> ## Documentation Index
> Fetch the complete documentation index at: https://docs.interchange.io/llms.txt
> Use this file to discover all available pages before exploring further.
# Seller analytics
> The sellerAnalytics payload Murph returns — win rate, buyer asks, top products, seasonality, recommended negotiation posture, and how that learning is converting.
## Overview
When you ask Murph for seller analytics, Murph can attach a `sellerAnalytics`
payload to the chat response for visual rendering. The payload summarizes your
recent intelligence runs — discovery-run mix, buyer asks, top surfaced
products, and the commercial outcomes attributed to those runs — and turns them
into directional negotiation guidance.
This page is the canonical reference for that payload. Each section below is one
top-level field of `sellerAnalytics`.
Seller analytics outcome totals use last-touch product-overlap attribution and
are directional. Seller recommendations are deterministic coaching signals
derived from the same window — not automated pricing or packaging changes. Your
[operating instructions](/v2/storefront/operating-instructions/overview) and
[buyer instructions](/v2/storefront/buyer-instructions/overview) remain
authoritative over these signals. For audited campaign delivery reporting, use
the [reporting endpoints](/v2/guides/reporting-overview).
## historicalPerformance
`historicalPerformance` summarizes the analytics window into the negotiation
signals the Merchandising Agent can use.
| Field | Type | Description |
| ------------------------------- | -------------- | --------------------------------------------------------------------------------------------------------------------------------------------------- |
| `runCount` | integer | Recent intelligence runs considered |
| `bookedRunCount` | integer | Runs with at least one attributed booked media buy |
| `winRate` | number | Share of recent runs that booked, from `0` to `1` |
| `askRunCount` | integer | Runs with buyer asks, price objections, or packaging asks |
| `askToBookConversionRate` | number \| null | Share of ask runs that booked, or `null` when no ask runs exist |
| `priceObjectionConversionRate` | number \| null | Share of price-objection runs that booked, or `null` when none exist |
| `packagingAskConversionRate` | number \| null | Share of packaging-ask runs that booked, or `null` when none exist |
| `averageBookedBudget` | number \| null | Average booked budget per attributed booked media buy |
| `averageProductsPerRun` | number | Average products shown per run |
| `averageProductsPerBookedRun` | number \| null | Average products shown on booked runs |
| `averageProductsPerUnbookedRun` | number \| null | Average products shown on unbooked runs |
| `deliveryRate` | number \| null | Delivery-report events divided by booked media buys, or `null` when none booked |
| `repeatBuyerCount` | integer | Buyer identities with at least two runs in the window |
| `recommendedPosture` | string \| null | Directional negotiation posture: `hold_value`, `value_preserving_compromise`, `tradeoff_ladder`, `direct_fit`, `price_first`, or `wholesale_mirror` |
## strategySignals\[]
`strategySignals[]` turns `historicalPerformance` into deterministic strategy
guidance.
| Field | Type | Description |
| ---------- | --------------------------------- | ----------------------------------------------------------- |
| `id` | string | Stable signal key within the generated analytics payload |
| `priority` | `"high"` \| `"medium"` \| `"low"` | Suggested attention level |
| `label` | string | Short display label |
| `detail` | string | Human-readable reason for the signal |
| `posture` | string | Suggested negotiation posture |
| `evidence` | array | Small list of `{ label, value }` metrics backing the signal |
## outcomes
`outcomes` summarizes attributed outcomes across the window.
| Field | Type | Description |
| ----------------------- | -------------- | --------------------------------------------------------------------------------------------- |
| `attributedRunCount` | integer | Number of recent runs with at least one attributed outcome event |
| `eventCount` | integer | Total attributed outcome events |
| `submittedCount` | integer | Media buys submitted for seller approval |
| `forwardedCount` | integer | Media buys forwarded to inventory sources |
| `forwardFailedCount` | integer | Media-buy forward attempts that failed |
| `rejectedCount` | integer | Media buys rejected by the seller |
| `deliveryReportedCount` | integer | Delivery report events attributed to recent runs |
| `bookedMediaBuyCount` | integer | Unique attributed media buys with a booked budget |
| `bookedBudget` | number | Sum of attributed booked budget in the window, counted once per media buy |
| `deliveredImpressions` | integer | Sum of attributed delivered impressions |
| `deliveredSpend` | number | Sum of attributed delivered spend |
| `deliveredCurrency` | string \| null | Delivery spend currency, or `null` when no delivery currency is known or currencies are mixed |
Each item in `runs[]` carries an `outcome` object for that run:
| Field | Type | Description |
| ----------------------- | -------------- | --------------------------------------------------------------------------------------------------------------- |
| `eventCount` | integer | Attributed outcome events for the run |
| `latestType` | string \| null | Latest attributed event type, or `null` when no events are attributed |
| `latestStatus` | string \| null | Latest media-buy status, or `null` when no status is available |
| `mediaBuyId` | string \| null | Latest attributed media-buy ID, or `null` when no media buy is attributed |
| `submittedCount` | integer | Media buys submitted for seller approval |
| `forwardedCount` | integer | Media buys forwarded to inventory sources |
| `forwardFailedCount` | integer | Media-buy forward attempts that failed |
| `rejectedCount` | integer | Media buys rejected by the seller |
| `deliveryReportedCount` | integer | Delivery report events attributed to the run |
| `bookedMediaBuyCount` | integer | Unique attributed media buys with a booked budget |
| `bookedBudget` | number \| null | Sum of attributed booked budgets for the run, counted once per media buy, or `null` when no budget is available |
| `deliveredImpressions` | integer | Delivered impressions attributed to the run |
| `deliveredSpend` | number | Delivered spend attributed to the run |
| `deliveredCurrency` | string \| null | Delivery spend currency, or `null` when no delivery currency is known or currencies are mixed |
`latestType` is one of `media_buy_submitted_for_approval`,
`media_buy_forwarded`, `media_buy_forward_failed`, `media_buy_rejected`, or
`delivery_reported`.
## buyers\[]
`buyers[]` rolls the same window up by buyer identity.
| Field | Type | Description |
| ------------------------- | -------------- | --------------------------------------------------------------------------------------------- |
| `operatorDomain` | string \| null | Buyer operator domain when known |
| `brandDomain` | string \| null | Buyer brand domain when known |
| `country` | string \| null | Buyer country when known |
| `firstRunAt` | string \| null | Earliest run timestamp for this buyer in the window |
| `lastRunAt` | string \| null | Latest run timestamp for this buyer in the window |
| `runCount` | integer | Recent intelligence runs for this buyer identity |
| `bookedRunCount` | integer | Runs for this buyer with at least one attributed booked media buy |
| `winRate` | number | Share of this buyer's recent runs that booked, from `0` to `1` |
| `askRunCount` | integer | Runs for this buyer with buyer asks, price objections, or packaging asks |
| `askToBookConversionRate` | number \| null | Share of this buyer's ask runs that booked, or `null` when no ask runs exist |
| `attributedRunCount` | integer | Runs with at least one attributed outcome event |
| `eventCount` | integer | Attributed outcome events for this buyer identity |
| `totalShownProducts` | integer | Products shown across the buyer's runs |
| `averageShownProducts` | number | Average products shown per run |
| `priceObjectionCount` | integer | Runs with detected price objections |
| `packagingAskCount` | integer | Runs with detected packaging asks |
| `requestAskCount` | integer | Buyer request-level asks detected in the window |
| `productAskCount` | integer | Product-level asks detected in the window |
| `proposalAskCount` | integer | Proposal-level asks detected in the window |
| `bookedMediaBuyCount` | integer | Unique attributed media buys with a booked budget |
| `bookedBudget` | number | Sum of attributed booked budget for this buyer identity |
| `averageBookedBudget` | number \| null | Average booked budget for this buyer's attributed booked media buys |
| `deliveredImpressions` | integer | Delivered impressions attributed to this buyer identity |
| `deliveredSpend` | number | Delivered spend attributed to this buyer identity |
| `deliveredCurrency` | string \| null | Delivery spend currency, or `null` when no delivery currency is known or currencies are mixed |
| `recommendedPosture` | string \| null | Buyer-specific directional negotiation posture |
## seasonality\[]
`seasonality[]` buckets the same window by UTC month.
| Field | Type | Description |
| ------------------------- | -------------- | ---------------------------------------------------------------------- |
| `period` | string | Month bucket in `YYYY-MM` form |
| `runCount` | integer | Intelligence runs in the period |
| `bookedRunCount` | integer | Runs in the period with at least one attributed booked media buy |
| `winRate` | number | Share of period runs that booked, from `0` to `1` |
| `askRunCount` | integer | Period runs with buyer asks, price objections, or packaging asks |
| `askToBookConversionRate` | number \| null | Share of period ask runs that booked, or `null` when no ask runs exist |
| `priceObjectionCount` | integer | Period runs with detected price objections |
| `packagingAskCount` | integer | Period runs with detected packaging asks |
| `bookedBudget` | number | Sum of attributed booked budget in the period |
| `averageBookedBudget` | number \| null | Average booked budget across booked runs in the period |
| `averageShownProducts` | number | Average products shown per run in the period |
| `recommendedPosture` | string \| null | Directional posture inferred for the period |
## postureConversion
`postureConversion` is the learning readout: which negotiation posture your
Merchandising Agent actually used is booking deals, and whether following the
history-derived recommendation booked better than diverging from it. It is the
proof behind the `recommendedPosture` that appears throughout this payload.
It distinguishes two postures per run: the **selected** posture (what the agent
used this time) and the **recommended** posture (the history-derived suggestion —
the same one documented under `historicalPerformance.recommendedPosture`).
### postureConversion.byPosture\[]
Conversion rolled up by the posture the agent actually used (the `selected`
posture). Runs where no composition ran — and so have no selected posture — are
excluded from every denominator.
| Field | Type | Description |
| ---------------- | ------- | ------------------------------------------------------------------------------------------------ |
| `posture` | string | The negotiation posture used (see `historicalPerformance.recommendedPosture` for the six values) |
| `runCount` | integer | Recent runs that used this posture |
| `bookedRunCount` | integer | Those runs with at least one attributed booked media buy |
| `winRate` | number | Share of this posture's runs that booked, from `0` to `1` |
| `bookedBudget` | number | Sum of attributed booked budget for runs using this posture |
### postureConversion.adherence
How often the agent followed its own history-derived recommendation, and whether
following it converted better than diverging from it.
| Field | Type | Description |
| -------------------- | -------------- | ----------------------------------------------------------------------------- |
| `comparableRunCount` | integer | Runs where both a selected and a recommended posture are present |
| `followedCount` | integer | Comparable runs where the selected posture matched the recommendation |
| `followedWinRate` | number \| null | Share of followed runs that booked, or `null` when there are no followed runs |
| `divergedWinRate` | number \| null | Share of diverged runs that booked, or `null` when there are no diverged runs |
**How to read it:** when `followedWinRate` is higher than `divergedWinRate`,
following the learned recommendation is converting better — the signal the
learning loop is working. The reverse is a prompt to revisit your
[operating instructions](/v2/storefront/operating-instructions/overview). Both
rates stay `null` until there are enough comparable runs to compare.
## learnedDefault
`learnedDefault` is the negotiation posture your Merchandising Agent has saved as
its own default — the first rule the agent writes for itself. Where
`postureConversion` is the live readout of what is converting, `learnedDefault`
is the persisted rule distilled from it: a durable, visible, revertible default
that survives across sessions.
| Field | Type | Description |
| ----------- | -------------- | --------------------------------------------------------------------------------------------------------------------- |
| `posture` | string | The saved default negotiation posture (one of the six values listed under `historicalPerformance.recommendedPosture`) |
| `reason` | string \| null | Why it was learned — e.g. `"Hold value booked 64% over 14 runs"`, or `"Pinned by you"` when you set it yourself |
| `checkedAt` | string \| null | ISO 8601 timestamp of when the default was last written |
`learnedDefault` is `null` until a default has been saved. Two conditions must
both hold before the agent saves one:
1. **A sustained, evidence-gated signal.** A single posture must be both
converting above the floor and backed by enough recent runs — a one-off win
never becomes a saved default.
2. **The learning loop is switched on for your storefront.** Saving a default is
gated and off by default; until it is enabled, the agent reads its posture
live on every request and saves nothing.
**How it is used:** the agent applies `learnedDefault` only as a *fallback* —
when it has no fresher, buyer-specific read for the request in front of it. It is
never a forcing override. Your
[operating instructions](/v2/storefront/operating-instructions/overview) and any
buyer-specific rules always take precedence, so you stay in control of how your
storefront sells.
**Clearing or pinning it:** you stay in control of the saved default. Ask your
agent to **pin** a specific posture as the default yourself — the saved `reason`
then reads `"Pinned by you"` and the agent will **not** relearn over it, even
while it keeps learning, until you change it. Or ask it to **clear** the default
(revert to agent-managed): the saved default is removed and the agent may learn a
new one from outcomes again. Either way the learned default remains a fallback the
agent uses only when it has no fresher read; pinning or clearing never changes
which postures the agent considers, only which one it falls back to.
## sellerRecommendations\[]
`sellerRecommendations[]` turns those directional signals into seller actions.
| Field | Type | Description |
| ---------- | ----------------------------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------- |
| `id` | string | Stable recommendation key within the generated analytics payload |
| `priority` | `"high"` \| `"medium"` \| `"low"` | Suggested attention level |
| `kind` | `"protect_expand"` \| `"prioritize_follow_up"` \| `"packaging_friction"` \| `"price_resistance"` \| `"tighten_selection"` \| `"learned_auto_approve"` | Recommendation category |
| `title` | string | Short display title |
| `detail` | string | Human-readable reason for the recommendation |
| `action` | string | Suggested next seller action |
| `buyer` | object \| null | Buyer identity the recommendation is about, or `null` for global seller guidance |
| `evidence` | array | Small list of `{ label, value }` metrics backing the recommendation |
### Learned auto-approve suggestions
When the media buys from one buyer that came to you for review have all cleared
and forwarded with no rejections, the agent surfaces a `learned_auto_approve`
recommendation flagging that buyer as consistently low-friction and pointing you
at the [per-buyer auto-approve](/v2/storefront/buyer-auto-approvals/overview)
control — so you can opt that buyer in with one step if you trust them to keep
clearing. This looks only at **media-buy approvals** — not creative review (a
separate gate) and not delivery performance. It is **suggest-only**: the agent
recommends, but you author the carve-out — it never changes an approval gate on
its own. It learns the pattern from your own decisions, and it's evidence-gated —
a short or mixed approval history yields no suggestion.
## Related
What a storefront is and how its pieces fit together.
The per-run records these analytics aggregate.
How to ask Murph for analytics in the storefront workspace.
Audited delivery reporting for finished campaigns.