Competitive Intelligence Dashboard
We built a Python scraping system to crawl competitor prices and track market trends daily.
script tracks 500 product endpoints automatically every day.
monitor 10 competitor sites without crashing when page designs change.
System Bottlenecks
Sales teams spent hours checking competitor websites manually to build pricing tables, which led to delayed responses to market price changes.
Architectural Move
We wrote a Python tool that scrapes product listings daily. It cleans and structures pricing tables using Pandas, saving the results to a dashboard to track price fluctuations over time.
Execution Path
Scraping Scripts: We wrote BeautifulSoup and Selenium parsers to fetch pricing grids.
Data Normalization: We built Pandas jobs to unify raw text inputs and filter out formatting quirks.
Dashboard Interface: We built Streamlit charts to plot price movements over time.
"The tool gives us pricing updates every morning, letting us adjust our quotes quickly."
— Bilal Ejaz, B2B Sales Director
Core Stack
- Python (Crawler and Pandas engine)
- Streamlit (Dashboard visualization)
Under-the-hood Challenge
Preventing crawlers from failing when websites change their HTML layouts. We solved this by writing regex pattern matchers that extract pricing patterns directly from raw text, avoiding reliance on specific HTML CSS classes.
CID tracks competitor pricing automatically. By combining Python crawlers with Pandas processing, we extract structured datasets from dynamic webpages without using complex database pipelines.