A sales marketing SLA (service level agreement) defines what marketing delivers, what sales commits to in return, and how both teams measure success. Without it, you get predictable failure modes: marketing celebrates MQL volume while sales ignores the queue, SDRs reject half the list for vague reasons, and leadership discovers conversion problems only when pipeline misses forecast.

The highest-leverage clause in most B2B SLAs is speed-to-lead—the maximum time allowed between a qualified handoff and meaningful sales outreach. Research across B2B categories consistently shows that response delay erodes conversion. A lead contacted within five minutes behaves differently than one contacted tomorrow afternoon. Your SLA should specify:

• Clock start — when the timer begins (form submit, MQL status change, round-robin assignment, or CRM task creation).
• First meaningful touch — what counts as response (live call attempt, personalized email, or booked meeting—not an automated nurture enrollment alone).
• Business hours vs calendar hours — whether nights and weekends pause the clock or trigger escalation rules.
• Escalation path — who gets notified when SLA breach exceeds threshold (SDR manager, AE backup queue, RevOps alert).

Practical starting targets by motion:

Speed-to-lead only works when routing is deterministic. Round-robin with no backup, broken CRM assignment rules, and reps on vacation without coverage create breaches that look like performance problems but are system failures. RevOps should wire SLA timers to CRM workflows and surface breach rates on the same dashboard marketing and sales review—not buried in a report nobody opens.

FunnelWon builds SLA-backed handoffs as part of a broader RevOps foundation: lifecycle design, routing logic, and dashboards that show whether leads convert or stall before you scale spend to fix a leak downstream.

An SLA built on fuzzy lifecycle definitions is a contract both sides interpret differently. Before response-time targets or rejection workflows, marketing and sales must agree—in writing—what MQL (marketing qualified lead) and SQL (sales qualified lead) mean in your CRM.

MQL: marketing’s promise of fit and intent

An MQL should represent a contact that matches ICP criteria and demonstrated intent above baseline engagement. Document four components:

1. Firmographic fit — industry, company size, geography, tech stack, or other ICP filters scored or enforced at form level.
2. Behavioral threshold — specific actions (demo request, pricing page, multiple high-intent pages, webinar attendance with duration minimum).
3. Exclusions — competitors, students, job seekers, existing customers, and territories outside coverage.
4. Scoring vs hard rules — whether MQL is score-based (e.g., 100+ points) or rule-based (demo form = automatic MQL).

Marketing owns MQL volume and quality at creation. Sales owns whether that quality converts—but only if the definition was co-authored, not inherited from a marketing automation default.

SQL: sales acceptance of real opportunity potential

An SQL (or SAL—sales accepted lead—in some orgs) means sales reviewed the record and confirmed it warrants active pursuit. Document:

• Acceptance criteria — budget signal, timeline, authority, need confirmed (formally or via discovery call).
• Who can mark SQL — SDR after qualification call, AE on inbound, or manager override with reason code.
• Time limit on MQL review — sales must accept, reject, or recycle within X business days or record auto-recycles to nurture.
• Opportunity creation trigger — whether SQL automatically creates an opportunity or requires a separate stage.

The handoff artifact

Every MQL passed to sales should carry context sales can act on without opening five tabs:

• Source and campaign (preserved first-touch field)
• Form responses or chat transcript summary
• Pages viewed and content downloaded
• Known pain point or use case from form
• Assigned owner and SLA deadline timestamp

When MQL and SQL definitions live in one lifecycle map—signed by both functions—rejection rates become diagnostic instead of political. Pair definitions with funnel stage design so every status change maps to a reportable conversion metric.

Lead rejection is not failure—it is feedback—when reasons are structured, mandatory, and reviewed weekly. Unstructured “bad lead” notes create resentment. A defined rejection taxonomy tells marketing exactly what to fix: targeting, offers, forms, or scoring.

Build a closed-list of rejection reasons

RevOps should configure CRM picklists (required on reject) with ten to fifteen reasons max. Common categories:

• Fit — wrong industry, company too small/large, wrong geography, not ICP
• Intent — research only, no project, competitor shopping, student or personal email misuse
• Reachability — invalid phone/email, no response after defined attempt cadence
• Duplicate or existing relationship — already in pipeline, open opp, active customer
• Data quality — incomplete form, obvious test submission, bot traffic
• Timing — budget next fiscal year, project paused (recycle to nurture with date)

Each reason should map to an action:

Rejection rate guardrails

Track MQL rejection rate (rejected MQLs ÷ total MQLs passed to sales) and reason mix monthly. Healthy ranges vary by motion—inbound demo programs should see low fit rejections; content syndication may run higher. Alert when:

• Rejection rate spikes more than ten points week-over-week
• One reason dominates (often “no response” masking weak SLA compliance)
• Sales rejects before SLA first-touch attempt (process violation)

The recycle path

Rejections without recycle logic waste demand. Define when a lead returns to marketing automation, who owns re-engagement, and when sales can receive the same contact again. Timing-based rejects should auto-enroll in a dated nurture branch—not disappear from reporting.

Structured rejection feeds CRM hygiene: clean picklists, consistent lifecycle history, and dashboards that show whether marketing quality or sales follow-through broke down—not both teams guessing.

A sales marketing SLA survives only if one scorecard sits in the weekly operating rhythm. Not separate marketing dashboards and sales dashboards that disagree—one view with shared KPI definitions, owned by RevOps, reviewed by both leaders.

Core SLA metrics for the scorecard

Include metrics that connect handoff behavior to pipeline outcomes:

• MQL volume and MQL-to-SQL conversion rate — quality and acceptance
• SQL-to-opportunity rate — sales qualification effectiveness
• Speed-to-lead median and SLA breach rate — response discipline
• MQL rejection rate by reason — quality feedback loop
• Pipeline created from marketing-sourced leads — dollar or count, same source field leadership trusts
• Win rate and cycle time by source cohort — whether accepted leads become revenue

Sample weekly scorecard layout

Operating rules that keep the scorecard honest

1. One source of truth — CRM lifecycle timestamps, not spreadsheet exports.
2. Definitions linked in the dashboard header — MQL, SQL, and source fields documented inline.
3. Slice by channel and campaign — aggregate numbers hide broken programs.
4. Action column required — every red metric gets an owner and due date before meeting ends.
5. No attribution debates in SLA review — save model discussions for monthly; weekly is execution.

The scorecard connects to attribution and reporting but stays simpler: did we pass the right leads, did sales work them fast enough, and did pipeline result? When those three align, budget conversations get easier because both teams optimize toward the same outcome.

SLAs fail when they live in a Google Doc nobody opens after signing. Treat the sales marketing SLA as a living operating system: documented standards, automated enforcement where possible, and quarterly refinement based on scorecard evidence.

What the SLA document must include

• Lifecycle definitions (MQL, SQL, opportunity) with CRM field names
• Routing and ownership rules by segment, territory, and product line
• Speed-to-lead targets by lead type with clock-start and breach escalation
• Rejection reason picklist and recycle policies
• Attempt cadence before “unreachable” rejection (calls, emails, LinkedIn—specified)
• Shared scorecard metrics, targets, review cadence, and owners
• Exception process (enterprise named accounts, partner referrals, executive intros)

Enforcement without culture damage

Accountability works when breaches are visible and systemic issues get fixed:

• Automate SLA timers — CRM tasks, Slack alerts, or RevOps workflow on assignment.
• Report breach rate by rep and team — coaching conversation, not public shaming; investigate capacity first.
• Marketing SLA commitments too — lead delivery volume bands, data completeness scores, and campaign pause rules when rejection rate exceeds threshold.
• Quarterly SLA review — adjust targets when motion changes (new SDR team, ABM pivot, longer cycle).

Implementation timeline

Weeks 1–2: Workshop MQL/SQL definitions with marketing, sales, and RevOps; audit current rejection notes and speed-to-lead baseline.

Weeks 3–4: Publish one-page SLA; configure CRM picklists, routing, and SLA timestamps; build scorecard v1.

Weeks 5–8: Weekly scorecard reviews; fix top rejection reason and top breach cause before adding complexity.

Month 3+: Tie SLA metrics to planning—channel mix, SDR headcount, and nurture investment—using forecast inputs leadership already inspects.

SLAs do not create alignment—they expose whether lifecycle design and ownership were clear enough to align. When handoffs convert reliably, marketing scales demand with confidence and sales trusts the queue. FunnelWon helps B2B teams build that system through RevOps consulting: shared definitions, SLA-backed routing, rejection workflows, and scorecards that turn lead flow into predictable pipeline.