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Global Matching


Say Goodbye to Duplicates

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On average, a database contains 8-10% duplicate records. These duplicates result in waste and inefficiencies and cloud your ability to get a single, accurate view of the customer.

Melissa MatchUp is the most powerful and accurate matching and deduping solution on the market to combat the problem of duplicate records. What sets it apart from the rest is its intelligent parsing capability to understand and parse the various components of international addresses. By combining deep domain knowledge of international address formats and advanced fuzzy matching techniques, MatchUp gives you the ability to identify and merge/purge even the most difficult-to-spot duplicate records.

  • Eliminate clutter and duplicates that prevent a clear view of your customers
  • Increase the accuracy of your database – saving you time and money
  • Reduce postage and mailing costs by eliminating duplicates using advanced matching technology

4 Ways to Access MatchUp

API Integration 

Data Cleansing 

Service Bureau /
Automated List Processing 

Data Cleansing For
Popular Programs 

Find a Match Yourself

Try to find the duplicate records using some of MatchUps matchcodes.

Name Company Address City, State, Zip
John Smith United Data Machines 12 Main St Boston, MA 02134
John Smyth United Data Machines Co. Twelve Main St Boston, Massachusetts 02134
John Smith UDM 12 Main Street Boston, MA 02134
Mary Smith eSolutions 12 Main St Boston, MA 02134
Mary Smith eSolutions, Inc. 12 North Main St Boston, MA 02134
Fred Jones United Data Machines Corp. 4 Elm Avenue Boston, MA 02134
F. Jones Jones Consulting 4 Elm Ave Boston, MA 02134

Select a Matchcode below to compare records.

Enter a record for MatchUp to search the database.

First Name



Address 2

Last Name



ZIP Code

How MatchUp Works

MatchUp employs a Matchcode or set of rules to determine if two records should be considered duplicates. MatchUp uses a predefined Matchcode, or one that you have created using the Matchcode Editor. The following matchcode components (data types) are available for use in identifying duplicates:

  • Prefix
  • First Name
  • Middle Name
  • Last Name
  • Suffix
  • Gender
  • First/Nickname
  • Middle/Nickname
  • Department/Title
  • Company
  • Company Acronym
  • Street Number
  • Street Pre-Directional
  • Street Name
  • Street Suffix
  • Street Post-Directional
  • PO Box™
  • Street Secondary
  • Address
  • City
  • State/Province
  • ZIP9
  • ZIP5
  • ZIP+4®
  • Postal Code
  • Country
  • Phone/Fax
  • Email Address
  • Credit Card Number
  • Date
  • Numeric
  • Proximity
  • General ID

MatchUp performs a two-step process to determine a match score between records. First, MatchUp finds exact matches. Then, MatchUp performs a fuzzy match on the remaining records to calculate a match score between pairs of records. If the match score of the two records meets the match score threshold, then MatchUp considers the two records a match. One example of the fuzzy matching algorithms MatchUp can use is Levenshtein shown above. MatchUp employs many other fuzzy matching algorithms, including Melissa proprietary ones.

Fuzzy Matching

MatchUp combines Melissa’s deep domain knowledge of contact data with over 20 fuzzy matching algorithms to match similar records and quickly dedupe your database. MatchUp employs the following fuzzy matching algorithms to identify “non-exact matching” duplicate records:

  • Phonetex
  • Soundex
  • Containment
  • Frequency
  • Fast Near
  • Accurate Near
  • Frequency Near
  • UTF-8 Near
  • Vowels Only
  • Consonants Only
  • Alphas Only
  • Numerics Only
  • MD Keyboard
  • Jaro
  • Jaro-Winkler
  • n-Gram
  • Needleman-Wunch
  • Smith-Waterman-Gotoh
  • Dice’s Coefficient
  • Jaccard Similarity Coefficient
  • Overlap Coefficient
  • Longest Common Substring
  • Double MetaPhone

Global Deduping

The World Edition of MatchUp can match international records with support for 12 countries, including Canada, Germany, U.K., and Australia and upcoming quarterly additions. MatchUp’s advanced deduping can see through diacritic equivalents to Latin characters and interpret keywords that are the same but spelled differently (i.e. Germany and DEU).

Golden Records and Survivorship

Survivorship/Golden Record (SQL Server® and Pentaho® only)

MatchUp’s most unique method for determining the most accurate view of the customer – the Golden Record – is called Survivorship. The Golden Record process identifies the best record of a matched group based on criteria including most complete, best overall data quality (derived from the validity of address, name, phone, and email data), most frequent specified value or a custom criterion you create. Duplicate entries can be collapsed into a single customer record while retaining any additional information that may be accurate and applicable through unlimited Survivorship configuration options.

Unique Matching Scenarios

MatchUp has some unique attributes which can be employed to help identify duplicates geographically or by household.

1. Proximity Matching

MatchUp’s patented distance algorithm uses latitude-longitude coordinates and proximity thresholds to identify duplicate records that are geographically close together. For instance, using location attributes, MatchUp can detect matching records at different addresses (for example, a company with two different entrances) but within a specified distance to each other.

2. Householding

MatchUp can identify and consolidate records that are members of the same household to better understand customer relationships, lifecycle, and needs. You can also use MatchUp to bring together multiple business accounts into “corporate families” to build insight and better evaluate the total sales relationship. Householding can also be used to eliminate unnecessary multiple mailings to the same household to cut down on wasted print, production, and postage costs.

Three Ways to Dedupe Your Data

MatchUp offers three methods of operation (or ways to match records):

  1. Read/Write Deduping – compares records and one or more databases at once. Each unique group will have one record that receives an “output” status; the other matching records receive a “duplicate” status. Ideal for matching entire databases at one time.

  2. Incremental Deduping – enables real-time matching by comparing each record as it comes in (like from a web form or call center) against the existing master database. If the incoming record is not a duplicate, it can be added.

  3. Hybrid Deduping – provides a combination of the first two methods with the flexibility to customize the process to match an incoming record against a small cluster of potential matches. With hybrid deduping you can store the match keys in a proprietary manner. Ideal for real-time data entry or batch processing of entire lists.

What Our Customers Say


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