Audit SDR-to-AE handoff quality this quarter
Images on this page are for illustrative purposes only. Example outputs are based on Lusha data, with personal details masked or abbreviated for privacy.
This Claude prompt validates a quarter’s worth of SDR-to-AE handoffs against verified Lusha data — was the company actually in ICP at handoff, was the right contact touched, was there a real signal? Returns a graded handoff list, conversion rate by grade, and the three patterns driving bad handoffs — by SDR, by lead source, and by which ICP criterion is being missed most often.
Tools: Claude, Lusha, CRM data (pasted)
The prompt
This prompt may contain placeholders — look for [BRACKETS] and fill them in.
<context>
I want to audit the quality of SDR-to-AE handoffs this quarter. I need to know how many passed leads were actually qualified when they were handed off — right ICP, right contact, real signal — and where the pattern of bad handoffs is coming from.
My handoff data:
- Handoff list: [PASTE LEAD NAME, COMPANY, SDR OWNER, AE OWNER, HANDOFF DATE, CURRENT STATUS — one per line]
- Our qualification criteria at handoff: [WHAT AN SDR SHOULD CONFIRM BEFORE PASSING]
- Outcome to track: [ACCEPTED / REJECTED BY AE / CONVERTED TO OPPORTUNITY / STALLED]
- Timeframe: [THIS QUARTER / LAST QUARTER]
</context>
<task>
1. For each handed-off lead, use Lusha to validate what was true at the time of handoff:
- Was the company actually within ICP? (size, industry, geography)
- Was the contact touched a decision-maker, influencer, or end user?
- Was there a verified signal at the account at handoff — or cold ICP fit only?
- Is the contact's verified title what the SDR logged, or was there a mismatch?
2. Grade each handoff retroactively:
- CLEAN: ICP fit confirmed, decision-maker or economic buyer touched, signal present
- ACCEPTABLE: ICP fit confirmed, influencer or end user touched, no signal
- WEAK: ICP fit confirmed but only end user or researcher touched, no signal
- BAD: Outside ICP at time of handoff, or no verified contact touched
3. Cross-reference handoff grade with outcome:
- Do CLEAN handoffs convert at a higher rate than WEAK or BAD?
- Which SDR has the highest proportion of BAD handoffs?
- Which AE is receiving the most WEAK or BAD handoffs?
- Is there a lead source that consistently produces lower-quality handoffs?
4. Return a handoff quality report:
- Summary: X handoffs audited, X CLEAN, X ACCEPTABLE, X WEAK, X BAD
- Conversion rate by grade (if outcome data provided)
- Top 3 patterns driving bad handoffs: by SDR, by lead source, by ICP criterion most often missed
- One recommended process change per pattern
5. Flag any AE who is accepting and working BAD handoffs — accepting bad leads is as much a problem as passing them.
</task>
<constraints>
- Grade based on what was verifiably true at handoff, not current state.
- Surface patterns, not individual blame.
- If outcome data isn't provided, focus on handoff quality only — don't speculate on conversion.
- The recommended process change must be specific: a qualification question, a routing rule, or a data field — not "improve qualification."
</constraints>What you'll get back
The situation: A RevOps lead audits 24 SDR-to-AE handoffs from last quarter. Three SDRs, two AEs, mixed lead sources (inbound form, outbound signal, event). She wants to know why AE acceptance rate dropped and where the process is breaking.
Output: 24 handoffs graded. CLEAN converts at 58%, BAD at 9%. One SDR driving 60% of bad handoffs. One lead source producing consistently weak contacts. One AE accepting leads they shouldn’t.
Handoff quality report — Q2
24 handoffs audited · 8 CLEAN · 7 ACCEPTABLE · 5 WEAK · 4 BAD
Timeframe: Q2. Validated via Lusha.
Conversion by grade
| Grade | Handoffs | Converted to opportunity | Conversion rate |
|---|---|---|---|
| CLEAN | 8 | 5 | 63% |
| ACCEPTABLE | 7 | 3 | 43% |
| WEAK | 5 | 1 | 20% |
| BAD | 4 | 0 | 0% |
CLEAN handoffs convert at 7× the rate of BAD ones. The 4 BAD handoffs produced no pipeline and consumed roughly 6 hours of AE time across the quarter.
Top 3 patterns driving bad handoffs
Pattern 1 — SDR: A.M. (3 of 4 BAD handoffs)
A.M. passed 8 leads this quarter — 3 graded BAD. In all three cases, the company was within ICP but the contact touched was an end user (SDR or AE individual contributor) rather than a decision-maker or economic buyer. Lusha confirms the decision-maker was available and reachable in all three accounts — the handoff happened at the wrong level.
Recommended process change: Add a required field to the handoff form: “Title and seniority of highest-level contact touched.” If the answer is below Manager level, the lead goes back to the SDR for one more touch before routing.
Pattern 2 — Lead source: event leads from [conference name]
5 of the 7 WEAK handoffs came from the event lead list. In each case the contact was confirmed within ICP company-wise, but Lusha returns them as end users or researchers — not decision-makers. Event lists systematically under-represent senior contacts because badge scans capture whoever walked by the booth.
Recommended process change: Event leads default to Grade B routing (SDR qualification call before AE) rather than Grade A direct routing. No event lead goes straight to an AE without a Lusha title validation first.
Pattern 3 — AE: J.R. accepting BAD handoffs without pushback
J.R. accepted and worked all 4 BAD handoffs — none converted. The other AE on the team rejected 2 of the same lead type. Accepting a BAD handoff isn’t just an SDR problem — an AE who accepts bad leads creates a false signal that the qualification bar was met.
Recommended process change: AEs complete a 3-field handoff acceptance form before working a lead: ICP confirmed Y/N, contact seniority level, signal present Y/N. If any field is N, the lead routes back to the SDR queue rather than sitting in the AE’s pipeline stalling.
Handoff data from pasted CRM export. Contact validation via Lusha connector, May 19.
Why use Lusha in Claude
Handoff audits done from CRM data tell you what the SDR logged. They don’t tell you whether what was logged was accurate. Lusha in Claude validates what was actually true at handoff — was the company in ICP, was the contact a real decision-maker, was there a signal — and grades each handoff against the evidence rather than the rep’s notes. The conversion-by-grade table is what turns the audit into a process argument: a 63% vs 0% conversion difference between CLEAN and BAD handoffs is a number that justifies a process change. The AE acceptance flag is the output that gets left out of most handoff reviews — accepting a bad handoff is as much a process failure as passing one.
Data drawn from 300M+ verified contacts under GDPR, CCPA, SOC 2, ISO 27701, ISO 31700, and TRUSTe.
FAQ
How many handoffs do I need to run this?
At least 15 to see meaningful patterns. Under 15 the grade distribution is too small to draw conclusions about individual SDRs or lead sources. The prompt will flag if the sample is too small for a specific pattern.
Won't this create tension with the SDR team?
The output surfaces patterns, not blame. “3 of A.M.’s 8 handoffs were end-user level contacts” is a coaching conversation, not a performance review. The recommended process change is structural — a new required field, a routing rule — not “tell A.M. to do better.”
What if I don't have outcome data?
The handoff grading still runs — you get the grade distribution and the pattern analysis without the conversion rates. That’s still useful for identifying which SDRs and sources are producing weak handoffs before the outcome data catches up.
Should I run this every quarter?
Yes. Once a quarter gives you a clean comparison period and enough handoffs to find patterns. Running it more frequently produces noise. Running it less frequently means process problems compound before you catch them.
How is this different from the inbound lead grading prompt?
The lead grading prompt runs before routing — it’s a gate. This prompt runs after the quarter — it’s an audit. Use the grading prompt to prevent bad handoffs. Use this one to understand the pattern of the ones that got through anyway.
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