Avia - Dashboard API (1.6.0)

Returns service health status. No authentication required.

Returns key dashboard metrics for this store only (same shape as all-stores summary). Traffic and session metrics from BigQuery; CVR, AOV, and revenue per visitor from ShopifyQL. Defaults to yesterday when startDate and endDate are omitted.

Return the full dashboard payload for a single store location.

Includes demographics, heatmaps, opportunity-loss table, anomalies, and forecasts. Same data shape as the all-stores dashboard, filtered to this location. Defaults to yesterday when dates are omitted.

Returns the list of registered devices for the specified store.

Same as all-stores GET /api/{shopify_domain}/visits with locationId set: three parallel BigQuery series (dup uniques, dup row counts, normalized in_count), merged dailyUnique and dailyTraffic as sorted [{date, traffic}, ...].

Predicts the rolling 14-day forecasted traffic per hour per store starting with today's date. Operating hours are always applied automatically; data is filtered to only include hours when the store is open.

Returns detected visit anomalies with hourly and 4-hour bucket groupings for the specified store. Each anomaly includes the anomaly value (integer) and type ('positive' or 'negative').

Returns detected conversion rate (CVR) anomalies with hourly and 4-hour bucket groupings for the specified store. CVR is expressed as a percentage. Each anomaly includes the anomaly value (CVR percentage as integer) and type ('positive' or 'negative').

Returns stockout alerts for a location: top products and variants with inventory and 14-day demand projections. Response includes success, shop, serverTime, received (locationId), averageCvr14d, cvr14dDetails, dailyCvr14d, dailyTraffic14d, forecastDailyVisits14d, expectedOrdersByDate14d, expectedProductOrdersByDate14dByProductId, expectedUnitsDemandByDate14dByProductId, stockoutProjectionByProductId, totalUnitsSoldAllVariants14d, topVariants, and topProducts. See StockoutAlertsResponseSchema for the full response shape.

Returns all stores (locations) for the specified client.

List registered entrance devices across every store for the client, for use by UI location/device pickers. Excludes POS devices (same scope as the serial numbers fed into BigQuery traffic queries) and unregistered devices. Pair a device's id with its locationId as the deviceId query param on /summary, /summary/full, /opportunity-loss, /demographics, /visits, and /heatmap.

Returns locations in the same format the dashboard expects: list of { id, name, active }. Only includes stores that have at least one registered (ENTRANCE) traffic device; active is true when the location has at least one active license. hasActiveLicenses is true when at least one returned location is active.

Returns key dashboard metrics for each requested locationId, computed in parallel. Each result pairs a location_id with a data object holding the same kind of metrics as /summary, plus store-economics ratios (revenue per store size, revenue per rent, traffic-to-rent) when store size/rent are configured — those ratio fields are null otherwise. Also includes sales_per_hour (gross sales for the period divided by the store's total operating hours in that period, from its configured store_hours schedule — not staff/labor hours); null when the store has no operating-hours schedule configured, or when it was closed the whole period.

Returns key dashboard metrics aggregated across all stores for the specified client, or for a single store when locationId is set. Traffic and session metrics from BigQuery; CVR, AOV, and revenue per visitor from ShopifyQL. Defaults to yesterday when startDate and endDate are omitted.

Two breakdowns: unique visitors (genderUnique, ageUnique, ageByGenderUnique)—latest enter per visitor key (global_id + local date + camera SN)—and all duplicate rows (genderTotal, ageTotal, ageByGenderTotal), one count per device_duplicates_normalized row. Empty views are filled from in_count on device_events_normalized with the same filters as dashboard traffic.

Returns the full dashboard payload expected by the Shopify app frontend. All data (locations, user tier, shop info, summary, heatmaps, revenue, anomalies, opportunity loss) is computed here. Scoped to a single store when exactly one locationId is given, otherwise aggregated across all stores for the client.

Three parallel BigQuery loads (same camera / date / operating-hours filters):

• dailyUniqueByGlobalId — distinct visitor key (global_id + local date + camera SN) per day (summed across cameras) from device_duplicates_normalized.
• dailyDuplicateTableRows — COUNT(*) per day on device_duplicates_normalized (no per-visitor deduplication).
• dailyNormalizedTraffic — summed in_count per day from device_events_normalized.

dailyUnique / dailyTraffic — sorted lists of { "date", "traffic" } merged from the above (unique ↔ normalized; dup rows ↔ merged unique). Raw series dailyUniqueByGlobalId, dailyDuplicateTableRows, dailyNormalizedTraffic are unchanged.

Return heatmap payload in the same format as the full dashboard: heatmap, rawHeatmapData, heatmapDailyTotals, allHeatmapData (visits, cvr, aov, combined), forecastResult, forecastUpliftResult, operatingHours, etc. Single-store or aggregate depending on whether exactly one locationId is provided.

Returns a JSON object keyed by Shopify locationId, where each value matches the payload from GET /api/{shopify_domain}/heatmap?locationId=... (heatmap, rawHeatmapData, dailyTotals, heatmapDailyTotals, allHeatmapData, forecastResult, operatingHours, etc.).

• locationId: repeat per store, e.g. ?locationId=1&locationId=2. Comma-separated values in a single parameter are also accepted.
• startDate / endDate: required (YYYY-MM-DD).
• applyOperatingHours: accepted but ignored — this endpoint always forces operating-hours filtering OFF (false), regardless of the value passed.
• heatmapMetric: visits | cvr | aov | combined (default visits).

Non-numeric location IDs yield an empty heatmap/heatmapDailyTotals payload for that key.

Returns a JSON object keyed by Shopify locationId, where each value is { rawHeatmapData, startDate, endDate, locationId }. Unlike /heatmap/by-location, rawHeatmapData rows are keyed by real calendar date (YYYY-MM-DD) + hour instead of weekday + hour, so one range request returns actual daily history rather than a 7-weekday rollup.

• locationId: repeat per store, e.g. ?locationId=1&locationId=2. Comma-separated values in a single parameter are also accepted.
• startDate / endDate: required (YYYY-MM-DD).
• Always ignores operating-hours filtering (same as /heatmap/by-location), regardless of any applyOperatingHours value — there is no such parameter on this endpoint.
• orders/sales come from ShopifyQL and default to 0/0.0 when ShopifyQL data is absent; rows with orders == 0 from refund-only lines are filtered out upstream (same rule as the weekday-grouped heatmap endpoints).

Returns opportunity loss data for the given date range and filters. Same semantics as /dashboard: startDate, endDate, locationId (optional), applyOperatingHours, comparisonPeriod ("wow" or "dod").

Returns a full dashboard-style summary (demographics, opportunity loss, daily traffic, raw heatmap rows, forecasts, revenue metrics) used to populate the comparison pane. Same filter semantics as /opportunity-loss and /dashboard.

Note: unlike /dashboard, the heatmap field here is the raw per-hour row array (same content as rawHeatmapData), not a pivoted hour-to-week map.

Returns POS metrics in the same structure as the Shopify app api.pos.metrics route. Used by the POS extension. Traffic and demographics from BigQuery; revenue and orders from ShopifyQL. Same filter semantics as /dashboard and /summary: startDate/endDate default to yesterday.

Predicts the rolling 14-day forecasted traffic per hour aggregated across all stores for the specified client, starting with today's date. Built by aggregating each store's forecast rows by (day-of-week, hour) and projecting that pattern onto the next 14 calendar dates. Unlike the per-store variant, this aggregate response never includes an "insight" or "accuracy" field.

Returns a JSON object keyed by Shopify locationId, where each value is the exact same payload as GET /api/{shopify_domain}/stores/{shopify_location_id}/ai/traffic-forecast (traffic_forecast_hourly, traffic_forecast_4h, insight, accuracy) — so a forecast sync can fetch N locations in one request instead of N serial per-location calls.

• locationId: repeat per store, e.g. ?locationId=1&locationId=2. Comma-separated values in a single parameter are also accepted.
• Unlike the aggregate /ai/traffic-forecast endpoint, each per-location value always includes an insight field (empty object when no insight is available), matching the per-store variant.
• accuracy is computed per location, from that location's own devices only — a store with multiple devices gets one weighted accuracy percentage combining all of them, not a per-device breakdown.