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Stop wasting time on bad leads. Start with our powerful, configurable scoring system to prioritize effort.
Use our advanced AI Conversion Score to predict the exact probability of a lead closing.
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Without a scoring system, every lead looks the same. Manual prioritization is slow,
subjective, and leads to high-potential customers getting ignored.
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Assign structured scores to leads based on attributes and interactions to determine initial readiness.
Create qualitative scoring rules based on lead data like source, requirement, or deal size.
Award score points for actions such as email opens, link clicks, calls, and SMS responses.
Automatically assign and update lead scores so reps focus on the most engaged prospects.
Use artificial intelligence to compute predictive scores that reflect each lead’s likelihood to convert along with actionable suggestions.
Predict conversion probability instantly for every lead for smarter prioritization.
AI identifies key engagement patterns that indicate lead intent or risk.
Receive guided recommendations to optimize engagement and next steps with each lead.
Use artificial intelligence to compute predictive scores that reflect each lead’s likelihood to convert along with actionable suggestions.
Predict conversion probability instantly for every lead for smarter prioritization.
AI identifies key engagement patterns that indicate lead intent or risk.
Receive guided recommendations to optimize engagement and next steps with each lead.
Lead Scoring works in tandem with our complete Sales Box, all-in-one sales CRM
built to boost productivity and efficiency. Sell smarter, not harder.
Rule-based scoring uses pre-configured rules set by your team, for example adding 10 points for leads from a website enquiry. AI Conversion Score uses machine learning to predict conversion probability based on real-time CRM data and historical patterns, providing a deeper layer of prioritisation beyond static rules.
The AI model processes internal CRM activity such as call logs, task completion rates, follow-up history, and sales cycle velocity, alongside external engagement signals to calculate the probability that a specific lead will convert. The more interaction data available, the more accurate the prediction.
The AI Suggested Actions panel highlights positive signals that indicate high intent, negative signals that may suggest the lead is cooling, and gives each rep a specific next-step recommendation such as schedule a demo or send a pricing guide, so the rep always knows what to do next.
Lead scoring is a method of ranking leads based on their likelihood to convert, so sales reps can prioritise the highest-potential opportunities first. Without lead scoring, reps often spend equal time on every lead regardless of quality, which results in missed conversions on high-intent leads and wasted effort on low-quality ones.
Start by identifying the characteristics and behaviours of your best-converted leads historically, such as their source, industry, company size, and engagement actions. Assign points to each characteristic. The total score tells you how qualified a lead is. In Corefactors, you can set these rules directly in the lead scoring module without any technical help.
Yes. For inbound leads, scoring is based on source quality and engagement behaviour. For outbound leads, scoring is based on fit criteria like industry, company size, and designation. Corefactors supports both models and lets you run separate scoring rules for different lead types if needed.
Take the guesswork out of lead prioritization. Get a personalized demo to see how our
Rule-Based Scoring and advanced AI scoring will transform your sales productivity.
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