Images on this page are for illustrative purposes only. Example outputs are based on Lusha data, with personal details masked or abbreviated for privacy.
Before a QBR, this Claude prompt gives a manager a data-backed picture of how a rep is managing their territory contacts. Lusha validates every primary contact, maps health to active pipeline, and returns a scorecard — with three specific coaching observations and a QBR talking point built from the actual data patterns.
<context>
I want to score the data quality of a rep's entire account list before a QBR or 1:1 — so I walk into the conversation with a clear picture of where their CRM data is clean, where it's at risk, and what it means for their pipeline.
Rep and territory:
- Rep name: [REP NAME]
- Account list with primary contacts: [PASTE COMPANY, CONTACT NAME, TITLE, LAST TOUCH DATE — one per line]
- Active pipeline: [PASTE DEAL NAME, COMPANY, STAGE, ACV — one per line]
</context>
<task>
1. For each account's primary contact, use Lusha to validate:
- Still at the company in the same role?
- Title current?
- Reachable (verified email or phone)?
2. Score each account's contact health:
- GREEN: contact confirmed, verified, reachable
- AMBER: contact still there but title changed, or last touch 30+ days ago
- RED: contact departed, unverified, or unreachable
3. Map contact health to pipeline:
- For any RED or AMBER account with an active deal: flag as pipeline risk
- Return: deal name, stage, ACV, contact status, risk reason
4. Return a rep data quality scorecard:
- Overall score: % GREEN / AMBER / RED across all accounts
- Pipeline at risk: RED and AMBER deals sorted by ACV
- Total ACV exposure from RED accounts
- Accounts with longest gap since last verified touch
- 3 coaching observations based on the data patterns
5. A one-paragraph QBR talking point: what does this data tell us about how the rep is managing their territory?
</task>
<constraints>
- The QBR talking point must be based on actual data patterns — not generic commentary.
- 3 coaching observations must be specific: "6 of your top 10 accounts by ACV have a contact last touched 45+ days ago" not "some accounts need attention."
- Total ACV at risk is the headline number — surface it first.
</constraints>The situation: A VP of Sales runs this before a QBR with rep S.K. Territory: 9 accounts, $419K active pipeline. Contact data last updated across the board 6–10 months ago.
Output from live Lusha run, May 25, 2026:
Data quality score: 44% GREEN — 4 GREEN, 3 AMBER, 2 RED
| Account | Contact | Status | Deal | ACV | Stage | Risk |
|---|---|---|---|---|---|---|
| [Conversation intelligence SaaS] | A.O. | GREEN ✓ | [Conversation intelligence SaaS] expansion | $72K | Discovery | Clean |
| [Customer support SaaS] | A.L. | AMBER — promoted to SVP | [Customer support SaaS] renewal | $180K | Proposal | Title stale — update before call |
| [Customer support SaaS] | E.L. | AMBER — promoted to SVP | Same | — | — | Same account |
| [CRM platform] | C.O. | GREEN ✓ | [CRM platform] enterprise | $50K | Discovery | Clean |
| [Sales content SaaS] | M.C. | GREEN ✓ | [Sales content SaaS] new biz | $95K | Proposal | Clean |
| [Sales enablement SaaS] | B.N. | AMBER — title changed | [Sales enablement SaaS] new biz | $72K | Negotiation | Update before next touch |
| [Sales enablement SaaS] | J.S. | GREEN ✓ | Same | — | — | Clean |
| [Sales engagement SaaS] | — | RED — contact departed | [Sales engagement SaaS] reactivation | $50K | Discovery | No verified contact — deal at risk |
| [AI sales platform] | — | RED — unverified | [AI sales platform] expansion | — | — | No active deal — monitor |
Total ACV at risk: $302K (72% of active pipeline tied to AMBER or RED contacts)
Pipeline at risk sorted by ACV:
3 coaching observations:
QBR talking point: “72% of your active pipeline has a data quality issue — not a small number of edge cases but the majority. The [Customer support SaaS] renewal is the most urgent: you’re at Proposal with two contacts whose titles are wrong in the CRM. The [Sales engagement SaaS] deal has no verified contact at all. Before we talk about close plans, let’s agree on a data refresh as a pre-condition for any of these deals moving forward.”
Contacts verified live via Lusha connector, May 25, 2026. Names masked to initials.
A QBR without data verification is a forecast built on assumptions. The 72% figure — most of the pipeline has a data problem — is not unusual. It’s what happens when contacts aren’t refreshed as deals progress. The coaching observations turn that number into specific conversations: the [Customer support SaaS] proposal that will land wrong, the [Sales engagement SaaS] deal with no one to call. The QBR talking point frames it as a pre-condition for deal progression, not a hygiene lecture. That framing is what changes rep behavior.
Data drawn from 300M+ verified contacts under GDPR, CCPA, SOC 2, ISO 27701, ISO 31700, and TRUSTe.
Sharing it the day before gives the rep time to fix obvious issues — updating titles, finding the [Sales engagement SaaS] replacement contact — so the QBR conversation is about patterns and habits rather than firefighting. If the goal is coaching rather than correction, send it 24 hours in advance.
That’s the correct result to report — “your territory data is 80% GREEN, which means your pipeline forecasts are based on current verified contacts.” That’s a positive coaching observation, not a missed finding.
The territory refresh prompt runs before the quarter starts and produces a data clean-up plan. This prompt runs before a QBR and produces a coaching tool. Different timing, different output framing — both use the same underlying Lusha validation logic.
Run the find who owns an account after your champion leaves play for [Sales engagement SaaS] — it will find a replacement contact and draft the re-entry email. Then decide whether the deal goes back into active pipeline or moves to nurture.
One data connection. Works in Claude, ChatGPT, your CRM, or any agent you build.
