We Built a Scalable Amazon Seller List for a USA-Based Client From 2023 Til Now
Clients Background:
A USA-based client was looking for a consistent supply of high-quality leads of Amazon sellers. He wanted 1000 leads weekly and asked us if we could help. So we took a closer look at all his requirements and agreed to do the job.
Requirements:
The client gave us some niche-specified keywords and asked for a structured dataset with the following fields:
- Seller Name
- Website
- Amazon Store Link
- Decision Maker’s First and Last Name
- Job Title
- Phone Number
- LinkedIn Profile
- Location
Challenges:
The main challenge of this project was to find seller brand names using specific keywords as the keywords were limited, and we needed a huge amount of data to fulfill clients’ requirements. Besides, Manual data collection requires significant effort, plus this was a long-term project, and we had to maintain consistency to deliver results every week without failing.
Our Approach:Â
Step 1: Lead Identification
We went to Amazon and started searching for sellers based on the keywords to find their store names, and we collected those Amazon store links. Then, we Google-searched those store names to find their website URLs and other information like their phone numbers (business), and decision-makers’ information.
Step 2: LinkedIn search
It was crucial to enrich the dataset so we searched every decision-maker on LinkedIn and gathered their additional contact details.
Step 3: Data Validation & Accuracy Check
We used Million Verifier to validate email addresses and ensure deliverability.
Step 4: Weekly Lead Submission
Once all leads were verified and formatted, we organised them in an Excel sheet and submitted them to the client every week.
Overcoming Challenges:Â
When we found duplicate data, we teamed up with the client to add more keywords, so we got fresh and good leads. We also made our work faster and better by following a clear step-by-step process, making sure everything was done on time and correctly.
Conclusion:
This project describes our expertise in handling large data sets with consistency and precision. As we worked hard and didn’t miss any details, this helped us build a strong and long-lasting partnership. And we are still working on that project.
