Our eCommerce data scraping services played a pivotal role in scraping sports apparel brand data across the US, Asia, the Middle East, and Europe. The objective was to understand their marketing strategies and implement similar tactics to save millions on ad spend. We gained insights into their promotional activities, target demographics, and pricing strategies by scraping data from these regions. Hence, it enabled our client to optimize their marketing campaigns, target the right audience, and allocate their budget more efficiently, resulting in significant cost savings.
The Client
Our US-based sports apparel brand aimed to save millions on ad spend by scraping sports apparel brand data across the US, Asia, the Middle East, and Europe. They leveraged our eCommerce data scraping service to gather insights into competitor marketing strategies. By analyzing this data, they could optimize their marketing campaigns, target the right audience, and allocate their budget more effectively. It resulted in significant cost savings and improved ROI for their advertising efforts.
Key Challenges
The challenges faced by our client, a sports brand, were multifaceted. They struggled with budget overshoots from excessive advertising, needing more adequate data to make informed decisions on digital channel investments. The brand also needed more competitive insights spread across numerous online channels, which their in-house analytics team needed help to gather due to resource constraints. Their self-service tools had issues ranging from coverage to quality, further complicating data extraction. Additionally, the brand required frequent updates from websites like Amazon and Walmart, with a tight timeframe of 90 minutes from extraction to data integration into their analytics tool.
Key Solutions
Our e-commerce data scraper provided a comprehensive solution to help the sports brand save millions on ad spend. We quickly extracted competition data from over 50 websites, including Amazon and Walmart, and pushed it into a database for analysis. The data was refreshed automatically every three hours to ensure it remained up-to-date. We scaled our servers and increased the data extraction speed to meet the data delivery deadline within 1 hour. We also implemented a Quality Assurance tool to ensure data accuracy. We also built a connector to integrate our data with the brand's proprietary BI tool, enabling the business teams to identify anomalies and optimize ad spending faster. This automated process eliminated manual data collection, reducing the time to get advertising intelligence from days to minutes and enabling significant changes in their marketing campaigns.
Advantages of Collecting Data Using Product Data Scrape
Industry Expertise: With a deep understanding of various industries, we tailor our scraping solutions to meet your specific industry needs, providing insights that others may miss.
Ethical Scraping Practices: We adhere strictly to ethical scraping practices, ensuring that our scraping activities are legal and ethical, giving you peace of mind.
Robust Infrastructure: Our robust infrastructure can handle large-scale scraping projects, ensuring you get the data you need, no matter how big the task.
Data Security: We prioritize data security, implementing stringent measures to protect your data throughout scraping.
Seamless Integration: Our scraping solutions seamlessly integrate with your existing systems, making incorporating the scraped data into your workflow easy.
Dedicated Support: We offer dedicated support to address any issues or concerns you may have, ensuring that your scraping project runs smoothly from start to finish.
Final Outcomes: Thanks to our solutions, the sports apparel brand achieved an 8.4% improvement in ROI within 89 days. By gaining better visibility into market dynamics, they reduced ad expenditure wastage, improved marketing campaign ROI, and responded faster to opportunities. Our insights also optimized their revenue management team's efforts, helping them determine the best timing for advertising campaigns based on actionable competition data.
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