Pricing Optimization

Pricing Optimization

Determine the price/tier structure that maximizes expected total value (ARR), and back the recommendation with proof at the standard of an empirically skeptical reader. The deliverable is: a ranked set of candidate grids, the revenue-maximizing grid, and evidence that the ranking survives hostile assumptions — plus an explicit statement of what only a live A/B test can settle.

Core principle

A model optimum is not an empirical optimum. The model narrows the search to the best candidate and proves dominated options are dominated. Report the ROBUST direction and Monte Carlo bands, never a false-precision single number. The final word on pricing is a live price test post-launch. Say so explicitly.

The 5-step method

1. Anchor to VERIFIED substitutes (do not skip, do not estimate)

Willingness-to-pay is bounded by what a buyer can switch to. For every buyer segment, find the feature+volume-equivalent competitor and record its current, observed price — pull it live, cite the source and observation date. Training- data prices rot fast (this market moved 30%+ in one quarter). Delegate the price gathering to the researcher agent or research_deep if many anchors are needed.

For each segment capture: reservation price R (max it will pay), best substitute price S, and a signed gap adjustment g (coverage/quality edge or penalty vs the substitute). The binding constraint is c = min(R, S + g).

2. Model capture per segment

Marginal cost per unit ≈ 0, so maximize total value == maximize ARR. Each segment routes to the cheapest tier whose features+volume satisfy its need; it buys with logistic probability around c (50% at price==c, falls off by the configured elasticity). This makes the optimum interior, not a band-cap artifact.

3. Grid-search the optimum

Hold the tier structure fixed (which tier first unlocks each capability) and sweep prices over defensible bands (bounded by competitor anchors). Report the revenue-max grid. Cross-check that it is a true interior maximum, not a search bug.

4. Adversarially falsify the ranking

Try to BREAK the conclusion. Monte Carlo with wide jitter on every assumption (segment sizes, R, S, g, elasticity, funnel). Report the share of hostile draws in which each alternative beats the proposed winner, and example assumption sets where it flips. A ranking that survives 50k hostile draws is robust.

5. Isolate the break-point

For the single anchor the conclusion most depends on, find the value at which the decision flips. State how wrong the verified anchor would have to be to change the answer ("the substitute would have to be 50% higher than its published price").

Then write model-free deductions: 1–2 proofs from verified prices + the substitution principle alone, independent of the simulation. These are the strongest arguments — they hold even if every modeling assumption is wrong.

How to run

The engine is config-driven. Build a JSON config (segments + candidate grids + search bands + jitter), then:

cd <skill-dir>
python3 scripts/pricing_model.py CONFIG.json --mc 4000 --adv 50000 \
  --breakpoint "GRID_A:GRID_B:segment_name:S"
python3 scripts/pricing_model.py --schema     # full config schema + field docs

A complete worked example ships at examples/wxa-vpn-2026-05.json (run it first to see the expected output shape). Copy it as a starting template for a new product.

Verify the engine itself is arithmetically correct (independent reimplementation proof — capture curve, tier selection, ARR accounting, conservation, grid-search-is-a-true-max) with:

python3 scripts/verify_model.py            # exits non-zero if any proof fails

Run this after any edit to pricing_model.py.

Depth modes

Parse $ARGUMENTS for --depth (default standard):

• quick — steps 1–3 only (anchors → model → grid search). One recommended grid.
• standard — all 5 steps. Full proof: grid search + MC + adversarial + break-point + deductive proofs.
• deep — standard, plus: per-segment break-point sweep on every anchor, a written proof artifact committed to the project's docs/, and a draft stakeholder recommendation (e.g. an email arguing a team off a dominated grid using the segment-loss numbers as evidence).

Workflow

1. Gather verified anchors (step 1). If >3 anchors needed, delegate to researcher.
2. Write the config. One segment per distinct buyer type; shares sum to 1.0. Cite every R/S with a _source field (provider + price + observation date).
3. Run the engine at the chosen depth. Confirm the grid search optimum is an interior max and the ranking holds under adversarial draws.
4. Write deductive proofs — model-free arguments from prices alone.
5. Report: ranked grids, the optimum, robustness evidence, the break-point, and an explicit "what only A/B testing can prove" caveat.
6. (deep) Commit the config + a verification write-up to docs/ as a proof artifact; optionally draft the stakeholder recommendation.

Honesty requirements (non-negotiable)

• Never present a model number as empirical fact. Always pair with MC bands.
• Always state the break-point — readers must know how fragile the conclusion is.
• Always include the "only A/B testing is rigorous proof" caveat.
• If an anchor is an estimate not a verified price, label it as such; flag the conclusion's sensitivity to it via the break-point.

See references/method.md for the economic model derivation and the WTP capture math.