Lead Scorer: prioritize buying readiness
Qualify inbound leads as Hot, Warm, or Cold using budget, timing, and product fit. Keep missing information separate from low interest.
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Three ordered categories, one explicit sales policy
Hot, Warm, and Cold describe relative readiness under your sales process, not a person’s worth or a verified probability of closing. Define what evidence moves a lead between stages before comparing results. A clear purchase timeline and a known product fit may justify a prompt conversation; company size alone does not. This tool supports follow-up prioritization. It does not assess creditworthiness, employment eligibility, or any other personal qualification, and it does not update your CRM automatically.
Work through the cases
Illustrative inputs and review guidance, not recorded model outputs. Run your own context to inspect a decision.
A concrete buying process
We need 250 seats, have an approved budget, and want to start this quarter. Can procurement review your contract?
Hot is an intended category if your policy requires an active timeline, a relevant need, and access to a buying process. Verify the product actually supports the requested deployment. Large seat counts alone should not outweigh a mismatch that prevents you from serving the account.
Interest without a committed timeline
The pilot looks promising. We are comparing three vendors, but budget approval will not happen until next year.
This illustrates the Warm/Hot boundary: fit and engagement are present, but the buying window is not immediate. Preserve the reason for the stage so a salesperson knows what to clarify next. Adjacent-stage disagreement still matters if it changes the promised response time.
Too little evidence to assess readiness
Please send more information.
This message does not establish a budget, timeline, or lack of interest. Missing data should not automatically mean Cold. With only three default categories, treat the result as provisional and collect the missing buying context before using it to suppress follow-up.
Set the boundaries before you classify
Suggested review rules for these options. Include the rules relevant to your team in the question or context when you run a decision.
| Choice | Use it when | Check the boundary |
|---|---|---|
| Hot | Relevant need with concrete evidence of an active buying process. | Enthusiastic wording is not a substitute for timing or product fit. |
| Warm | A plausible fit and interest, with important buying conditions still unresolved. | Record what is missing rather than using the middle category as a catch-all. |
| Cold | Evidence of low current intent or a mismatch with the offered product. | An incomplete message is not evidence that the lead is unqualified. |
Where to take a closer look
Marketing language mistaken for a buying signal
A lead may repeat your landing-page claims without describing their own need. Look for a specific workflow, deadline, constraint, or procurement step. Keep engagement claims separate from evidence that the product solves the account’s problem.
Personal attributes substituted for business fit
Use relevant business requirements, requested capacity, timing, and source context. Do not include names, gender, nationality, or other personal attributes as scoring criteria. Minimize contact details in the input; outreach systems can retain those separately.
Balance sales capacity with missed opportunities
Review false Hot assignments alongside promising leads sent to lower-priority queues. Prioritizing scarce sales time can justify stricter evidence for Hot, but an overly restrictive rule can miss early-stage buyers. Compare outcomes within a consistent follow-up window.
Historical outcomes can help evaluate category definitions and thresholds outside this tool. They do not retrain the model through this page. Check for feedback loops: leads that received little attention may have poor outcomes because of that decision, not because the original score was correct.
A Jev API example for your next integration
Call the iajev REST API with cURL. Receive a choice, probabilities, and confidence as JSON.
curl https://iajev.com/api/v1/decisions/run \
-H 'Authorization: Bearer YOUR_API_KEY' \
-H 'Idempotency-Key: YOUR_UNIQUE_REQUEST_ID' \
-H 'Content-Type: application/json' \
--data '{
"state": {
"message": "VP of Operations at a 400-person logistics company. Asked for pricing for 250 seats and wants to start this quarter.",
"source": "pricing page"
},
"decision": {
"type": "choice",
"question": "How likely is this lead to buy?",
"choices": [
"Hot",
"Warm",
"Cold"
]
}
}'Questions about this tool
Can I write the result directly to a CRM?
Your integration can store the suggested stage, source context, and review status. Keep a human override and avoid replacing a salesperson’s confirmed stage with an unreviewed classification. This page does not connect to or write to a CRM.
Would five stages work better than three?
Only if your sales team can define the boundaries and takes a different action for each stage. Extra labels do not supply missing evidence. Start with consistent Hot/Warm/Cold definitions, then evaluate whether another stage resolves an actual handoff problem.
Using iajev
Add context
Include the facts needed for this decision.
Review the choices
Customize the question and options to match your workflow.
Run and review
Inspect the distribution before taking action.
Is confidence the probability that a decision is correct?
No. Confidence is a separate model estimate; the choice probabilities describe the distribution over your options. Neither is a measured accuracy guarantee.
Can I compare two inputs?
Edit the context and run the same question again to compare probabilities. Changing the question or choices starts a new comparison.