Database scraping and contact information prospecting
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Post a project like this$125
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Website Developer, Graphic Designer, Transcriber, Content writer, CAD Expert, Python Developer, Photo Editor, Web Scrapper, JAVA developer, Android developer, Wix/Shopify Expert,
Regensburg
LinkedIn Lead Expert, Web Scraping, Data Entry, Data mining, Web Crawling, Data extraction and Email Lists
London
Customer Support, Bookkeeping, Data entry, Product listing, Virtual assitant
Coimbatore
2543778274675212354233715410128591925573696451142054414297133422475450104302406
Description
Experience Level: Entry
I need an expert in data parsing and prospecting. I need you to do:
1) parse information about franchise companies from the US websites (I'll give you the links)
I expect you to parse this info about 4160 companies:
- the company name, minimum cash required (royalty), total investment, franchise fee, financing assistance,
- if the page has this information - Year founded, Number of units,
- and the rest of the franchise's general description
2) I think there will be some duplicates after parsing, it should be checked and fixed.
3) Then you have a list of franchises with data about them. You find 5-7 C-level decision-makers from the franchise headquarters (not branch offices and franchisees' employees as we need the very franchisors' C-level contacts), find and extract their first and last names, job titles, and up-to-date email addresses and phone numbers.
4) As a result, you give me a CSV. Here is a template for one company: https://monosnap.com/file/hKBKC8UQJrGIqJwJlauFyA7nqPB1jO
1) parse information about franchise companies from the US websites (I'll give you the links)
I expect you to parse this info about 4160 companies:
- the company name, minimum cash required (royalty), total investment, franchise fee, financing assistance,
- if the page has this information - Year founded, Number of units,
- and the rest of the franchise's general description
2) I think there will be some duplicates after parsing, it should be checked and fixed.
3) Then you have a list of franchises with data about them. You find 5-7 C-level decision-makers from the franchise headquarters (not branch offices and franchisees' employees as we need the very franchisors' C-level contacts), find and extract their first and last names, job titles, and up-to-date email addresses and phone numbers.
4) As a result, you give me a CSV. Here is a template for one company: https://monosnap.com/file/hKBKC8UQJrGIqJwJlauFyA7nqPB1jO
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26 Apr 2024
Russian Federation
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