Documentation Index Fetch the complete documentation index at: https://docs.abconvert.io/llms.txt
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Preview — this page is an early draft. We’re still verifying the API reference against the current endpoints. Fields, response shapes, and examples may change before this section is marked stable. Follow PRO-1459 or ask in #abconvert-dev if you’re integrating against this today.ABConvert provides four analytics endpoints for a single experiment: two product-based and two visitor-based (v1 and v2). A fifth endpoint returns a timeseries of statistical significance values so you can visualize how confidence builds over the run of an experiment.
All analytics endpoints are session endpoints, require Shopify authentication, and accept the same standard request body described below.
Standard analytics request All analytics endpoints except the timeseries endpoint accept this request body:
{ "dateStart" : "2024-01-01T00:00:00.000Z" , "dateFinish" : "2024-01-31T23:59:59.999Z" , "filterParams" : { "countries" : [ "US" , "CA" ], "productIds" : [ "123456" ] }, "deviceType" : "ALL" , "visitorType" : "ALL" }
ISO 8601 start of the reporting window, inclusive.
ISO 8601 end of the reporting window, inclusive.
Optional filters to narrow the result set. Array of two-letter ISO country codes. If omitted, all countries are included.
Array of Shopify product ID strings. If omitted, all products in the experiment are included.
Filter by device category. One of "ALL", "DESKTOP", "MOBILE", or "TABLET". Defaults to "ALL".
Filter by visitor type. One of "ALL", "VISITOR" (new visitors), or "CUSTOMER" (returning customers). Defaults to "ALL".
Product-based analytics Returns conversion metrics aggregated at the product level. Use this view when you want to compare raw counts of views, add-to-carts, and revenue across test groups.
POST /api/analytics/:experimentId
Path parameters The _id of the experiment to query.
Example request curl -X POST "https://app.abconvert.io/api/analytics/64a1f2b3c4d5e6f7a8b9c0d1" \ -H "Authorization: Bearer <shopi...en>" \ -H "Content-Type: application/json" \ -d '{ "dateStart": "2024-01-01T00:00:00.000Z", "dateFinish": "2024-01-31T23:59:59.999Z", "filterParams": { "countries": ["US"] }, "deviceType": "ALL", "visitorType": "ALL" }'
Response { "views" : [ 1200 , 1185 ], "addToCarts" : [ 340 , 390 ], "revenue" : [ 8500.00 , 9750.50 ], "conversionRates" : { "addToCart" : [ 0.283 , 0.329 ], "purchase" : [ 0.071 , 0.082 ] } }
Total product page views per test group. Index 0 is the control group; index 1 is the first variant.
Total add-to-cart events per test group.
Total revenue in store currency per test group.
Computed conversion rates for add-to-cart and purchase events, indexed by test group.
Visitor-based analytics v1 Returns conversion metrics at the unique visitor level rather than per product view. This is the standard analytics view for most experiment types.
POST /api/analytics/v1/:experimentId
Path parameters The _id of the experiment to query.
Example request curl -X POST "https://app.abconvert.io/api/analytics/v1/64a1f2b3c4d5e6f7a8b9c0d1" \ -H "Authorization: Bearer <shopi...en>" \ -H "Content-Type: application/json" \ -d '{ "dateStart": "2024-01-01T00:00:00.000Z", "dateFinish": "2024-01-31T23:59:59.999Z", "deviceType": "DESKTOP", "visitorType": "ALL" }'
Response { "totalViews" : [ 950 , 940 ], "totalAddToCarts" : [ 210 , 255 ], "totalOrders" : [ 67 , 81 ], "totalRevenue" : [ 6800.00 , 8200.00 ], "statisticalSignificance" : { "pValue" : 0.043 , "tStat" : 2.04 , "confidenceInterval" : [ 0.02 , 0.18 ] } }
Unique visitors who viewed the experiment per test group.
Unique visitors who added to cart per test group.
Show statisticalSignificance fields
The p-value for the primary conversion metric. Values below 0.05 indicate statistical significance at the 95% confidence level.
The t-statistic for the two-sample test.
95% confidence interval for the difference in conversion rates between control and variant.
Visitor-based analytics v2 An enhanced version of visitor-based analytics that includes journey tracking — how visitors moved through the funnel across multiple sessions. Use v2 when you need more granular attribution.
POST /api/analytics/v2/:experimentId
Path parameters The _id of the experiment to query.
Example request curl -X POST "https://app.abconvert.io/api/analytics/v2/64a1f2b3c4d5e6f7a8b9c0d1" \ -H "Authorization: Bearer <shopi...en>" \ -H "Content-Type: application/json" \ -d '{ "dateStart": "2024-01-01T00:00:00.000Z", "dateFinish": "2024-01-31T23:59:59.999Z", "deviceType": "ALL", "visitorType": "ALL" }'
The response shape is an extension of v1, with additional journey-level breakdown fields included for each test group.
Statistical significance timeseries Returns a day-by-day (or period-by-period) breakdown of statistical significance over the life of the experiment. Use this to visualize how confidence grows and identify when the experiment reached significance.
POST /api/analytics/timeseries/:experimentId
Path parameters The _id of the experiment to query.
Request body ISO 8601 start of the reporting window.
ISO 8601 end of the reporting window.
Total cumulative visitor counts per group at the time of the query. Example: [950, 940].
Metric to use for the significance calculation. Pass "sales" to use revenue; omit for the default conversion rate calculation.
Example request curl -X POST "https://app.abconvert.io/api/analytics/timeseries/64a1f2b3c4d5e6f7a8b9c0d1" \ -H "Authorization: Bearer <shopi...en>" \ -H "Content-Type: application/json" \ -d '{ "dateStart": "2024-01-01T00:00:00.000Z", "dateFinish": "2024-01-31T23:59:59.999Z", "totalViews": [950, 940], "type": "sales" }'
Response { "timeSeriesData" : [ { "date" : "2024-01-01" , "significance" : 0.12 }, { "date" : "2024-01-07" , "significance" : 0.31 }, { "date" : "2024-01-14" , "significance" : 0.67 }, { "date" : "2024-01-21" , "significance" : 0.89 }, { "date" : "2024-01-31" , "significance" : 0.96 } ], "overallSignificance" : 0.96 }
Array of date + significance pairs. significance ranges from 0 to 1, where values above 0.95 indicate 95% confidence.
The aggregate statistical significance for the full date range.
