The MQL Handoff That Isn’t Working
Northwind Analytics is a 180-person B2B SaaS company selling a $24K annual subscription. Marketing generates about 1,200 marketing-qualified leads per quarter. Sales accepts only 38% of them, the median time to first touch is 31 hours, and 22% of accepted leads never receive a call or email. Pipeline created from MQLs is down 15% year over year. Marketing says sales ignores good leads; sales says most MQLs are students, competitors, or tiny accounts. The current SLA states only that sales will follow up within 24 hours. There is no agreed definition of accepted versus rejected, no rejection reason taxonomy, no closed-loop reporting, and no consequence for missing the commitment. The CRO wants a redesigned SLA within 60 days and has ruled out adding headcount. The CRM has lead status fields, but 40% of rejected leads have no reason recorded. The sales ops analyst can pull lead source, firmographic, activity, and opportunity data.
1. Before rewriting the SLA, what evidence would you gather to determine whether the problem is lead quality, follow-up discipline, or both? Explain how each data point changes your recommendation.
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Start by separating quantity, quality, and behavior. Pull a 90-day sample of MQLs and segment by source, company size, industry, and score. For each segment, measure contact rate within 5 minutes, 1 hour, and 24 hours; acceptance rate; rejection reason, even if free text; conversion to opportunity; and lost reason. Compare accepted-but-never-contacted leads with contacted leads.
If contacted leads convert at a reasonable rate but contact rate is low, the issue is follow-up discipline and routing. If contact rate is high but conversion is near zero for a segment, it is targeting or qualification. If both are weak, you need segmentation and a service-level model, not a single blanket SLA. Interview six reps and two marketing managers to understand what accepted means in practice. Check rep capacity: 1,200 MQLs per quarter may be manageable, but uneven routing will hurt speed-to-lead. The 40% missing rejection reasons is itself a finding: you cannot improve quality without a closed-loop reason taxonomy. Bring a baseline to the SLA workshop so the debate is about evidence, not blame.
2. Design the core terms of a two-way SLA. Specify commitments from marketing, from sales, definitions, response-time tiers, and the governance cadence. Justify each design choice.
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Make the SLA two-way and segmented. Marketing commits to define MQL with explicit fit and intent criteria; pass only records with company, role, source, and score; route by territory and segment; and respond to sales rejection feedback within two business days. Sales commits to contact accepted MQLs within one business hour for enterprise, four hours for mid-market, and 24 hours for self-serve; log disposition; use a required rejection reason from a controlled list; and update opportunity stage within one business day of a buyer interaction.
Define accepted as a record that meets ICP and has a named contact with a business email. Define rejected with reasons such as no fit, no authority, competitor, student, duplicate, or bad data. Add a weekly 30-minute pipeline council with marketing, sales, and RevOps reviewing acceptance rate, speed-to-lead, rejection reasons, and conversion by source. Escalate two consecutive weeks of missed commitments to the CRO, not to individual reps. Segmentation prevents a 24-hour promise from being meaningless for enterprise. A rejection taxonomy creates the feedback loop. A council fixes systemic issues rather than public shaming.
3. What leading and lagging indicators would you report at 30 and 60 days, and how would you avoid the SLA becoming a blame exercise?
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At 30 days, report leading indicators: median speed-to-lead by segment, percentage of accepted leads contacted within SLA, rejection reason completion rate, and acceptance rate by source. At 60 days, add lagging indicators: MQL-to-opportunity conversion, opportunity creation, and pipeline coverage by segment.
Avoid blame by reporting at the team and segment level, not rep leaderboards, and pair every miss with a root cause. If speed-to-lead improves but conversion does not, revisit targeting. If rejection reasons cluster around no budget, adjust qualification. Publish a one-page scorecard and review it in the pipeline council. Success at 60 days is not 100% compliance; it is a visible improvement in contact rate and a reason taxonomy marketing can act on. Also track unintended effects: are reps cherry-picking easy leads, and are enterprise leads aging? If so, adjust routing or capacity.