Create a Data Mapping Schema
Contents
Learn how to create a data mapping schema and apply the schema to a contact list.
The data mapping feature is an alternative to using an input specification file to map contact data from an external file (input file) into CX Contact contact list fields. It eliminates the need to create and upload a detailed specification file, which is written using regular expression. Instead, all mapping can be defined in the CX Contact application.
The way you map data depends largely on the type of contact data file you're working with - whether it's a fixed position file or a delimited file.
This article explains how to create a data mapping schema for both file types and then describes how to apply the schema to a contact list being imported into CX Contact.
The page is broken down into sections:
To get started, go to Lists -> Data Mapping and then select New -> Data Mapping.
The New Data Mapping page consists of two sections. On the left-hand side of the page, you'll provide key details about the contact list you're working with. On the right-hand side of the page, you'll define the mapping table for the data contained in the contact list.
Create a Data Mapping Schema for a Fixed Position File
In a fixed position file, all contact data contained in the file is output to a fixed-position field and each of these fields has a defined fixed length to determine the start and end character positions.
Provide Details about the Contact List
On the left-hand side of the screen, provide key details about the contact list you're working with, as described in the table below.
| Option | Description |
|---|---|
| Name | The name of the mapping schema you're creating. |
| Description | A description of the mapping schema. |
| For Import/For Export | Use For Import, which means the mapping schema you create applies to lists being imported into CX Contact. For Export is reserved for future use. |
| The following rows include the General tab settings. For Advanced settings refer to the Advanced Input - Preprocessing section below. | |
| Caller ID Number | The caller ID number associated with the contact list. |
| File type | Options are Fixed-length and Delimited. For this task, select Fixed-length. |
| Header records |
The header record count tells CX Contact where to look for the header row in a contact list that identifies the type of data stored in that column (first name, last name, phone number, etc.). By default, the count is set at 0, which is the first row of data in the header record. If you have a line space between a header row and the first row of data in the file, count that as a header row, because CX Contact will import all records below the defined header row position. In the example below, you would set the Header record count to 2, because you want to import all records starting from Row 3.  |
| Trailer record | The trailer record is often used to summarize data in a file - for example, the total number of records in the file, the total amount paid on accounts, etc. Setting the trailer record count allows CX Contact to exclude this information from being imported into the contact list. Simply specify the number of records from the bottom of the file you want to exclude. |
| Label set | If you select a labeling schema here, the labels defined in that labeling schema will populate in the Label (Contact Field) menu where you define the mapping (on the right-hand side of the screen). For more information about labeling schemas, go to the Field Labels page in this manual. |
| Sample File | You can import a fixed-position file that contains the contact data so that when you define the mapping, the data being mapped will display in the Sample field. Important: This option is enabled only after you define the mapping for at least one field. |
Advanced Input - Preprocessing
Periodically, to achieve your goals data must be modified when it is loaded. The technique used to alter data during import is called preprocessing.
This section explains the tips and techniques used to achieve some of the more common preprocessing requests.
When using an input specification file, the pre-process lines in the input spec must appear before everything else that is grabbed.
Also, you can use several preprocessing statements in one input spec. The statements must be added in the order you want them to run. Preprocessing can be added to Data Mapping Schema using the Advance tab (see the image below) when creating or editing a Data Mapping Schema.
Replacing Abbreviations With Full Words For Proper TTS
The following statement should be used when searching for occurances of case insensitive data, and it can significantly reduce the number of required preprocessing statements.
For example, the statement below will find and replace instances of dr, Dr, DR, and dR.
| Find | Replace |
|---|---|
| ^([^,]*,[^,]*,[^,]*,["]*[^",]*)((?i) dr,)(.*)$ | $1 Drive,$3 |
# pull out Dr from address and replace with Drive
| Find | Replace |
|---|---|
| ^([^,]*,[^,]*,[^,]*,[^,]*,[^,]*,["]*[^",]*) (Dr)(.*)$ | $1 Drive$3 |
| ^([^,]*,[^,]*,[^,]*,[^,]*,[^,]*,["]*[^",]*) (dr)(.*)$ | $1 Drive$3 |
# pull out St from address and replace with Street
| Find | Replace |
|---|---|
| ^([^,]*,[^,]*,[^,]*,[^,]*,[^,]*,["]*[^",]*) (St)(.*)$ | $1 Street$3 |
| ^([^,]*,[^,]*,[^,]*,[^,]*,[^,]*,["]*[^",]*) (st)(.*)$ | $1 Street$3 |
| ^(.*) DR,(.*)$ | $1 Drive,$2 |
| ^(.*) ST,(.*)$ | $1 Street,$2 |
| ^(.*) HWY,(.*)$ | $1 Highway,$2 |
| ^(.*) LN,(.*)$ | $1 Lane,$2 |
| ^(.*) PL,(.*)$ | $1 Place,$2 |
| ^(.*) CT,(.*)$ | $1 Court,$2 |
| ^(.*) WY,(.*)$ | $1 Way,$2 |
| ^(.*) CIR,(.*)$ | $1 Circle,$2 |
| ^(.*) NE,(.*)$ | $1 Northeast,$2 |
| ^(.*) NW,(.*)$ | $1 Northwest,$2 |
| ^(.*) SE,(.*)$ | $1 Southeast,$2 |
| ^(.*) SW,(.*)$ | $1 Southwest,$2 |
| ^(ST)(.*)$ | Saint$2 |
| ^(St)(.*)$ | Saint$2 |
TTS evaluates certain abbreviations in an unintended manner. For example, Dr is spoken as doctor and St is spoken as saint if the TTS engine cannot put abbreviations in the proper context.
This could be a problem is you performed TTS on an address, since TTS would result in 65 Burlington Doctor, instead of 65 Burlington Drive.
The following preprocess statements are examples of how it is possible to convert some of these abbreviations (in this case, the 6th comma-separated field), into what they should be.
To perform this type of abbreviation replacement on a fixed width file, you should perform the following so that you do not encounter a situation in which data will be replaced with variable length words.
| Find | Replace |
|---|---|
| ^(.*) DR (.*)$ | $1 $2Drive |
| ^(.*) ST (.*)$ | $1 $2Street |
| ^(.*) HWY (.*)$ | $1 $2Highway |
| ^(.*) LN (.*)$ | $1 $2Lane |
| ^(.*) PL (.*)$ | $1 $2Place |
| ^(.*) CT (.*)$ | $1 $2Court |
| ^(.*) WY (.*)$ | $1 $2Way |
| ^(.*) CIR (.*)$ | $1 $2Circle |
| ^(.*) NE (.*)$ | $1 $2Northeast |
| ^(.*) NW (.*)$ | $1 $2Northwest |
| ^(.*) SE (.*)$ | $1 $2Southeast |
| ^(.*) SW (.*)$ | $1 $2Southwest |
In the following example, specific abbreviations with specific amounts of space around them are replaced with the a specific amount of space with the addition of words at the end of the record. For example, if you insert an address in a field, you will pull the old street address, skip a bunch of characters that would normally bring you directly to the end of the record, and finally grab the amount of characters that you added at the end. For instance, note that the word replacements in the above example are all 9 characters long.
#remove ND only if it is preceded with a number. As a result, SECOND does not become SECO
| Find | Replace |
|---|---|
| ^(.*)([0-9]+)ND (.*)$ | $1$2 $3 |
| ^(.*)([0-9]+)Nd (.*)$ | $1$2 $3 |
| ^(.*)([0-9]+)nd (.*)$ | $1$2 $3 |
#remove TH only if it is preceded with a number. As a result, NORTH does not become NOR
In the following table, TH or ND are removed from an address so that 19th street is replaced with 19 street.
| Find | Replace |
|---|---|
| ^(.*)([0-9]+)TH (.*)$ | $1$2 $3 |
| ^(.*)([0-9])Th (.*)$ | $1$2 $3 |
| ^(.*)([0-9]+)th (.*)$ | $1$2 $3 |
Replace Text With Something Else
Use pre process to format any part of the data file before the rest of the input spec looks at the data file.
In the first example, an attempt is made to verify if either one of the telephone fields only includes zeros. If it does only include zeros, the zeros will be replaced by the number 9.
#Phone number replace
| Find | Replace |
|---|---|
| ^(.{103})0000000000(.{40})0000000000(.*)$ | $19999999999$2 $3 |
In the second example, an attempt is made to verify if one of the calling fields only includes zero.
Both the first and second examples should be used together to avoid records being rejected.
Blank the phone fields
| Find | Replace |
|---|---|
| ^(.{103})0000000000(.*)$ | $1 $2 |
| ^(.{153})0000000000(.*)$ | $1 $2 |
In the third example, the delimiter is changed. It is possible to use this example in various ways to show a field with its own special delimiter, appear as its own field.
#change all ~ to,
| Find | Replace |
|---|---|
| ^([^~]*)~([^~]*)~([^~]*)~([^~]*)$ | $1,$2,$3,$4 |
In the fourth example, a static value is replaced with a different value.
#find a value and change it
| Find | Replace |
|---|---|
| ^([^|]*|[^|]*|[^|]*|)(6)(.*)$ | $18882493432$3 |
Add some fields after an entire record
#Add a campaign name to the end of a record
In the following example, the campaign name always appears in the data. If for example, the campaign name is imported as Other1, you can create a field in an output spec to display a Campaign Name as well as a Sub Campaign name.
| Find | Replace |
|---|---|
| ^(.*)$ | $1,Campaign Name |
# Add a campaign name to the end of a record
In the following example, the campaign name always appears in the data. If for example, the campaign name is imported as Other1, you can create a field in an output spec to display a Campaign Name as well as a Sub Campaign name.
| Find | Replace |
|---|---|
| ^(.*)$ | $1,Campaign Name |
#If you want to add some fields when you see specific numbers in a specific position
In the following example, 8001231234 is added to the end of each record in the original file. 8001231234 is then treated as part of the original file and is grabbed into one of the Other fields for use as a DC number, and so on. The position for this additional field is one more than the number of field positions in the original data file.
| Find | Replace |
|---|---|
| ^([^,]*,)(12345)(.*)$ | $1$2$3,STUFF |
The following is another example in which the client supplies 2 phone numbers in the 9th and 10th fields of each row. These 2 fields are the last 2 fields in each record. All records should always have a phone number in the 9th field. Some records have a number in the 10th field. If a valid number exists in the 10th field, then that should be the only contact number. If the number in the 10th field is missing or mangled, then the number in the 9th field will be used instead. In addition, notice that a comma is added to the end of each data line so that a series of comparisons can be performed to see which number should be grabbed.
| Find | Replace |
|---|---|
| ^(.*)$ | $1, |
| ^([^,]*),([^,]*),([^,]*),([^,]*),([^,]*),([^,]*),([^,]*),([^,]*),(\d{10}),,$ | $1,$2,$3,$4,$5,$6,$7,$8,$9,,$9 |
| ^([^,]*),([^,]*),([^,]*),([^,]*),([^,]*),([^,]*),([^,]*),([^,]*),(\d{10}),([w|+|.]{1}),$ | $1,$2,$3,$4,$5,$6,$7,$8,$9,$(10),$9 |
| ^([^,]*),([^,]*),([^,]*),([^,]*),([^,]*),([^,]*),([^,]*),([^,]*),(\d{10}),([w|+|.]{2}),$ | $1,$2,$3,$4,$5,$6,$7,$8,$9,$(10),$9 |
| ^([^,]*),([^,]*),([^,]*),([^,]*),([^,]*),([^,]*),([^,]*),([^,]*),(\d{10}),([w|+|.]{3}),$ | $1,$2,$3,$4,$5,$6,$7,$8,$9,$(10),$9 |
| ^([^,]*),([^,]*),([^,]*),([^,]*),([^,]*),([^,]*),([^,]*),([^,]*),(\d{10}),([w|+|.]{4}),$ | $1,$2,$3,$4,$5,$6,$7,$8,$9,$(10),$9 |
| ^([^,]*),([^,]*),([^,]*),([^,]*),([^,]*),([^,]*),([^,]*),([^,]*),(\d{10}),([w|+|.]{5}),$ | $1,$2,$3,$4,$5,$6,$7,$8,$9,$(10),$9 |
| ^([^,]*),([^,]*),([^,]*),([^,]*),([^,]*),([^,]*),([^,]*),([^,]*),(\d{10}),([w|+|.]{6}),$ | $1,$2,$3,$4,$5,$6,$7,$8,$9,$(10),$9 |
| ^([^,]*),([^,]*),([^,]*),([^,]*),([^,]*),([^,]*),([^,]*),([^,]*),(\d{10}),([w|+|.]{7}),$ | $1,$2,$3,$4,$5,$6,$7,$8,$9,$(10),$9 |
| ^([^,]*),([^,]*),([^,]*),([^,]*),([^,]*),([^,]*),([^,]*),([^,]*),(\d{10}),([w|+|.]{8}),$ | $1,$2,$3,$4,$5,$6,$7,$8,$9,$(10),$9 |
| ^([^,]*),([^,]*),([^,]*),([^,]*),([^,]*),([^,]*),([^,]*),([^,]*),(\d{10}),([w|+|.]{9}),$ | $1,$2,$3,$4,$5,$6,$7,$8,$9,$(10),$9 |
| ^([^,]*),([^,]*),([^,]*),([^,]*),([^,]*),([^,]*),([^,]*),([^,]*),(\d{10}),([w|+|.]{10}),$ | $1,$2,$3,$4,$5,$6,$7,$8,$9,$(10),$(10) |
| ^([^,]*),([^,]*),([^,]*),([^,]*),([^,]*),([^,]*),([^,]*),([^,]*),,(\d{10}),$ | $1,$2,$3,$4,$5,$6,$7,$8,,$(10),$(10) |
Past custom example - force variable-length field to trim or be padded
In the following examples, the client has a score value in the 13th data field. The score value can be up to 4 digits long (for example, 1, 12, 123, 1234). If the score is less than 3 digits long, you must pad zeroes at the beginning to turn it into 3 digits. If the score is 3 digits long, do not make any changes. If the score is 4 digits long, remove the first digit to turn it into a 3 digit number. This solution incorporates the preprocessing field order. Begin by reducing 4 digit scores into 3 digits. Add 3 zeros in front of all the scores. Finally, reduce scores of 6, 5, or 4 digits into 3 digits.
# Turns a 4 character score into 3 by removing the first character
| Find | Replace |
|---|---|
| ^([^,],[^,],[^,],[^,],[^,],[^,],[^,],[^,],[^,],[^,],[^,],[^,],["])(\d)(\d{3})(["].*)$ | $1$2$3 |
# Add 3 zeros in front anytime digits are found
| Find | Replace |
|---|---|
| ^([^,]*,[^,]*,[^,]*,[^,]*,[^,]*,[^,]*,[^,]*,[^,]*,[^,]*,[^,]*,[^,]*,[^,]*,["]*)(\d)(["]*.*)$ | $1000$2$3 |
# Turns 6 character numbers into 3
| Find | Replace |
|---|---|
| ^([^,],[^,],[^,],[^,],[^,],[^,],[^,],[^,],[^,],[^,],[^,],[^,],["])(\d{3})(\d{3})(["].*)$ | $1$2$3 |
# Turns 5 character numbers into 3
| Find | Replace |
|---|---|
| ^([^,],[^,],[^,],[^,],[^,],[^,],[^,],[^,],[^,],[^,],[^,],[^,],["])(\d{2})(\d{3})(["].*)$ | $1$2$3 |
# Turns 4 character numbers into 3
| Find | Replace |
|---|---|
| ^([^,],[^,],[^,],[^,],[^,],[^,],[^,],[^,],[^,],[^,],[^,],[^,],["])d(\d{3})(["]*)$ | $1$2$3 |
Grab required data even if records are not the same length
In the following example, the client supplied data where the first 2 fields were the ClientID followed by the name. The 3rd field represents the home phone number. If the home phone number is the only data available, the row ends with no placeholder commas to indicate that there were additional fields without a value. Also, when a cell number does not exist, but a home phone number and a work phone number do exist, the data will not indicate that a cell number does not exist.
#Fun
| Find | Replace |
|---|---|
| ^["]*([^",]*,[^",]*)["]*.*$ | $1, , , |
For example:
12345,JAY DEE,7818972639
12346,FRANK DEE,7818971234,7818972354
12348,SUSIE DEE,7818975142,7818971092,7818976523
Using preprocessing, the solution adds extra fields to all records. In the preprocess statement above, there are 10 spaces between each comma. The length of a record row is not significant as long as the rightmost field the system is trying to grab can be grabbed (For example, in this case the cell phone number in the 5th field). Therefore, by adding empty fields to all the rows, all possible data situations are satisfied. If there is only 1 phone number, extra fields are added to allow the Work and Cell grabs to work as expected. If there are only have 2 phone numbers, the extra fields are added to allow the Cell grab to work as expected. When all 3 numbers are available there is no significance to appending blank fields at the end of the row since they are already referenced.
Change Inconsistent Dates To Consistent Dates
The following is an example of when a client sends dates with a single digit month, single digit day, or both (refer to the fourth field of example below). In this example, that data must be changed so that is can be used with the makeDate function.
Travis Nelson,7818972639,53214,1/22/1979,$325.45 ,M,50000001
David Durski,7818972640,53211,12/3/1975,$148.45 ,M,T0050000002
Monika Mitchell,7818972641,85954,3/4/1980,$123.45 ,M,50000003
When importing into an Other field, the date format for the date format should be MMddyyyy. In this example, the preprocess removes the slashes from the date. Three preprocess lines are required to handle the possible month or day single digit scenarios. The result is that the dates are all the same and do not include slashes. The plus character (+) is added to the preprocess statement to enable slashes within the statement.
| Find | Replace |
|---|---|
| ^([^,]*,[^,]*,[^,]*,)(\d)/(\d{2}) | (.*)$+$10$2$3$4+ |
| ^([^,]*,[^,]*,[^,]*,)(\d{2})/(\d) | (.*)$+$1$20$3$4+ |
| ^([^,]*,[^,]*,[^,]*,)(\d)/(\d) | (.*)$+$10$20$3$4+ |
Replace More Than One Instance Of Something
In the following example, the client wanted to remove the asterisks from their data and replace it with spaces. Each record could contain one or two stars depending on how many names were in the record.
Example data:
Joe A*Schmoe Lucy K*Schmoe 6031231234 12345
Before performing the replacement, you must remember that preprocess matches and replaces the first instance it finds in a record. If the record contains another instance in the same record, the same preprocess statement will not replace the second instance. To overcome this, repeat the preprocess statement as many times as possible for the data you are trying to replace. The following was performed for the above example. The first statement replaces the first instance and the second statement finds the second instance.
| Find | Replace |
|---|---|
| ^(.*)\*(.*)$ | $1 $2 |
| ^(.*)\*(.*)$ | $1 $2 |
Secondary name in fixed position data
In the following example, there is no delimiter, commas in the name field, and the second name fields are predominately empty. The second name field is 40 bytes long and since the system needs to skip 62 bytes, a pipe must be seeded at byte 103 to use as a reference point later on.
# seed a pipe at byte 103 in order to grab the secondary name (other2)
| Find | Replace |
|---|---|
| ^(.{102})(.*)$ | $1|$2 |
Remove commas from business names
Some data includes commas in business names. Typically, the <CSV> technique is used to remove the commas, but occasionally the data must be preprocessed. The following is an example of removing the commas using preprocessing. In this example, the field in question is the 3rd field. Anytime commas are embedded in a field, double quotes are entered around the field. In this example, the 3 lines will remove instances of 3 commas, 2 commas, or 1 comma respectively in that field. Since ampersands have been entered in the business names and it is important to keep them, the ampersands are included as part of the character classes for the match (word characters, white space characters, and ampersands).
| Find | Replace |
|---|---|
| ^([^,]*,[^,]*,)"([ws&]+),([ws&]+),([ws&]+),([ws&]+)"(.*)$ | $1$2$3$4$5$6 |
| ^([^,]*,[^,]*,)"([ws&]+),([ws&]+),([ws&]+)"(.*)$ | $1$2$3$4$5 |
| ^([^,]*,[^,]*,)"([ws&]+),([ws&]+)"(.*)$ | $1$2$3$4 |
Remove leading zeros in a currency field
To remove leading zeros in a fixed position field you will need to replace them with spaces. The first statement replaces the first zero with a space, the second statement detects the space and then a zero and replaces the zero with a space, and so on
For example:
device other1 clientid
781897252000020134 1
781897252151781258 2
781897252200000134 3
The following solution turns 00000134 tp 1.34, or 51781258 to 517812.58, or 00000004 to 0.04.
| Find | Replace |
|---|---|
| ^(\d{10})[0]{1}(.*)$ | $1 $2 |
| ^(\d{10}\s{1})[0]{1}(.*)$ | $1 $2 |
| ^(\d{10}\s{2})[0]{1}(.*)$ | $1 $2 |
| ^(\d{10}\s{3})[0]{1}(.*)$ | $1 $2 |
| ^(\d{10}\s{4})[0]{1}(.*)$ | $1 $2 |
Add static values to variable length records in one field
In this scenario, the client must add a static value to the end of all records, based on a specific flag. For example purposes, refer to the Add some fields after an entire record section above.
When static values are added to variable length records in one field the record appears as follows (for example, Test Camp is added at the end of all records with a colon delimiter ahead of it):
TEST,20408,TEST2:Test Camp
Using a unique delimiter with this static value for all records, enables you to locate specific information within the record.
This technique can only be used if the secondary delimiter is not used anywhere else in the original data file.
In the example below, the second field and the added campaign value are merged into a single field.
| Find | Replace |
|---|---|
| ^[^,]*,([^,]*),[^:]*:(.*)$ | $1:$2 |
Map the Data
To set up the mapping schema, click New or New Multiple and provide details about each field in your contact list, as described in the table below.
| Option | Description |
|---|---|
| Type |
The type of data being mapped. Currently, the string option is only available. All other options are reserved for future use. |
| Start | This is the starting position of each field within a row of data. The starting point is 0. |
| Length | The maximum number of characters allowed for that field. |
| Strict | Reserved for future use. |
| R/O | Reserved for future use. |
| Unique | Reserved for future use. |
| Label (Contact Field) | If you selected a labeling schema from the Label Set menu, the defined labels in that schema will populate here. |
Create a Data Mapping Schema for a Delimited File
In a delimited file, columns are not confined to a pre-defined maximum length. Each record is one continuous line in the text file, with each field separated by a delimiter, such as a comma or a pipe.
Provide Details about the Contact List
On the left-hand side of the screen, provide key details about the contact list you're working with, as described in the table below.
| Option | Description |
|---|---|
| Name | The name of the mapping schema you're creating. |
| Description | A description of the mapping schema. |
| For Import/For Export | Use For Import, which means the mapping schema you create applies to lists being imported into CX Contact. For Export is reserved for future use. |
| Caller ID Number | The caller ID number associated with the contact list. |
| File type | Options are Fixed-length and Delimited. For this task, select Delimited. |
| Delimiter |
Select the delimiter used in your contact list. Options are as follows:
|
| Header records |
A header record is the header row in a contact list that identifies the type of data stored in that column (firstName, lastName, etc). If you have a line space between a header row and the first row of data in the file, count that as a header row. |
| Trailer record | The trailer record is often used to summarize data in a file - for example, the total number of records in the file, the total amount paid on accounts, etc. Setting the trailer record count allows CX Contact to exclude this information from being imported into the contact list. Simply specify the number of records from the bottom of the file you want to exclude. |
| Use Header Record # | If your contact list has at least one header row, you must specify the line number in your contact list that contains the field headers (firstName, lastName, Device1, etc). For example, if you have three header rows, and your field headers are on the second header row, specify 2 in the Use Header Record # menu. |
| Select fields using |
This allows you to map the fields in your contact list either by the contact list Field Name or the contact list Field Number. If you do not have a header row in your contact list, your only option here is to map by Field Number. |
| Label set | If you select a Label schema here, the labels defined in that labeling schema will populate in the Label (Contact Field) menu where you define the mapping (on the right-hand side of the screen). For more information about labeling schemas, go to the Field Labels page in this manual. |
| Sample File | You can import a delimited file that contains the contact data so that when you define the mapping, the data being mapped will display in the Sample field. Important: This option is enabled only after you define the mapping for at least one field. |
Map the Data
To set up the mapping schema, click New or New Multiple and provide details about each field in your contact list, as described in the table below.
Notice that each field entry has an associated Edit (pencil) icon and Delete (trash can) icon so that you can easily modify or delete an entry.
| Option | Description |
|---|---|
| # |
This option is useful if you do not have a header row in your contact list and therefore cannot specify a field name. You simply identify the position of the data within the row of data in your contact list. The starting position in a row is 1. In the example below, the client ID is in the 4th position in each row, so you would enter a value of 4 in this field to map the client ID data.  |
| Field Name in File | This option is only available if you're identifying the fields to be mapped by their field names (meaning you selected Field Name in the Select fields using menu), which means your contact list has a header row containing field names. You can use this field in conjunction with the # field, but only the field name is mandatory. |
| Type |
The type of data being mapped. Options are as follows:
|
| Strict | Reserved for future use. |
| R/O | Reserved for future use. |
| Unique | Reserved for future use. |
| Label (Contact Field) | If you selected a labeling schema from the Label Set menu, the defined labels in that schema will populate here. |
Once the data is mapped and you saved the schema, return to the main Data Mapping page to see a summary of the schema. This page includes the following details about each data mapping schema:
- Name - The name of the mapping schema.
- Description - The description of the mapping schema.
- Type - Whether the mapping applies to lists being imported or lists being exported. For now, the only option is Import.
- File Type - Whether the mapping is for a fixed-length file or a delimited file.
- Last Modified Date - The date the schema was last modified
- Created Date - The date the schema was created.
- Actions - Use this menu to do any of the following:
- Edit - Click the pencil icon to open and edit the mapping schema.
- Delete - Click the trash can icon to delete the mapping schema.
- Duplicate - Click the document icon to duplicate the mapping schema. The new mapping schema will inherit all the properties from the original mapping schema.
Apply a Data Mapping Schema to a Contact List
When you upload a new contact list or edit an existing list, you can apply a data mapping schema to the list via manual upload or via List Automation.
Manual Upload
On the New List page (or Edit List page), turn the Use Specification File to the Off position.
The Select Specification File menu changes to the Select Data Mapping menu, where you select the data mapping scheme you want to apply to the contact list.
For more information about contact lists, refer to the Import and Manage Contact Lists page in this manual.
List Automation Tasks
When you create a list automation task that involves a list import (import file, send campaign group, import suppression file), you can apply a data mapping schema to the list being imported.
On the New List Automation page (or Edit List Automation page), go to the Rules section and turn the Use Specification File to the Off position.
The Select Specification File menu changes to the Select Data Mapping menu, where you select the data mapping schema you want to apply to the contact list.
For more information about List Automation, refer to the Create and Manage List Automation Tasks page in this manual.