| name | pricing-pocket-waterfall |
| description | Build and analyze the pocket-price waterfall — every step where margin leaks between list price and final pocket. Backed by a deterministic Python script that takes line-item discount data and produces the per-customer + cohort waterfall + leak ranking. Loaded by the main pricing skill when the operator asks "my discounts are eating margin", "where is margin leaking", "pocket price", or "discount policy review." |
| user-invocable | false |
| allowed-tools | ["Read","Write","Bash"] |
Pricing Pocket Waterfall — sub-skill
The discount-leak detector. Headline price ≠ revenue. The waterfall
surfaces every step where margin leaks between list and pocket.
Activation
Routed here when operator says:
- "My discounts are eating margin"
- "Where is margin leaking?"
- "Pocket price"
- "Discount policy review"
- "Why is our effective price so much lower than list?"
The canonical waterfall
List price
- Ramp discount (first-year, "introductory")
- Volume discount (per-seat / per-unit breakpoints)
- Bundle discount (multi-product)
- Annual prepay discount
- Strategic / champion / "executive sponsor" discount
- Promotional / quarter-end close discount
- Off-invoice rebates (back-end)
- Credits / SLA penalties (rolling)
- Payment-term cost (NET-60 vs NET-30 = ~0.5-1.5% of revenue)
- Implementation discount (often "free")
= Pocket price
Pocket-price waterfall is run per-customer, then aggregated to the
cohort level.
Workflow
1. Gather the data
Required (operator provides or pulls from CRM/billing):
- For each customer (or representative sample of ~30):
- List price (per-tier, per-seat / per-unit)
- Cadence (monthly / annual)
- Each discount line-item with %, dollar value, and reason
- Payment terms
- Implementation / onboarding fees (and discounts)
- Credits / penalties applied in the year
If the operator says "we don't track that" — flag this as the first
finding. You can't manage what you can't measure.
2. Run the waterfall script
python3 scripts/pocket_price_waterfall.py \
--input customers.csv \
--output waterfall_report.json
The script:
- Reads each customer's line-item data
- Computes pocket price per customer
- Aggregates to cohort-level mean/median
- Ranks discount steps by total margin impact
- Identifies the top 3 leak sources
3. Diagnostic questions
After the script runs, ask:
- What's the largest single leak step? (Often: ramp discount or
strategic discount.)
- Is the leak step tied to commercial value? (Annual prepay
tied to cash flow = real concession. "Strategic discount" with no
commercial concession = pure leak.)
- Is the leak step bounded by policy or sales-rep discretion?
(Discretion = leak source; policy with thresholds = managed.)
- What % of revenue moves through the largest leak? (>20% = top
priority for policy work.)
- Are the leaks PSP-correlated? (One PSP gets bigger discounts
= a pricing-vs-WTP issue for that segment.)
4. Build the discount policy
For each leak step that's >5% of revenue, output a policy:
Discount step: {name}
Current state:
- Frequency: applied to {N}% of customers
- Mean discount: {X}%
- Total $ leak: ${Y}/year
- Tied to commercial concession: {Yes / No}
- Bounded by policy: {Yes / No}
Recommended policy:
- Approval threshold: 0-{N}% rep / {N}-{M}% manager / {M}+% VP
- Required commercial concession: {longer term / prepay / case study / etc.}
- Documentation requirement: {what gets logged}
- Reason codes: {list of accepted reasons}
- Forbidden reasons: {"close the deal" alone is not a reason}
5. Quantify the recovery
Estimate margin recovery if policy is implemented:
- Pre-policy mean discount on this step: X%
- Post-policy mean discount estimate: Y%
- Cohort revenue affected: $Z
- Estimated recovery: (X - Y) × Z
Typical first-year recovery from documenting + bounding discount
policy: 3-8% of total revenue. (Hermann Simon empirical baseline.)
6. Output the waterfall report
Pocket-Price Waterfall — {company}
─────────────────────────────────────────────────────────────────
LIST PRICE (cohort weighted average): ${X}/customer/year
Waterfall steps (ranked by leak magnitude):
1. Ramp discount: -${Y1}/customer (Z1% of customers, mean A1%)
2. Strategic discount: -${Y2}/customer (Z2%, A2%)
3. Volume discount: -${Y3}/customer (Z3%, A3%)
...
POCKET PRICE (cohort weighted average): ${P}/customer/year
LEAK FROM LIST: ${L} (M%)
TOP 3 PRIORITY POLICIES:
1. {Policy for highest leak}
2. {Policy for second leak}
3. {Policy for third leak}
ESTIMATED FIRST-YEAR RECOVERY: ${R} (S% of revenue)
PSP-CORRELATED LEAKS:
- {PSP X} discounted Y% more than median — investigate
Self-check
Script reference
scripts/pocket_price_waterfall.py:
- Input: CSV with columns
customer_id, list_price, cadence, discount_steps_json, payment_terms, implementation_fee, credits_applied
- Output: JSON with per-customer breakdown + cohort aggregates + leak ranking
- No dependencies. Pure stdlib. Python 3.8+.
References
../../pricing/references/pricing-framework.md — the waterfall in context../pricing-tribunal/SKILL.md — for any policy change that affects >10% of customers../pricing-quarterly-review/SKILL.md — where the waterfall is re-run quarterly
