Brand monitoring is how companies know about what customers, shoppers, and prospects are thinking about their brand, products, and services. In conversations on social media, forums, discussion boards, and customer complaint platforms, customers reveal their experiences and shopping activities.
These conversations contain mentions about brands, their impact, qualities, service, features, and gaps. Monitoring these online conversations can be a strategic advantage for brands as they will know the exact perception of their brand among users.
However, monitoring it all manually on so many platforms is not easy. Moreover, manual methods may not work at all. This is where you need tools and services that monitor the web to keep track of your brand’s mentions and sentiments.
This article shows how to scale your brand monitoring efforts through automated means, such as data scraping, to turn your online conversations into insights.
Why is Brand Monitoring Required?
Social media opinions, trends, and hashtags are not something that brands can neglect. They have the power to change their brand’s reputation overnight.
Even the most well-positioned and reputable brands have faced capital erosion of millions in cases of slander campaigns against them by rivals. In some cases, your employees or grieved customers may start a hashtag that can cause immense reputation damage.
This is why brand monitoring is required for safeguarding your brand equity, which takes years to build but seconds to get ruined.
Brand monitoring works best when you tap into data from multiple sources:
- Scraping E-commerce Platforms for Pricing and Reviews
- Monitoring Forums and Blogs for Niche Brand Discussions
- Social media channels, pages, posts, and comments
- App store reviews
- News and Media Scraping for Public Perception Analysis
Core Challenges in Scaling Online Brand Monitoring
Brands face their most important challenges when they try to scale their monitoring operations. Brand monitoring requires them to track social media conversations, online feedback, consumer forums, etc., to keep an eye on current sentiments and build sentiments in favor or against them.
1. Fragmented Data Across Platforms
Brand data exists on social media, online platforms, and other avenues. However, this data is in a disconnected format. It is difficult to monitor all the brand data on various platforms. Gathering data across channels requires specialized tools, as manual methods won’t work.
2. Delayed Detection of Brand Sentiment Moves
Brand sentiment can change dramatically within hours. Delayed detection creates substantial damage as negative sentiments start from niche forums, specific regions, or among certain demographic groups. Without real-time monitoring of thousands of datasets on various social platforms, any negative sentiments often go unnoticed until they’ve damaged your reputation. With traditional monitoring approaches, brands find conversations long after they happen. Negative sentiment builds faster than positive sentiment. It can create a full-blown PR crisis.
3. Manual Monitoring Limitations in High-Volume Environments
Traditional monitoring methods break down quickly as your brand’s reach extends to multiple platforms. Manual processes fail to track thousands of mentions across several platforms and in many languages. High volumes of brand mentions can overwhelm human reviewers or social media reputation managers. Who misses important mentions, especially in image/video content or untagged brand references? Large global brands need AI-powered brand monitoring systems for global reputation monitoring coverage. Also, with manual methods, it is difficult to address each comment on social media.
Web Scraping for Brand Monitoring: Key Capabilities
As we discussed the challenges brands face in dealing with brand mentions across dozens of platforms in multiple languages, it is clear that manual brand monitoring won’t work in today’s high-volume data environments. This is where web scraping comes as a natural solution. Web scraping tools can track and extract specific keywords, brand mentions, and comments to analyze them with other NLP tools to gauge sentiments.
Scraping tools extract data from thousands of web sources and online platforms where sentiment data is available. No matter how scattered, unstructured, and disorganized the data is, web scrapers have the competency to extract and tabulate the data into an organized and structured format for easy NLP analysis.
Scraping tools extract brand mentions from e-commerce platforms, social media, forums, news sites, and review platforms. The automated scrapers monitor all major platforms non-stop for the detection of sentiments. Advanced scrapers can spot rising trends.
1. Real-Time Brand Mention Tracking with BeautifulSoup
Python-based libraries like BeautifulSoup or Scrapy are the lifeblood of real-time brand monitoring systems. These tools help developers create crawlers that scan for brand mentions on a number of social media platforms.
A simple implementation might include scripts that:
| import requests
from bs4 import BeautifulSoup # Example: Scrape forum for brand-related discussions url = ‘https://exampleforum.com/brand-mentions’ response = requests.get(url) soup = BeautifulSoup(response.text, ‘html.parser’) # Extract brand mentions from posts posts = soup.find_all(‘div’, class_=’post-content’) for post in posts: print(post.text) |
You can expand this approach and script to include forums, news sites, and blogs where brand discussions happen often.
2. Social Media Conversations Monitoring with Web Scraping APIs
Another method for web scraping effectively includes using Web Scraping APIs for monitoring social media conversations. Web scraping APIs help monitor conversations on Twitter, Instagram, Facebook, and other networks where customers discuss your products and services. All major platforms have their own unique APIs that can work flawlessly to scrape brand mentions on the platform.
For instance, Twitter monitoring works through their API:
| import tweepy
# Twitter API credentials setup auth = tweepy.OAuthHandler(api_key, api_secret) auth.set_access_token(access_token, access_token_secret) api = tweepy.API(auth) # Search for brand mentions tweets = api.search_tweets(q=’YourBrandName’, lang=’en’, count=100) for tweet in tweets: print(f”{tweet.user.screen_name}: {tweet.text}”) |
This method tracks mentions, hashtags, and user comments for sentiment analysis and engagement trends. If you use advanced web scraping APIs, you can also track videos and images.
Qualities, Features, and Competencies Brand Monitoring Tools Must Possess:
| Resilient Scraping Infrastructure
A reliable scraping system serves as the foundation of effective online brand monitoring. Your scraping system must do more than just collect data. It needs to overcome anti-scraping barriers, handle dynamic content, and turn raw data into practical findings. |
Rotating Proxies
A dependable scraping setup needs strategies to avoid detection and blocking. Your brand monitoring efforts will stop within minutes from IP blocks if you don’t manage proxies properly when scraping thousands of web pages. |
Headless Browsers for Anti-Bot Evasion
Headless browsers like Puppeteer and Selenium help you slip past detection by acting like real users. These tools work behind the scenes and access websites like regular browsers without showing visual interfaces. |
| Dynamic Content Handling
Today’s websites heavily use JavaScript to load content dynamically—creating a major challenge for simple scraping tools. Both Puppeteer and Selenium offer ways to handle these challenges. |
Data Deduplication for Clean Insights
Raw scraped data often has inconsistencies that make it less useful for brand monitoring. Data normalization turns this mixed information into standard formats that help with accurate analysis. |
Custom Scraping
The scrapers must be designed as per your brand monitoring needs. It must be built as per the platform’s specifics and terms of use. They should follow rate limits, GDPR guidelines, etc. |
Integrating Scraped Data into Brand Intelligence Workflows
Brand data becomes truly valuable when it merges with your business intelligence tools. This is key for sentiment analysis. Your scrapers will provide you with all the data about brand mentions from various online platforms. However, this data needs to go through quality sentiment analysis tools (powered preferably by NLP) to correctly understand the context of the sentiments. By doing so, you get a fair idea of what is driving consumer sentiment.
From product feature intelligence to customer service issues, from delivery problems faced by shoppers to price satisfaction, and from brand experience to product quality perception, all these can be tracked using web scraping tools. These metrics give a fair idea of the exact sentiments that your brand creates in consumers.
Conclusion
Your scraping practices need strong ethical guidelines despite these technical capabilities. You protect your reputation and legal standing by respecting robots.txt files, following GDPR and CCPA rules, and avoiding personally identifiable information. X-Byte’s specialized solutions help address all these challenges.
X-Byte’s professional brand monitoring services help you merge scraped data into your business intelligence workflows.
✯ Alpesh Khunt ✯
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