Lead evaluation and sales prioritization
Growth IntelSeparate profile fit, purchase intent and missing information.
Do not collapse every inquiry from WhatsApp, Instagram, ad forms and your website into a single static number. See criteria fit, behavioral signals and unanswered questions separately so your sales team knows the right next step.
A live walkthrough using your customer criteria, channels and sales workflow.
- Inquiry
- Pricing and delivery
- Service area
- Istanbul
- Profile fit
- 100%
100% - Information coverage
- 100%
100%
The inquiry and area match the sample profile. Buying intent is evaluated separately.
Core concept
What is Lead Intent Scoring?
Lead Intent Scoring is a dynamic prioritization method that evaluates an inbound prospect’s purchase readiness from 0 to 100 using channel interactions, response speed, pipeline progression, and commercial signals. In Growth Intel, purchase intent is kept distinct from profile fit (ICP) and information coverage so incomplete inquiries are never discarded as unqualified.
Explainable prioritization instead of inflated scores.
Deterministic rules that prevent rapid-fire messages from inflating scores and stop missing details from being treated as disqualifications.
Dimension separation
Missing information is not a disqualification
Profile fit (ICP), purchase intent and information coverage are measured separately. A lead whose region or budget is not yet known is marked for completion rather than penalized with a zero score.
Profile fit · Purchase intent · Information coverage
Separate evaluationNoise protection
Prevents multi-step form and message inflation
Duplicate form steps within 30 minutes count as a single signal. Repeated actions over a 7-day window apply diminishing returns (1, 1/2, 1/3) to prevent artificial score spikes.
30-min window + 7-day diminishing returns (1 / n)
Score calibrationData privacy
Personal identifiers are stripped before AI evaluation
Names, phone numbers and email addresses are excluded from AI quality evaluation calls; only company, title, industry, region and source context are processed.
Company · Title · Industry · Region · Source
GDPR & KVKK privacyMetrics reflect default parameters of the Growth Intel scoring engine; thresholds and weights are configurable per business profile.
Evaluation architecture
How is a lead evaluated and prioritized?
Growth Intel processes every inquiry across 4 complementary layers to give sales reps an explainable priority queue:
When information coverage is low, the system does not guess; it highlights the missing field as the next question for the rep.
- 01 · Profile Fit (ICP Fit) — Rules and weights tailored to each buyer group
- Define separate ideal customer profiles for B2B, B2C, dealer or project inquiries. Industry, region, product group and commercial criteria are scored with their own weights.
- 02 · Behavioral Intent (Intent) — Interaction depth balanced by diminishing returns
- WhatsApp and Instagram messages, proposal views, form details and conversation signals update the 0–100 intent score without letting rapid consecutive messages inflate the result.
- 03 · Information Coverage (Coverage) — Making unknown details visible
- Track what percentage of decision-critical fields are known. Missing fields are never treated as a mismatch; they guide the conversation workflow.
- 04 · Priority & Ad Feedback — Industry recency horizon and conversion signals
- Intent combines with interaction recency, pipeline stage and source weight to rank the queue. Recorded qualification and sales outcomes can be reported back to configured Meta and Google Ads accounts.
Approach comparison
Single-score rules vs. multi-dimensional lead evaluation
How separating fit, intent and information coverage compares with collapsing every signal into a single static point total:
| Evaluation Criterion | Single-Dimension Static Scoring | Growth Intel Multi-Dimensional Evaluation |
|---|---|---|
| Inquiries with missing details | Blank form fields reduce the total score, making promising buyers look unqualified | Information coverage is tracked separately; missing fields appear as the next follow-up step |
| Repeated messages & multi-step forms | Every message or form step adds flat points, artificially inflating the score | 30-min same-action deduplication and 7-day diminishing returns measure genuine progression |
| Distinct buyer groups (B2B / B2C) | A single point table is applied across all inbound inquiries | Consumer buyers, dealers and enterprise projects are evaluated against their own ICP profiles |
| Advertising & sales connection | Ad reporting stops at click and form counts while sales outcomes live elsewhere | Recorded qualification and sales outcomes connect to the source campaign and report back to Meta & Google Ads |
| Data privacy & agent readiness | Raw customer contact fields are often passed into third-party prompts or connectors | Zero-PII model evaluation plus authenticated MCP and REST access for authorized teams |
Frequently asked questions
Common questions about lead intent scoring and prioritization
How does lead intent scoring change the sales team’s daily workflow?
Instead of working strictly in chronological order, reps see high-fit and high-intent inquiries first, along with which details are still missing and the recommended next action on the same record.
Why isn’t a lead with missing information given a low fit score?
A prospect messaging on WhatsApp or Instagram rarely shares every detail in the first message. Because Growth Intel measures information coverage separately, incomplete records are flagged with the next question to ask rather than rejected.
Do rapid consecutive messages artificially inflate the intent score?
No. A 30-minute same-action window deduplicates repeated form or message triggers, and a 7-day diminishing-returns rule scales down repeated actions of the same type.
Do we need historical sales data before scoring works?
No. Industry templates and configurable ideal customer profiles evaluate leads from day one, and priority weights can be calibrated as your team records outcomes.
How does this connect back to Meta and Google Ads campaigns?
You can compare which channels and campaigns generate form fills versus qualified leads and recorded sales. When ad integrations are configured, qualification and sales events are sent back to Meta and Google Ads.
Are personal contact details sent to AI models during evaluation?
No. Names, phone numbers and email addresses are stripped before AI quality evaluation; only company, title, industry, region and source signals are evaluated.
See multi-dimensional lead evaluation with your own criteria.
Let’s review your ad channels, WhatsApp/Instagram workflows and customer profiles together in a live demo.