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Prompt

Score and route an MQL list before handoff to sales

Built by: Lusha
Time to build: 4 min
Difficulty: Easy
Tools: Claude ▪ Gmail ▪ Lusha ▪ Slack

Images on this page are for illustrative purposes only. Example outputs in this play are illustrative — the structure, fields, and format reflect real Lusha, Gmail, and Slack connector output, but were not pulled from a live session. Run the prompt with your own MQL list and connectors to see live results. Personal details in any live examples are masked or abbreviated for privacy.

Scoring and routing an MQL list before sales handoff means replacing the form-fill firmographic data — guessed job titles, self-reported company sizes, unverified industries — with Lusha-verified facts, re-grading every lead based on what the company actually is, and routing to AE or SDR based on the verified grade rather than the behaviour score alone. This Claude prompt runs every MQL through Lusha to replace form-fill data with verified titles, headcount, and funding stage, checks Gmail for active deals before routing, flags divergence between the original behaviour score and the verified grade, and posts routed leads with rep briefs to Slack. Sales gets leads worth working. Marketing gets a routing log that holds up to scrutiny.

The prompt

Open in Claude Open in ChatGPT 
<context>
I have a batch of MQLs ready to route to sales. Before routing, I want to replace form-fill firmographic data with verified Lusha data — actual titles, actual company sizes, actual funding stages — so the MQL grade reflects reality. I also want to check for prior sales contact and flag any lead already in an active deal before it hits the queue.

My MQL batch:
- MQL list: [PASTE NAME, EMAIL, COMPANY, TITLE (submitted), LEAD SOURCE, BEHAVIOUR SCORE — one per line]
- What we sell: [PRODUCT / SOLUTION]
- ICP criteria: [COMPANY SIZE / INDUSTRY / FUNCTION / SENIORITY]
- Routing rule: [e.g. "AE if Grade A + score 60+, SDR if Grade B, nurture if C or D"]
- Sales Slack channel: [CHANNEL NAME OR "skip"]
</context>

<task>
1. For each MQL, use Lusha to replace form-fill data with verified information:
   - Verified current title — flag if different from submission
   - Still at the company — flag departed contacts
   - Actual headcount, industry, funding stage, tech stack, revenue range
   - ICP fit grade: A (decision-maker, strong match), B (influencer or partial),
     C (weak match), D (outside ICP)
   - Flag any lead where verified grade diverges significantly from behaviour score

2. Check Gmail for prior sales contact per lead or company domain:
   - Already in an active deal? Flag as HOLD — don't re-route
   - Prior outreach, no response? Flag as warm re-engagement
   - No prior contact? Route as standard MQL

3. Apply the stated routing rule using the Lusha-verified grade:
   - AE route: verified Grade A + behaviour score meets threshold
   - SDR route: Grade B + threshold met, or Grade A below threshold
   - Nurture: Grade C or D, or any grade below threshold
   - HOLD: active deal detected, or contact has departed

4. For every AE and SDR routed lead, produce a one-line rep brief:
   - Verified title, company context, ICP grade, behaviour score, lead source
   - Prior contact note if applicable
   - One suggested first line for outreach specific to role and lead source

5. Return:
   - Full MQL table: original vs verified grade, routing decision
   - AE leads with rep briefs
   - SDR leads with rep briefs
   - Nurture summary
   - Hold list with reason
   - Slack alert if channel specified
   - Summary: X to AE, X to SDR, X to nurture, X on hold
</task>

<constraints>
- Never route a lead already in an active deal — flag HOLD, notify account owner.
- Never route a departed contact.
- Grade comparison mandatory — flag where verified grade diverges from behaviour score.
- Rep brief must be specific — not "follow up on content download" but the exact context.
</constraints>

What you'll get back

The situation: A marketing ops manager has 6 MQLs from the past week — pricing page visits, content downloads, and a demo request. All have behaviour scores between 45–85. Running the verification before the Monday morning sales handoff.

MQL table — original vs verified

LeadSubmitted titleVerified titleCompanySubmitted sizeVerified sizeBehaviour scoreOriginal gradeVerified gradeRouting
K.B.Operations DirectorVP Revenue OperationsCrestline Software25034082BA⬆ AE
T.K.VP SalesVP of Sales ✓Dunmore Analytics50058074AAAE
R.F.Marketing ManagerMarketing Manager ✓Ashford Platforms40042061BD⬇ Nurture
P.O.VPVP of Sales ✓Briarway SaaS30029055BBSDR
M.L.Head of OpsHead of Sales OperationsElmont Systems10061048CB⬆ SDR
D.R.Director RevOps—Ashford Platforms——67B—🔴 HOLD

Grade divergence flags

⬆ K.B. upgraded A → AE route Submitted “Operations Director” at a “250-person” company. Lusha verified: VP Revenue Operations at a 340-person Series A SaaS company. Original behaviour score of 82 was under-routing a decision-maker to SDR. Re-routed to AE.

⬇ R.F. downgraded D → Nurture Submitted “Marketing Manager” — Lusha confirms Marketing Manager. Company is 420 employees but Marketing function is outside ICP. High behaviour score (61) from content downloads doesn’t reflect sales-readiness. Moved to nurture.

⬆ M.L. upgraded B → SDR route Submitted “Head of Ops” at “100 employees.” Lusha verified: Head of Sales Operations at Elmont Systems, 610 employees — well above the ICP floor. Was being routed to nurture based on the wrong company size. Re-routed to SDR.

HOLD — do not route

🔴 D.R. at Ashford Platforms — Gmail shows an active deal at Negotiation stage with @sarah.ae. Routing this MQL to a separate rep would create a conflict. Hold and notify Sarah.

AE leads — route today

K.B. · Crestline Software · @james.ae Verified: VP RevOps, 340-person Series A SaaS. Demo request. Behaviour score 82. No prior contact. Brief: “K.B. submitted a demo request via the pricing page. VP RevOps at a 340-person Series A — decision-maker, high intent. Open with: ‘You requested a demo from the pricing page — happy to walk you through what that looks like for a RevOps team your size.'”

T.K. · Dunmore Analytics · @sarah.ae Verified: VP Sales, 580-person Series B SaaS. Pricing page visit. Behaviour score 74. ⚠ Prior contact: pricing thread 54 days ago, no reply. Brief: “Warm account — T.K. asked about pricing 54 days ago. Series B just closed 18 days ago. Don’t open cold. ‘With the Series B just closed, the pricing conversation from March is probably more relevant now.'”

SDR leads — sequence this week

P.O. · Briarway SaaS · behaviour 55 · content download · no prior contact M.L. · Elmont Systems · behaviour 48 · webinar registration · no prior contact

Nurture: R.F. at Ashford Platforms — Marketing function, outside ICP. High behaviour score from content consumption. Add to marketing nurture, exclude from sales routing.

Summary: 2 to AE · 2 to SDR · 1 to nurture · 1 on hold

Illustrative example — fictional company names used. Run with your own MQL list to see live results.

What to do next

Lusha in Claude exposes the three failure modes in every MQL batch that behaviour scoring alone can’t catch. K.B. was under-routed — a VP Revenue Operations at a Series A company was going to an SDR because the form said “Operations Director” and “250 employees.” M.L. was under-routed for the same reason — “Head of Ops at 100 employees” looked like a nurture lead until Lusha returned “Head of Sales Operations at 610 employees.” R.F. was over-routed — a high behaviour score from content downloads masked a Marketing function that sits outside the ICP entirely. And D.R.’s HOLD flag stops a second rep from walking into an account that Sarah is already at Negotiation stage with. All four findings come from combining Lusha’s verified firmographic layer with Gmail’s deal history — neither source alone is enough.

Data drawn from 300M+ verified contacts under GDPR, CCPA, SOC 2, ISO 27701, ISO 31700, and TRUSTe.

FAQ

  • Why does form-fill data produce wrong MQL grades?

    People self-report job titles and company sizes on forms — and both are frequently inaccurate. Titles get simplified (“Operations Director” instead of “VP Revenue Operations”), companies get misreported (especially headcount), and the firmographic grade the marketing automation platform assigns is based on those inaccurate fields. Lusha replaces the guessed data with the verified record before the grade is applied.

  • What does the grade divergence flag actually mean?

    It means the lead was being routed to the wrong queue. An upgrade flag (like K.B.) means a decision-maker was going to SDR instead of AE. A downgrade flag (like R.F.) means a high behaviour score was routing an outside-ICP contact to sales. Both waste rep time. The flag makes the divergence visible before the routing happens rather than after the rep calls and realizes the mistake.

  • Should I run this on every MQL batch or just weekly?

    Daily for teams with high inbound volume. Weekly for smaller teams. The key is running it before the batch hits the sales queue — not after reps have already worked the leads. Once a rep reaches out to a misgraded MQL, the damage to the relationship and to sales-marketing trust is already done.

  • How is this different from a standard lead scoring model in HubSpot or Marketo?

    A standard lead scoring model grades on behaviour and on whatever firmographic data the contact submitted. Lusha adds a third layer — verified real-world firmographics that replace the self-reported data. The behaviour score stays; Lusha corrects the firmographic component. The two work together.

  • What happens to HOLD leads?

    HOLD leads are not routed to any rep or queue. The prompt notifies the account owner via Slack that a lead from the same account came through as an MQL. The account owner decides whether to loop the lead into the existing deal or treat the inbound as a separate signal.

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