Predictive Contact Lead Scoring models

The Predictive Contact Lead Scoring model is a ready-to-use data science model that scores B2B contacts on their likelihood to convert based on profile and engagement patterns.

The Predictive Contact Lead Scoring model explained

The Predictive Contact Lead Scoring model generates numerical scores for leads based on profile, revenue, and behavior data. The output values allow you to identify contacts that are active in different levels of the sales funnel and the potential for them to make purchases. Lead score values with lead score timestamps are generated for every contact, allowing you to more effectively target customer segments and align sales and marketing strategies.

Parameters of the model

When creating the model, you will need to define the following parameters for the model:

  • Algorithm: The algorithm is the piece of code that runs the model. When you select the Predictive Contact Lead Scoring B2B algorithm, you will need to define the additional lookback window parameter. Select the number of days as a lookback window: 90, 180, 270, or 360.

  • Queries: The queries selected for the model generate a dataset for model training and scoring.

  • Inputs: The inputs are query attributes from the Unity data model that are used for model training and scoring. You can't make changes to model inputs.

  • Outputs: The outputs are data objects and attributes from the Unity data model that are used to store the output values of the model. You can make updates to the default mapping of model outputs.

Model inputs

To generate values, the Predictive Contact Lead Scoring model uses the following data.

For the model to successfully run:

  • Data needs to be ingested into all the input attributes below.
  • The custom data object SalesData needs to be created with the following required attributes.
    • OpportunityDate: Represents the date the opportunity was created (when the conversion occurred).
    • Is_Converted: The value 1 represents conversion. The value 0 represents non-conversion.
    • SourceAccountID: Account for which the conversion is captured.
Data object Attribute Data type Description
Account Employees Integer The number of employees in the company.
Account Annual_Revenue Float

The annual revenue of the company.
Customer SourceID String The unique identifier for the source.
Customer ContactID String The unique identifier for the contact.
Customer Contact_Company String The company name for the contact.
Customer Contact_Industry String The industry of the contact.
Customer Contact_Email_Domain String The contact's email domain.
Customer Contact_Title String The title for the contact, such VP, Manager, and Analyst.
Customer Contact_Country String The contact's country.
Customer Country_State_Prov String The contact's state or province.
Customer Contact_City String The contact's city.
Customer Contact_Zip_Postal String The zip code or postal code that the contact resides.
Event Medium String The channel for the message, such Email, SMS, and Push.
Event Type String The type of event, such as View, Purchase, Buy, and Click.
Event EventTS Timestamp The date and time when the event occurred.
SalesData Is_Converted String Indicates when conversions occurred. The value 1 represents conversion. The value 0 represents non-conversion.
SalesData OpportunityDate Timestamp The date when an opportunity was created (when the conversion occurred).
SalesData SourceAccountID String Unique identifier for the account.

Model outputs

Output values will be stored in the Contact_LeadScore data object. You can review the lead score values for each contact in the Status attribute. Each status (cold, cool, warm, and hot) is based on a lead score generated from 0 to 100. Review the specific values and assessments below.

The following attributes will generate output values.

Attribute Description Data Type Is Key attribute?
SourceContact_LeadScoreID The unique identifier for the data object. String Yes
SourceID The unique identifer for the source. String Yes
SourceContactID The unique identifier for the contact. String Yes
Lead Score Timestamp The date and time when the lead score was generated. String No
Score The score generated for the contact. It is a numerical value from 0 to 100. Integer Yes
Status The status that assesses the contact based on the lead score. String Yes

The Status attribute will generate one of the following values based on the lead score.

Value Assessment of lead Score
Cold lead Not very active 0-25
Cool lead Slightly active 26-50
Warm lead Active 51-75
Hot lead Very active 76-100

Create and use a Lead Scoring model

To create and use a Lead Scoring model, you will need to do the following:

  1. Follow the steps for Creating Predictive Contact Lead Scoring models.

  2. After creating the model, follow the steps for Running training and scoring jobs.

After the model runs and creates output values, you can do the following:

Attribute Description
SourceContactID The unique ID for the contact.
Lead Score Timestamp The date and time when the lead score was generated.
Contact Score The score generated for the contact. It is a numerical value from 0 to 100.
Contact Status/Category The status that assesses the contact based on the lead score.
Account Score The score generated for the account. It is a numerical value from 0 to 100.
Account Status/Category The status that assesses the account based on the lead score.
Value Assessment of lead Score
Cold lead Not very active 0-25
Cool lead Slightly active 26-50
Warm lead Active 51-75
Hot lead Very active 76-100

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