What specific types of data are typically used for lead scoring?

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Here are some queries to understand "Lead scoring data" better:
How is lead scoring data collected and integrated into CRM and marketing automation platforms?
What are the different models or methodologies for lead scoring?
What are the common challenges in collecting and maintaining accurate lead scoring data?
How does lead scoring data influence the handover of leads from marketing to sales?
What are some examples of lead scoring data in practice?

Lead scoring data.
Lead scoring best practices.
Data points for lead scoring.
Lead scoring models.
Lead scoring software integration.
Importance of lead scoring data.
Lead scoring challenges.
Impact of lead scoring on sales.
Lead scoring analytics.
Predictive lead scoring data.
"Lead scoring data" refers to the specific attributes finland mobile database and behaviors of a potential customer (lead) that are collected, analyzed, and assigned numerical values (scores) to assess their likelihood of converting into a paying customer. This data forms the basis of a lead scoring model, which helps sales and marketing teams prioritize their efforts on the most promising leads.

What Specific Types of Data are Typically Used for Lead Scoring?
Lead scoring data generally falls into two main categories:

Explicit Data (Fit/Profile Information): This is information directly provided by the lead or gathered about their company, indicating how well they fit your Ideal Customer Profile (ICP).

Demographic Data (B2C):
Age, Gender, Income Level
Location (city, state, zip code)
Job title/Role (if applicable)
Education Level
Firmographic Data (B2B):
Industry (e.g., healthcare, SaaS, manufacturing)
Company Size (number of employees)
Annual Revenue
Location (headquarters, regional offices)
Public vs. Private company
Funding rounds and investors