Challenge

A UK-based B2B SaaS startup expanding into the EU needed targeted contacts — Heads of Operations and IT Directors at logistics companies with 50–500 employees across Germany, the Netherlands, and France. Their existing purchased lists had 40%+ bounce rates and were generating almost no outreach replies.

Approach

Sourced contacts from industry-specific directories, professional networks, and company websites across all three target markets. Each record was cross-referenced for role accuracy and verified with email deliverability checks. Unverifiable records were excluded. Final dataset: full name, job title, company name, verified email, LinkedIn URL, and company size.

Result

Delivered 2,100 verified contacts in 8 working days. The client reported a 6.2% positive reply rate on their first outreach campaign — up from under 1% with previous lists. Email bounce rate dropped to under 4%.

Challenge

A US-based consumer electronics brand selling on Amazon, eBay, and their own Shopify store needed to track competitor pricing on 350+ SKUs across all three marketplaces. Manual spreadsheet tracking was consuming two full days per week and still missing price changes.

Approach

Built a structured data pipeline collecting pricing, stock availability, and seller ratings for each competitor SKU across all three platforms daily. Output was deduplicated and delivered as a formatted CSV compatible with their pricing dashboard. Set up automated email alerts for price drops exceeding 10% on key products.

Result

Freed up roughly 8 hours per week of manual work. The client could now respond to competitor price changes within 24 hours instead of 5–7 days, and reported a measurable improvement in Amazon Buy Box win rate within the first month.

Challenge

A property investment firm covering the Netherlands and UK needed rental listings from Funda, Rightmove, Pararius, and several local portals combined into a single structured dataset. Their goal was to identify undervalued properties by comparing asking rents to area averages, but data was scattered across incompatible platforms with no way to compare them side by side.

Approach

Extracted listing data from each platform — property type, location, asking price, square footage, rooms, listing date, and agent details. Normalised everything into a single schema, deduplicated across platforms (the same property frequently appears on multiple sites), and enriched with calculated price-per-square-metre fields. Delivered weekly as a pivot-ready Excel file.

Result

First delivery: a unified dataset of 4,800+ active listings across both markets. Weekly updates followed. The client identified 12 properties meeting their investment criteria within the first two weeks — opportunities they confirmed they would have missed without the aggregated view.