Blog/E-Commerce Price Monitoring Case Study: How One Online Store Increased Profit Margins by 30%

E-Commerce Price Monitoring Case Study: How One Online Store Increased Profit Margins by 30%

Discover how a mid-sized online store used ScrapeGraphAI for hourly competitor price tracking and dynamic pricing to boost profit margins.

Case Studies•4 min read min read•Mohammad Ehsan AnsariBy Mohammad Ehsan Ansari
E-Commerce Price Monitoring Case Study: How One Online Store Increased Profit Margins by 30%

In the world of data-driven decisions, social media platforms like Instagram, LinkedIn, and Reddit are goldmines of public opinion, user behavior, and emerging trends. Whether you're building a trend monitoring dashboard, training an AI model, or running competitive analysis — the first step is structured data extraction. Enter ScrapeGraphAI: a smart web scraping framework that leverages LLMs (Large Language Models) to convert messy HTML into structured, ready-to-use JSON.

This blog will walk you through how to collect social media insights programmatically from Instagram, LinkedIn, and Reddit using ScrapeGraphAI, and how you can use that data for trend analysis.

Why Scrape Social Platforms?

Scraping Instagram, LinkedIn, and Reddit allows companies, researchers, and developers to:

  • Track hashtags, topics, or keywords over time.
  • Analyze engagement across communities or audiences.
  • Understand sentiment toward products, services, or competitors.
  • Detect trending discussions, pain points, or user needs in real time.
  • Build trend dashboards, AI training datasets, or market intelligence tools.

Why Use ScrapeGraphAI?

ScrapeGraphAI combines the power of Python with LLM-powered extraction, meaning:

  • You don't need to write brittle regex or custom parsers.
  • You define your schema (like "title", "author", "timestamp"), and the model fills it in.
  • It’s modular — choose different browser engines, proxies, and LLMs (OpenAI, Groq, Mistral, etc.)
  • You get clean JSON directly from semi-structured pages.

Perfect for dynamic, changing pages like Reddit threads or Instagram profiles.

🔧 Setting Up the Project

First, install ScrapeGraphAI:

bash
pip install scrapegraphai

You’ll also need an LLM key (OpenAI, Groq, or any compatible provider) and optionally a proxy service like BrightData.

Let’s extract top posts from a subreddit.

python
from scrapegraphai.graphs import SmartScraperGraph
from scrapegraphai.utils import convert_md_to_html

config = {
    "llm": {
        "model": "gpt-3.5-turbo",
        "api_key": "your-openai-key"
    },
    "headless_browser": {
        "browser": "firefox"
    }
}

prompt = "Extract post title, upvotes, author, and timestamp"
url = "https://www.reddit.com/r/artificial/"

graph = SmartScraperGraph(
    prompt=prompt,
    source=url,
    config=config
)

output = graph.run()
print(output)

Output (structured JSON)

json
[
  {
    "title": "Google's Gemini outperforms ChatGPT in math",
    "upvotes": "3200",
    "author": "u/techinsider",
    "timestamp": "2025-06-01T13:45:00Z"
  },
  ...
]

Example 2: Scraping LinkedIn Posts (Public)

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LinkedIn requires authentication and IP rotation. Once you have access (via cookies or session-based browser automation), you can still use ScrapeGraphAI.

python
prompt = "Extract author name, post content, reactions, and posted date"
url = "https://www.linkedin.com/in/satyanadella/detail/recent-activity/shares"

graph = SmartScraperGraph(
    prompt=prompt,
    source=url,
    config=config
)

output = graph.run()
print(output)

📸 Example 3: Scraping Instagram Profiles or Hashtags

Instagram also requires headless browsing and anti-bot protections (BrightData helps here).

python
prompt = "Extract image URL, caption text, likes, and comments count"
url = "https://www.instagram.com/explore/tags/ai/"

graph = SmartScraperGraph(
    prompt=prompt,
    source=url,
    config=config
)

output = graph.run()

Using the Data for Trend Analysis

Once you’ve collected structured data across platforms, you can:

Use tools like Pandas, Matplotlib, or Plotly to visualize hashtag usage, post frequency, or sentiment over time.

2. Train Models

Use collected posts for NLP tasks like topic modeling, summarization, or classification.

3. Build Dashboards

Integrate with Streamlit, Dash, or Superset for real-time social analytics.

Best Practices & Ethics

  • Always respect "robots.txt" and platform TOS.
  • Use proxies, randomized headers, and rate-limiting.
  • Never scrape private data or send spam.
  • Use your data responsibly and only for public, permitted insights.

🔚 Conclusion

ScrapeGraphAI simplifies the complex and error-prone process of scraping dynamic social platforms. Whether you're tracking Reddit discussions, LinkedIn engagement, or Instagram trends — the power of LLM-driven extraction gives you structure, flexibility, and speed.

Build your trend analysis engine today — the future is structured.

Want the code? Dive into ScrapeGraphAI GitHub or use the Python SDK to start scraping smarter.

Want to learn more about intelligent agents and web scraping? Explore these guides:

These resources will help you understand how to effectively integrate web scraping capabilities into your intelligent agents.