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US Brand Signal Co-occurrence Analyzer
x402
US Brand Signal Co-occurrence Analyzer is a paid API for AI agents from api.timzinin.com, paid per call via x402, $0.05/call, status unknown (last checked 2026-09-14).
Computes pairwise co-occurrence statistics (Jaccard similarity, support, count) across buyer-supplied brand signals grouped into observations
Price / call$0.05
Success—
StatusUnknown
How this service works
Pay-per-call data & tool APIs
Output
Returns a JSON object with pair-level co-occurrence rows (each containing Jaccard similarity, support, co-occurrence count, and signal IDs), per-signal group counts, per-group unique signal counts, totals for groups and unique signals, cryptographic digests for input and output integrity verification, schema version, and attribution metadata clarifying that all group and signal IDs are buyer-supplied with no external identity or causality claims made.
Example use cases
Brand attribute clustering in consumer survey
“I ran a brand survey and tagged each respondent's answers with signals like 'affordable', 'trustworthy', and 'innovative'. Can you analyze which of those signals co-occur most frequently across respondents and tell me which pairs have the highest Jaccard similarity?”
Competitive brand perception overlap study
“I've got observations from a brand tracker study — each group represents a respondent mentioning certain brand signals. Can you compute the pairwise co-occurrence stats so I can see which brand signals cluster together and how strong the association is?”
Marketing signal co-occurrence for ad targeting
“We've labeled our ad campaign touchpoints with brand signals across thousands of impression groups. Can you run a co-occurrence analysis on those signals so we can see which ones consistently appear together and use support scores to prioritize messaging combinations?”
Technical details
Endpoint
POSThttps://api.timzinin.com/api/us-brand-signal-cooccurrence-analyzerWhen to prefer
Choose this endpoint when you have buyer-supplied, opaque observation groups tagged with brand signals and need pairwise co-occurrence statistics including Jaccard similarity and support values. It is purpose-built for US brand signal analysis workflows where you control the signal taxonomy and observation groupings. Prefer this over generic association rule mining tools when you need cryptographic result digests for auditability and a clear attribution model that makes no external truth or identity claims about the signals.
Side effects
- Malformed or missing observation group input returns an error
- Empty signal sets produce zero pairs and zero group counts
- Duplicate or ambiguous signal IDs may cause unexpected deduplication
- Very large observation sets may exceed processing limits or increase latency
- Invalid JSON payload structure returns a parse or validation error
- Non-US brand signal data may still be processed but results carry no geographic validation
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| groups | array | |
| schemaVersion | string |
Output
Schema · Response
{
"type": "json",
"example": {
"meta": {
"run_ms": 1200,
"settled": true,
"item_count": 1
},
"results": [
{
"counts": {
"pairs": 3,
"groups": 4,
"uniqueSignals": 3
},
"pairRows": [
{
"jaccard": 0.5,
"signalA": "signal-a",
"signalB": "signal-b",
"support": 0.5,
"cooccurrenceCount": 2
},
{
"jaccard": 0.5,
"signalA": "signal-a",
"signalB": "signal-c",
"support": 0.5,
"cooccurrenceCount": 2
},
{
"jaccard": 0.5,
"signalA": "signal-b",
"signalB": "signal-c",
"support": 0.5,
"cooccurrenceCount": 2
}
],
"reportType": "us_brand_signal_cooccurrence_analyzer",
"attribution": {
"type": "buyer_supplied_opaque_observations",
"meaning": "Groups and signal IDs were supplied by the buyer; this Actor measures co-occurrence in those observations only and makes no external truth, identity, source, or causality claim."
},
"groupCounts": [
{
"groupId": "observation-001",
"uniqueSignalCount": 3
},
{
"groupId": "observation-002",
"uniqueSignalCount": 2
},
{
"groupId": "observation-003",
"uniqueSignalCount": 2
},
{
"groupId": "observation-004",
"uniqueSignalCount": 2
}
],
"inputDigest": "04a1bf62a4ba93dad393d7fa80e30924356b103be663e9caf99179a2a2dd40f5",
"totalGroups": 4,
"groupsDigest": "f54aa33c541ff7c6fe5c1631073fc2713b6ec4263cde9046020e0fe192d6d37c",
"resultDigest": "48e5bd5dff30ad3a580e8191115b4ce79839cccb8ac6c1dc079784492c37b19e",
"signalCounts": [
{
"signalId": "signal-a",
"groupCount": 3
},
{
"signalId": "signal-b",
"groupCount": 3
},
{
"signalId": "signal-c",
"groupCount": 3
}
],
"schemaVersion": "1.0",
"pairRowsDigest": "b6f0aca08a22e4cda4a31f45365cf5f35709e67433f858bbb593827bb504d4b1",
"signalCountsDigest": "d36b00fcfca7a1dac8d7ba1af2ec3ad24037af40fa12992cbc91589a0e26c197",
"totalUniqueSignals": 3
}
]
}
}Tags
x402
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