How we on board CRM data

Using your data in Lexer is easy, but setting it up can take time. Lexer’s Customer Data Platform ingests data from a variety of sources, and one of the most common is CRM data. Sending Lexer this data allows you to understand the demography of your customers and prospects, their location, and contact information, as well as combine it with your other data for an enriched single-customer-view.

What we do with data

Auto-validation we check the format of the file, and the records in the file, to make sure they can be imported.
Validation we look at the data to make sure it’s what we expect it to be.
Enrichment we transform the data to turn it from what it is into a set of value-add attributes.
Unification we unify your transaction data to other data you have provided, as well as third-party data sets.
Import we take the validated, enriched, and unified data and import it into the Lexer product.

What we build

Once we receive the data, we will build a set of attributes you can query in Lexer. We either import it without transformation, or we change it so you can get more value from it. Here is a list of the attributes we can build with this data.

Standard attributes

Lexer cleans and transforms your data into straightforward representations of the raw data, making it easy for your team to search, analyse and activate.

Attribute Code Description Value
Customer ID Your internal customer ID A customer ID allows you to unify your data across different files and sources.
Email The customer’s email address Email allows us to expand your data universe to third parties.
First name Customer’s first name Names can be used to improve 1:1 communication across sales and service channels.
Last name Customer’s last name  
Date of birth Customer’s date of birth Birth dates allow you to understand a customer’s age, as well as anticipate behaviors around their birthday.
Birth year Customer’s year of birth  
Address 1 The customer’s street address Know where your customers live, so you can improve delivery and other in-person service inquiries.
Address 2 The customer’s street address  
Suburb/City The customer’s suburb/city Suburbs allow you understand customers at a demographic level, as well as providing insight around geographic distribution.
Postcode/ZIP Customer’s postal code Demographic data sources use postal and zip codes, so this data enables the understanding of behaviors and traits of people living in the area.
State The customer’s state States allow you to understand customers at a demographic level, as well as providing insight around geographic distribution.
Country The customer’s country A customer’s country helps you personalize language, understand seasonal trends and build regional strategies.
Mobile/Cell The customer’s phone number A phone number can be unified to other sources, as well as deliver phone-based service interactions.
Gender Customer’s gender A customer’s gender is often useful for basic segmentation, especially when your products are gender specific.

Value-add attributes

Lexer takes all the data you have provided and enriches it, often combining multiple data points to make them more useful than they are on their own.

Attribute Code Description Value
Record Has some sort of relationship with your company This is used within the product to run a quick search, highlighting your customer and prospect universe.
Age The customer’s age Capture the customer’s age at a glance, to improve segmentation and service interactions.
Email sha256 The customer’s hashed email address (sha256) Hashed email addresses can be used to target customers on several online channels, without sacrificing the customer’s privacy.
Lives in Country The customer’s address is within nominated countries - e.g. Lives in Australia, Lives in USA, Lives in England A simple flag to highlight the customer’s country.
Closest Store The customer’s closest store If you’re going to direct a customer to a store, we want to make it as convenient as possible.
Geo-location The Geo-point on a map See where one, some, or all of your customers are on a map, at a glance, to better understand where they are.
Inferred Gender Lexer’s modeled gender, scored against the probability that their first name would be given to a male or a female If you don’t capture a customer’s gender, we can help you predict what it is.
Generation The customer’s generation: Baby Boomer’s are born after 1946, Gen X after 1965, Xennial born after 1977, Millennial born after 1984, Gen Z born after 1997 A customer’s generation can be used in basic segmentation, rather than splitting out customers by explicit ages.

How we unify

Customer ID

This field is typically used to match to your other data sources, such as Transactional, and Email Engagement platforms. If this ID is used across your business, it will be the field that links across all systems.

Email address

This field is used to unify your data to Twitter, enable activation on various sales channels, and can be used to unify your data to certain second and third party sources.

Phone number

This field is used to unify on other data sets, such as Experian. Lexer transforms your mobile numbers to make sure they’re in a consistent format, ensuring the highest possible unification rates.

Detail for the IT team

To get up and running with this data right away, you need to make sure your data is formatted in a certain way. Although we work hard to make data easy for businesses to use, this process can be quite technical.

Each column is transformed in its own way, with rules around what we can import. Here is some guidance on the sort of data we accept:

  1. Lexer will automatically accept files that are in UTF-8 format, and will clean out rows that are not.
  2. Lexer only accepts flat CSV rows in files. Rows containing fields with newline-delimited text will be rejected.
  3. Lexer prefers the “PSV” format, where the pipe (“|”) character is used as the field separator. This helps to lessen the number of rejected rows. We also accept tab and “,” separated values.
  4. Supported quote characters are “ and ‘.
  5. Quotes inside quoted fields can either be escaped with "" or doubled.

Files you can send

Some fields are necessary to get basic CRM data up and running in Lexer. All of these fields should be in a single file, and every row in the file should contain information about a single customer.

Required data

Customer ID  
Data Formats Accepted Numeric ID, Alphanumeric ID
Examples 995435, john.smith, tqbf123
Comments Along with Email address, this ID is ideally an identifier that is used in other files. If you do not have this ID, email address will be used as the primary identifier.
First Name  
Data Formats Accepted text strings
Examples John, Mary, Ólafur
Comments If this data is sent as text in a UTF-8 format, it will be accepted. If this column also contains non-name string records, they will be imported as is.
Last Name  
Data Formats Accepted text strings
Examples Smith, Brown, Kröger
Comments If this data is sent as text in a UTF-8 format, it will be accepted. If this column also contains non-name string records, they will be imported as is.
Email Address  
Data Formats Accepted Email Address
Examples john.smith@gmail.com
Comments Lexer automatically identifies email addresses as a block of text with a string (e.g. john.smith) before an ‘@’ symbol, another set of letters (e.g. gmail), then a full stop, then another set of letters (com). If any of these strings are less than 2 characters, they will be rejected.
Mobile/Cell  
Data Formats Accepted Phone Numbers
Examples +61400000000, (303) 555-1234
Comments Lexer automatically identifies phone numbers, and strips out the characters ” +()-”. If there are any other non-numeric characters in the field, they will be rejected.
Date of Birth  
Data Formats Accepted datetime_iso8601, date_dmy, datetime_dmy, date_mdy, datetime_mdy, date_ydm, datetime_ydm, date_ymd, datetime_ymd
Date Examples  
Datetime ISO8601 2018-07-23T14:22:18+10:00, 2018-07-23T14:22:18Z
Date DMY 23/07/2018
Datetime DMY 23/07/2018 13:13:13, 23/07/2018 13:13, 23/07/2018 1:01 PM
Date MDY 07/23/2018
Datetime MDY 07/23/2018 13:13:13, 07/23/2018 13:13, 07/23/2018 1:01 PM
Date YMD 2018/07/23
Datetime YMD 2018/07/23 13:13:13, 2018/07/23 13:13, 2018/07/23 1:01 PM
Date YDM 2018/23/07
Datetime YDM 2018/23/07 13:13:13, 2018/23/07 13:13, 2018/23/07 1:01 PM

A date column in this data will be identified as one of many date formats. If the column has multiple date formats in it, those not matching the identified format will be rejected.

Address  
Data Formats Accepted text strings
Examples 86 Inkerman Street, 500 Wilson Road
Comments If this data is sent as text in a UTF-8 format, it will be accepted. Lexer analyses strings to automatically identify columns as addresses, however, If this column contains non-address string records, they will be imported as is.
Suburb  
Data Formats Accepted text strings
Examples St Kilda, Santa Monica
Comments If this data is sent as text in a UTF-8 format, it will be accepted. If this column contains non-address string records, they will be imported as is.
State  
Data Formats Accepted text strings
Examples Australian States (VIC, Victoria), American States (CA, California)
Comments If this data is sent as text in a UTF-8 format, it will be accepted. Lexer automatically recognises Australian and US state names and codes but will import any string if there are other states in the file. If this column contains non-address string records, they will be rejected.
Country  
Data Formats Accepted text strings
Examples Country Name (Australia, United States), Country Code (AU, US)
Comments If this data is sent as text in a UTF-8 format, it will be accepted. Lexer automatically recognises ISO 3166 country names and codes, and names. If this field contains values that do not match ISO 3166, they will be rejected.
Postcode/ZIP  
Data Formats Accepted Numeric ID
Examples 3182, 90210
Comments If this data is sent as an integer, it will be accepted. If this column contains non-zip records, they will be rejected.

Preferred data

Address 1  
Data Formats Accepted text strings
Examples Level 1, Unit 6
Comments If this data is sent as text in a UTF-8 format, it will be accepted. Lexer analyzes strings to automatically identify columns as addresses, however If this column contains non-address string records, they will be imported as is.
Address 2  
Data Formats Accepted text strings
Examples 86 Inkerman Street, 500 Wilson Road
Comments If this data is sent as text in a UTF-8 format, it will be accepted. Lexer analyses strings to automatically identify columns as addresses, however If this column contains non-address string records, they will be imported as is.
Gender  
Data Formats Accepted text strings
Examples Male, M, Female, F
Comments If this data is sent as text in a UTF-8 format, it will be accepted. Lexer analyses strings to automatically identify columns as addresses, however If this column contains non-address string records, they will be imported as is.
Latitude  
Data Formats Accepted floats
Examples -37.8640, -37.8
Comments This data will be combined with the longitude field to derive a Geo-location for the customer. If either value is incorrect, the point will also be incorrect.
Longitude  
Data Formats Accepted floats
Examples 144.9820, 144.98
Comments This data will be combined with the latitude field to derive a Geo-location for the customer. If either value is incorrect, the point will also be incorrect.
Geo  
Data Formats Accepted Geo-location
Examples ‘39.13093,173.30921’
Comments Valid Geo-location coordinates are accepted, and will be resolved to a geo-point in the CDP.