Event Attendance Signals

event-attendance-signals

Structured lookup against the event_contact entity (one row per person + event) via ask_onfire. This entity records every tracked event attendance signal — who attended which conference or industry gathering, their employer, and their location. For per-company attendee counts, use the event_company entity instead.

ask_onfire takes a single query argument — a structured QueryIR, never SQL:

ask_onfire(query={
  entity: "<entity>",
  select: ["<field>", ...],          // logical fields on the entity
  filters: [{dimension, op, value}], // op: eq | in | gte | lte | contains
  joins: [{entity, filters}],        // FILTER-ONLY single-hop; cannot SELECT joined columns
  order_by: [{field, direction}],
  distinct_by: "<field>",
  limit: <int>,                      // ROW BUDGET — see Billing
  confirmed: <bool>
})

When to use this

• "Who attended CloudCon 2025?"
• "Find all HackSummit 33 attendees from US-based companies"
• "Which events did people from Meridian Bank go to?"
• "Show me every event a list of LinkedIn profiles attended"
• "Who from Germany was at the MediaExpo?"
• Any question about conference attendance or event participation

Skip this for:

• GitHub star/fork activity → use github-repo-signals
• Community membership (Slack/Discord/Reddit) → use community-join-signals
• People search by current job title / company → use entity-people-search

Entity structure

event_contact — one row per (person, event)

event_contact has no profile name/title fields. To return attendee names and titles, query entity: "contact" and add a filter-only join to event_contact (see the first template below).

event_company — one row per (company, event)

Resolving the event name

The event value is a concept, not a literal. The server resolves it from the events vocabulary (Event - <name>). Pass the human term and let ask_onfire resolve it; if you want to confirm the canonical wording first, call resolve_insights. Never LOWER/LIKE/substring-match the event field.

Billing — read before calling

ask_onfire bills the client 1 credit per row returned.

• Always set a small explicit limit. For most attendee lookups 10-50 rows is plenty.
• If you do not set a limit (or the match is bigger than an unconfirmed budget over the server threshold), ask_onfire returns needs_confirmation with stage: "row_budget" — nothing is billed and no rows come back. It reports how many rows match so you can lower limit to what you need and resubmit. Do not reflexively set confirmed: true to push a large pull through.

QueryIR templates

Who attended a specific event (with names + titles)

event_contact has no profile fields, so query contact and filter-join to event_contact on the event:

ask_onfire(query={
  entity: "contact",
  select: ["full_name", "job_title", "current_company_name", "linkedin_url"],
  joins: [{entity: "event_contact", filters: [
      {dimension: "event", op: "eq", value: "CloudCon 2025"}   // resolved to 'Event - CloudCon 2025'
  ]}],
  limit: 50
})

If you only need the raw attendee + employer URLs (no names), query event_contact directly:

ask_onfire(query={
  entity: "event_contact",
  select: ["contact_url", "company_url", "contact_country", "contact_region"],
  filters: [{dimension: "event", op: "eq", value: "CloudCon 2025"}],
  limit: 50
})

All events attended by people from a specific company

ask_onfire(query={
  entity: "event_contact",
  select: ["contact_url", "event", "contact_country", "active_employment"],
  filters: [{dimension: "company_url", op: "eq", value: "https://www.linkedin.com/company/meridian-bank"}],
  limit: 50
})

Did specific people attend any events?

ask_onfire(query={
  entity: "event_contact",
  select: ["contact_url", "event", "company_url", "contact_country"],
  filters: [{dimension: "contact_url", op: "in", value: [
      "https://www.linkedin.com/in/alice-smith",
      "https://www.linkedin.com/in/bob-jones"
  ]}],
  limit: 50
})

How many people from a company attended an event (count)

Use the event_company entity — attendee_count is pre-aggregated:

ask_onfire(query={
  entity: "event_company",
  select: ["event", "attendee_count"],
  filters: [
    {dimension: "company_url", op: "eq", value: "https://www.linkedin.com/company/meridian-bank"},
    {dimension: "event", op: "eq", value: "CloudCon 2025"}
  ],
  limit: 5
})

Companies with the largest presence at an event

ask_onfire(query={
  entity: "event_company",
  select: ["company_url", "attendee_count"],
  filters: [
    {dimension: "event", op: "eq", value: "CloudCon 2025"},
    {dimension: "attendee_count", op: "gte", value: 25}
  ],
  order_by: [{field: "attendee_count", direction: "desc"}],
  limit: 20
})

Attendees from a region at a specific event

ask_onfire(query={
  entity: "event_contact",
  select: ["contact_url", "company_url", "contact_region"],
  filters: [
    {dimension: "event", op: "eq", value: "SecureCon"},
    {dimension: "contact_country", op: "eq", value: "united states"},
    {dimension: "contact_region", op: "eq", value: "california"}
  ],
  limit: 50
})

Not expressible with ask_onfire

ask_onfire cannot do GROUP BY distributions over a free dimension. Two of the old query shapes have no direct equivalent:

• "Which events had the most attendees from a country?" (group by event, count attendees) — event_contact has no per-event count measure (attendee_count lives on event_company, keyed by company, not country). Workaround: name the candidate events and pull each one's event_company.attendee_count separately, or query event_contact filtered to the country with a large limit and tally events client-side from the returned dataset.
• "Which events attracted the most attendees from a set of target companies?" (group by event across many companies) — same limitation. Pull event_company rows for each target company_url (or each candidate event) and aggregate the attendee_count values yourself; there is no single GROUP-BY call.

Output handling

ask_onfire returns a dataset handle + preview rows (and total_count when relevant).

1. Show the preview rows as a table.
2. State total_count; offer download_dataset for the full CSV.
3. Follow-up slices ("only active employees", "who appears most") → use query_datasets against the dataset_id — do not re-run ask_onfire (re-running bills again).

Common pitfalls

• event is a bound concept — pass the human term ("CloudCon 2025"); the server resolves it to the stored Event - CloudCon 2025. Never LOWER/LIKE/substring-match it.
• event_contact has no name/title fields — to return profiles, query entity: "contact" and filter-join event_contact.
• Per-company counts live on event_company (attendee_count, pre-aggregated) — do not try to COUNT over event_contact.
• contact_country / contact_region are lowercase — filter against "united states", not "United States".
• Joins are filter-only — you cannot select columns from a joined entity, only filter by them.
• Billing — every row costs 1 credit; set a small limit and slice follow-ups with query_datasets instead of re-running.