This section provides an example of how you can develop a match rule. This example focuses on the scoring and threshold components, detailing the thought process you might take to create an effective match rule.

1. Create a list of all of the attributes that should match between two matching records. This list should include attributes that are really important as well as attributes that are just good to have as matches.

   For this example, this table shows the following list of attributes:

   Attribute Name	Entity	Type
   Party Name	Party
   Phone Number	Contact Point
   Address1	Address
   Country	Address	Lookup
   Postal Code	Address
   Contact Last Name	Address

2. Rank the order of importance of the attributes, as shown in this table:

   Rank	Attribute Name	Entity	Type
   1	Party Name	Party
   2	Phone Number	Contact Point
   3	Contact Last Name	Contact
   4	Address1	Address
   5	Country	Address	Lookup
   6	Postal Code	Address

   This ranking indicates that the attribute score you assign to party name is the highest and the scores are lower or stay the same as you go down the ranking.

3. Identify the minimum sets of attributes you require to match for records to be considered matches, for example:

   • Only the party name

   • Only the phone number

     This requirement indicates that your acquisition attributes should at least include party name and phone number and that you should select the Match Any search operator.

4. Identify the sets of attributes that by themselves are not good enough to indicate that you have matching records, but which, if they were close enough matches, might give additional credence to a match on the minimum set of party name and phone number.

   • Only address

   • Only country

   • Only postal code

   • Only address and country

   • Only address and postal code

   • Only country and postal code

   • Only contact last name

     This selection determines the attributes that you need to include as scoring attributes.

1. You should select the Match Any search operator because you have two sets in step 3 of Preparing for the Match Rule.

2. Choose attributes from step 1 of Preparing for the Match Rule that would get you all of the possible matches. You must include the attributes from step 3 of Preparing for the Match Rule. For this example, you select:

   • Party Name

   • Phone Number

   • Contact Last Name

3. Select attributes from step 1 of Preparing for the Match Rule that you want to use to score the records. You must include the attributes from step 4 of Preparing for the Match Rule.

   This table shows the scoring attributes.

   Attribute Name	Entity	Type
   Party Name	Party
   Phone Number	Contact Point
   Address1	Address
   Country	Address	Lookup
   Postal Code	Address
   Contact Last Name	Contact

4. Assign scores to the scoring attributes following the ranking in step 2 of Preparing for the Match Rule. The most important attributes receive the highest scores. For this example, the score assignments should reflect the following:

   • Matches on party name provide the best match results, so you assign the highest score to party name.

   • Matches on a phone number might be the second best matching criterion, so you assign the next highest score to phone number.

   • Combinations of the address components and contact last name are the third best, so you assign scores by relative importance.

   • The contact last name attribute is estimated to have about the same value as the address1 attribute.

   For this example, the scores in this table are assigned to the scoring attributes.

   Scoring Attributes	Scores
   Party Name	40
   Phone Number	30
   Address1	15
   Country	10
   Postal Code	10
   Contact Last Name	15

   The total score for the attributes in this table is 120.

1. Obtain minimum sets from step 3 of Preparing for the Match Rule and total attribute scores from step 4 of Selecting Attributes and Assigning Scores.

   • For Party Name the total attribute score is 40.

   • For Phone Number the total attribute score is 30.

2. Set your match threshold based on the lower score of the two minimum sets, in this example, 30.

With the match threshold at 30, you can interpret scoring as follows:

• If only the phone number is a match, the record is a match because the score equals the match threshold of 30.

• If only the party name is a match, then the record is a match because the score exceeds the match threshold of 30.

• If the country, postal code, and contact last name are a match, then the record is a match because the attributes' combined score of 35 exceeds the match threshold of 30.

• If the address1, country, and postal code are a match, then the record is a match because the attributes' combined score is 35, which exceeds the match threshold of 30.

With the match threshold at 30, this table shows results of possible matches:

If you have transformation weights other than 100%, then you might need to tune your threshold. With weights other than 100%, the total score for the record can be lower than the match threshold that you assigned. The total score is the sum of attribute scores that are multiplied by the weight.

For example, a minimum set of attributes required for match consists of party name. The following table shows the transformations and weights assigned to the Party Name attribute, as well as the weighted attribute scores calculated for each transformation.

Depending on the transformations, a matching party name can have a weighted attribute score below 40. With a weighted score of 20, for example, this minimum set might not exceed the match threshold of 30. If you want all possible matches that originate from any of the transformations, you might want to adjust some of your values.

You have three options:

1. Decrease the match threshold to the lowest possible weighted attribute score. Performing this option might affect the scores of other attributes and thresholds.

2. Increase the weight of the transformations so that the lowest possible weighted attribute score exceeds the match threshold. This option might not always be possible because weights must be less than or equal to 100.

3. Increase the attribute score so that the lowest possible weighted attribute score exceeds the match threshold.

For example, you can increase the Party Name attribute score to 60 and the Cleanse transformation weight to 70%. This table shows the adjusted assignments with each possible weighted attribute score exceeding the match threshold of 30.