Sellzone toolbox is designed to help Amazon sellers make the most out of each Amazon product listing by influencing its visibility, traffic, and conversions. Sellzone is brought to you by the Semrush team, and its 12 years of experience in providing solutions for marketers.
Sellzone toolbox contains 7 tools:
Keyword Wizard – Amazon keyword research tool enabling sellers to improve Amazon listing visibility by finding high-volume search terms to rank for.
Traffic Insights – allows you to run reverse ASIN lookup, evaluate and compare Amazon organic and external traffic sources of any product listed on Amazon
PPC Optimizer – helps Amazon sellers launch and manage their Amazon ads campaigns by building the semantic core and tracking the effectiveness.
Product Research – identifies the most profitable products and categories to sell on Amazon, provides the FBA calculator, and shows the breakdown of Amazon fees associated with selling a particular product.
Listing Quality Check - audits listing content for errors and provides advice on improving its performance.
Split Testing - runs automated split testing of product pages, so you can find the best-performing parameters for your listings based on live test data.
Listing Protection - monitors listings of your own or competing products and instantly alerts you of any changes via email or SMS.
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Based on our record, FuzzyWuzzy seems to be more popular. It has been mentiond 11 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
Do fuzzy matching (something like fuzzywuzzy maybe) to see if the the words line up (allowing for wrong words). You'll need to work out how to use scoring to work out how well aligned the two lists are. Source: over 1 year ago
Convert the original lines to full furigana and do a fuzzy match. (For reference, the original line is 貴方がこれまでに得てきた力、存分に発揮してくださいね。) You can do a regional search using the initial scene data (E60) first, and if the confidence is low, go for a slower full search. Source: over 1 year ago
It's now known as "thefuzz", see https://github.com/seatgeek/fuzzywuzzy. Source: almost 2 years ago
You can have a look at this library to use fuzzy search instead of looking for plaintext muck: https://github.com/seatgeek/fuzzywuzzy. Source: over 2 years ago
To deal with comparing the string, I found FuzzyWuzzy ratio function that is returning a score of how much the strings are similar from 0-100. Source: over 2 years ago
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