kununu Company Profile Scraper — DACH Ratings & Awards

What does the kununu Company Profile Scraper do?

The kununu Company Profile Scraper turns any kununu company profile into a single, structured record — one clean row per company across the DACH region (Germany, Austria and Switzerland). Paste profile URLs or bare company slugs and get back the overall kununu score, the full rating-factor breakdown (up to 14 factors such as Gehalt/Benefits, Work-Life-Balance and Vorgesetztenverhalten), salary ranges by job title (min / max / median / average), the benefits list with the share of employees reporting each, total and segmented review counts, the recommendation rate, the recent-vs-older score trend, and the company's awards and Top Company badges — plus location, industry and claimed/verified status. It runs with no code, exports to JSON, CSV, Excel or the API, and can watch employers on a schedule and ping you only when their numbers move.

Why this scraper

• ⚡ Fast & low-cost — one compact record per company, so large DACH watchlists finish quickly and stay cheap.
• 🧾 Rich, typed records — 40+ structured fields per company, not raw HTML.
• ♻️ Cheap to monitor — incremental mode re-emits only the companies whose score or review counts changed (see below).
• 🔔 Notifications built in — Telegram, Slack, Discord or any webhook (n8n / Make / Zapier).
• 🤖 AI- & API-ready — compact and drop-empty output modes keep payloads small for LLMs and MCP.

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✨ Key features

• 🏢 One record per company — paste kununu profile URLs, country/slug paths or bare slugs; you get exactly one tidy employer snapshot back per company.
• 🌍 Full DACH coverage — Germany, Austria and Switzerland (de / at / ch), with a default country for bare slugs and graceful skipping of company slugs that no longer exist.
• ⭐ Complete rating breakdown — overall kununu score, rounded score, applicants' score, industry-average score, and the full rating-factor list (up to 14 factors, each with its own name and score).
• 💶 Salary & benefits — salary ranges per job title (min / max / median / average with the number of data points), a salary-satisfaction split, and the full benefits list with the share of employees reporting each perk.
• 🗣️ Review depth — total reviews, reviews with text, employee vs. candidate reviews, reviews in the last two years, reviews with an employer response, and the year of the first review.
• 👍 Recommendation signal — recommendation rate plus the raw recommended / not-recommended counts.
• 📈 Score trend — recent-period vs. older-period scores and the delta, so you can see whether an employer's rating is rising or falling.
• 🏆 Awards & badges — Top Company years, award names with year, and whether each badge was paid.
• ♻️ Incremental monitoring — schedule it and get only what changed (NEW / UPDATED / UNCHANGED / EXPIRED); unchanged companies are skipped so a daily watch costs a fraction of a full re-scan.
• 🔔 Notifications — push matched changes to Telegram, Slack, Discord or any webhook.
• 🤖 AI-ready — compact output plus a drop-empty mode for lean LLM and MCP payloads.

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📚 What data can you extract?

• Company identity — name, simpleName, slug, countryCode, url, website, profileType, isClaimed, isVerified, logo, industryId, plus stable id / profileUuid.
• Location — city, state, companyCountryCode, totalLocations.
• Scores — kununuScore, roundedScore, applicantsScore, industryAverageScore, and the trend triple scoreTrendRecent / scoreTrendOlder / scoreTrendDelta.
• Reviews — totalReviews, totalReviewsWithText, totalReviewsEmployees, totalReviewsCandidates, totalReviewsLastTwoYears, totalReviewsWithResponse, firstReviewYear.
• Recommendation — recommendationRate, recommendedCount, notRecommendedCount.
• Rating factors — factorRatings (each factor's name and score).
• Salary — salaryRanges (per job title: min, max, median, average, number of entries) and salarySatisfaction (positive / neutral / negative split).
• Benefits — benefits (each perk with the percentage of employees reporting it and the raw count) plus benefitsTotalReviews.
• Awards & badges — awards, topCompanyYears, isTopCompanyPaid.
• Run metadata — source, scrapedAt, contentHash, changeType (in incremental mode).

Every field is present in standard mode (missing values are null); compact mode returns the core fields only — profileUuid, name, url, countryCode, kununuScore, roundedScore, totalReviews, recommendationRate (plus changeType in incremental mode) — for lean AI/MCP payloads.

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⚙️ Input & output

Configure it in the visual editor — no code needed — or pass JSON via the API.

Field	What it does
companies	The kununu companies to scrape — full profile URLs, country/slug paths, or bare slugs.
country	Default country (de / at / ch) for bare slugs that don't include one.
maxResults	Cap the number of company profiles (0 = no limit).
ignoreUrlFailures	Skip slugs that don't exist instead of failing the run.
includeFactorRatings	Include the per-factor rating breakdown; turn off for a faster, cheaper run when you only need the overall score.
includeSalaryBenefits	Include salary ranges by job title and the benefits list; turn off for leaner records.
compact	Emit only the core fields — ideal for AI agents and MCP.
incrementalMode	Emit only companies whose score or review counts changed since the last run.
proxyConfiguration	Optional proxy settings for reliable high-volume runs.

…and 21 inputs in total — the table shows the essentials; the rest cover the drop-empty output mode, incremental state and unchanged/expired handling, notification channels (Telegram / Slack / Discord / webhook) and advanced tuning, all in the visual editor.

📥 Example input

Basic — two German companies by URL:

{ "companies": ["https://www.kununu.com/de/sap", "https://www.kununu.com/de/bmwgroup"], "maxResults": 25 }

Mixed DACH — slugs and country paths with a default country:

{ "companies": ["sap", "at/red-bull-gmbh", "ch/swisscom"], "country": "de" }

Incremental monitoring with a Telegram alert on every change:

{ "companies": ["https://www.kununu.com/de/sap"], "incrementalMode": true, "telegramToken": "123456:ABC", "telegramChatId": "@yourchannel" }

📤 Example output

{
  "id": "a558284b-93ad-4015-a649-a1e152c479c8",
  "profileUuid": "a558284b-93ad-4015-a649-a1e152c479c8",
  "name": "SAP SE",
  "simpleName": "SAP",
  "slug": "sap",
  "countryCode": "de",
  "url": "https://www.kununu.com/de/sap",
  "website": "https://jobs.sap.com",
  "profileType": "Claimed",
  "isClaimed": true,
  "isVerified": true,
  "industryId": 6,
  "city": "Walldorf / Baden-Württemb.",
  "state": "Baden-Württemberg",
  "companyCountryCode": "de",
  "totalLocations": 42,
  "kununuScore": 4.3,
  "roundedScore": 4.5,
  "applicantsScore": 3.1,
  "industryAverageScore": 4.0,
  "scoreTrendRecent": 4.1,
  "scoreTrendOlder": 4.4,
  "scoreTrendDelta": -0.3,
  "totalReviews": 4918,
  "totalReviewsWithText": 2638,
  "totalReviewsEmployees": 4658,
  "totalReviewsCandidates": 260,
  "totalReviewsLastTwoYears": 631,
  "totalReviewsWithResponse": 9,
  "firstReviewYear": 2007,
  "recommendationRate": 84.0,
  "recommendedCount": 502,
  "notRecommendedCount": 96,
  "factorRatings": [
    { "name": "Gehalt/Benefits", "score": 4.2 },
    { "name": "Work-Life-Balance", "score": 4.1 },
    { "name": "Vorgesetztenverhalten", "score": 4.3 },
    { "name": "Kollegenzusammenhalt", "score": 4.5 }
  ],
  "topCompanyYears": [2026, 2025, 2024, 2023, 2022],
  "awards": [
    { "name": "Top Company 2026", "group": "kununu Top Company", "year": 2026, "wasPaid": false },
    { "name": "Top Rated-Siegel Gehaltszufriedenheit 2026", "group": "kununu Top Rated-Siegel Gehaltszufriedenheit", "year": 2026, "wasPaid": false }
  ],
  "isTopCompanyPaid": false,
  "salaryRanges": [
    { "jobTitle": "Softwareentwickler:in", "jobTitleSlug": "softwareentwickler-in", "min": 43500, "max": 109500, "median": 70000, "average": 71800, "entries": 428 },
    { "jobTitle": "SAP-Berater:in", "jobTitleSlug": "sap-berater-in", "min": 46800, "max": 123700, "median": 75000, "average": 78100, "entries": 150 }
  ],
  "salarySatisfaction": { "positivePercentage": 80, "neutralPercentage": 11, "negativePercentage": 9, "totalReviews": 4573 },
  "benefits": [
    { "id": "flexWorkingHours", "percentage": 83, "count": 3855 },
    { "id": "homeOffice", "percentage": 79, "count": 3689 },
    { "id": "pensionPlan", "percentage": 72, "count": 3338 }
  ],
  "benefitsTotalReviews": 4665,
  "source": "kununu.com",
  "scrapedAt": "2026-08-12T20:14:12.827667+00:00"
}

The factorRatings, salaryRanges, benefits and awards arrays are trimmed above for readability — a real record returns every rating factor, salary band, benefit and award for the company.

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💡 Use cases

• Employer benchmarking — compare score, recommendation rate and rating factors across a set of competitors or your own subsidiaries in one sheet.
• Employer-brand & HR monitoring — watch how your kununu score, review volume and awards move over time and get alerted when they do.
• Recruiting & talent intelligence — score prospective employers for candidates, or size up a market before you enter it.
• Market & competitive research — pull rating and review data for a whole industry or region across Germany, Austria and Switzerland.
• Enrichment & aggregation — feed clean employer data into your own CRM, dashboard, sheet or warehouse.
• AI agents & pipelines — compact output plugs straight into LLM / MCP workflows.

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♻️ Incremental monitoring — pay for changes, not repeats

Schedule the actor and turn on incremental mode: each run compares against the last and emits only NEW / UPDATED / EXPIRED companies — those whose kununu score or review counts actually moved. Unchanged companies are skipped, so a daily watch over a large employer list costs a fraction of a full re-scan.

Daily churn	of 1,000 tracked	billable records	you save
5 %	1,000	50	95 %
15 %	1,000	150	85 %
30 %	1,000	300	70 %

The first run seeds the baseline and bills in full; every run after that bills only the delta. Pair it with a notification channel to get a live change feed straight into Telegram, Slack, Discord or your own webhook.

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🚀 How to run it

1. Open the actor and paste your company URLs or slugs (e.g. https://www.kununu.com/de/sap or at/red-bull-gmbh).
2. Pick a default country for bare slugs and set Max companies.
3. (Optional) Turn on compact or drop-empty output for AI pipelines.
4. (Optional) Turn on incremental mode and a notification channel, then Schedule it.
5. Click Start, then download the data as JSON, CSV or Excel, or pull it from the API.

New to Apify? Create a free account — it comes with monthly credit, no credit card required.

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🔌 Integrations & export

Export to JSON, CSV, Excel or an HTML table, or pull from the REST API and the JavaScript / Python clients. Runs on a schedule, connects to Google Sheets, Slack, Make, Zapier and n8n, and works as an MCP tool for AI agents — compact mode keeps token usage small.

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❓ FAQ

What do I give it — search terms or URLs? A list of companies: full kununu profile URLs, country/slug paths, or bare slugs. You get one record per company.

Which countries are covered? The whole DACH region — Germany (de), Austria (at) and Switzerland (ch).

Does it return the individual review texts? No — it returns the company-level snapshot: overall and factor scores, salary ranges by job title, benefits, review counts, recommendation rate, score trend and awards. It's the fastest, most complete way to track employers at the company level.

Can I get only companies that changed on a schedule? Yes — turn on incremental mode and schedule it; each run emits only what changed and can notify your channel.

What happens if a company slug doesn't exist? With "Skip missing companies" on (the default), it's skipped and the run continues instead of failing.

What formats can I export? JSON, CSV, Excel, HTML table, or via the API.

Is it good for AI agents? Yes — enable compact mode; the output is MCP-friendly and drop-empty keeps payloads lean.

Is scraping this legal? The actor collects only publicly available data. You are responsible for how you use it, including any personal data and GDPR-style obligations.

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⚖️ Disclaimer

This actor accesses only publicly available data on kununu. You are responsible for how you use the extracted data — in particular any personal information — and for complying with the site's terms and applicable law (including the GDPR where it applies). Not affiliated with, endorsed by, or sponsored by kununu or New Work SE.

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