Methodology

How every lead gets scored

LeadQualify uses a two-layer system. The first layer is a transparent rules-based engine every point is traceable to a specific signal. The second layer is an AI explanation engine that reads your Ideal Customer Profile and tells you exactly what to do with each lead.

Layer 1

Rules-based scoring

Six factors are evaluated per lead title, company size, industry, growth signals, LinkedIn presence, and red flags. Each adds or subtracts points. The final score is 0–100, clamped so no single factor can dominate.

Deterministic · Transparent · Auditable

Layer 2

AI explanation engine

Once scored, every Priority and Cultivate lead is passed to Gemini AI alongside your Ideal Customer Profile. The AI generates a plain-English explanation, identifies risks, recommends an action with timing, and writes a personalised opening line for outreach.

Personalised to your ICP · Actionable · Not generic

Example AI output for a lead

Explanation

Sarah scores 95/100 because she is a VP-level decision maker at a 150-person SaaS company exactly matching your ICP. The Series B funding and active hiring signals suggest the company is in a growth phase where new tools get approved faster.

Risks

  • No direct budget confirmation in notes

Recommended action

Contact within 48 hours mention their Series B and reference the SDR hiring as context for why lead qualification matters now.

Opening line

"Hi Sarah saw Innovate Inc just closed Series B and you're scaling the sales team. Curious whether qualifying inbound leads is eating into your reps' time."

Score formula

Title+Company size+Industry+Growth signals+LinkedIn-Red flags=0–100

Score is clamped between 0 and 100. No single factor can push it above 100 or below 0. Email presence adds +10 if a valid email address is present.

The three verdicts

Priority75–100

Strong fit across multiple dimensions. Budget authority, company fit, and active growth signals all present. Contact within 48 hours.

Cultivate50–74

Good signals but missing one or two key factors. Add to a nurture sequence and revisit in 2–4 weeks.

Defer0–49

Poor fit, junior contact, or active red flags. Remove from active pipeline revisit only if circumstances change.

Scoring factors

Job Title & Seniority

0 to +30 pts

The strongest single signal. Decision-making authority correlates directly with seniority. C-level titles score highest, junior titles score zero.

CEO, CTO, CFO, President+30
VP of Sales, VP of Marketing+25
Director, Head of+20
Manager, Lead+15
Analyst, Coordinator, Intern0

Company Size

-5 to +20 pts

Mid-size companies (20–500 employees) are the sweet spot large enough to have budget, small enough to move fast. Very small companies score negatively.

20–500 employees+20
500–5,000+15
5,000++10
Under 20-5
Unknown0

Industry Fit

0 to +20 pts

SaaS, software, and tech companies score highest because they understand and buy tools like this. Unrelated industries score zero.

SaaS, Software, Tech+20
B2B, Enterprise+15
Sales, Marketing agencies+15
Finance, Consulting+10
Retail, Consumer, Other0

Growth Signals

0 to +15 pts

Companies actively growing are far more likely to invest in new tools. Signals are read from the notes field of your CSV.

Series A / B / C mentioned+15
Hiring aggressively+10
Expanding to new markets+10
New product launch+10
No growth signals0

LinkedIn Presence

0 to +5 pts

A LinkedIn URL signals the lead is professionally visible and the data is more likely to be accurate.

LinkedIn URL present+5
No LinkedIn URL0

Red Flags

-20 to -10 pts

Certain notes override positive signals. These are hard stops that reduce the score regardless of other factors.

Unsubscribed / Do not contact-20
Competitor mentioned-15
Not interested-10
No response (repeated)-10

Your Ideal Customer Profile

The rules above are generic starting points. The AI explanation layer goes further it reads your specific ICP and scores each lead against YOUR best customers, not a generic template.

When you set up your ICP in the dashboard, you tell us who your best customers are their titles, company sizes, industries, and the signals you look for. Every lead you score after that receives an explanation referencing your specific profile. A CEO at a 10-person startup might score 85 on generic rules, but if your ICP is mid-market SaaS companies, the AI will flag the company size as a risk and adjust the recommendation accordingly.

Set up your ICP in the dashboard →

Expected CSV columns

All columns are optional except at least one identifier. More data means more accurate scoring and better AI explanations.

ColumnUsed forImpact
NameDisplay and AI personalisationAI opening line quality
EmailPresence check+10 pts if valid
CompanyIndustry and size scoring+0 to +20 pts
Job TitleSeniority scoring+0 to +30 pts
Company SizeDirect size input+0 to +20 pts
LinkedInPresence check+5 pts if present
NotesGrowth signals and red flags+15 pts or -20 pts
PhoneDisplay onlyNo score impact

See it in action

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