---
title: "Brand monitoring"
description: "Brand monitoring data from Google News, Reddit, Trustpilot, G2 and forums: every brand mention as clean Markdown for sentiment, alerts and share of voice."
canonical: https://www.zenrows.com/use-cases/brand-monitoring
updated: 2026-10-18
---

# Brand monitoring

Brand monitoring is tracking where your brand is mentioned online, in news, forums, review sites, video and search results, so you can measure reputation and respond before an issue spreads. Zenrows fetches those pages, including protected ones, and returns each mention as clean Markdown or JSON for your sentiment model, alerting rules and share-of-voice reports.

## Sample output: one brand mention

```json
{
  "mention_id": "m-20261014-0193",
  "brand": "Acme Cloud",
  "source_type": "forum",
  "site": "reddit.com",
  "url": "https://www.reddit.com/r/example/comments/abc123/",
  "title": "Acme Cloud invoice doubled overnight?",
  "published_at": "2026-10-14T07:41:00Z",
  "first_seen_at": "2026-10-14T08:00:12Z",
  "excerpt": "Our monthly charge went up after the plan update and support has not replied yet...",
  "other_brands": ["Example Host"],
  "engagement": { "comments": 48, "score": 212 },
  "sentiment": "negative",
  "topic": "pricing",
  "alert": true,
  "content_format": "markdown"
}
```

An illustrative mention record. Zenrows returns the page as Markdown or fields; sentiment, topic and the alert flag are added by your own classifier.

## Where brand mentions come from

Owned social accounts are only a small part of what people say about a brand. The mentions that move reputation tend to sit in threads, reviews and articles you do not control.

| Job | Data you collect | Typical sources |
|---|---|---|
| News and press coverage | Headline, outlet, date, article text, quoted names | Google News, trade publications, press release wires |
| Forum and community threads | Thread title, post text, replies, upvotes, subreddit or board | Reddit, Hacker News, Stack Exchange, niche forums |
| Review site tracking | Star rating, review text, date, reply from the company | Trustpilot, G2, Capterra, Google Maps, app store pages |
| Search result monitoring | Page one results for your brand, plus "brand + reviews" and "brand + scam" | Google Search, Bing |
| Video and creator mentions | Video title, description, channel, views, comment text | YouTube, public creator pages |
| Share of voice | Mention counts for you and named competitors, by source and week | All of the above, filtered to the same date range |

## Why brand mention tracking misses things

Mention tracking fails quietly. Nothing errors when a source stops returning results; you simply stop hearing about it.

| What breaks | What it looks like | How Zenrows handles it |
|---|---|---|
| Sources that block crawlers | Review sites and forums answer with CAPTCHAs or 403s, and their mentions drop out of reports | Zenrows' stealth gets through Cloudflare, DataDome and similar protections, so those sources stay in the feed |
| Comments loaded by JavaScript | The thread title arrives but replies, where the complaint is, do not | Adaptive Stealth Mode escalates to browser rendering when a page needs it, so replies have loaded before the page returns |
| Noisy page text | Menus, cookie banners and ads end up in your sentiment model and skew the score | Markdown output returns the readable content without page chrome |
| Local results | Search results and review pages differ by country, so a local issue goes unseen | Geotargeting checks the same query from each market |
| Spikes in volume | A viral thread brings hundreds of new URLs in an hour | Batch takes the whole URL list as one job and calls your webhook when done |

## How to set up brand monitoring with Zenrows

1. **List your queries.** Brand name, product names, executives' names in a public role, common misspellings, and the same for two or three competitors.
2. **Discover new mentions.** Fetch search pages for each query on each source on a schedule, for example Google News sorted by date and Reddit search, and collect result URLs you have not seen.
3. **Fetch each mention as Markdown.** Send new URLs to Fetch or Batch with Markdown output so your classifier reads text instead of HTML.
4. **Classify and alert.** Score sentiment and topic, then alert when negative mentions cross a threshold or appear on a high-reach source.

```python Python
# pip install requests
import requests

API = "https://api.zenrows.com/v1/"
seen = set(open("seen_urls.txt").read().split())

# 1. Discover result links for one query on one source
search = requests.get(API, params={
    "apikey": "YOUR_ZENROWS_API_KEY",
    "url": SEARCH_URL,  # e.g. a news or forum search for "Acme Cloud", newest first
    "mode": "auto",  # Adaptive Stealth Mode
    "css_extractor": '{"links": ".result a.title @href"}',
}).json()

# 2. Fetch each new mention as Markdown for your classifier
for link in search["links"]:
    if link in seen:
        continue
    page = requests.get(API, params={
        "apikey": "YOUR_ZENROWS_API_KEY",
        "url": link,
        "mode": "auto",
        "response_type": "markdown",
    })
    classify_and_store(link, page.text)  # your sentiment and alert step
```

```bash CLI
npx -y @zenrows/cli init
zenrows extract SEARCH_URL --css '{"links":".result a.title @href"}' --out links.json
# new_mentions.jsonl holds one {"url": "..."} per line from today's discovery run
zenrows batch create new_mentions.jsonl --output markdown --wait
zenrows batch results JOB_ID --out mentions.jsonl
```

```text Claude Code (MCP)
# Connect once
claude mcp add zenrows -e ZENROWS_API_KEY=YOUR_ZENROWS_API_KEY -- npx -y @zenrows/mcp

# Then ask:
Use Zenrows to read these five Reddit threads and two Trustpilot pages about Acme Cloud. Group the complaints by topic, quote the most upvoted one per topic, and tell me which ones mention refunds.
```

## Which Zenrows product fits

| If you need to | Use | Why |
|---|---|---|
| Check a search page or a single mention on demand | [Fetch](/products/fetch) | Returns the rendered page as HTML, Markdown or named fields |
| Pull hundreds of new mention URLs each hour | [Batch](/products/batch) | One job for the whole list, results by webhook or download |
| Run monitoring jobs from scripts and cron | [CLI](/cli) | `batch create` with `--output markdown` from any shell |
| Ask an assistant what people are saying this week | [MCP Server](/mcp) | Claude, Cursor and other MCP clients read the pages through Zenrows |

## What success looks like

| Measure | What good looks like | How to check it |
|---|---|---|
| Source coverage | Every source on your list returns results on every run | Log result counts per source and alert when one drops to zero |
| Time to detection | A negative thread is in your queue within one discovery cycle of being posted | Compare `published_at` on the page with your `first_seen_at` |
| Text quality | Mentions reach the classifier without menus, banners or cookie text | Sample ten Markdown files per source each week and read them |
| Duplicate rate | Syndicated articles and reposts are counted once | Group mentions by canonical URL and text similarity |
| Share-of-voice consistency | You and each competitor are measured on the same sources and dates | Run every brand's queries in the same Batch job |

## Brand monitoring FAQ

### What is brand monitoring?

Brand monitoring is the ongoing tracking of mentions of your brand, products and people across news sites, forums, review platforms, video, social media and search results. Teams use it to measure reputation and sentiment over time, compare share of voice with competitors, find customer issues early and spot coverage worth amplifying. It differs from brand protection, which hunts for counterfeits and misuse to remove.

### How do I track brand mentions online?

Build a list of queries for your brand and products, then run them on a schedule against each source's search page: news, Reddit, review sites, YouTube and Google Search. Store every result URL, fetch new ones as clean text, and classify them for sentiment and topic. Zenrows handles the fetching, including sources with bot protection.

### Is Zenrows a brand monitoring tool?

Zenrows is the data layer underneath brand monitoring tools. It gives your pipeline, or the tool you build, the page behind each mention as Markdown, JSON or HTML. Use it to add sources your current brand monitoring tools miss, or to run monitoring on your own list of sources with your own classifier.

### Which sources should brand monitoring cover?

Start with where your buyers talk. For software that usually means Reddit, Hacker News, G2, Capterra and Google Search. Consumer brands add Trustpilot, Google Maps reviews, YouTube and app store pages. Add trade news for B2B and local news for retail. Track a few competitors on the same sources so share-of-voice numbers are comparable.

### How often should I check for new mentions?

Hourly for sources where issues spread fast, such as Reddit and news. Daily for review sites and search results. Weekly is enough for slow forums and blogs. Match the cycle to how quickly you would act on what you find.

### How do I measure brand reputation from mentions?

Score every mention for sentiment and topic, then weight it by reach: a thread with thousands of upvotes or a story in a national outlet counts for more than a reply with no engagement. Track the share of negative mentions per week, per source and per topic, next to the same numbers for two or three competitors. A rise in one topic, such as delivery or support, tells you where to act.

### Can I monitor mentions in other countries and languages?

Yes. Use geotargeting so each request comes from the country you are checking, and search engines and review sites return the results local users see, and run your queries in each local language. The Markdown output keeps the original language, so your classifier or translation step receives the text as written.

## Related use cases

- [Review scraping](https://www.zenrows.com/use-cases/review-scraping.md): Review text, ratings, dates and replies from Amazon, Google, Yelp, Trustpilot and G2, every page.
- [AI search monitoring](https://www.zenrows.com/use-cases/ai-search-monitoring.md): Answers, brand mentions and cited sources from ChatGPT, Perplexity, Gemini and AI Overviews, prompt by prompt.
- [Sentiment analysis data](https://www.zenrows.com/use-cases/sentiment-analysis-data.md): Review text with ratings, forum threads and comments, cleaned and labeled for sentiment and NLP models.
- [Brand protection](https://www.zenrows.com/use-cases/brand-protection.md): Unauthorized sellers, trademark misuse and impersonation stores across marketplaces, with screenshot evidence for every takedown.

Start free: https://app.zenrows.com/register. Docs: https://docs.zenrows.com