Deal Desk

Deal Desk

End-to-end deal-desk operational practice: charter, approval thresholds, deal-review packet design, routing automation, velocity analysis, and the governance that turns "every deal is a snowflake" into "we close non-standard deals in 48 hours predictably."

This skill is provider-agnostic: works whether your CRM is Salesforce, HubSpot, Pipedrive, or homegrown. The patterns and decisions transfer.

When to use this skill

| Situation | Skill applies |

|-----------|---------------|

| Starting a deal-desk function from scratch | Yes — start with charter design |

| Reviewing existing deal-desk for slowness / inconsistency | Yes — use scripts/deal_velocity_analyzer.py + bottleneck patterns |

| Defining who can approve what discount / term | Yes — use approval threshold matrix + scripts/discount_authority_router.py |

| Building the deal-review packet template | Yes — see deal-review packet section + scripts/deal_review_packet.py |

| Approving / declining a specific deal | Use the packet generator + approval router |

| Setting pricing strategy | Use business-growth/pricing-strategy first |

| Forecasting / measuring pipeline | Use business-growth/revenue-operations |

| Negotiating an individual contract | Pair with business-growth/contract-and-proposal-writer |

What deal desk does (and doesn't)

Does:

• Review non-standard deals: discounts beyond rep authority, custom legal terms, custom SLAs, multi-product bundles, payment terms outside policy
• Make the approval decision (or route to the right approver)
• Structure the deal: pricing, terms, ramp schedule, success criteria
• Maintain the deal-desk policy — what's standard, what needs approval
• Track deal velocity (time from request → decision → signature)
• Produce evidence for finance / audit (every concession traceable)

Doesn't:

• Set the published pricing (that's pricing strategy)
• Negotiate with the customer (that's the sales rep / AE)
• Close the sale (that's the rep + customer success)
• Run the order-to-cash workflow (that's billing / RevOps)
• Replace legal review (legal is one of the approvers, not the function itself)

A clean deal-desk = the lubricant. Without it, every non-standard deal turns into a multi-week negotiation among engineering / product / legal / finance / executive. With it, those people are consulted by deal desk as needed and the rep gets a yes/no in days.

Deal-desk charter (template)

Every deal desk needs a written charter. Use this template:

purpose:
  Deal Desk reviews, approves, and structures non-standard deals to enable
  sales to close faster while keeping commercial / legal / financial risk
  within company tolerance.

scope:
  In-scope:
    - All deals > $X ARR
    - All deals with discount > Y%
    - All deals with non-standard terms (custom SLAs, custom legal language,
      payment terms beyond Net 30, ramp deals, multi-year discounts > 12 months
      of standard, bundles spanning multiple product lines)
    - All renewals with > 20% expansion or > 10% contraction
    - All deals to enterprise (>1000 employees) or regulated industries
  Out-of-scope:
    - Self-serve / PLG transactions
    - Standard renewals within auto-renewal terms
    - Trial extensions < 30 days
    - Add-ons < $X per existing customer

sla:
  - Standard deal-desk review (no exec approval needed): 1 business day
  - Deal needing CFO/CRO approval: 2 business days
  - Deal needing CEO/Board approval: 5 business days
  - Legal-only review (no commercial concession): 2 business days

intake_format:
  Sales submits via [Salesforce form / CPQ tool / Slack form]. Required fields:
    - Customer name + size + industry
    - Product(s) + ACV
    - Requested deviation from standard (specific list)
    - Justification (competitor situation, customer constraint, strategic value)
    - Standard-pricing total + requested total
    - Contract length + payment terms
    - Implementation / SLA requirements

decision_inputs:
  - Customer LTV estimate
  - Strategic value (logo, reference, vertical foothold)
  - Risk (credit, compliance, integration)
  - Margin impact

outputs:
  - Approve / decline / counter
  - If approve: signed approval packet with terms, conditions, expiration date
  - If counter: list of negotiable items + non-negotiables
  - If decline: reasoning + alternatives

team:
  Deal-desk lead: <name>
  Deal-desk analysts: <names>
  Standing approvers: CRO, CFO, General Counsel, VP Product (escalation paths)
  Consulted as-needed: Engineering Lead, Security Lead, Customer Success Lead

metrics:
  - Median time-to-decision (target: 1 business day)
  - Decision distribution (% approved, % declined, % countered)
  - Discount-on-discount %  (deals where requested discount was further negotiated up)
  - Discount % vs ACV (correlation; outliers reviewed monthly)
  - Win rate of deal-desk-approved deals
  - Concession follow-through (did the customer keep their side?)

See references/deal-desk-charter-and-process.md for the full charter template, including sub-charters per region, intake form spec, and the standard SLAs.

Approval threshold matrix

The matrix defines: for each deal characteristic (discount %, contract length, custom term type), who can approve it.

Standard matrix template

| Deal characteristic | Rep | Sales Manager | Director | VP Sales | CRO | CFO | CEO |

|---------------------|-----|---------------|----------|----------|-----|-----|-----|

| Discount 0-10% | ✓ | | | | | | |

| Discount 10-20% | | ✓ | | | | | |

| Discount 20-30% | | | ✓ | | | | |

| Discount 30-40% | | | | ✓ | | | |

| Discount 40-50% | | | | | ✓ | | |

| Discount > 50% | | | | | | | ✓ |

| ACV > $250k | | ✓ | | | | | |

| ACV > $1M | | | | ✓ | | | |

| ACV > $5M | | | | | | | ✓ |

| Multi-year > 12mo standard | | ✓ | | | | | |

| Non-standard payment terms | | | | | | ✓ | |

| Custom SLA / penalties | | | | (with CCO) | | | |

| Custom legal language | | | | | | | (Legal must concur) |

| MSA red-line on liability cap | | | | | | | (Legal must concur) |

| Most-favored-nation clause | | | | | | ✓ | |

| Acceptance criteria / payment-on-acceptance | | | | | | ✓ | |

| Multi-product / cross-BU bundle | | | (each BU lead approves) | | | | |

| Whitelabel / OEM rights | | | | | | | ✓ |

Customize per company stage, ACV distribution, and authority preference (some orgs want CRO at 30%, others delegate further down).

Stacking rule

When multiple non-standard items apply, the highest required approver applies. A $1M deal at 25% discount with custom SLA needs VP Sales (ACV) AND Director (discount) AND VP Sales+CCO (custom SLA) → effectively requires VP Sales sign-off + CCO + Legal concurrence.

Use scripts/discount_authority_router.py --deal deal.yaml to compute the required approvers for any deal.

See references/approval-thresholds-and-routing.md for the full matrix design guide, regional variants, escalation paths, and routing automation patterns.

The deal-review packet

Every non-standard deal gets a packet. Without it, approvers ask the same questions repeatedly and decisions take days instead of hours.

Standard packet structure

# Deal Review: <Customer Name>

## Summary
- Customer: <name, size, industry>
- ACV: $<amount>
- Discount %: <%> (vs standard $<list-price>)
- Contract: <length>, <payment terms>
- Decision needed by: <date>

## Standard vs Requested
| Item | Standard | Requested | Delta |
|------|----------|-----------|-------|
| ACV  | $X       | $Y        | -Z%   |
| Term | 12mo     | 36mo      | +24mo |
| Payment | Net 30 | Net 60   | +30d  |
| SLA  | 99.5%    | 99.9%     | +0.4% |
| Liability cap | 1x fees | 2x fees | +1x |
| Termination for convenience | No | Yes (90d) | New |

## Justification
- Why customer wants this: <competitor situation, budget cycle, etc.>
- Why we're considering: <strategic value, logo, vertical>
- Customer leverage: <alternatives they have>

## Financial impact
- Standard ARR: $X
- Discounted ARR: $Y (Z% off)
- Net new gross margin: $A (with cost overlay)
- Projected LTV with this discount: $B
- Discount payback if customer renews: <years>

## Strategic value
- Logo value: <high/medium/low — reasoning>
- Reference value: <will they be a public ref? case study?>
- Vertical foothold: <do we want this vertical?>
- Competitive replacement: <who are we displacing?>

## Risk
- Credit risk: <score / payment history>
- Compliance risk: <regulated? data residency?>
- Technical fit risk: <integration complexity>
- Concession follow-through: <are they likely to honor commitments?>

## Required approvers (per matrix)
- [ ] Director: <name>
- [ ] VP Sales: <name>
- [ ] CFO: <name>
- [ ] Legal: <name>

## Recommendation (from deal desk)
<Approve / Counter / Decline> — with reasoning

## Conditions if approved
- Discount expires <date>
- Customer must agree to: <reference call, case study, etc.>
- Customer agrees this is single-instance (not precedent)
- Payment must close by <date>

Use scripts/deal_review_packet.py --deal deal.yaml to generate this packet from a deal spec.

Velocity analysis

A slow deal desk strangles sales. Measure and tune.

Key metrics

| Metric | Healthy | Warning |

|--------|---------|---------|

| Median time-to-decision | < 1 business day | > 3 days |

| 90th percentile time-to-decision | < 3 business days | > 7 days |

| % of deals waiting on a single approver > 24h | < 10% | > 30% |

| Deals stuck > 7 days | 0 | > 5 |

| Sales rep satisfaction with deal desk (NPS) | > 50 | < 0 |

| % approved (high approval rate may mean threshold too low) | 60-80% | > 95% or < 40% |

| Discount-on-discount: deals where customer negotiated up after deal-desk approval | < 10% | > 30% |

Run scripts/deal_velocity_analyzer.py --deals deals.csv to compute these from a CRM export.

Common bottlenecks

| Bottleneck | Diagnosis | Fix |

|------------|-----------|-----|

| Single approver bottleneck (one person on everything) | Routing matrix concentrated authority | Delegate; add back-ups; raise thresholds |

| Legal review takes a week | Legal sees every deal | Standard MSA + pre-approved clause library; Legal only on deviations |

| Engineering needed for SLA review | Custom SLAs every time | Publish standard SLA tiers; only deviations route to eng |

| Approval cycle back-and-forth | Packet missing key info | Use the standard packet template; reject incomplete submissions |

| Long executive lag | Exec doesn't have context for every deal | Weekly deal review meeting for batch decisions on smaller items |

| Sales submits incomplete packets | Reps don't know what to include | Intake form that enforces required fields |

| No SLA enforcement | Deals sit in queue with no urgency | Publish + report SLA; aging dashboard visible to leadership |

See references/discount-and-concession-playbook.md for the discount/concession patterns: legitimate reasons for each concession type, how to evaluate, alternatives to discounting, and how to structure performance-based discounts.

Clarify First

Before generating the deal-desk artifact, confirm these inputs. If any is unknown or vague, ASK — do not assume:

• [ ] Task type — standing up the desk (charter + matrix) vs reviewing one deal (packet) (determines which template you produce)
• [ ] Deal specifics: ACV + requested deviation — discount %, term, payment, custom SLA/legal (sets the Standard-vs-Requested table and which approvers the matrix requires)
• [ ] Approval authority structure — who can approve what (Rep→Manager→Director→VP→CRO/CFO/CEO + Legal) (drives the threshold matrix and routing)
• [ ] Strategic value + risk — logo/reference value, credit/compliance risk (drives the packet's justification and recommendation)

Stop rule: ask only the 2-3 that most change the output. If the user says "just draft it," proceed and list your assumptions at the top of the artifact.

End-to-end workflows

Workflow: A rep submits a non-standard deal

• Rep submits via intake form: customer + ACV + requested deviation + justification
• Deal desk triages within 4h: assigns analyst, validates packet completeness, requests missing info
• Deal desk reviews within 1 business day: financial impact, strategic value, risk
• Deal desk recommends approve / counter / decline
• Route to approver(s) per matrix (auto via scripts/discount_authority_router.py)
• Approver decides within SLA
• If approved: packet signed off, conditions sent to rep with expiration
• If countered: deal desk works with rep on alternative structure
• If declined: clear reason + alternatives sent to rep + customer

Workflow: Stand up a deal desk from scratch

• Draft charter with sales, finance, legal sign-off
• Build the approval matrix — interview key stakeholders, document existing tribal knowledge
• Design intake form — CRM-integrated or Slack-bot
• Hire / appoint deal desk lead + analyst(s)
• Train sales — what triggers deal desk, what info is needed, what to expect
• Soft launch — manual operation for 1 month; track metrics
• Iterate — refine thresholds, automate routing, publish SLAs
• Quarterly review — metrics, threshold adjustments, charter updates

Workflow: Audit deal-desk performance

• Export deals from CRM for the period (CSV with deal IDs, stages, approval timestamps, discounts)
• Run velocity analyzer — compute medians, percentiles, aging, approver bottlenecks
• Sample 10-20 deals for qualitative review (was the packet complete? were conditions met?)
• Identify patterns — are certain reps over-discounting? are certain customers getting MFN clauses inappropriately?
• Propose adjustments — to charter, thresholds, intake form, training
• Present to leadership with metrics + recommendations

Workflow: Quarterly threshold review

Thresholds drift. Quarterly:

• Pull discount distribution for the quarter
• Identify outliers — deals where discount % was anomalous for ACV / segment
• Compare approval rates by threshold — if 30%+ discount deals get approved 95%+ of the time, the threshold is too low
• Compare win rates by discount band — does deeper discount actually improve win rate, or does it just give up margin?
• Adjust thresholds based on data + market shift
• Publish new matrix with effective date; train sales

Anti-patterns

• Deal desk as bottleneck. SLAs published but ignored; deals stack up; sales builds workarounds. Measure + enforce SLAs.
• Deal desk that always says yes. Approval rate > 95% means thresholds are too low — you're rubber-stamping. Tighten or raise thresholds.
• Deal desk that always says no. Approval rate < 40% means policy is too strict OR sales doesn't understand it. Investigate root cause.
• No deal-desk policy. Every deal evaluated case-by-case. Inconsistent decisions; legal exposure; reps gaming the system.
• Concentrated authority. One person approves everything → bottleneck + bus factor. Delegate.
• Pricing strategy disguised as deal-desk policy. If 80% of deals need discounting, the published price is wrong. Fix pricing.
• Discount creep. Each deal raises the bar for the next; eventually published price is irrelevant. Track + reset.
• Concession with no quid pro quo. Customer asks for 20% discount; you give 20% discount. Always trade: 20% for case study, 20% for 3yr contract, etc.
• No expiration on quotes. Customer can come back in 6 months and demand the same terms. Always time-box (typically 30-60 days).
• Single-instance language never enforced. "This is a one-time exception" → next year the customer cites it as precedent.

Tooling outputs

| Script | Input | Output |

|--------|-------|--------|

| scripts/deal_review_packet.py | Deal spec YAML | Markdown deal-review packet with summary, financials, strategic value, risk, approver list, recommendation template |

| scripts/discount_authority_router.py | Deal spec YAML + approval matrix YAML | Required approver(s), routing order, escalation path, SLA-aware ordering |

| scripts/deal_velocity_analyzer.py | CSV of deals from CRM export | Median / p90 time-to-decision, aging dashboard, approver bottleneck identification, discount-on-discount analysis |

All scripts: stdlib only, argparse CLI, JSON or markdown output.

References

• deal-desk-charter-and-process.md — full charter template, intake form spec, SLA framework
• approval-thresholds-and-routing.md — matrix design, regional variants, escalation paths, automation patterns
• discount-and-concession-playbook.md — concession types, legitimate reasons, alternatives, performance-based structures

Related skills

• business-growth/pricing-strategy — sets the prices that deal desk enforces deviations from
• business-growth/revenue-operations — measures the pipeline; deal-desk metrics flow into RevOps dashboards
• business-growth/contract-and-proposal-writer — drafts the final contract once deal desk approves
• business-growth/channel-economics — channel deals have their own deal-desk patterns
• business-growth/partnerships-architect — partner-mediated deals route through both deal desk + partnerships
• business-growth/commercial-policy — the broader governance framework deal desk enforces
• sales-success/sales-engineer — provides technical validation in packet
• sales-success/sales-operations — owns CRM / forecast accuracy that deal desk feeds