The overview page in your Paddle dashboard gives you a summary of your business's performance. It presents revenue, subscription, and conversion metrics as charts designed for a fast check-in.
When you want a closer look at that data with flexible filtering and breakdown options, go to the Explore page instead. Explore is built for ad hoc investigations and lets you spot trends or investigate unexpected changes without exporting your data or using a separate analytics tool.
Choose the metric you want to examine, apply filters, and group the results to answer specific questions about your business.
How it works
The Explore page and metrics operations use the same underlying data as the overview page, with more control and precision over how you view it.
Opening a chart directly from the overview page takes you into Explore with the same filters already applied, so you can continue your investigation without starting over.
Dimensions
With Explore, you can tailor the page view and the data it displays by making selections across four dimensions: metrics, filters, breakdown, and granularity.
Your view selections are captured in the page URL, so you can share the exact charts with your team by sharing the URL.
Metrics
Metrics are the numbers that represent what you want to understand about your business.
The total transaction value minus discounts, before deductions like tax, Paddle's fees, refunds, or chargebacks are taken out.
The number of completed transactions and paid B2B invoices in the period.
The total amount refunded to customers, not including chargebacks.
The number of chargebacks raised against completed transactions, excluding warnings and reversals.
The proportion of checkout sessions that resulted in a completed payment.
Your total monthly recurring revenue from active subscriptions.
The net change in MRR in a given period (new, expansion, and reactivation revenue) minus contraction and churn.
Filters
Filters help you narrow down the data behind your chosen metric before it's grouped or totaled. Use a single filter for a broad view, or combine multiple filters to narrow the data to a more specific scenario:
- Date range: must be a single, continuous period.
- Frequency: narrow down to a subscription's billing cycle.
- Product: choose one or more products.
- Country: choose one or more countries.
You can select multiple products, countries, and frequencies to filter your data. You can only select a single date range at a time up to 2 years in the past.
Breakdown
Breakdown groups your chosen metric into categories so you can compare them, instead of seeing a single total. You can pick one breakdown at a time, or none at all:
- None
- Product
- Country
- Frequency
- Subscription actions (only available for MRR and MRR growth)
Choosing None as your breakdown displays the data as a single totals line.
Granularity
Granularity controls how your data is grouped over time in both the chart and the table that follows:
- Daily
- Weekly, starting on Monday
- Monthly
Paddle picks a default based on your selected date range, but you can change it at any time.
Things to know
The data in Explore refreshes on a 24-hour cycle, so it can take up to 24 hours for new activity to appear.
- Total rows aren't always a sum of the preceding rows.
Some breakdowns associate a single record with more than one row, for example a transaction that includes more than one product. The total always matches the figure you'd see with no breakdown applied, so it can be lower than the sum of the individual rows. - Other is a total of everything that can't be displayed in a chart.
When a breakdown has more distinct values than can be displayed on the chart, Explore displays the top values on individual lines and rolls up everything else into a single "Other" line.
View your metrics in Explore
- Go to Paddle > Analytics > Explore.
- Choose the metric you want to view and make selections for filters, breakdown, and granularity to narrow down the output. See Dimensions for what each one does.
- Review the chart and the table that follows, now updated with your selections.
You can also open Explore by selecting a chart on the overview page, or from a direct link shared with you. The existing filters are carried across.
Use the Paddle API to query and explore your account data programmatically using the same filtering and breakdown options available in the dashboard.
Query your data using the API in three steps:
- Discover what you can query
Query the relevant entity to see what dimensions, measures, and filters are available. - Query an entity
Send a request with the entity, a date range, and one or more selections to get the data for the relevant metric back. - Paginate through results
Where a request has more results, get the next page by sending another POST request.
Each metric shown in Explore maps to a specific entity in the API:
| Metric | Entity |
|---|---|
| Revenue | transactions.completed |
| Transactions | transactions.completed |
| Refunds | adjustments.refunds |
| Chargebacks | adjustments.chargebacks |
| Checkout conversion | checkouts |
| MRR | subscriptions |
| MRR growth | subscriptions |
The subscriptions entity isn't available in sandbox, so MRR and MRR growth return no data there. Use a live account to query them.
Discover entities
Send a GET request to /metrics/explore/entities to see what dimensions, measures, and filters are available.
Use the entity query parameter to look up a single entity. Omit to list everything available in your account.
This example returns dimensions, measures, and filters for revenue metrics by passing transactions.completed as a value to the entity query parameter.
{ "data": [ { "entity": "transactions.completed", "time_dimension": { "intervals": ["day", "week", "month"], "max_lookback_days": 1096 }, "dimensions": [ { "name": "product", "type": "string", "filter_ops": ["in"], "multi_valued": false }, { "name": "country", "type": "string", "filter_ops": ["in"], "multi_valued": false }, { "name": "billing_cycle", "type": "string", "filter_ops": ["in"], "multi_valued": false } ], "measures": [ { "name": "gross_revenue", "type": "currency", "aggs": ["sum"] }, { "name": "id", "type": "string", "aggs": ["count", "count_distinct"] } ] } ], "meta": { "request_id": "b93d9c94-c28f-4e5d-af2e-044854d7afe8" }}Query an entity
Send a POST request to /metrics/explore with the entity, from and to dates, an interval, and one or more measures and dimensions.
from is inclusive and to is exclusive, so a to of 2026-08-01 with a monthly interval returns buckets up to and including July. A to in the future is clamped to the current time.
order_by is optional. Leave it out and Explore orders by the entity's default, which you can see in the catalog. Set it when you break results down by a dimension, because it decides which segments land on the page.
Each value in the response is named after the aggregation and the field it came from, so { "field": "gross_revenue", "agg": "sum" } comes back as sum_gross_revenue. Derived measures have no aggregation, so they keep their field name — mrr and checkout_conversion come back unchanged.
Dimensions return Paddle IDs rather than display names, so a product dimension returns a pro_ ID that you can look up using the get a product operation.
Each combination of the dimensions you break down by is a segment: one product, or one product and country pair. Segments are the API equivalent of the categories a breakdown creates in the dashboard. Explore returns one entry in series for every segment, and each one carries its complete timeseries.
This example returns gross revenue for the five highest-earning products, bucketed by month across three months. order_by and per_page together narrow 42 products down to the top five, and each series carries one datapoint per bucket so you can see the trend as well as the ranking.
{ "entity": "transactions.completed", "from": "2026-05-01", "to": "2026-08-01", "interval": "month", "dimensions": ["product"], "measures": [ { "field": "gross_revenue", "agg": "sum" } ], "order_by": [ { "field": "gross_revenue", "dir": "desc" } ], "per_page": 5}{ "data": { "entity": "transactions.completed", "interval": "month", "currency_code": "USD", "starts_at": "2026-05-01T00:00:00Z", "ends_at": "2026-08-01T00:00:00Z", "updated_at": "2026-08-01T06:00:00Z", "fields": { "dimensions": [{ "name": "product", "type": "string" }], "measures": [{ "name": "sum_gross_revenue", "type": "currency", "field": "gross_revenue", "agg": "sum" }] }, "series": [ { "dimensions": { "product": "pro_01h1vjfevh5etwq3rb416a23h2" }, "timeseries": [ { "timestamp": "2026-05-01T00:00:00Z", "measures": { "sum_gross_revenue": "410500" } }, { "timestamp": "2026-06-01T00:00:00Z", "measures": { "sum_gross_revenue": "455200" } }, { "timestamp": "2026-07-01T00:00:00Z", "measures": { "sum_gross_revenue": "482000" } } ] } ] }, "meta": { "request_id": "b93d9c94-c28f-4e5d-af2e-044854d7afe8", "pagination": { "per_page": 5, "next": "https://api.paddle.com/metrics/explore?after=eyJ2IjoxLCJxaWQiOiIwMWpuOHg0azJwN3E5ciIsIm9mZiI6NSwicWgiOiJzaGEyNTY6OWYyYzRkMWUiLCJleHAiOjE3ODU1Njc2MDB9", "has_more": true, "estimated_total": 42 } }}This example returns the total refunded across July 2026, with no breakdown applied. Omit dimensions and order_by when you want a single figure rather than a set of segments. The response holds one series with an empty dimensions object.
{ "entity": "adjustments.refunds", "from": "2026-07-01", "to": "2026-08-01", "interval": "month", "measures": [ { "field": "amount", "agg": "sum" } ]}{ "data": { "entity": "adjustments.refunds", "interval": "month", "currency_code": "USD", "starts_at": "2026-07-01T00:00:00Z", "ends_at": "2026-08-01T00:00:00Z", "updated_at": "2026-08-01T06:00:00Z", "fields": { "dimensions": [], "measures": [{ "name": "sum_amount", "type": "currency", "field": "amount", "agg": "sum" }] }, "series": [ { "dimensions": {}, "timeseries": [ { "timestamp": "2026-07-01T00:00:00Z", "measures": { "sum_amount": "15400" } } ] } ] }, "meta": { "request_id": "b93d9c94-c28f-4e5d-af2e-044854d7afe8", "pagination": { "per_page": 5, "has_more": false, "estimated_total": 1 } }}One page can return up to 10,000 datapoints: per_page multiplied by the number of time buckets in your date range. To stay under it, narrow the range between from and to, use a coarser interval, or lower per_page.
Paginate through results
Pagination for the Explore metrics operation works differently to pagination for list operations.
Explore returns five segments per page by default, up to a maximum of 50. Set per_page in the request body to change it.
When meta.pagination.has_more is true, take the after value from meta.pagination.next and send it as an after query parameter, resending the same request body to get the next page.
Repeat until has_more is false. Paddle returns next on the last page too, so check has_more rather than the presence of next to decide whether to request another page.
For more details, see Pagination.
Common errors
unknown_entity |
| The entity you're querying isn't a recognized queryable metric. |
unknown_field |
| A dimension, measure, or filter field in your query isn't available for this metric. |
invalid_aggregation |
| The aggregation you requested isn't allowed for this measure field. |
invalid_operator |
| The filter operator you used isn't valid for this field's type. |
max_lookback_exceeded |
| The date range you requested goes further back than this metric allows. |
too_many_datapoints |
| The query would return more datapoints on one page than the Explore endpoint allows. |
invalid_cursor |
| The pagination cursor is malformed, expired, or doesn't match the query you sent. |
query_timeout |
| The metrics query took too long to complete. |