Amazon Product Researcher | Online Arbitrage Specialist | Amazon FBA

used Keepa every day to analyse product history on Amazon UK. The main things I looked at were BSR history, buy box price trends, and how many sellers were on a listing over time. These three things together told me whether a product sells consistently or just occasionally, and whether the competition was growing or stable. Reading Keepa graphs became second nature to me. I could quickly spot a product with healthy, steady demand versus one that had a single sales spike and nothing else. That saved a lot of time and helped me avoid recommending leads that looked good on the surface but had no real selling history behind them.

My main focus was finding profitable Online Arbitrage leads for Amazon UK sellers. Every day, I would scan UK retail websites — including high street stores and online-only retailers — looking for products that could be resold on Amazon.co.uk at a healthy margin. I wasn't just browsing; I had a structured process for every lead I evaluated.

I used SellerAmp SAS daily to evaluate product profitability for Amazon UK. It was my go-to tool for calculating ROI, net profit, FBA fees, and referral fees quickly without having to manually run the numbers. I also used it to check for IP complaints, restricted ASINs, and hazmat flags before adding anything to a sourcing sheet. What I found most useful was being able to scan products fast and filter out the ones that didn't meet the target margin right away. It kept my research efficient and made sure every lead I passed on to the seller was already verified and profitable.

I researched products for Amazon FBA sellers by analysing demand, competition, and profitability before recommending any lead. I used Keepa to check BSR history and buy box trends, and SellerAmp SAS to calculate ROI, FBA fees, and net profit. I also checked for IP complaints, restricted brands, and category gating so sellers could source with confidence. Every lead I submitted came with a complete sourcing sheet — clean, organised, and ready to act on. Over time I developed a good eye for spotting which products have real, consistent demand versus ones that just look good on the surface. I focused on accuracy and making sure the numbers were always reliable before anything reached my client.

I used Google Sheets to build and maintain sourcing sheets for Amazon product research. Every lead I found was logged with all the important details — ASIN, source link, buy price, sell price, ROI, profit, and any notes on restrictions or competition. This made it easy for sellers to review leads quickly and make purchasing decisions without having to dig for information. I also used it to track supplier lists, monitor product performance over time, and organise research data across multiple client accounts. I kept everything clean, clearly labelled, and easy to navigate so nothing got missed and updates were simple to make.

I used Jungle Scout to support my product research by validating demand and estimating monthly sales volume. It gave me a clearer picture of how well a product was actually selling and helped me confirm whether the BSR I was seeing in Keepa translated into real, consistent sales numbers. I also used it to check competitor listings, spot trends in specific categories, and identify niches with steady demand but lower competition. It worked well alongside Keepa and SellerAmp SAS as part of my overall research process — each tool filled a different gap and together they gave me a more complete view of whether a product was worth recommending or not.

I worked specifically with Amazon FBA sellers in the UK marketplace, so I understood how the FBA process works from a sourcing perspective. I knew how FBA fees, referral fees, and VAT affect the final profit margin, and I always factored those into my calculations before recommending any product. I was familiar with how the buy box works on Amazon.co.uk, how seller count affects pricing, and what to watch out for when sourcing — things like restricted brands, oversaturated listings, and products with inconsistent sales history. Everything I researched was done with the FBA seller's actual costs and risks in mind, not just the surface numbers.