What properties are taken into account when creating my predictive lead score?

Last updated: February 8, 2018

Applies to:

Marketing Hub: Enterprise
Sales Hub: Professional

When HubSpot sets up your predictive lead scoring, it looks at data from your contact's properties. It will look at all the default properties in your HubSpot account and also take into consideration the following nuances when compiling scores:

  • Email is a free account (e.g.,,
  • Emails clicked / emails delivered
  • Phone number length
  • Company name upper case
  • Nonsense score of last name1
  • Similarity between email and last name2
  • Last name upper case
  • Nonsense score of first name1
  • Similarity between email and first name2
  • Company name length
  • Similarity between first name and company name2
  • Nonsense score of Company Name1
  • Similarity between email domain and company name2
  • Last name contains digits
  • Length of email3
  • Company name is capitalized
  • First name is all lowercase
  • Length of last name
  • Last name contains special characters
  • First name is all upper case
  • Last name is all upper case
  • Similarity between first name, last name and company name2
  • Length of first name
  • Email bounced over delivered
  • Similarity between last name and company name2
  • Similarity between first and last name2
  • Nonsense score of email1
  • Emails bounced over emails delivered
  • Email is valid
  • Company name contains digits
  • First name contains special characters
  • Email address length
  • Nonsense score of last name1
  • First name contains digits
  • Email is globally ineligible4
  • ASDF keyboard row mashing scrore5

[1] Nonsense score determines if a value looks like a real string (e.g. the user didn't mash on the keyboard or type the same letter repeatedly). A low score here indicates that the value had a low distribution of characters (e.g. "aaaaaaa") while a high score indicates a high distribution (e.g. "abcdefghijklmnop"). A good score is around 2-3. 

[2] The Similarity between email and last name score looks at the last name of the contact and compares it to their email address. Since most valid professional email addresses include the contact's last name (e.g., similarity between email and last name is seen as a positive indicator (as opposed to This same logic is applied to similarity between first and last name, similarity between last name and company name, etc.

[3] For Email address length, HubSpot will compare the number of characters in a contact's email address to other high quality email addresses. 

[4] Read more about how an email address becomes globally ineligible here.

[5] Similarly to Nonsense score, ASDF keyboard row mashing score measures the likelihood that a user entered random keystrokes when filling out a field.

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