Multiple Techniques
Because one methodology cannot be relied on exclusively to deal with all variations found in a typical database, the matchIT engine uses multiple algorithms and lexicons to ensure that all types of differences are detected. It takes a 3-dimensional view of the data, never relying on any single item of data being correct or consistent.
- Phonetic matching to match “sounds like” names such as Deighton and Dayton
- Lookup tables to match names such as Bill and William, The Guthrey Group and Guthries Ltd
- Acronym and initial matching to match inconsistencies like Bill and W. (e.g. Bill Deighton and Mr. W. Dayton), The Guthrey Group and TGG Ltd
- Non-phonetic fuzzy matching to match keying errors (such as transpositions like Wilson and Wislon) and reading errors (such as Morton and Horton)
- Element matching to match names with elements missing or reversed, such as Mr J R Gonzalez, Jose Gonzalez Esq. and Gonzalez Jose
Read more about how the matchIT engine outperforms its competitors in terms of matching effectiveness and accuracy here.
Examples of matchIT’s ability to find significantly more accurate matches than its competitors abound: 80% more matches to the Mailing Preference Service than a leading UK online service, nine times as many suppression matches as its leading UK on-premise competitor, 226% more matches than a major US competitor. These are valid matches that the other solutions can’t detect.
What our customers say
“We have seen a significant increase in accuracy and a reduction in false positives. We often see a match rate to the master file in excess of 90%, compared to 40-60% previously. This increase, when applied to our millions of rows of address data, is huge!”
“This product is significantly faster, more flexible and easier to use than the competitor products we have used previously.”