Use Cases / Brand Monitoring
Brand Monitoring
What people say about your brand online shapes purchasing decisions. Review sites, social platforms, and forums are where customers share their experiences - both good and bad. Automated monitoring lets you track these conversations across the web and respond before small issues become big problems.
What to monitor
Review sites
Trustpilot, Google Maps reviews, G2, Capterra, and industry-specific platforms. Track new reviews, rating trends, and common themes in customer feedback.
Social media
Twitter/X, Reddit, and TikTok for public mentions, complaints, and praise. Social posts often surface issues before they appear in formal reviews.
Competitor reviews
Track what customers say about your competitors too. Common complaints about a competitor are opportunities for your marketing and product teams.
Forum discussions
Reddit threads, community forums, and Q&A sites where people discuss products and services. These conversations are often more candid than formal reviews.
Collect reviews from Trustpilot
This example scrapes reviews for a company on Trustpilot, extracting the author name, star rating, and review text. Run it on a schedule to track new reviews as they come in.
from browser7 import Browser7
from bs4 import BeautifulSoup
import json, re
client = Browser7(api_key="b7_your_api_key")
result = client.render(
"https://www.trustpilot.com/review/amazon.com",
country_code="US",
)
soup = BeautifulSoup(result.html, "html.parser")
reviews = []
for article in soup.select("article"):
author = article.select_one("span[data-consumer-name-typography]")
if not author:
continue
star_img = article.select_one("img[alt*='Rated']")
rating = None
if star_img:
match = re.search(r"Rated (\d) out of 5", star_img.get("alt", ""))
if match:
rating = int(match.group(1))
text_el = article.select_one("p[data-relevant-review-text-typography]")
reviews.append({
"author": author.get_text(strip=True),
"rating": rating,
"text": text_el.get_text(strip=True)[:150] if text_el else None,
})
print(json.dumps(reviews[:5], indent=2))See our Trustpilot scraping guide for complete examples including date extraction and pagination.
What you can do with brand data
Alert on negative reviews
Set up a pipeline that scrapes review sites daily and flags new 1-2 star reviews. Early detection lets your support team respond quickly, often turning a negative experience into a positive one before it influences other buyers.
Track rating trends over time
Monitor your average rating and review volume week over week. A gradual decline in ratings might indicate a product quality issue. A sudden spike in reviews might mean a viral social media post or a competitor's campaign.
Competitive benchmarking
Compare your review scores, volume, and sentiment against competitors on the same platforms. Identify areas where competitors are praised that you could improve, or weaknesses you can highlight in your marketing.
Product feedback analysis
Review text contains specific product feedback that surveys often miss. Common themes in negative reviews point to product issues. Common themes in positive reviews tell you what to emphasize in marketing.
What this costs
Every page costs $0.01. A Trustpilot company page shows 20 reviews, so monitoring 5 companies daily (first page of reviews each) costs $0.05/day. Scraping deeper - 10 pages of reviews across 20 companies - costs $2/day. Brand monitoring is one of the most cost-effective scraping use cases because the data volume is relatively small but the business value is high.
Related guides
- How to Scrape Trustpilot - reviews, ratings, and dates
- How to Scrape Google Maps - business listings and review data
- How to Scrape Reddit - community discussions and sentiment