a refund or billing issue). You should recompute metrics for past time periods periodically to take these changes into account. You can use the updated_at field to detect if a transaction may have changed since a prior export.
• Data is up to date as of the export beginning to be generated. Thus, changes could occur between the start of an export generating and its delivery that would not be reflected in that export.

We try to normalize or at least annotate these quirks as much as possible, but by and large we consider receipts as the sources of truth, so any inconsistencies in the transaction data can always be traced back to the receipt.

The date and time set in Next export start time via the dashboard is when the next export should start getting generated and is not when the next export should be delivered.

Updating to the latest version

If you're on an older version of our exports, updating is easy:

1. Open app.revenuecat.com
2. Navigate to your Project in Project Settings
3. Click on your Scheduled Data Exports integration in the “Integrations” side panel
4. Click UPDATE TEMPLATE
5. After confirming that your data pipeline is configured to ingest the described changes, click UPDATE in the confirmation modal to complete the update.

⚠️Data Format Changes

Please note that Version 4 and all subsequent templates include data format changes which must be incorporated into your data pipeline before updating. Learn more here.

Handling updated transactions correctly

We strongly recommend keeping the option to "Receive new and updated transactions only" enabled to significantly reduce the amount of data that you need to process in each daily export.

However, handling transaction updates can be tricky, so consider these tips to make it easier:

1. For most stores, store_transaction_id will be unique for each transaction, but for Stripe it is not; so for best results we recommend treating every unique set of [store_transaction_id + renewal_number] as a unique transaction.
2. Instead of overwriting prior transaction states when receiving an updated transaction, consider adding them as new rows to your output table and setting a property like is_latest to ensure you're never double-counting different versions of the same transaction. Or, you could set an ingested_time property to order the transactions by the most recent version you received from RevenueCat.
3. When in doubt, use updated_at (provided by RevenueCat in your export) as a reference point to determine the latest version of a transaction if you have multiple prior versions and can't otherwise confidently determine which one is latest.

Sample queries for RevenueCat measures

You can use the following sample queries (written in Postgresql) as starting points for reproducing common RevenueCat measures.

Active Subscriptions

-- Active Subscriptions as of a specified date

SELECT
  COUNT(*)
FROM
  [revenuecat_data_table]
WHERE date(effective_end_time) > [targeted_date]
  AND date(start_time) <= [targeted_date]
  AND is_trial_period = 'false'
  AND DATE_DIFF('s', start_time, end_time)::float > 0
  AND ownership_type != 'FAMILY_SHARED'
  AND store != 'promotional'
  AND is_sandbox != 'true'

-- The RevenueCat Active Subscriptions chart excludes trials,
-- promotional transactions, and transactions resulting from family sharing
-- since they do not reflect auto-renewing future payments.

Active Subscriptions Movement

-- Active Subscriptions Movement within a specified date range

WITH

filtered_subscription_transactions AS (
    SELECT
        *
    FROM [revenuecat_data_table]
    /* Filter down to the date range that you want to measure MRR Movement for */
    WHERE (start_time BETWEEN [targeted_start_date] and [targeted_end_date] 
        OR effective_end_time BETWEEN [targeted_start_date] and [targeted_end_date])
        /* Exclude trials, which do not contribute to MRR */
        AND is_trial_period = 'false'
        AND DATE_DIFF('s', start_time, end_time)::float > 0
        AND ownership_type != 'FAMILY_SHARED'
        AND store != 'promotional'
        AND is_sandbox != 'true'),

actives AS (
  SELECT
    DATE(start_time) AS date,
    COUNT(
        CASE 
            WHEN renewal_number = 1
                OR is_trial_conversion = 'true'
            THEN 1 
            ELSE NULL 
        END) AS num_new_actives,
    COUNT(
        CASE 
            WHEN renewal_number > 1
                AND is_trial_conversion = 'false'  
            THEN 1 
            ELSE NULL 
        END
    ) AS num_renewals
    
  FROM filtered_subscriptipon_transactions
  GROUP BY 1),
  
expirations AS (
  SELECT
    DATE(effective_end_time) AS date,
    COUNT(*) AS num_expirations
  FROM filtered_subscriptipon_transactions
  GROUP BY 1)

SELECT
    COALESCE(a.date, e.date) AS date,
    COALESCE(a.num_new_actives, 0) AS new_actives, /* "New Actives" in the Active Subscriptions Movement Chart */
    COALESCE(a.num_renewals, 0) AS num_renewals,
    COALESCE(e.num_expirations, 0) AS num_expirations,
    num_expirations - num_renewals AS churned_actives, /* "Churned Actives" in the Active Subscriptions Movement Chart */
FROM actives a
FULL JOIN expirations e ON a.date = e.date
WHERE a.date BETWEEN [targeted_start_date] AND [targeted_end_date]
    AND e.date BETWEEN [targeted_start_date] AND [targeted_end_date]

MRR

-- MRR as of a specified date

SELECT
    SUM(
        CASE WHEN effective_end_time IS NOT NULL THEN
            CASE 
                /* Handle cases where product_duration cannot be used for the transaction first */
                WHEN (is_in_intro_offer_period = 'true' OR product_duration IS NULL) THEN 
                CASE
                    WHEN DATE_DIFF(day, start_time, end_time) BETWEEN 0 AND 1 
                        THEN (30 * price)::DECIMAL(18,2)
                    WHEN DATE_DIFF(day, start_time, end_time) = 3 
                        THEN (10 * price)::DECIMAL(18,2)
                    WHEN DATE_DIFF(day, start_time, end_time) BETWEEN 6 AND 8 
                        THEN (4 * price)::DECIMAL(18,2)
                    WHEN DATE_DIFF(day, start_time, end_time) BETWEEN 12 AND 16 
                        THEN (2 * price)::DECIMAL(18,2)
                    WHEN DATE_DIFF(day, start_time, end_time) BETWEEN 27 AND 33 
                        THEN (1 * price)::DECIMAL(18,2)
                    WHEN DATE_DIFF(day, start_time, end_time) BETWEEN 58 AND 62 
                        THEN (0.5 * price)::DECIMAL(18,2)
                    WHEN DATE_DIFF(day, start_time, end_time) BETWEEN 88 AND 95 
                        THEN (0.333333 * price)::DECIMAL(18,2)
                    WHEN DATE_DIFF(day, start_time, end_time) BETWEEN 179 AND 185 
                        THEN (0.1666666 * price)::DECIMAL(18,2)
                    WHEN DATE_DIFF(day, start_time, end_time) BETWEEN 363 AND 375 
                        THEN (0.08333 * price)::DECIMAL(18,2)
                    ELSE ((28 / (DATE_DIFF('s', start_time, end_time)::float / (24 * 3600))) * price)::DECIMAL(18,2)
                END
                /* Then handle cases where product_duration can be used */
                WHEN product_duration = 'P1D' 
                    THEN (30 * price)::DECIMAL(18,2)
                WHEN product_duration = 'P3D' 
                    THEN (10 * price)::DECIMAL(18,2)
                WHEN product_duration = 'P7D' 
                    THEN (4 * price)::DECIMAL(18,2)
                WHEN product_duration = 'P1W' 
                    THEN (4 * price)::DECIMAL(18,2)
                WHEN product_duration = 'P2W' 
                    THEN (2 * price)::DECIMAL(18,2)
                WHEN product_duration = 'P4W' 
                    THEN (1 * price)::DECIMAL(18,2)
                WHEN product_duration = 'P1M' 
                    THEN (1 * price)::DECIMAL(18,2)
                WHEN product_duration = 'P2M' 
                    THEN (0.5 * price)::DECIMAL(18,2)
                WHEN product_duration = 'P3M' 
                    THEN (0.333333 * price)::DECIMAL(18,2)
                WHEN product_duration = 'P6M' 
                    THEN (0.1666666 * price)::DECIMAL(18,2)
                WHEN product_duration = 'P12M' 
                    THEN (0.08333 * price)::DECIMAL(18,2)
                WHEN product_duration = 'P1Y' 
                    THEN (0.08333 * price)::DECIMAL(18,2)
                ELSE ((28 / (DATE_DIFF('s', start_time, end_time)::float / (24 * 3600))) * price)::DECIMAL(18,2)
            END
        END 
    ) AS active_mrr
FROM [revenuecat_data_table] 

/* Filter down to the date range that you want to measure MRR for */
WHERE date(effective_end_time) > '2024-02-06'
  AND date(start_time) <= '2024-02-06'
  /* Exclude trials, which do not contribute to MRR */
  AND is_trial_period = 'false'
  AND DATE_DIFF('s', start_time, end_time)::float > 0
  AND ownership_type != 'FAMILY_SHARED'
  AND store != 'promotional'
  AND is_sandbox != 'true'

MRR Movement

-- MRR Movement for a specified date range

WITH

filtered_subscription_transactions AS (
    SELECT
        *,
        CASE WHEN effective_end_time IS NOT NULL THEN
            CASE 
                /* Handle cases where product_duration cannot be used for the transaction first */
                WHEN (is_in_intro_offer_period = 'true' OR product_duration IS NULL) THEN 
                CASE
                    WHEN DATE_DIFF(day, start_time, end_time) BETWEEN 0 AND 1 
                        THEN (30 * price)::DECIMAL(18,2)
                    WHEN DATE_DIFF(day, start_time, end_time) = 3 
                        THEN (10 * price)::DECIMAL(18,2)
                    WHEN DATE_DIFF(day, start_time, end_time) BETWEEN 6 AND 8 
                        THEN (4 * price)::DECIMAL(18,2)
                    WHEN DATE_DIFF(day, start_time, end_time) BETWEEN 12 AND 16 
                        THEN (2 * price)::DECIMAL(18,2)
                    WHEN DATE_DIFF(day, start_time, end_time) BETWEEN 27 AND 33 
                        THEN (1 * price)::DECIMAL(18,2)
                    WHEN DATE_DIFF(day, start_time, end_time) BETWEEN 58 AND 62 
                        THEN (0.5 * price)::DECIMAL(18,2)
                    WHEN DATE_DIFF(day, start_time, end_time) BETWEEN 88 AND 95 
                        THEN (0.333333 * price)::DECIMAL(18,2)
                    WHEN DATE_DIFF(day, start_time, end_time) BETWEEN 179 AND 185 
                        THEN (0.1666666 * price)::DECIMAL(18,2)
                    WHEN DATE_DIFF(day, start_time, end_time) BETWEEN 363 AND 375 
                        THEN (0.08333 * price)::DECIMAL(18,2)
                    ELSE ((28 / (DATE_DIFF('s', start_time, end_time)::float / (24 * 3600))) * price)::DECIMAL(18,2)
                END
                /* Then handle cases where product_duration can be used */
                WHEN product_duration = 'P1D' THEN (30 * price)::DECIMAL(18,2)
                WHEN product_duration = 'P3D' THEN (10 * price)::DECIMAL(18,2)
                WHEN product_duration = 'P7D' THEN (4 * price)::DECIMAL(18,2)
                WHEN product_duration = 'P1W' THEN (4 * price)::DECIMAL(18,2)
                WHEN product_duration = 'P2W' THEN (2 * price)::DECIMAL(18,2)
                WHEN product_duration = 'P4W' THEN (1 * price)::DECIMAL(18,2)
                WHEN product_duration = 'P1M' THEN (1 * price)::DECIMAL(18,2)
                WHEN product_duration = 'P2M' THEN (0.5 * price)::DECIMAL(18,2)
                WHEN product_duration = 'P3M' THEN (0.333333 * price)::DECIMAL(18,2)
                WHEN product_duration = 'P6M' THEN (0.1666666 * price)::DECIMAL(18,2)
                WHEN product_duration = 'P12M' THEN (0.08333 * price)::DECIMAL(18,2)
                WHEN product_duration = 'P1Y' THEN (0.08333 * price)::DECIMAL(18,2)
                ELSE ((28 / (DATE_DIFF('s', start_time, end_time)::float / (24 * 3600))) * price)::DECIMAL(18,2)
            END
        END AS transaction_mrr
    FROM [revenuecat_data_table]
    /* Filter down to the date range that you want to measure MRR Movement for */
    WHERE (start_time BETWEEN [targeted_start_date] and [targeted_end_date] 
        OR effective_end_time BETWEEN [targeted_start_date] and [targeted_end_date])
        /* Exclude trials, which do not contribute to MRR */
        AND is_trial_period = 'false'
        AND DATE_DIFF('s', start_time, end_time)::float > 0
        AND ownership_type != 'FAMILY_SHARED'
        AND store != 'promotional'
        AND is_sandbox != 'true'),

actives AS (
  SELECT
    DATE(start_time) AS date,
    SUM(
        CASE
            WHEN renewal_number = 1
                OR is_trial_conversion = 'true' 
            THEN transaction_mrr
            ELSE null
        END
    ) AS new_mrr,
    
    SUM(
        CASE
            WHEN renewal_number > 1 
                AND is_trial_conversion = 'false' 
            THEN transaction_mrr
            ELSE null
        END
    ) AS renewal_mrr
    
  FROM filtered_subscription_transactions
  GROUP BY 1),
  
expirations AS (
  SELECT
    DATE(effective_end_time) AS date,
    SUM(transaction_mrr) AS expired_mrr
  FROM filtered_subscription_transactions
  GROUP BY 1)

SELECT
    COALESCE(a.date, e.date) AS date,
    COALESCE(a.new_mrr, 0) AS new_mrr, /* "New MRR" in the MRR Movement Chart */
    COALESCE(a.renewal_mrr, 0) as renewal_mrr,
    COALESCE(e.expired_mrr, 0) as expired_mrr,
    expired_mrr - renewal_mrr as churned_mrr /* "Churned MRR" in the MRR Movement Chart */
FROM actives a
FULL JOIN expirations e ON a.date = e.date
WHERE a.date BETWEEN [targeted_start_date] AND [targeted_end_date]
    AND e.date BETWEEN [targeted_start_date] AND [targeted_end_date]

Revenue

-- Revenue generated on a specified date

SELECT
  SUM(purchase_price_in_usd) as gross_revenue,
  SUM(price_in_usd) as revenue_net_of_refunds, /* "Revenue" in the Revenue Chart */
  SUM(price_in_usd * (1 - tax_percentage)) as revenue_net_of_taxes, /* "Revenue (net of taxes)" in the Revenue Chart */
  SUM(price_in_usd * (1 - tax_percentage - commission_percentage)) as proceeds /* "Proceeds" in the Revenue Chart */
FROM
  [revenuecat_data_table]
WHERE date(start_time) = [targeted_date]
  AND is_trial_period = 'false'
  AND ownership_type != 'FAMILY_SHARED'
  AND store != 'promotional'
  AND is_sandbox != 'true'

-- Transactions which have been refunded can be identified through the refunded_at field.

Subscription Retention

-- Subscription Retention per product

WITH filtered_transactions AS (
    SELECT
        *
    FROM [revenuecat_data_table] rc
    WHERE end_time IS NOT NULL /* only include subscriptions */
        AND NOT (billing_issues_detected_at IS NOT NULL
            AND (store = 'play_store' OR store = 'stripe'))
        AND is_sandbox <> 'true'
        AND is_trial_period = 'false'
        AND is_in_intro_offer_period = 'false' /* exclude introductory offers */
        AND ownership_type = 'PURCHASED'
),
  
subs AS (
    SELECT
        ft.rc_original_app_user_id,
        product_identifier,
        product_duration,
        DATE(MIN(ft.start_time)) AS first_start_time, /* first start time is used to define each cohort */
        MAX(DATE_DIFF('day', start_time, end_time)) as max_transaction_duration
    FROM filtered_transactions ft
    GROUP BY 1, 2, 3
),

/* some products don't have a set duration, so this CTE will calculate the duration on a per subscription basis */
calculated_product_duration AS (
    SELECT
        rc_original_app_user_id,
        product_identifier,
        CASE
            WHEN product_duration IS NOT NULL THEN 
            CASE
                WHEN product_duration = 'P7D' THEN 'P1W'
                WHEN product_duration = 'P30D' THEN 'P1M'
                WHEN product_duration = 'P4W' THEN 'P1M'
                WHEN product_duration = 'P12M' THEN 'P1Y'
                WHEN product_duration = 'P200Y' THEN 'lifetime'
                WHEN product_duration = 'P999Y' THEN 'lifetime'
            ELSE product_duration
            END
        ELSE 
            CASE
                WHEN max_transaction_duration BETWEEN 0 AND 1 THEN 'P1D'
                WHEN max_transaction_duration = 3 THEN 'P3D'
                WHEN max_transaction_duration BETWEEN 6 AND 8 THEN 'P1W'
                WHEN max_transaction_duration BETWEEN 12 AND 16 THEN 'P2W'
                WHEN max_transaction_duration BETWEEN 27 AND 37 THEN 'P1M'
                WHEN max_transaction_duration BETWEEN 58 AND 62 THEN 'P2M'
                WHEN max_transaction_duration BETWEEN 88 AND 95 THEN 'P3M'
                WHEN max_transaction_duration BETWEEN 179 AND 185 THEN 'P6M'
                WHEN max_transaction_duration BETWEEN 363 AND 375 THEN 'P1Y'
            ELSE NULL
            END
        END AS calculated_product_duration
    FROM subs s
    GROUP BY 1, 2, 3
),
  
retention AS (
    SELECT
        subs.first_start_time,
        subs.product_identifier,
        cpd.calculated_product_duration,
        /* Each period number represents the number of billing cycles the subscriber was active for */
        CASE
            WHEN calculated_product_duration = 'P1D' THEN DATE_DIFF('day', subs.first_start_time, start_time)
            WHEN calculated_product_duration = 'P1W' THEN DATE_DIFF('week', subs.first_start_time, start_time)
            WHEN calculated_product_duration = 'P1M' THEN CAST(ROUND(DATE_DIFF('day', subs.first_start_time, start_time) / CAST(30 AS NUMERIC)) AS INTEGER)
            WHEN calculated_product_duration = 'P2M' THEN CAST(ROUND(DATE_DIFF('day', subs.first_start_time, start_time) / CAST(60 AS NUMERIC)) AS INTEGER)
            WHEN calculated_product_duration = 'P3M' THEN CAST(ROUND(DATE_DIFF('day', subs.first_start_time, start_time) / CAST(90 AS NUMERIC)) AS INTEGER)
            WHEN calculated_product_duration = 'P6M' THEN CAST(ROUND(DATE_DIFF('day', subs.first_start_time, start_time) / CAST(180 AS NUMERIC)) AS INTEGER)
            WHEN calculated_product_duration = 'P1Y' THEN CAST(ROUND(DATE_DIFF('month', subs.first_start_time, start_time) / CAST(12 AS NUMERIC)) AS INTEGER)
        END AS period_number,
        count(1) AS subscriptions
    FROM filtered_transactions ft
    INNER JOIN subs ON 
        subs.rc_original_app_user_id = ft.rc_original_app_user_id AND
        subs.product_identifier = ft.product_identifier
    INNER JOIN calculated_product_duration cpd ON 
        cpd.rc_original_app_user_id = ft.rc_original_app_user_id AND
        cpd.product_identifier = ft.product_identifier
    WHERE period_number IS NOT NULL
    GROUP BY 1, 2, 3, 4
),
  
pending_retention AS (
    SELECT
        subs.first_start_time,
        subs.product_identifier,
        cpd.calculated_product_duration,
        CASE
            WHEN calculated_product_duration = 'P1D' THEN DATE_DIFF('day', subs.first_start_time, start_time) + 1
            WHEN calculated_product_duration = 'P1W' THEN DATE_DIFF('week', subs.first_start_time, start_time) + 1
            WHEN calculated_product_duration = 'P1M' THEN CAST(ROUND(DATE_DIFF('day', subs.first_start_time, start_time) / CAST(30 AS NUMERIC)) AS INTEGER) + 1
            WHEN calculated_product_duration = 'P2M' THEN CAST(ROUND(DATE_DIFF('day', subs.first_start_time, start_time) / CAST(60 AS NUMERIC)) AS INTEGER) + 1
            WHEN calculated_product_duration = 'P3M' THEN CAST(ROUND(DATE_DIFF('day', subs.first_start_time, start_time) / CAST(90 AS NUMERIC)) AS INTEGER) + 1
            WHEN calculated_product_duration = 'P6M' THEN CAST(ROUND(DATE_DIFF('day', subs.first_start_time, start_time) / CAST(180 AS NUMERIC)) AS INTEGER) + 1
            WHEN calculated_product_duration = 'P1Y' THEN CAST(ROUND(DATE_DIFF('month', subs.first_start_time, start_time) / CAST(12 AS NUMERIC)) AS INTEGER) + 1
        END AS period_number,
        count(1) AS subscriptions
    FROM filtered_transactions ft
    INNER JOIN subs ON 
        subs.rc_original_app_user_id = ft.rc_original_app_user_id AND
        subs.product_identifier = ft.product_identifier
    INNER JOIN calculated_product_duration cpd ON 
        cpd.rc_original_app_user_id = ft.rc_original_app_user_id AND
        cpd.product_identifier = ft.product_identifier
    WHERE unsubscribe_detected_at IS NULL /* count only subscriptions that are set to renew */
        AND
            ((calculated_product_duration = 'P1D' AND DATE_ADD(start_time, '1 day') > CURRENT_DATE)
            OR (calculated_product_duration = 'P1W' AND DATE_ADD(start_time, '1 week') > CURRENT_DATE)
            OR (calculated_product_duration = 'P1M' AND DATE_ADD(start_time, '1 month') > CURRENT_DATE)
            OR (calculated_product_duration = 'P2M' AND DATE_ADD(start_time, '2 months') > CURRENT_DATE)
            OR (calculated_product_duration = 'P3M' AND DATE_ADD(start_time, '3 months') > CURRENT_DATE)
            OR (calculated_product_duration = 'P6M' AND DATE_ADD(start_time, '6 months') > CURRENT_DATE)
            OR (calculated_product_duration = 'P1Y' AND DATE_ADD(start_time, '1 year') > CURRENT_DATE))
        AND period_number IS NOT NULL
    GROUP BY 1, 2, 3, 4
)

SELECT
    COALESCE(retention.first_start_time, pending_retention.first_start_time) AS first_start_date,
    COALESCE(retention.calculated_product_duration, pending_retention.calculated_product_duration) AS product_duration,
    COALESCE(retention.product_identifier, pending_retention.product_identifier) AS product_identifier,
    COALESCE(retention.period_number, pending_retention.period_number) AS period_number,
    CASE
        WHEN retention.period_number = 0 THEN 'Subscriptions'
        WHEN retention.calculated_product_duration = 'P1Y' THEN CONCAT('Year ', retention.period_number)
        WHEN retention.calculated_product_duration = 'P6M' THEN CONCAT('Month ', 6 * retention.period_number)
        WHEN retention.calculated_product_duration = 'P3M' THEN CONCAT('Month ', 3 * retention.period_number)
        WHEN retention.calculated_product_duration = 'P2M' THEN CONCAT('Month ', 2 * retention.period_number)
        WHEN retention.calculated_product_duration = 'P1M' THEN CONCAT('Month ', retention.period_number)
        WHEN retention.calculated_product_duration = 'P1W' THEN CONCAT('Week ', retention.period_number)
        WHEN retention.calculated_product_duration = 'P1D' THEN CONCAT('Day ', retention.period_number)
    ELSE CONCAT('Period ', retention.period_number)
    END AS period_name,
    COALESCE(retention.subscriptions, 0) + COALESCE(pending_retention.subscriptions, 0) AS subscriptions
FROM retention
FULL OUTER JOIN pending_retention ON
    pending_retention.first_start_time = retention.first_start_time AND
    pending_retention.calculated_product_duration = retention.calculated_product_duration AND
    pending_retention.product_identifier = retention.product_identifier AND
    pending_retention.period_number = retention.period_number
WHERE first_start_date >= /* desired date range */
ORDER BY product_identifier, first_start_date, period_number

Sample queries for customized measures

Scheduled Data Exports are a powerful way to add your own customizations on top of the core measures provided by RevenueCat. Check out the following sample queries (written in Postgresql) for some ideas.

Active Subs by Custom Attribute

-- How many Active Subscriptions do I have with a given custom attribute value?
  
SELECT
  COUNT(*)
FROM
  [revenuecat_data_table] rc
  
WHERE date(effective_end_time) > [targeted_date]
  AND date(start_time) <= [targeted_date]
  AND is_trial_period = 'false'
  AND DATE_DIFF('s', start_time, end_time)::float > 0
  AND ownership_type != 'FAMILY_SHARED'
  AND store != 'promotional'
  AND is_sandbox != 'true'
  AND json_extract_path_text(custom_subscriber_attributes, '[custom_attribute_key].value') = [custom_attribute_value]

Active Subs by Auto Renew Status

-- What is my split of Active Subs by auto renew status?
  
SELECT
  CASE 
    WHEN unsubscribe_detected_at IS NOT NULL THEN 'Set to cancel' 
    ELSE 'Set to renew' 
  END as auto_renew_status,
  COUNT(*) as active_subscriptions
FROM
  [revenuecat_data_table]
  
WHERE date(effective_end_time) > [targeted_date]
  AND date(start_time) <= [targeted_date]
  AND is_trial_period = 'false'
  AND DATE_DIFF('s', start_time, end_time)::float > 0
  AND ownership_type != 'FAMILY_SHARED'
  AND store != 'promotional'
  AND is_sandbox != 'true'
  GROUP BY 1

Weekly Revenue (starting Monday)

-- What is my weekly revenue, where Monday is set as the start day of the week?

SELECT
  date_trunc('week', start_time) as week,
  SUM(price_in_usd) as total_revenue
FROM
  [revenuecat_data_table]
WHERE date(start_time) BETWEEN [targeted_period_start_date] AND [targeted_period_end_date]
  AND is_trial_period = 'false'
  AND ownership_type != 'FAMILY_SHARED'
  AND store != 'promotional'
  AND is_sandbox != 'true'
GROUP BY week

Realized LTV Segments

-- What is my Realized LTV of each monthly subscription cohort, segmented by whether they were offered a trial?
  
WITH 
(SELECT
  MIN(start_time) as subscription_start_time,
  original_store_transaction_id,
  MAX(is_trial_period) as had_a_trial,
  SUM(price_in_usd) as realized_ltv
FROM
  [revenuecat_data_table]
WHERE date(start_time) > [targeted_period_start_date]
  AND ownership_type != 'FAMILY_SHARED'
  AND store != 'promotional'
  AND is_sandbox != 'true'
  GROUP BY original_store_transaction_id) as subscriptions
  
SELECT
  date_trunc('month', subscription_start_time) as subscription_start_month,
  had_a_trial,
  COUNT(*) as subscriptions,
  SUM(realized_ltv) as realized_ltv,
  SUM(realized_ltv) / COUNT(*) as realized_ltv_per_subscription
FROM
  subscriptions

Active Trials by Grace Period Status

-- What portion of my Active Trials are in a grace period?
  
SELECT
  CASE
    WHEN grace_period_end_time IS NOT NULL THEN 'in_grace_period'
    ELSE 'in_trial_period'
    END as period_type,
  COUNT(*) as active_trials
FROM
  [revenuecat_data_table]
WHERE date(effective_end_time) > [targeted_date]
  AND date(start_time) <= [targeted_date]
  AND is_trial_period = 'true'
  AND DATE_DIFF('s', start_time, effective_end_time)::float > 0
  AND ownership_type != 'FAMILY_SHARED'
  AND store != 'promotional'
  AND is_sandbox != 'true'
GROUP BY period_type

Realized LTV Per Paying Customer by First Purchase Date

-- What is my Realized LTV per Paying Customer cohorted by First Purchase Date?
  
WITH filtered_transactions AS
  (SELECT *
  FROM [revenuecat_data_table]
  WHERE is_trial_period = 'false'
    AND ownership_type != 'FAMILY_SHARED'
    AND store != 'promotional'
    AND is_sandbox != 'true'
    AND refunded_at IS NULL
    AND price > 0),

first_purchase_dates AS
  (SELECT
    rc_original_app_user_id,
    MIN(start_time) as first_purchase_date
  FROM filtered_transactions
  GROUP BY 1)

SELECT

  DATE(fpd.first_purchase_date) AS first_purchase_date,
  COUNT(DISTINCT rc_original_app_user_id) AS paying_customers,
  SUM(CASE WHEN DATEADD(day, 7, first_purchase_date) > start_time 
    THEN price_in_usd ELSE 0 END)::DECIMAL(18,2) AS total_ltv_7_days,
  SUM(CASE WHEN DATEADD(day, 30, first_purchase_date) > start_time 
    THEN price_in_usd ELSE 0 END)::DECIMAL(18,2) AS total_ltv_30_days,
  SUM(CASE WHEN DATEADD(month, 6, first_purchase_date) > start_time 
    THEN price_in_usd ELSE 0 END)::DECIMAL(18,2) AS total_ltv_6_months,
  SUM(CASE WHEN DATEADD(month, 12, first_purchase_date) > start_time 
    THEN price_in_usd ELSE 0 END)::DECIMAL(18,2) AS total_ltv_12_months,
  SUM(CASE WHEN DATEADD(month, 24, first_purchase_date) > start_time 
    THEN price_in_usd ELSE 0 END)::DECIMAL(18,2) AS total_ltv_24_months,
  SUM(price_in_usd)::DECIMAL(18,2) AS total_ltv_unbounded,

  (SUM(CASE WHEN DATEADD(day, 7, first_purchase_date) > start_time 
    THEN price_in_usd ELSE 0 END)/COUNT(DISTINCT rc_original_app_user_id))::DECIMAL(18,2) AS avg_ltv_7_days,
  (SUM(CASE WHEN DATEADD(day, 30, first_purchase_date) > start_time 
    THEN price_in_usd ELSE 0 END)/COUNT(DISTINCT rc_original_app_user_id))::DECIMAL(18,2) AS avg_ltv_30_days,
  (SUM(CASE WHEN DATEADD(month, 6, first_purchase_date) > start_time 
    THEN price_in_usd ELSE 0 END)/COUNT(DISTINCT rc_original_app_user_id))::DECIMAL(18,2) AS avg_ltv_6_months,
  (SUM(CASE WHEN DATEADD(month, 12, first_purchase_date) > start_time 
    THEN price_in_usd ELSE 0 END)/COUNT(DISTINCT rc_original_app_user_id))::DECIMAL(18,2) AS avg_ltv_12_months,
  (SUM(CASE WHEN DATEADD(month, 23, first_purchase_date) > start_time 
    THEN price_in_usd ELSE 0 END)/COUNT(DISTINCT rc_original_app_user_id))::DECIMAL(18,2) AS avg_ltv_24_months,
  (SUM(price_in_usd)/COUNT(DISTINCT rc_original_app_user_id))::DECIMAL(18,2) AS avg_ltv_unbounded

FROM filtered_transactions ft
LEFT JOIN first_purchase_dates fpd 
  ON fpd.rc_original_app_user_id = ft.rc_original_app_user_id
GROUP BY 1

Trial Conversion Rate by Trial End Date

-- What was the conversion-to-paid of all trials that ended each day?

WITH trials AS
(SELECT
  rc_original_app_user_id,
  DATE(effective_end_time) AS end_time
  FROM [revenuecat_data_table] 
  WHERE is_trial_period
),

conversions AS
(SELECT
  rc_original_app_user_id,
  DATE(start_time) AS start_time
  FROM [revenuecat_data_table] 
  WHERE is_trial_conversion
)

SELECT
  DATE(t.end_time) AS date,
  COUNT(t.*) AS trials_ending,
  COUNT(c.*) AS conversions,
  (COUNT(c.*)::real / COUNT(t.*)::real) AS cvr
  FROM trials AS t
  LEFT JOIN conversions AS c ON c.rc_original_app_user_id=t.rc_original_app_user_id
  WHERE t.end_time < CURRENT_DATE
  GROUP BY date;