Published at: 2026-09-17

Lead conversion rate prediction

Learn how the system predicts the probability that a Sales Lead converts into an Opportunity or an Account based on historical data and intelligent algorithms.

Business value

The system uses an AI model to evaluate Sales Leads across multiple dimensions and predict their conversion probability. This helps sales teams identify the highest-potential prospects from a large Lead pool. Managers can also use the result to plan sales targets and optimize marketing investment.

Core concepts

Concept	Definition	Related objects
Conversion rate prediction	A probability score, shown as a percentage, that the system calculates with a machine learning model to estimate whether a Lead will convert into an Account or an Opportunity.	Sales Lead, Account, Opportunity
Feature factors	The key attributes that affect the prediction, including basic attributes such as industry and region, plus Follow-Up Activity signals such as communication frequency and response time.	Sales Lead
Confidence level	An evaluation of prediction reliability. It depends on how much historical converted and discarded Lead data is available.	Sales Lead

Operating mechanism

The system predicts conversion probability through a closed loop of historical data, model training, and new Lead evaluation.

graph TD
    A[Historical converted and failed Lead data] -->|Training input| B[Machine learning prediction model]
    C[New Sales Lead] -->|Extract feature fields| D[Match prediction factors]
    D -->|Run calculation| B
    B -->|Output| E[Generate predicted conversion probability]
    E -->|Display and trigger rules| F[Sales follow-up decisions]

Model training: The system learns from large volumes of historical converted Accounts and discarded Leads to build a tenant-specific prediction model.
Factor matching: When you create a new Lead, the system parses fields such as Industry, Registered Capital, and Lead Source. It can also monitor follow-up signals such as Open Email activity.
Dynamic updates: The prediction is not fixed. As follow-up deepens, such as after a successful visit or after you add related contacts, the predicted value updates in real time.

Applicable roles

Role	Main actions
Sales manager	Use predicted conversion rates and Lead grades to optimize resource allocation and Lead routing.
Sales rep	Sort daily work by predicted conversion probability and focus first on the most promising Sales Leads.
CRM administrator	Maintain feature-factor field configuration and monitor model accuracy.

Business scenarios

Scenario 1: Intelligent assignment and precise distribution

A fintech company imports 1,000 industry Leads. After model evaluation, the system identifies 50 Leads with a predicted conversion probability above 75%. The main features are East China region, enterprise size above 1,000 employees, and registered capital above CNY 50 million. The system assigns those 50 Leads automatically to senior sales reps, while the remaining Leads stay in the general Lead pool for team claiming.

Scenario 2: Daily follow-up prioritization

A sales rep opens the workspace and sees hundreds of Leads that need attention. They sort the list by Predicted conversion probability from high to low. They then focus first on the 10 prospects with a predicted conversion rate above 60%. This improves the productivity of the day.