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Determine likelihood to close with predictive lead scoring

Last updated: May 31, 2022

Applies to:

Marketing Hub Enterprise
Sales Hub Enterprise

Using predictive machine learning algorithms, HubSpot analyzes your customers and industry customer sets to determine the probability that your open contacts will close as customers within 90 days.

The Likelihood to close and Contact priority properties allow you to analyze and segment your contacts based on this predictive lead scoring model.

  • In your HubSpot account, click the settings settings icon in the main navigation bar.
  • In the left sidebar menu, navigate to Properties.
  • Search or browse in the Contact Information property group for Likelihood to close and Contact priority:
    • Likelihood to close: a score that represents the percentage probability of a contact closing as a customer within the next 90 days based on standard contact properties and behavior. For example, contacts with a close probability value of 22 have a 22% chance of closing as a customer in the next 90 days.
    • Contact priority: categories of contacts based on the Likelihood to close score, which can be used as CRM filters to segment your best and/or worst leads.
      • The Very High, High, Medium and Low categories will each contain 25% of your contacts based on the Likelihood to close score, with the Very High category applying to the top 25% of scores.
      • The Closed won category applies to contacts whose lifecycle stage is Customer.
      • Because the categories are relative groupings, the range of scores in each category may shift over time. If you have a newer HubSpot account, you may see lower scores in the top tier. As more data accumulates on your contacts and customers over time, you may see higher scores in the top tier. 

Please note: you need at least 100 contacts in your HubSpot account to see values for the Contact priority property.

To set values for these properties, HubSpot analyzes:

  • Analytics information (e.g., web page visits, time of last visit, email interactions including clicks, opens, and replies, and form submission events).
  • Firmographic information provided by HubSpot Insights about the contact’s company.
  • Firmographic information about your business and HubSpot account.
  • Interactions logged in the HubSpot CRM (e.g., tracked email clicks, meetings booked).

HubSpot uses the most current predictive machine learning algorithms known as black boxes to provide accurate predictions. With a black box, data scientists understand the input and outputs of the model, but how the input is transformed into the output is unknown. These models have been proven to outperform white box models, but it is not possible to break down how each individual input contributes to a contact’s score. Instead, the focus is on the overall predictive performance of the model. 

To learn how to use lead scoring to prioritize your outreach efforts, check out the HubSpot Academy lesson on Understanding HubSpot Lead Scoring.

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