matchit^®

After normalization, matchit uses intelligent phoneticization. Once its isolated values like first name, company name, street name, and city, it can generate phonetic tokens for these fields to help cope with errors in your data.

Instead of using off-the-shelf algorithms like Soundex, Double-Metaphone or Metaphone 3 that were never designed for the nuances of contact and business data, matchit uses a proprietary phonetic algorithm that breaks each word into syllables and works out the sound of each syllable.

While picking up names that sound the same but are spelled quite differently like Leighton and Layton, matchit’s phonetic algorithm rejects different-sounding names which these other algorithms equate, like Mason and MacEwan.

Unlike humans, matchit doesn’t get tired – its job is to enable you to accept the vast majority of matches without having to review them, so there are very few that could be considered “in the gray area.” Here’s how it does this…

As candidate groups are created, matchit’s scoring algorithms compare the records contextually. All the relevant data is graded for similarity and assigned a component score for each aspect of the data.

Where conventional approaches rely on the comparison and scoring of match keys, matchit is comparing and scoring the contact name, company name, address, zip, telephone, email, and even custom fields individually – so you can score things like website, account numbers, date of birth, license plates or any other information you hold.

Finally, the component scores are brought together into a single composite score which establishes the overall similarity between the records.

To learn more about what’s going on under the hood of matchit – click the button below!