pricing-strategist

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English

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Chinese

pricing-strategist

定价策略工具

Purpose

用途

Help Commercial, Product Marketing, and CMO functions answer three questions at the pricing-design moment:

Which pricing model fits this product + customer + market? (subscription seat-based, usage-based, value-based, freemium, hybrid)
What does the customer actually pay before it feels too expensive? (Van Westendorp PSM on WTP survey responses)
How should we package this into tiers? (Good / Better / Best — with anti-pattern detection)

The skill recommends a model and a range. The human picks the number, owns the trade-offs, and runs the GTM.

协助商业部门、产品营销团队及首席营销官在定价设计阶段解答三个核心问题：

哪种定价模型适配当前产品+客户+市场？（基于订阅席位、基于使用量、基于价值、免费增值、混合模式）
客户在觉得价格过高前实际愿意支付多少？（基于WTP调研反馈的Van Westendorp PSM分析）
如何将产品包装为不同层级？（基础/进阶/高级套餐，并检测反模式）

该工具仅推荐模型和价格区间，具体定价数值由人工决策，同时需自行权衡利弊并负责上市推广（GTM）。

When to use

使用场景

Launching a new SaaS / API / AI tool and choosing the first pricing model
Revisiting pricing after 18+ months of GTM data (model shift, not just price increase)
Designing or redesigning tier packaging (Good/Better/Best, Bronze/Silver/Gold)
You have Van Westendorp survey data and want the optimal price range
A board / exec is asking "what should we charge?" and you need the structured answer
You suspect your packaging has anti-patterns (decoy tier, feature dump, no upgrade trigger)

Do not use for:

Per-deal discount approval →
```
deal-desk
```
Strategic CMO positioning, brand, category creation →
```
c-level-advisor/cmo-advisor
```
Whole-company revenue strategy →
```
c-level-advisor/cro-advisor
```
Technical-sale enablement →
```
business-growth/sales-engineer
```

推出新SaaS/API/AI工具并选择首个定价模型时
积累18个月以上GTM数据后重新评估定价（调整模型，而非仅涨价）
设计或重新设计套餐层级（基础/进阶/高级、青铜/白银/黄金等）
拥有Van Westendorp调研数据，需确定最优价格区间时
董事会/高管询问“我们该定价多少？”，需给出结构化答案时
怀疑现有套餐存在反模式（诱饵层级、功能堆砌、无升级触发点等）时

请勿用于：

逐单折扣审批 →
```
deal-desk
```
首席营销官的战略定位、品牌打造、品类创建 →
```
c-level-advisor/cmo-advisor
```
全公司营收战略 →
```
c-level-advisor/cro-advisor
```
技术销售赋能 →
```
business-growth/sales-engineer
```

Workflow

工作流程

Step 1 — Assess customer context

步骤1 — 评估客户背景

Fill

assets/pricing_brief_template.md

(≈ 20 min). Capture: industry, deal size avg, customer count, value drivers, adoption curve, consumption pattern (seat / usage / value / hybrid), competitor models.

填写

assets/pricing_brief_template.md

（约20分钟），记录：行业、平均交易规模、客户数量、价值驱动因素、采用曲线、消费模式（席位/使用量/价值/混合）、竞品模型。

Step 2 — Pick the pricing model

步骤2 — 选择定价模型

Run

scripts/pricing_model_picker.py --input brief.json --profile saas --output markdown

. Output ranks 5 models by fit-score 0-100 with trade-offs. Decision logic is deterministic: low usage variance + high seat-attach → subscription wins; power-law usage + variable customer value → usage-based wins.

运行

scripts/pricing_model_picker.py --input brief.json --profile saas --output markdown

。输出结果会按适配度得分（0-100）对5种模型进行排序，并说明各模型的权衡点。决策逻辑为确定性：使用量差异小+席位关联度高→订阅模式胜出；使用量呈幂律分布+客户价值差异大→基于使用量的模式胜出。

Step 3 — Validate WTP with Van Westendorp PSM

步骤3 — 用Van Westendorp PSM验证支付意愿

If you have survey data (≥ 4 questions per respondent: too cheap / bargain / getting expensive / too expensive), run

scripts/wtp_analyzer.py --input survey.json --output markdown

. Output: 4 intersection points (OPP, IDP, PMC, PME) and the Range of Acceptable Prices.

PSM gives a range, not the price. See

references/van_westendorp_methodology.md

for common misinterpretations.

若拥有调研数据（每位受访者需回答至少4个问题：太便宜/划算/开始变贵/太贵），运行

scripts/wtp_analyzer.py --input survey.json --output markdown

。输出内容包括4个交叉点（OPP、IDP、PMC、PME）及可接受价格区间。

PSM仅提供价格区间，而非具体定价。关于常见误解可参考

references/van_westendorp_methodology.md

。

Step 4 — Design packaging

步骤4 — 设计套餐

Run

scripts/packaging_designer.py --input features.json --profile saas --output markdown

. Output: 3-tier Good/Better/Best assignment with anti-pattern flags (decoy tier, feature dump, no upgrade trigger, Bronze loss leader, Enterprise no-anchor).

运行

scripts/packaging_designer.py --input features.json --profile saas --output markdown

。输出结果为基础/进阶/高级三层套餐分配方案，并标记反模式（诱饵层级、功能堆砌、无升级触发点、青铜层级亏本引流、企业级无锚点等）。

Step 5 — Decide

步骤5 — 决策

Take model + range + packaging into the pricing committee. Skill does not commit the number — you do.

将模型+价格区间+套餐方案提交至定价委员会。该工具不会确定最终价格——此决策由人工完成。

Scripts

脚本

```
scripts/pricing_model_picker.py
```
— 5-model fit scorer (subscription / usage / value / freemium / hybrid)
```
scripts/wtp_analyzer.py
```
— Van Westendorp PSM implementation
```
scripts/packaging_designer.py
```
— Good/Better/Best tier designer with anti-pattern detection

All scripts: stdlib only.

--help

and

--sample

work on all three.

```
scripts/pricing_model_picker.py
```
— 5种模型适配度评分工具（订阅/使用量/价值/免费增值/混合）
```
scripts/wtp_analyzer.py
```
— Van Westendorp PSM实现工具
```
scripts/packaging_designer.py
```
— 基础/进阶/高级套餐设计工具，带反模式检测

所有脚本仅依赖标准库。三个脚本均支持

--help

和

--sample

参数。

References

参考资料

```
references/saas_pricing_canon.md
```
— Skok, Tunguz, Campbell, Ramanujam, BVP, Shevlin, Stanford GSB
```
references/van_westendorp_methodology.md
```
— original 1976 paper, NMS refinement, Conjoint.ly, Sawtooth, ESOMAR, Lipovetsky, Decision Analyst
```
references/packaging_anti_patterns.md
```
— ProfitWell, OpenView, BVP vertical SaaS, Ramanujam, Poyar, SaaS Capital

```
references/saas_pricing_canon.md
```
— Skok、Tunguz、Campbell、Ramanujam、BVP、Shevlin、斯坦福商学院相关内容
```
references/van_westendorp_methodology.md
```
— 1976年原始论文、NMS优化方案、Conjoint.ly、Sawtooth、ESOMAR、Lipovetsky、Decision Analyst相关资料
```
references/packaging_anti_patterns.md
```
— ProfitWell、OpenView、BVP垂直SaaS、Ramanujam、Poyar、SaaS Capital相关内容

Assumptions

假设前提

Pricing decisions are joint: Commercial owns the model + tier shape, Product owns the features-per-tier, Finance owns the discount envelope, Legal owns the contract.
Van Westendorp PSM is a directional tool. N ≥ 30 minimum, N ≥ 100 preferred. Below 30, the script emits a sample-size warning.
"Value-based pricing" requires a measurable customer value driver (revenue lift, cost saved, time recovered). If you can't measure it, don't pick value-based.
Industry profiles tune defaults — they don't override your data.
This is a decision-support skill, not a price oracle. Output is a model + range, never the number.

定价决策是跨部门协作的结果：商业部门负责模型+层级框架，产品部门负责各层级功能配置，财务部门负责折扣范围，法务部门负责合同条款。
Van Westendorp PSM是方向性工具。样本量最低要求N≥30，推荐N≥100。若样本量低于30，脚本会发出样本量警告。
“基于价值的定价”需要可衡量的客户价值驱动因素（收入提升、成本节约、时间节省）。若无法衡量，请勿选择该模式。
行业配置文件会调整默认值，但不会覆盖用户提供的数据。
这是决策支持工具，而非定价预言机。输出内容为模型+区间，绝不会是具体数值。

Anti-patterns

反模式

Recommending a specific number. This skill emits a model and a range. Final price is a human commercial decision involving deal-desk policy, competitive intel, and strategic intent that this skill cannot know.
Using PSM with N < 30. Statistical noise dominates. The script warns; respect the warning.
Treating PSM as "the price." PSM gives a Range of Acceptable Prices (RAP) and an Optimal Price Point (OPP). Test the range in market, don't anchor on a single intersection.
Picking value-based pricing without a measurable value metric. Without instrumentation to show customer ROI, value-based collapses into "whatever they'll pay" — which is just bad usage-based pricing.
Designing tiers before picking a model. Tier structure depends on the model. Run pricing_model_picker first.
Packaging "feature dumps" into the Best tier. If Best has 3x the features for 2x the price, customers buy Better and never upgrade. See
```
packaging_anti_patterns.md
```
.
Hidden usage-based pricing inside subscription tiers. "Up to 100k API calls/mo, then $X per 1k" disguised as a "Pro tier" is two pricing models in one. Customers notice. Pick one.
Confusing this skill with deal-desk. Pricing strategy = the menu. Deal-desk = approving discounts off the menu. Different decision, different cadence, different owner.

推荐具体价格数值：该工具仅输出模型和区间。最终定价是人工商业决策，涉及该工具无法获取的交易台政策、竞争情报和战略意图。
使用样本量N<30的PSM分析：统计噪声占主导。脚本会发出警告，请务必重视。
将PSM结果视为“最终定价”：PSM提供可接受价格区间（RAP）和最优价格点（OPP）。需在市场中测试该区间，而非锚定单一交叉点。
选择基于价值的定价但无可衡量的价值指标：若无工具展示客户投资回报率（ROI），基于价值的定价会退化为“客户愿意付多少就收多少”——这只是劣质的基于使用量的定价。
先设计层级再选择模型：层级结构依赖于定价模型。请先运行pricing_model_picker。
在高级套餐中堆砌功能：若高级套餐拥有3倍功能但仅定价2倍，客户会选择进阶套餐且从不升级。详情见
```
packaging_anti_patterns.md
```
。
在订阅层级中隐藏基于使用量的定价：“每月最多10万次API调用，超出后每千次收费X美元”伪装成“专业版”，实则包含两种定价模型。客户会注意到这一点，请选择单一模型。
混淆该工具与交易台工具：定价策略=菜单，交易台=批准菜单外的折扣。二者是不同的决策类型、不同的频率、不同的负责人。

Distinct from

与其他工具的区别

deal-desk — per-deal discount approval, MEDDIC, deal scoring. Operates daily on existing pricing.
c-level-advisor/cmo-advisor — strategic positioning, brand, category. Pricing strategist consumes positioning as input, doesn't generate it.
c-level-advisor/cro-advisor — full-funnel revenue strategy, comp plans, territory design. Pricing strategist is one input to CRO.
business-growth/sales-engineer — technical sale, POC scoping. Sales engineering operates after pricing is set.

deal-desk — 逐单折扣审批、MEDDIC、交易评分。基于现有定价每日运作。
c-level-advisor/cmo-advisor — 战略定位、品牌、品类创建。定价策略工具将定位作为输入，而非生成定位。
c-level-advisor/cro-advisor — 全漏斗营收战略、薪酬计划、区域设计。定价策略工具是首席营收官（CRO）的输入之一。
business-growth/sales-engineer — 技术销售、POC范围界定。销售赋能在定价确定后开展。

Forcing-question library (Matt Pocock grill discipline)

强制问题库（Matt Pocock 质询准则）

Walked one at a time by

/cs:grill-commercial

or the orchestrator. Recommended answer + canon citation per question. Never bundled.

"Is your customer paying for outcomes, seats, or usage?" Recommended: outcomes (value-based) if you can measure them; usage if marginal cost is variable; seats only if usage is roughly flat per user. Canon: Ramanujam 2016 (Monetizing Innovation) — Mistake #1 of 9: seat-based pricing on a usage-variable product caps TAM at ~20% of WTP.
"Do you have a measurable value metric, or are you guessing?" Recommended: instrument the value metric BEFORE going to market with value-based pricing. Canon: Patrick Campbell / ProfitWell research — value-based without instrumentation collapses into bad usage-based pricing.
"What's the variance in customer usage across your top decile vs. median?" Recommended: variance > 10x → usage-based wins; variance < 3x → subscription wins; in between → hybrid with usage overage. Canon: Kyle Poyar (Growth Unhinged) — high-variance products lose 60%+ of revenue on flat-rate plans.
"What's your competitor's pricing model, and why are you choosing the same or different?" Recommended: surface the differentiation hypothesis explicitly. Identical pricing = identical value claim. Canon: David Skok (For Entrepreneurs) — pricing is a positioning signal.
"What sample size do you have for WTP analysis, and is it segmented?" Recommended: N≥30 per segment for PSM, N≥100 for conjoint. Canon: van Westendorp 1976 / Sawtooth Software methodology — sub-30 PSM is statistical noise.
"What's the ONE feature that forces a tier upgrade?" Recommended: every Better and Best tier needs a single non-negotiable upgrade trigger. Canon: Ramanujam (Monetizing Innovation) — Mistake #4: tiers with no clear differentiator make 70% of customers pick the cheapest.

Walk depth-first. Lock 1-3 before opening 4-6. After all 6 are answered, invoke

pricing_model_picker.py

→

wtp_analyzer.py

→

packaging_designer.py

in sequence.

由

/cs:grill-commercial

或编排器逐一提出。每个问题配有推荐答案及权威引用。请勿批量提问。

“你的客户是为成果、席位还是使用量付费？” 推荐答案：若可衡量则选成果（基于价值）；若边际成本可变则选使用量；仅当每位用户的使用量大致稳定时才选席位。权威引用：Ramanujam 2016（《创新变现》）——9大错误之第1条：对使用量可变的产品采用基于席位的定价，会将总可寻址市场（TAM）限制在支付意愿（WTP）的约20%。
“你有可衡量的价值指标，还是在猜测？” 推荐答案：在推出基于价值的定价前，先部署价值指标监测工具。权威引用：Patrick Campbell / ProfitWell研究——无监测工具的基于价值定价会退化为劣质的基于使用量定价。
“顶级10%客户与中位数客户的使用量差异是多少？” 推荐答案：差异>10倍→基于使用量的模式胜出；差异<3倍→订阅模式胜出；介于两者之间→带使用量超额收费的混合模式。权威引用：Kyle Poyar（《增长失控》）——高差异产品采用固定费率计划会损失60%以上的收入。
“竞品的定价模型是什么，你为何选择相同或不同的模型？” 推荐答案：明确阐述差异化假设。相同的定价=相同的价值主张。权威引用：David Skok（《致创业者》）——定价是定位信号。
“你的支付意愿分析样本量有多大，是否经过细分？” 推荐答案：PSM分析每个细分群体的样本量需≥30，联合分析需≥100。权威引用：van Westendorp 1976 / Sawtooth Software方法论——样本量不足30的PSM分析属于统计噪声。
“促使客户升级套餐的核心功能是什么？” 推荐答案：每个进阶和高级套餐都需要一个明确的非必需升级触发点。权威引用：Ramanujam（《创新变现》）——第4大错误：无清晰差异化的层级会导致70%的客户选择最便宜的套餐。

按深度优先顺序提问。先确认1-3题，再开启4-6题。所有6题回答完毕后，依次调用

pricing_model_picker.py

→

wtp_analyzer.py

→

packaging_designer.py

。