I have been able to make this work with String::Approx. I had to match one list of companies against another. The lists were made by different organizations.

First, it helps to strip the noise from the company names, such as Inc, Co, Corp, GMBH, LTD, etc.

String::Approx finds a distance by looking at insertions, deletions, and substitutions needed to transform one string to another.

A different approach, which worked better for me, was to make lists of all the substrings of length n in the source string. I called these n-tuples. I compared the percentage overlap between the n-tuple sets for each name in one list to the n-tuples for each word in the other list. The best value for the length n of the tuples was three or four.

Very close matches could be completely automated this way. For matches that were not so close, I finished the matching task manually. I made a web user interface that had a selection list of the match candidates ranked by the closeness of the match. The closeness was determined by the percentage of n-tuples that matched. I selected the best match for each entry on the amongst these top-ranked match candidates.