Competitor Change Rollup — Weekly Watch Card

Watch one competitor domain through a stable named baseline and receive a review-ready card for observed technology, public hiring, and SEC Form D name-match changes. Three built-in scanners run in-process; no child Actor is invoked and there is no second Actor charge.

What you get

• One card per check: a successful first run establishes a baseline, and later runs mark exactly which comparable fields moved since the prior accepted snapshot.
• Three signals combined in one watch: tech-stack changes (new tools, CMS, ecommerce platform), hiring momentum shifts, and new SEC funding-filing mentions.
• Honest about uncertainty: a guessed hiring board is checked against its public owner signal when available. A confirmed mismatch is excluded from the snapshot and cannot create a paid change. Funding name matches remain unverified.
• Source failures stay source failures. A timeout, blocked/truncated website response, malformed source payload, or unavailable ATS board cannot be labelled as a completed check merely because the scanner returned an error-shaped row.
• A quiet week has no result-event charge. The tier-specific Actor-start event still applies to every run, including quiet and failed runs.
• Runs on Apify: schedule it weekly, call it from the API, export to JSON/CSV/Excel or push straight into your competitive-intel pipeline.

How to run it

1. Click Try for free and open the Input tab.
2. Paste the competitor's domain into Competitor domain.
3. Optionally set Role keywords to flag hiring for specific roles.
4. Hit Start. The first run creates the baseline; schedule it weekly to get a stream of change cards.

Pricing

Pay-per-event with the currently published six-tier contract: $0.005–$0.004 per Actor start and $0.05–$0.04 per delivered baseline/change card, depending on the buyer's Apify tier. Quiet and total-failure rows have no result-event charge, but the start event still applies. This Actor watches one competitor and can deliver at most one paid card per run. At the highest published prices, one paid run costs $0.055 in events and ten separate paid runs cost $0.55. A partial baseline/change card may still be delivered and charged when at least one source produced usable evidence; inspect partial, partialReason, checked flags, confidence, and gaps before acting. The live Pricing tab is authoritative.

Input

Field	Required	What it does
domain	yes	The competitor's domain to watch, e.g. "stripe.com".
role_keywords	no	Words to match against open job titles (default: ["sales","marketing"]).
company_name_override	no	Override our guessed ATS token/company name (see limitation below).
watch_name	no	Name for this watch, in case you run more than one on the same domain. Defaults to the domain itself.

Migration: older API clients and saved Tasks may still send baseline_key. Runtime accepts that legacy alias when watch_name is absent, so the same value continues to address the same stored baseline. New integrations should use watch_name; if both fields exist, watch_name wins, including when it is explicitly empty (which selects the domain fallback).

{
    "domain": "stripe.com",
    "role_keywords": ["sales", "marketing"],
    "company_name_override": "stripe",
    "watch_name": "stripe-weekly-watch"
}

⚠️ Important limitation: guessed company identity for 2 of the 3 signals

The tech-stack signal is requested directly from the submitted domain, so it does not need a guessed company token; the website request can still fail, be blocked, or hit the bounded response cap, in which case techStackChecked=false and the row is partial. The hiring and funding signals need a company NAME or ATS token, not a domain, so by default we guess one from the domain itself. This guess can attach an unrelated company's data to your watch — every week. Verified live (same underlying issue as our own lead-list-qualifier Actor): guessing "example" as a company slug for example.com returned real hiring data for a completely unrelated company that happens to sit under that token on one of the job boards we check. Every row exposes matchBasis, attributionConfirmed (true, false, or null), and attributionEvidence. A false hiring match is excluded and any legacy misattributed hiring values are removed from the baseline without charging for that correction. null means ownership evidence was unavailable, not confirmed. If you know the competitor's real ATS token or legal entity name, set company_name_override to remove the guesswork.

Baseline storage and privacy

The Actor stores one compact comparison snapshot in the named competitor-change-rollup-baseline Key-Value Store available to the account running the Actor. The snapshot contains normalized technology labels, three hiring counts, and previously observed SEC accession identifiers. It does not retain fetched HTML, job descriptions, contact details, cookies, credentials, or an Apify token. The sanitized watch_name selects the record inside that store. Named storage persists between runs according to the caller's Apify storage settings; delete the store or its record when the watch and its retained baseline are no longer needed. Do not place secrets or unnecessary personal data in domain, company_name_override, role_keywords, or watch_name.

Output

Historical baseline excerpt from the accepted 2026-08-09 Ryan canary 59pv6fSD95Z76U5jz on build iV66eUzGveg35dUuZ (Dataset FfEeGSz8rdJbwuNaU). The excerpt deliberately preserves the identity and SEC limits; the current release additionally fails closed when any scanner returns an error row:

{
    "domain": "stripe.com",
    "found": true,
    "baselineCreated": true,
    "changed": false,
    "changedFields": [],
    "hiringMomentum": "aggressive hiring",
    "openRolesCount": 550,
    "matchedRolesCount": 119,
    "fundingFilingsSeenCount": 11,
    "newFundingFilingsCount": 11,
    "companySlugUsed": "stripe",
    "identityConfidence": "override",
    "matchBasis": "buyer-override",
    "attributionConfirmed": null,
    "error": "",
    "recommendedAction": "SCHEDULE_NEXT_CHECK",
    "safeToAutomate": false,
    "checkedAt": "2026-08-09T15:38:27.757Z"
}

Change detected (real output, live run 30.07.2026 — reproduced via a controlled baseline change to prove the diff engine catches it, since a competitor's real headcount doesn't reliably shift within seconds):

{
    "domain": "stripe.com",
    "baselineKey": "selftest-live-run",
    "found": true,
    "baselineCreated": false,
    "changed": true,
    "changedFields": [
        { "field": "openRolesCount", "from": 1, "to": 539 }
    ],
    "newFundingFilingsCount": 0,
    "newFundingFilings": [],
    "identityConfidence": "guessed",
    "error": "",
    "summary": "Change detected for \"stripe.com\" (watch \"selftest-live-run\") (hiring/funding signals here are UNVERIFIED — guessed company slug, see README): openRolesCount: \"1\" -> \"539\".",
    "checkedAt": "2026-07-30T10:49:19.857Z"
}

Field	Meaning
found	true for a real, billable result (baseline established or a real change); false for a notice/error row.
baselineCreated	true only on the first-ever run for this watch.
changed / changedFields	Whether anything moved since the last check, and exactly which field(s) with old/new values.
techStack, ecommerce, cms	Current tech-stack snapshot.
hiringMomentum, openRolesCount, matchedRolesCount	Current hiring snapshot, keyed on companySlugUsed.
fundingFilingsSeenCount, newFundingFilingsCount, newFundingFilings	Cumulative SEC filings seen for this watch, and any new ones this run — always check newFundingFilings yourself, see the limitation above.
identityConfidence	"guessed" or "override" — see the limitation above.
matchBasis, attributionConfirmed, attributionEvidence	How the hiring token was chosen and whether the board owner's public signal matched this domain. null is unknown.
partial / partialReason	true when some (not all) of the three sources failed to respond this run — whatever DID answer is still reported/billed.
error	Empty on a delivered baseline/change or comparable quiet outcome. Non-empty on total source failure, baseline-state failure, budget stop, pricing failure, or delivery advisory; read it together with failureType and retryable.

Other tools we built

Related tools

Related tools for adjacent workflows in B2B lead generation and data enrichment, e-commerce.

Actor	What it does
Tech Stack Change Detector	Pair it in the B2B lead generation and data enrichment workflow: Detect a website's current technologies (CMS, ecommerce, analytics, marketing/CRM, framework, hosting/CDN,...
Company Profile Lookup	Pair it in the B2B lead generation and data enrichment workflow: Turn a domain or company name into one unified company card: website tech stack (CMS, ecommerce, key tech)...
Company Hiring Radar	Pair it in the B2B lead generation and data enrichment workflow: Pull every open role a company is hiring for from its public job board (Greenhouse, Lever, Ashby) and turn...
Shopify Price Change Monitor	Pair it in the e-commerce workflow: Watch a list of Shopify stores and get alerted when their price range, catalog size, or estimated revenue...
B2B Lead Enricher	Pair it in the B2B lead generation and data enrichment workflow: Turn a list of company websites into sales-qualified lead cards: detected tech stack, a rough revenue...

FAQ / Limitations

Can I watch several competitors? Yes — run one watch per domain (each gets its own domain/watch_name); this Actor scores one competitor per run.

What happens if I change role_keywords or company_name_override for an existing watch? The watch keeps comparing against whatever it last saw, so changing settings can produce a "change detected" row that simply reflects the new settings, not a real change at the competitor. Use a new watch_name for an intentionally different watch.

What this is NOT. This is not a verified company database and does not confirm that the domain and a matched hiring board or funding filing are the same legal entity — see the limitation section above. It is a change-tracking heuristic, not proof.

Found a bug or need another signal rolled into the card? Issues on the Actor's page.

Commercial guide: Competitor Change Rollup — Tech, Hiring, and Filing Watch

Watch one domain through comparable stateful snapshots and receive explicit technology, hiring, and SEC-name changes with before/after values, materiality, confidence, and attribution warnings.

This guide is written for buyers, operators, analysts, and automation builders. It explains what the Actor observes, how to turn the Dataset into a controlled workflow, and where human verification remains mandatory.

The decision this product supports

What changed in the same competitor watch since its last valid baseline, and which observations are strong enough for human competitive-intelligence review?

The Actor reduces collection and first-pass triage work. It does not remove responsibility for source verification or authorize an external business action. The commercial value comes from a structured, repeatable evidence layer: stable identity, observation time, source evidence, confidence, gaps, recommended action, and failure semantics travel with the raw facts.

Who uses it

User	Value
Small-business founders	Replace three repetitive competitor checks with one weekly evidence and action card.
Product marketers	Observe technology, ecommerce, CMS, hiring-function, and public filing-name changes as research context.
Competitive-intelligence teams	Retain baselines, before/after fields, materiality, confidence, and partial-source status in one audit trail.
Agencies	Operate named watches for approved client competitors without hidden child Actor charges.
Sales operations	Route material changes into research tasks while keeping guessed identity and unverified SEC matches visible.
Automation builders	Schedule one domain per run and send only reviewed state transitions to downstream systems.

Input contract

Input field	How to use it
domain	Required competitor domain. Website technology is checked directly against it; hiring and filing signals need additional identity handling.
role_keywords	Optional job-title words used to summarize relevant public roles. They do not prove strategy, hiring success, or buyer intent.
company_name_override	Optional known ATS token or legal-name lookup term. It reduces domain-derived guessing but is not legal-entity verification.
watch_name	Stable namespace for one comparable domain and settings definition. Use a new name when changing methodology intentionally.

Recommended first Input

{
  "domain": "stripe.com",
  "role_keywords": [
    "sales",
    "marketing"
  ],
  "company_name_override": "stripe",
  "watch_name": "stripe-weekly-watch"
}

Start with this bounded example, inspect every Dataset field, and only then expand the scope. Input limits are product controls, not inconveniences: they make cost, completeness, and error handling visible.

Field dictionary

Field or group	Meaning
entityId, eventId, domain, baselineKey	Stable competitor identity, monitor event, target domain, and comparison namespace.
baselineCreated, changed, changedFields, before, after	Explicit first-run versus state-transition semantics and old/new values.
changeFlags, materialityScore, materialityBand	Deterministic change labels and weighted triage significance, separate from evidence confidence.
techStack, ecommerce, cms	Current website-technology snapshot observed directly from the domain.
hiringMomentum, openRolesCount, matchedRolesCount	Bounded public job-board observations under the selected identity token.
companySlugUsed, identityConfidence, matchBasis	How the hiring/funding lookup identity was chosen.
attributionConfirmed, attributionEvidence	Public board-owner evidence when available; null remains unknown and false is excluded from the snapshot.
newFundingFilingsCount, newFundingFilings	New SEC name/keyword mentions that remain unverified as issuer-to-domain attribution or funding rounds.
partial, partialReason, confidenceScore, confidenceBand	Source coverage and evidence support for the precise change statement.
recommendedAction, actionPriority, safeToAutomate, failureType, retryable	Manual routing and recovery semantics.

Common decision fields

Field	Operational meaning
recordType	The semantic row family. Use it to distinguish a business result from an advisory or terminal record.
schemaVersion	Version of the additive decision-intelligence contract. Pin or validate it in strict consumers.
entityId	Stable entity identity for deduplication and joins. It is not necessarily a legal identifier.
inputRef	The relevant submitted input reference after normalization.
observedAt	When the Actor observed or finalized the evidence. It is not necessarily the source publication time.
firstSeenAt and lastSeenAt	Always-emitted observation boundaries. Stateful monitors use the compatible baseline/current boundary. Stateless rows set both equal to observedAt for the current run; that equality does not establish historical tenure.
freshness	A structured statement about evidence age or availability, not a prediction. Its basis and age unit follow the source-specific field definition.
eventId	For monitors, the stable identity of one observed transition or monitor outcome. It is distinct from entityId.
before and after	For monitors, the bounded comparable snapshots used for the decision. Null means that side of a comparison was not honestly available.
changedFields and changeFlags	Machine-readable monitor deltas and normalized change labels. Empty arrays mean no supported changed field was established, not that every possible real-world fact stayed constant.
materialityScore and materialityBand	Magnitude of an observed monitor change when the Actor can calculate it. Materiality is separate from evidence confidence and may be unknown when the source lacks the required facts.
confidenceScore	Evidence support on a 0–100 scale. It is separate from materiality, lead score, or business value.
confidenceBand	Readable high/medium/low/unknown grouping of evidence support.
confidenceReasons	Observed facts that raise confidence.
confidenceRisks	Missing, partial, ambiguous, inferred, or conflicting aspects that reduce confidence.
confidenceConflict	Explicit consistency warning when structured evidence does not reconcile.
sourceEvidence	Source-linked observations supporting the row. Preserve this during export.
dataGaps	Important evidence the Actor did not observe or cannot establish. Keep these gaps visible in CRM, spreadsheet, and automation exports.
negativeSignals	Machine-readable risks or gaps. A negative signal is not automatically a negative business outcome.
recommendedAction	Bounded review label produced from the available evidence.
actionPriority	Suggested queue priority, not urgency guaranteed by the source.
actionReason	Plain-language explanation for the recommended action.
safeToAutomate	Whether the narrow recommended action is deterministic enough for automation. Organizational policy still applies.
failureType	Normalized terminal or partial failure classification. Null means no classified failure.
retryable	Whether a later retry may legitimately change an operationally incomplete result.
recommendation	Human-readable handling guidance, especially for terminal rows.

Evidence, confidence, and honest boundaries

What the evidence supports

• The first valid run establishes a baseline; later rows compare the same normalized snapshot contract.
• Technology signals come from the submitted domain and do not depend on guessed company identity.
• Hiring board ownership mismatch is excluded; unknown attribution remains explicitly uncertain.
• SEC matches are retained as unverified name/keyword observations rather than labeled funding events.
• A partial scan cannot silently masquerade as a complete no-change result and lowers confidence.
• If a source was not checked, dataGaps names the exact missing technology, hiring, or SEC scan instead of returning an empty completeness signal.
• A pre-scan charge-limit stop is classified as BUDGET_EXHAUSTED, remains free, sets retryable=true, and never claims source evidence that was not collected.

What this Actor never claims

• The Actor does not prove a technology purchase, migration plan, hiring strategy, layoffs, growth, funding round, revenue change, or buyer intent.
• It does not prove a domain, ATS token, job board, legal entity, and SEC issuer all represent the same organization.
• It does not treat a quiet run as proof that nothing changed outside the three bounded source checks.
• It does not provide investment advice, valuation, company due diligence, or automatic campaign authorization.
• Changing watch settings under the same baseline can create configuration-driven differences and must be avoided.

Reading data gaps correctly

A data gap is part of the result. Nulls, partial flags, confidence risks, source failures, and unavailable fields must survive export. Removing these fields makes the remaining facts look more complete than they are. When two sources conflict or a required identity cannot be proven, lower confidence and keep safeToAutomate=false.

Source evidence is not permission

A public source proves only that a value or statement was observable at the recorded time and URL. It does not establish consent, contractual rights, legal status, accuracy after observation, or authorization for a downstream action. Your organization remains responsible for source terms, privacy rules, outreach policy, retention, and human review.

Decision policy and action routing

Action	How to use it
SCHEDULE_NEXT_CHECK	Keep the same watch definition and run again after the intended interval.
REVIEW_AND_ROUTE_COMPETITOR_CHANGE	Open before/after evidence, verify identity and relevance, then route a bounded internal research task.
RETRY_OR_REVIEW_PARTIAL_CHECK	Restore source coverage before interpreting apparent stability or change.
NO_MATERIAL_CHANGE	Keep the comparable quiet outcome in the audit history without creating an external action.
REPAIR_BASELINE_STATE	Resolve baseline corruption or incompatibility before collecting a new paid transition.

Confidence is not attractiveness

confidenceScore answers “how strongly does the available evidence support this factual classification?” It does not answer “how valuable is this lead, property, account, or address?” A high-confidence negative fact may be commercially uninteresting; a low-confidence positive signal may deserve research but not action. Keep the concepts separate in dashboards, exports, and CRM fields.

Why safeToAutomate is conservative

safeToAutomate is intentionally false whenever the next step could amplify an uncertain inference. It may be true only for narrow deterministic actions explicitly supported by the row, such as suppressing an email with invalid syntax. A true value does not waive legal, privacy, consent, contractual, or organizational rules.

Retry policy

• Retry when retryable=true and the failure is operational, such as a temporary source or DNS problem.
• Do not endlessly retry deterministic invalid input, policy refusal, or confirmed absence.
• A retry must preserve the original input reference and must not create duplicate downstream actions.
• Budget exhaustion is not negative evidence about the entity. Resume only the unprocessed scope with an authorized budget.
• A failed Actor run is an operational event. Never transform it into “no listing,” “no contact,” “bad lead,” or “invalid email.”

Commercial use-case playbooks

1. Weekly founder brief

Goal. Schedule one strategic competitor, review material changes, and retain direct source and limitation notes.

Recommended runbook.

1. Define the submitted cohort and write down why it is in scope.
2. Start with the smallest useful Input and preserve the exact run ID.
3. Inspect the Dataset overview before exporting anything.
4. Check failureType, retryable, completeness indicators, and confidenceBand.
5. Open the relevant sourceEvidence or source URL for material rows.
6. Apply the recommended action as a review label, not as an instruction to contact, buy, delete, accuse, or publish.
7. Record the analyst's final disposition in the destination system.

Do not skip. A change row proves that one or more normalized fields moved between comparable bounded snapshots. It remains research evidence, not proof of strategy, financial condition, hiring outcome, verified funding, or buyer intent.

2. Ecommerce platform watch

Goal. Route ecommerce or CMS transitions into product-marketing research without assuming a completed migration or contract.

Recommended runbook.

1. Define the submitted cohort and write down why it is in scope.
2. Start with the smallest useful Input and preserve the exact run ID.
3. Inspect the Dataset overview before exporting anything.
4. Check failureType, retryable, completeness indicators, and confidenceBand.
5. Open the relevant sourceEvidence or source URL for material rows.
6. Apply the recommended action as a review label, not as an instruction to contact, buy, delete, accuse, or publish.
7. Record the analyst's final disposition in the destination system.

Do not skip. A change row proves that one or more normalized fields moved between comparable bounded snapshots. It remains research evidence, not proof of strategy, financial condition, hiring outcome, verified funding, or buyer intent.

3. Hiring-function watch

Goal. Use role keywords to surface sales or marketing movement, then validate ownership and current roles manually.

Recommended runbook.

1. Define the submitted cohort and write down why it is in scope.
2. Start with the smallest useful Input and preserve the exact run ID.
3. Inspect the Dataset overview before exporting anything.
4. Check failureType, retryable, completeness indicators, and confidenceBand.
5. Open the relevant sourceEvidence or source URL for material rows.
6. Apply the recommended action as a review label, not as an instruction to contact, buy, delete, accuse, or publish.
7. Record the analyst's final disposition in the destination system.

Do not skip. A change row proves that one or more normalized fields moved between comparable bounded snapshots. It remains research evidence, not proof of strategy, financial condition, hiring outcome, verified funding, or buyer intent.

4. SEC mention review

Goal. Open each new filing reference and resolve issuer identity and filing facts before using it as company context.

Recommended runbook.

1. Define the submitted cohort and write down why it is in scope.
2. Start with the smallest useful Input and preserve the exact run ID.
3. Inspect the Dataset overview before exporting anything.
4. Check failureType, retryable, completeness indicators, and confidenceBand.
5. Open the relevant sourceEvidence or source URL for material rows.
6. Apply the recommended action as a review label, not as an instruction to contact, buy, delete, accuse, or publish.
7. Record the analyst's final disposition in the destination system.

Do not skip. A change row proves that one or more normalized fields moved between comparable bounded snapshots. It remains research evidence, not proof of strategy, financial condition, hiring outcome, verified funding, or buyer intent.

5. Agency client card

Goal. Give each client/domain/settings definition its own watch name and deliver change, confidence, gaps, and action together.

Recommended runbook.

1. Define the submitted cohort and write down why it is in scope.
2. Start with the smallest useful Input and preserve the exact run ID.
3. Inspect the Dataset overview before exporting anything.
4. Check failureType, retryable, completeness indicators, and confidenceBand.
5. Open the relevant sourceEvidence or source URL for material rows.
6. Apply the recommended action as a review label, not as an instruction to contact, buy, delete, accuse, or publish.
7. Record the analyst's final disposition in the destination system.

Do not skip. A change row proves that one or more normalized fields moved between comparable bounded snapshots. It remains research evidence, not proof of strategy, financial condition, hiring outcome, verified funding, or buyer intent.

6. CRM intelligence staging

Goal. Store eventId and before/after fields in an immutable event table; require analyst approval before updating account narratives.

Recommended runbook.

1. Define the submitted cohort and write down why it is in scope.
2. Start with the smallest useful Input and preserve the exact run ID.
3. Inspect the Dataset overview before exporting anything.
4. Check failureType, retryable, completeness indicators, and confidenceBand.
5. Open the relevant sourceEvidence or source URL for material rows.
6. Apply the recommended action as a review label, not as an instruction to contact, buy, delete, accuse, or publish.
7. Record the analyst's final disposition in the destination system.

Do not skip. A change row proves that one or more normalized fields moved between comparable bounded snapshots. It remains research evidence, not proof of strategy, financial condition, hiring outcome, verified funding, or buyer intent.

7. Partial-source quarantine

Goal. Branch partial and retryable outcomes away from no-change reporting so outages cannot create false reassurance.

Recommended runbook.

1. Define the submitted cohort and write down why it is in scope.
2. Start with the smallest useful Input and preserve the exact run ID.
3. Inspect the Dataset overview before exporting anything.
4. Check failureType, retryable, completeness indicators, and confidenceBand.
5. Open the relevant sourceEvidence or source URL for material rows.
6. Apply the recommended action as a review label, not as an instruction to contact, buy, delete, accuse, or publish.
7. Record the analyst's final disposition in the destination system.

Do not skip. A change row proves that one or more normalized fields moved between comparable bounded snapshots. It remains research evidence, not proof of strategy, financial condition, hiring outcome, verified funding, or buyer intent.

8. Methodology change

Goal. Create a new watch when role keywords or identity override change and document that the prior series ended.

Recommended runbook.

1. Define the submitted cohort and write down why it is in scope.
2. Start with the smallest useful Input and preserve the exact run ID.
3. Inspect the Dataset overview before exporting anything.
4. Check failureType, retryable, completeness indicators, and confidenceBand.
5. Open the relevant sourceEvidence or source URL for material rows.
6. Apply the recommended action as a review label, not as an instruction to contact, buy, delete, accuse, or publish.
7. Record the analyst's final disposition in the destination system.

Do not skip. A change row proves that one or more normalized fields moved between comparable bounded snapshots. It remains research evidence, not proof of strategy, financial condition, hiring outcome, verified funding, or buyer intent.

Integration recipes

All examples use placeholders. Keep the Apify token in a secret manager and never write it into a Dataset, README, screenshot, or client-side application.

cURL: start a run and wait briefly

curl -sS -X POST 'https://api.apify.com/v2/acts/zinin~competitor-change-rollup/runs?waitForFinish=60' \
  -H "Authorization: Bearer $APIFY_TOKEN" \
  -H 'Content-Type: application/json' \
  --data '{"domain":"stripe.com","role_keywords":["sales","marketing"],"company_name_override":"stripe","watch_name":"stripe-weekly-watch"}'

The run response includes defaultDatasetId. Read clean JSON rows with:

curl -sS "https://api.apify.com/v2/datasets/$DEFAULT_DATASET_ID/items?clean=true&format=json" \
  -H "Authorization: Bearer $APIFY_TOKEN"

JavaScript with apify-client

import { ApifyClient } from 'apify-client';

const client = new ApifyClient({ token: process.env.APIFY_TOKEN });
const input = {
  "domain": "stripe.com",
  "role_keywords": [
    "sales",
    "marketing"
  ],
  "company_name_override": "stripe",
  "watch_name": "stripe-weekly-watch"
};
const run = await client.actor('zinin/competitor-change-rollup').call(input);
const { items } = await client.dataset(run.defaultDatasetId).listItems({ clean: true });

for (const row of items) {
    console.log({
        entityId: row.entityId,
        confidenceBand: row.confidenceBand,
        recommendedAction: row.recommendedAction,
        safeToAutomate: row.safeToAutomate,
        failureType: row.failureType,
    });
}

Python with apify-client

import os
from apify_client import ApifyClient

client = ApifyClient(os.environ["APIFY_TOKEN"])
run = client.actor("zinin/competitor-change-rollup").call(run_input={
    "domain": "stripe.com",
    "role_keywords": [
        "sales",
        "marketing"
    ],
    "company_name_override": "stripe",
    "watch_name": "stripe-weekly-watch"
})

for row in client.dataset(run["defaultDatasetId"]).iterate_items(clean=True):
    print({
        "entityId": row.get("entityId"),
        "confidenceBand": row.get("confidenceBand"),
        "recommendedAction": row.get("recommendedAction"),
        "safeToAutomate": row.get("safeToAutomate"),
        "failureType": row.get("failureType"),
    })

Apify MCP call

{
  "name": "call-actor",
  "arguments": {
    "actor": "zinin/competitor-change-rollup",
    "input": {
      "domain": "stripe.com",
      "role_keywords": [
        "sales",
        "marketing"
      ],
      "company_name_override": "stripe",
      "watch_name": "stripe-weekly-watch"
    }
  }
}

Generic webhook consumer policy

1. Trigger on a terminal Actor run event.
2. Confirm the run status is SUCCEEDED before reading business rows.
3. Retrieve rows from defaultDatasetId.
4. Reject or quarantine rows whose failureType is non-null unless your policy explicitly handles that failure.
5. Send safeToAutomate=false rows to a human-review queue.
6. Store entityId, observedAt, sourceEvidence, confidence, action, and the Apify run ID together.
7. Make retries idempotent by keying the destination on the stable entity ID plus the intended observation or event identity.

Where this fits in a practical stack

Destination	Recommended pattern
Apify Console	Use the visual Input form, start the run, then open the default Dataset overview. This is the fastest path for a one-off review and the best place to inspect evidence before automating anything.
Apify API	POST JSON input to the Actor run endpoint, wait or poll for completion, then read the default Dataset through the URL returned by the run object.
JavaScript client	Use apify-client from a Node.js service, pass the same JSON object as the Console Input, and preserve the returned run and Dataset IDs in your own audit log.
Python client	Use apify-client in a Python enrichment job, iterate Dataset items, and route rows by recommendedAction, confidenceBand, failureType, and retryable.
Make	Start the Actor from a scenario, wait for the run, retrieve Dataset items, filter unsafe or low-confidence rows, then insert review-ready rows into the destination application.
Zapier	Use an Apify run action or webhook trigger, fetch Dataset items, apply a Filter step, and send only review-approved fields into the next sales or operations step.
n8n	Use HTTP Request or Apify nodes, branch on failureType and retryable, keep a manual-review lane for safeToAutomate=false, and write sourceEvidence together with the business fields.
Google Sheets	Export the Dataset directly or append rows from an automation. Keep stable entityId as a hidden key so reruns update the correct record instead of creating ambiguous duplicates.
Airtable	Map entityId to a primary or deduplication field, store confidence and evidence in separate columns, and expose recommendedAction as the triage view.
Webhook	Configure an Apify webhook for terminal run states, retrieve the Dataset after SUCCEEDED, and treat FAILED or TIMED-OUT runs as operational events rather than negative business evidence.

A safe automation shape

The Actor is a collection and decision-support component. A production workflow should keep raw evidence, decision metadata, and business action in distinct layers:

1. Collect: run the Actor with explicit bounded input.
2. Validate: require a successful run and schema-valid Dataset rows.
3. Triage: branch on failureType, retryable, confidenceBand, and safeToAutomate.
4. Review: open source evidence for rows that may affect a person, campaign, investment, compliance decision, or customer record.
5. Act: execute only the action approved by your own policy and authorized operator.
6. Audit: retain run ID, Dataset ID, observation time, input reference, source evidence, and the final human decision.

This separation prevents a common automation error: turning “data was observed” into “a business action is justified.”

Operating guide

Before the first production run

1. Write the business question in one sentence: What changed in the same competitor watch since its last valid baseline, and which observations are strong enough for human competitive-intelligence review?
2. Confirm every submitted input is within your authorized scope.
3. Use the prefilled small example and review all returned row types.
4. Map stable identifiers, confidence, evidence, actions, gaps, failure, and retry fields into the destination.
5. Establish a human owner for review exceptions.
6. Set a run budget and output bound appropriate to the test.
7. Verify that secrets are stored only in the platform or workflow secret manager.

After every scheduled run

1. Check terminal run status and logs.
2. Compare the number of submitted entities, produced business rows, and advisory rows.
3. Review partial, unknown, conflict, and low-confidence buckets.
4. Inspect a sample of source evidence, including at least one positive and one negative result.
5. Confirm the destination deduplicated on the intended stable key.
6. Verify that no downstream action was triggered from an error row.
7. Track cost per useful reviewed row rather than cost per raw request alone.

Production monitoring signals

Monitor source-unavailable rate, partial-row rate, low-confidence share, missing evidence, retry volume, run duration, Dataset row count, and spend. A sudden shift may indicate source drift, input drift, or an upstream outage. Stop automation and investigate before accepting a new pattern as business truth.

Cost control

Start with one domain and a new watch name. Inspect the baseline, then rerun unchanged to confirm quiet/change semantics and billing. The live Apify pricing panel is authoritative for current event prices.

Use maxTotalChargeUsd when calling a monetized Actor if your workflow supports it. Treat a buyer-set cap as a hard safety boundary. If the cap stops work, the unfinished items remain unprocessed; they do not become negative results.

Review templates and quality reporting

Row-review worksheet

For every material row, an analyst should be able to answer the following without relying on memory or an unstated assumption:

1. What submitted entity or query does this row refer to?
2. Is it a business result, a baseline/advisory row, a partial observation, or a failure?
3. Which exact source evidence supports the headline fact?
4. When was the evidence observed, and is there a different source publication time?
5. Which fields are direct observations, which are normalized, and which are deterministic derivations?
6. What important evidence is null, missing, partial, ambiguous, or conflicting?
7. Does confidence describe evidence support only, or has someone incorrectly treated it as business value?
8. What recommended action is present, and what additional verification does its reason require?
9. Is the narrow action marked safe to automate? If yes, does organizational policy also permit it?
10. What final human disposition was made, by whom, and from which run and Dataset item?

Field-group review prompts

1. entityId, eventId, domain, baselineKey

Contract meaning: Stable competitor identity, monitor event, target domain, and comparison namespace.

Reviewer prompts: Is the value present? Does its type match the schema? Is it supported by sourceEvidence or a documented deterministic transformation? Is any null being silently converted into a default? Would the value still mean the same thing after CSV export? Does the destination preserve the related confidence and gap fields?

2. baselineCreated, changed, changedFields, before, after

Contract meaning: Explicit first-run versus state-transition semantics and old/new values.

Reviewer prompts: Is the value present? Does its type match the schema? Is it supported by sourceEvidence or a documented deterministic transformation? Is any null being silently converted into a default? Would the value still mean the same thing after CSV export? Does the destination preserve the related confidence and gap fields?

3. changeFlags, materialityScore, materialityBand

Contract meaning: Deterministic change labels and weighted triage significance, separate from evidence confidence.

Reviewer prompts: Is the value present? Does its type match the schema? Is it supported by sourceEvidence or a documented deterministic transformation? Is any null being silently converted into a default? Would the value still mean the same thing after CSV export? Does the destination preserve the related confidence and gap fields?

4. techStack, ecommerce, cms

Contract meaning: Current website-technology snapshot observed directly from the domain.

Reviewer prompts: Is the value present? Does its type match the schema? Is it supported by sourceEvidence or a documented deterministic transformation? Is any null being silently converted into a default? Would the value still mean the same thing after CSV export? Does the destination preserve the related confidence and gap fields?

5. hiringMomentum, openRolesCount, matchedRolesCount

Contract meaning: Bounded public job-board observations under the selected identity token.

Reviewer prompts: Is the value present? Does its type match the schema? Is it supported by sourceEvidence or a documented deterministic transformation? Is any null being silently converted into a default? Would the value still mean the same thing after CSV export? Does the destination preserve the related confidence and gap fields?

6. companySlugUsed, identityConfidence, matchBasis

Contract meaning: How the hiring/funding lookup identity was chosen.

Reviewer prompts: Is the value present? Does its type match the schema? Is it supported by sourceEvidence or a documented deterministic transformation? Is any null being silently converted into a default? Would the value still mean the same thing after CSV export? Does the destination preserve the related confidence and gap fields?

7. attributionConfirmed, attributionEvidence

Contract meaning: Public board-owner evidence when available; null remains unknown and false is excluded from the snapshot.

Reviewer prompts: Is the value present? Does its type match the schema? Is it supported by sourceEvidence or a documented deterministic transformation? Is any null being silently converted into a default? Would the value still mean the same thing after CSV export? Does the destination preserve the related confidence and gap fields?

8. newFundingFilingsCount, newFundingFilings

Contract meaning: New SEC name/keyword mentions that remain unverified as issuer-to-domain attribution or funding rounds.

Reviewer prompts: Is the value present? Does its type match the schema? Is it supported by sourceEvidence or a documented deterministic transformation? Is any null being silently converted into a default? Would the value still mean the same thing after CSV export? Does the destination preserve the related confidence and gap fields?

9. partial, partialReason, confidenceScore, confidenceBand

Contract meaning: Source coverage and evidence support for the precise change statement.

Reviewer prompts: Is the value present? Does its type match the schema? Is it supported by sourceEvidence or a documented deterministic transformation? Is any null being silently converted into a default? Would the value still mean the same thing after CSV export? Does the destination preserve the related confidence and gap fields?

10. recommendedAction, actionPriority, safeToAutomate, failureType, retryable

Contract meaning: Manual routing and recovery semantics.

Reviewer prompts: Is the value present? Does its type match the schema? Is it supported by sourceEvidence or a documented deterministic transformation? Is any null being silently converted into a default? Would the value still mean the same thing after CSV export? Does the destination preserve the related confidence and gap fields?

Weekly quality report

Create a recurring internal report with these measures. The report is about pipeline health, not market demand unless the source contract explicitly measures demand.

Metric	Why it matters	Investigate when
Submitted inputs	Defines the actual denominator and scope of the run.	The count differs from the approved batch or schedule.
Business result rows	Shows how many usable observations were produced.	The rate changes sharply without an input explanation.
Advisory/failure rows	Prevents operational failures from disappearing in a results-only dashboard.	Any terminal class grows or is unmapped.
Partial-result rate	Measures incomplete source coverage or configured truncation.	It rises, or analysts stop seeing the partial warning.
Low-confidence rate	Shows the share of rows requiring more evidence.	It rises by source, cohort, or input pattern.
Retryable failure rate	Distinguishes temporary operational issues from deterministic outcomes.	Retries repeat without improving evidence.
Evidence-link coverage	Confirms material facts remain traceable after export.	Links or evidence objects are missing from delivered records.
Safe-automation share	Shows how little or much of the workflow can be deterministic.	A mapping change makes unsafe actions appear safe.
Manual-review backlog	Measures whether human verification capacity matches collection volume.	Rows age beyond the campaign or decision window.
Duplicate destination writes	Tests idempotency and stable identity mapping.	The same entity/run creates multiple external actions.
Cost per reviewed useful row	Relates platform spend to approved, decision-useful output.	Raw volume rises but reviewed utility falls.
Source-drift exceptions	Detects changed markup, response shape, policy, or source availability.	A new unknown pattern survives more than one bounded check.

Client-facing delivery note template

Use a note like this when delivering exports to a client or another team:

This Dataset contains bounded public-source observations produced by the Apify Actor for the submitted Input. Each row includes observation time, evidence confidence, recommended review action, and explicit gaps where available. A positive row is not proof of buyer intent, permission, legal status, future outcome, or any fact listed in the Actor's “never claims” section. Partial and failure rows are included so coverage is not overstated. Validate material rows at their source before acting.

Add the Actor URL, run URL, Dataset URL, build/version, exact Input scope, observation window, pricing model observed for the run, reviewer name, and date of approval.

CRM disposition vocabulary

Keep collection results and sales dispositions separate. A practical downstream vocabulary is:

• needs_evidence_review: useful signal exists but a reviewer has not approved it.
• needs_identity_review: entity or ownership association is not sufficiently proven.
• needs_policy_review: contact, privacy, suppression, legal, or contractual policy must be checked.
• approved_for_research: an analyst may perform more research; this is not approval for outreach.
• approved_for_authorized_action: a named operator approved one specific action under the organization's policy.
• retry_operational_failure: the source or infrastructure failed and a bounded retry is appropriate.
• closed_no_supported_signal: the completed bounded check found no supported signal; this is not a universal negative fact.
• closed_out_of_scope: the input should not have entered this workflow.

Never overwrite recommendedAction with the CRM disposition. The first is Actor-produced decision support; the second is your organization's accountable decision.

Sampling plan

For a new workflow, review every row in the first small run. When the contract is understood, sample all failure and partial rows plus a representative set of high-, medium-, and low-confidence results. Re-expand to full review whenever the source changes, the schema version changes, a new input cohort is introduced, the error distribution shifts, or a downstream user reports an unexplained result.

Change-management record

When you change field mappings or automation policy, record:

1. Previous mapping or rule.
2. New mapping or rule.
3. Actor build/version and schemaVersion used for validation.
4. Test run and Dataset URLs.
5. Positive, negative, partial, retry, and budget fixtures inspected.
6. Security and privacy review outcome.
7. Approver and activation time.
8. Rollback condition and responsible operator.

This makes a commercial data workflow supportable. Without the record, a later operator cannot distinguish a real source change from an undocumented mapping change.

Delivery patterns for marketing and small-business teams

One-off research

Run the Actor in Console, inspect the overview table, open evidence for each material row, and export only the approved subset. Record the run URL in the client or campaign notes.

Recurring watch or hygiene job

Use an Apify schedule. Write rows into a staging table keyed by entityId. Compare current and previous observations only when the Actor supplies valid state or your own pipeline implements an explicit comparable baseline. Never infer a change from a failed run.

Agency client delivery

Deliver three views: business results, evidence/quality exceptions, and operational failures. Include the run URL, observation time, configured scope, and a plain-language statement of what the Actor does not prove. This makes the deliverable auditable and reduces disputes caused by overclaiming.

CRM enrichment

Write into staging fields first. A human or approved policy promotes values into canonical CRM fields. Keep raw source values separate from normalized and decision fields, and do not replace a verified value with a lower-confidence observation.

AI-assisted review

An LLM can summarize rows, but it must receive the evidence, confidence risks, negative signals, and limitations. Require citations to sourceEvidence and prohibit invented identity, intent, legal, funding, mailbox, valuation, or availability facts.

Buyer and operator acceptance checklist

Use this checklist before calling the workflow production-ready.

Product fit

• The business question matches: What changed in the same competitor watch since its last valid baseline, and which observations are strong enough for human competitive-intelligence review?
• The submitted entities were selected through an authorized process.
• A human owner understands the positive, negative, partial, and failure row types.
• The team accepts the boundaries listed in “What this Actor never claims.”
• The destination keeps evidence confidence separate from business scoring.

Input and run controls

• domain is explicitly reviewed and bounded.
• role_keywords is explicitly reviewed and bounded.
• company_name_override is explicitly reviewed and bounded.
• watch_name is explicitly reviewed and bounded.
• The first production-like run uses a small representative sample.
• A maximum charge or internal spend alert is configured where appropriate.
• The workflow records Actor ID, build/version, run ID, Dataset ID, and input hash.

Data handling

• entityId is mapped to an idempotent destination key.
• observedAt and source-specific time fields remain distinct.
• sourceEvidence, gaps, and nulls are preserved.
• Advisory and failure rows cannot enter the positive-results lane.
• Low-confidence and partial rows have a visible manual-review view.
• Retention and deletion rules match the type of data collected.

Action safety

• recommendedAction is treated as a review label.
• safeToAutomate=false blocks automatic external action.
• Consent, suppression, legal, contractual, and platform rules are evaluated downstream.
• A reviewer can trace a material action back to source evidence and run metadata.
• Retry logic cannot duplicate a downstream action.

Ongoing quality

• The team monitors failure, retry, partial, low-confidence, and empty-result rates.
• A source-drift threshold pauses the workflow for inspection.
• Sample evidence is manually reviewed on a recurring basis.
• Cost per useful reviewed row is measured.
• Documentation and field mappings are updated when schemaVersion changes.

Frequently asked questions

Is this a database?

No. It is an on-demand observation tool. Each run collects or evaluates the submitted scope and records evidence at that time.

Does a found row prove commercial interest?

No. A found row proves only the factual observation described by its fields. Buyer intent is never inferred.

Can I automatically contact every result?

No. Use recommendedAction as triage, verify the evidence and identity, and apply your own consent, privacy, suppression, and outreach rules.

Why is safeToAutomate often false?

Because a useful observation can still require identity, context, legal, or source verification before action. Conservative routing prevents false certainty from scaling.

What should I do with low confidence?

Open confidenceRisks and sourceEvidence, close the important gap, or keep the row in a manual queue. Do not hide the confidence field.

What does partial mean?

The Actor obtained some usable evidence but could not support a complete observation of the configured scope. Partial is not the same as empty.

What is a confirmed zero?

Only an explicit source or deterministic rule can support a confirmed absence. An outage, truncation, or unreadable response is not a zero.

Should I retry every failure?

No. Retry only when retryable is true. Invalid input, policy refusal, or deterministic classification should be corrected or handled, not looped.

Can I delete failure rows?

You can exclude them from a business-results view, but retain them in operational logs so Dataset completeness and retry decisions stay explainable.

How should I deduplicate?

Use entityId for the entity and, for stateful monitors, eventId for the observed transition. Also retain the Apify run ID.

Can I treat confidence as conversion probability?

No. Confidence measures evidence support, not purchase probability, revenue, suitability, or expected return.

Can I change the recommended action?

Yes. It is an explainable default. Your downstream policy can be stricter, and should encode organization-specific authorization and risk tolerance.

How do I estimate cost?

Run the smallest representative input, inspect live event prices and run usage in Apify, then model the number of billable result events. The live pricing panel is authoritative.

Why use a small prefill?

It produces a cheap, fast, inspectable first run and reduces the chance of scaling a wrong input or workflow assumption.

Can I schedule it?

Yes. Use an Apify schedule, but make the destination idempotent and review changes in failure, partial, and confidence rates.

Can I export CSV or Excel?

Yes. Apify Datasets support common export formats. JSON is recommended when you need nested evidence and decision fields.

Can I send results to Sheets or Airtable?

Yes. Preserve entityId, confidence, evidence, gaps, actions, and failure fields instead of mapping only the headline value.

Can I use it from Make, Zapier, or n8n?

Yes. Start the Actor, wait for a successful terminal state, read Dataset items, then branch on decision and failure fields.

Can an LLM consume the output?

Yes, but pass the structured evidence and limitations together. Instruct the model not to invent missing facts and to cite sourceEvidence.

What happens when a source changes?

The run may become partial, unavailable, or fail validation. Monitor these rates and inspect logs before treating changed output as a real-world shift.

Does public mean unrestricted?

No. Public visibility does not remove source terms, privacy obligations, retention rules, or the need for a legitimate downstream purpose.

Is a source URL permanent?

Not necessarily. Store observation time and material facts because web content can change or disappear.

Can I rely on one row for a high-stakes decision?

No. High-stakes legal, financial, employment, compliance, safety, or personal decisions require appropriate primary evidence and qualified review.

How do I report a suspected parsing issue?

Provide the Actor run ID, a redacted input, affected field, expected source evidence, and whether the issue reproduces. Never include tokens or private data.

What does success mean?

A change row proves that one or more normalized fields moved between comparable bounded snapshots. It remains research evidence, not proof of strategy, financial condition, hiring outcome, verified funding, or buyer intent.

Support information to include with an issue

Provide the public Actor name, Apify run ID, Dataset item index or stable entity ID, a redacted Input, the relevant source URL, expected behavior, observed behavior, and whether retrying produced the same result. Do not include an Apify token, API key, private customer record, or unnecessary personal data.

Final interpretation rule

A change row proves that one or more normalized fields moved between comparable bounded snapshots. It remains research evidence, not proof of strategy, financial condition, hiring outcome, verified funding, or buyer intent.