One Engine. Multiple Techniques.

  • KEYWORD IDENTIFICATION
    • Business name matching requires specific logic to detect the primary words (e.g. Chubb Insurance Company of Europe matching against Chubb), and understand the similarity between words e.g. “Auto” and “Motors”, “Bijouterie” and “Jewelerie”.
  • NON-PHONETIC FUZZY MATCHING
    • to match keying errors and transpositions (such as Wilson and Wislon) and reading errors (such as Morton and Horton).
  • ELEMENT MATCHING
    • to match names with elements missing or reversed (e.g. Mr JR Clark, John Clark Jr and Clark John).
  • NON-STANDARDISED STRINGS AND WORDS
    • to match data in different languages, strings abbreviated in different ways, and/or hyphenated strings. (For example: The Haque and den Haag, Brussel & Bruxelles, Street & St, Stra8e & Str.)
  • PHONETIC MATCHING
    • to match names that sound the same such as Dayton and Deighton, or sound similar like Shaw and Shore.
  • NAME LEXICONS
    • to match names such as Bill and William.
  • ACRONYM AND INITIAL MATCHING
    • to match inconsistencies like Bill and W. (e.g. Bill Deighton and Mr. W. Dayton), and Oxford University Press and OUP.
  • RELOCATION OF DATA IN THE INCORRECT FIELD
    • to allow for e.g. names in address lines, postcode in the town field, P.O. Box number in the thoroughfaire field, town, county, postcode combined in one field.

 

More True Matches. Fewer False Matches.

  • THE matchIT API MATCHES ENTIRE RECORDS
    • using all available data to determine potential matches
  • THE matchIT API STANDARDISES & PARSES RECORDS BEFORE MATCHING
    • so records with inconsistent format, transposed or even missing data can still be compared
  • THE matchIT API WORKS TO UNDERSTAND THE PRONUNCIATION OF THE NAME
    • by converting names, companies and even streets into sophisticated phonetic equivalents
  • THE matchIT API INTELLIGENTLY SCORES RECORDS
    • so you can confidently automate some processes and leave only low scoring matches for manual review