Lead scoring is a crucial framework for aligning sales and marketing efforts by prioritizing leads based on their likelihood to convert and their potential for long-term retention. The webinar also delves into how artificial intelligence (AI), particularly machine learning, is revolutionizing lead scoring by enabling the analysis of vast amounts of data to predict high-potential leads. Furthermore, it highlights the importance of automation in acting on lead scores and establishing feedback loops to refine the lead scoring model continuously.
1. What is Lead Scoring?
Definition: Lead scoring is a framework that ranks leads coming to a website by assigning a numerical value based on specific behaviors (e.g., website visits, content downloads, demo requests) and demographics (e.g., job title, company size, industry).
Purpose: The numerical score indicates the probability of a lead converting into a customer and their potential for long-term value.
Prioritization: Lead scoring helps prioritize which leads sales should focus on, especially when dealing with a high volume of leads.
Distinction between MQL and SQL:Market Qualified Lead (MQL): A lead that has shown initial interest, often through website activity, but with minimal information known.
