More matches. Better matches. Automatically.
matchIT addresses the fundamental problem with contact data matching that every other Data Quality, Data Integration, MDM and Data Analytics software solution fails to tackle properly: it was built from the ground up to cope with the inconsistencies inherent in contact data:
matchIT easily matches poorly structured data, is amazingly tolerant to wide variations in customer data and by default uses all the contact information available to find matches. If there are significant items of data available such as email, phone and/or date of birth as well as name and address, it uses them. If they aren’t present in some records, or different emails and phone numbers were used at various times – no problem. If one record has an address and the other doesn’t but they have essentially the same phone number, it will automatically fuzzy match the names.
This AI logic enables users of all skill levels to easily use highly accurate contact data matching routines with greater accuracy and in a fraction of the time of conventional fuzzy matching methods. Scroll down to see how matchIT identifies all the thoroughly inconsistent contact records above as probable matches – using out of the box matching rules that enable a business user to just drag and drop the data and select the matching levels required!
matchIT identifies this group of records for comparison
matchIT identifies data types and common errors
matchIT identifies data types and common errors
matchIT automatically restructures and standardises the data
matchIT compares and scores the matches
The first 3 records match on name, company and address
The last record matches the first on name, phone and email address
and the second on name and phone.
The matching records are then all grouped together in a set of 4
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Intelligent fuzzy matching, built for contact data
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Finds the most true matches with least false matches
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Proven on UK and worldwide, consumer and business data
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Intelligently grades matches for automatic processing
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 online service, nine times as many suppression matches as its main UK competitor, over twice as many matches as 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.”
Connells Group
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