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Your competitors can change their prices overnight. Your data collection system shouldn't miss it. š
Imagine checking 50 product pages every morning just to find out which prices have changed.
Now imagine getting that information automatically.
With Python web scraping and automation, businesses can build systems that:
ā Monitor product prices across multiple websites
ā Track stock availability
ā Compare competitor pricing
ā Record changes over time
ā Send alerts when something important changes
ā Export updated data to Excel or CSV
Instead of repeatedly checking websites, you get organized information that helps you make better business decisions.
This can be especially useful for e-commerce businesses, online retailers, and teams that regularly research competitors.
At ScrapePro Solutions, I build custom Python scraping and automation solutions tailored to specific business needs.
A quick question for e-commerce business owners:
If a competitor changed their product price today, how would you find out ā manual checking or automatic alerts?
š¬ Share your answer in the comments.
ā¢ Oct 16
Good question to ask owners. In my experience alerts only get used if the threshold is set per product, otherwise people mute them after a week. Do you let clients set a percent change trigger, or alert on any change?
Thatās a great point! Iād recommend offering both options: alerts for any price change and customizable percentage thresholds, depending on the clientās needs. This helps keep notifications relevant and useful.
Its really great developer and services very good
Thankyou so much
Two things bit me while building a price tracker. A page can come back "successful" but empty (in my case, compressed content the client couldn't decode), and saving that blindly overwrites good data. So I refuse any page with no title and no price, and only record a change when...
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For the Onyx Data October 2026 Challenge, I worked on the Sunrise Coffee Chain analytics project, analyzing store performance, profitability, customer loyalty, revenue mix, and trading patterns across 60 UK stores (2023ā2024).
š¹ My Analytical Approach
1ļøā£ Python ā Data Preparation
Cleaned and prepared the source datasets to establish a reliable foundation for analysis.
2ļøā£ SQL ā Business Analysis
Investigated store-level profitability, cost pressure, customer loyalty, revenue contribution, and trading patterns.
3ļøā£ Power BI ā Interactive Dashboards
Built a 3-page interactive report featuring advanced DAX measures, KPI cards, YoY comparisons, profitability analysis, and business-focused visualizations.
š Key Findings
ā¢ Ā£89.7M Revenue | Ā£16.9M Net Profit
The business recorded an aggregate net profit margin of 18.8% across the analysis period.
ā¢ Store Location Matters
Transport-Hub stores achieved a 25.1% aggregate net profit margin, compared with 18.6% for High-Street stores.
ā¢ An Area Requiring Attention
South London Corner recorded a Ā£50.5K net loss over 24 months.
ā¢ Product Revenue Contribution
Espresso contributed 45.7% of daily revenue, while food contributed 26.1%.
ā¢ Trading Patterns
Average daily revenue was approximately 24% lower on weekends and 33% lower on UK bank holidays than on regular trading days.
š” My Biggest Takeaway
Revenue alone does not tell the whole story. Understanding profitability, cost pressure, product contribution, and trading patterns helps businesses identify where to improve performance and make better decisions.
š Explore the Complete Project
GitHub: https://github.com/SajeebTheAnalyst/sunrise-coffee-chain-analytics
If you were advising the management team, which finding would you investigate firstāand why?
#DataAnalytics #SQL #PowerBI #Python #DAX #BusinessIntelligence #DataVisualization #OnyxData
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