What is the best tool for fuzzy matching of company names?


Best tools for fuzzy matching algorithm?

chelseawatkins 2 weeks 2021-05-03T10:13:01+00:00 1 Answers 0

Answers ( 2 )


    In today’s world, the growth of the business is happening rapidly and business owners are getting more curious about spreading their business as much as possible. In this context, it becomes important for them to look after increasing their market continuously and making the link with more clients.

    And to make it happen business owners find ways to get the best leads for their business profit through sales reps or sales team, who can make it work and who can provide them as many as better qualified leads in minimum time.

    To help such business owners, sites like LeadAngel have created software to rout leads for them, it uses its cutting-edge technology of fuzzy matching algorithm (https://www.leadangel.com/fuzzy-match-algorithm/) for data matching and to filter out all unnecessary data from provided database and match each given data with given information before proceeding towards routing of those leads.

    Its Fuzzy Matching software provides rule and data dictionary to their clients to match company names by performing some settings like Strict Matching, Moderate Matching, and Lenient Matching which can be chosen by clients according to their convenience.

    It also identifies data with certain parameters like:
    1. email and web domain matching
    2. suffixes present in company names,
    3. geographically spelled words in the name,
    4. Nicknames,
    and such other rules that are set and controlled by LeadAngel to provide clients organized and satisfactory results in the shape of leads.

    Best answer

    Fuzzy matching allows you to identify non-exact matches of your target item. It is the foundation stone of many search engine frameworks and one of the main reasons why you can get relevant search results even if you have a typo in your query or a different verbal tense
    You can train a machine learning algorithm using fuzzy matching scores on these historical tagged examples to identify which records are most likely to be duplicates and which are not. Once trained, your new AI will predict whether or not a pair of customer records are truly duplicates.
    separate data sets in separate tabs. I make each one a table, by selecting the sheet and pressing CTRL-L on the data. The process to set up a match requires you to select one or more data points from each table to create a “fuzzy data binding”. In short, match rows by identifying similar matches between these columns.

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