Pricing Strategy

Overview

Pricing is the only marketing mix element that generates revenue — all others are costs. This skill covers five pricing approaches (cost-plus, value-based, competitive, penetration, skimming) plus psychological pricing techniques. The right approach depends on the product lifecycle stage, competitive landscape, and customer price sensitivity.

When to Use

Trigger conditions:

• User setting prices for a new product
• User evaluating whether current pricing is optimal
• User asks "how much should we charge?" or "why are our margins low?"
• User needs to choose between pricing models (subscription vs one-time, freemium vs premium)

When NOT to use:

• For comprehensive financial analysis → use financial ratios or DCF
• For customer segmentation → use STP
• For cost structure analysis → use Value Chain

Framework

IRON LAW: Price Communicates Positioning

Price is not just economics — it's a signal. Lowering price to compete
can permanently reposition a brand as "cheap." Raising price without
value justification creates distrust.

Every price change must be evaluated through BOTH a financial lens
(margins, volume) AND a positioning lens (what does this price say about us?).

Step 1: Understand the Three Price Anchors

Every pricing decision sits between three constraints:

Step 2: Choose a Pricing Approach

Step 3: Apply Psychological Pricing Techniques

Step 4: Validate with Price Sensitivity Analysis

Before committing:

• Van Westendorp: Survey-based method — ask customers "at what price is this too expensive / too cheap / a bargain / getting expensive?"
• Gabor-Granger: Show a price, ask if they'd buy. Vary the price across respondents.
• A/B test: If possible, test two price points with real transactions

Step 5: Monitor and Adjust

After launch:

• Track price elasticity: % change in demand / % change in price
• Monitor competitive response: Did competitors match your price?
• Watch customer perception: Did the price signal what you intended?

Output Format

# Pricing Strategy: {Product/Service}

## Three Anchors
- Cost floor: {$X} (based on: {cost breakdown})
- Competitor reference: {$X range} (competitors: {list})
- Customer ceiling: {$X} (based on: {value metric})

## Recommended Approach
**{Approach name}** — {rationale}

## Price Point
- Recommended price: {$X}
- Expected margin: {X%}
- Positioning signal: {what this price says about the brand}

## Psychological Techniques Applied
- {technique}: {how applied}

## Sensitivity Analysis
| Price Point | Est. Volume | Revenue | Margin | Risk |
|------------|------------|---------|--------|------|
| {low} | {high vol} | {$X} | {X%} | {positioning risk} |
| {recommended} | {med vol} | {$X} | {X%} | {balanced} |
| {high} | {low vol} | {$X} | {X%} | {volume risk} |

## Monitoring Plan
- Review frequency: {monthly/quarterly}
- Key metrics: {elasticity, competitive response, perception}

Examples

Correct Application

Scenario: Pricing a new SaaS project management tool for SMBs in Taiwan

Three anchors:

• Cost floor: NT$150/user/month (server + support costs)
• Competitors: Asana NT$350/user, Monday.com NT$300/user, Trello Free-NT$170/user
• Customer ceiling: NT$400/user (based on 30 customer interviews — value of time saved)

Approach: Value-based with decoy pricing

• Basic: NT$199/user/month (limited features — the decoy)
• Pro: NT$299/user/month (full features — the target)
• Enterprise: NT$499/user/month (with SSO, audit logs — anchor)

Why: Pro at NT$299 looks like great value vs Enterprise at NT$499, and much better than Basic at NT$199 for only NT$100 more.

Incorrect Application

What went wrong:

• Set price at cost + 20% (NT$180/user) without checking competitor reference or customer ceiling → Left NT$120+/user of value on the table. Customer would have paid NT$299.
• Cut price from NT$299 to NT$149 to match a new budget competitor → Signaled "we're a budget tool now," causing premium customers to leave. Violates Iron Law: price communicates positioning.

Gotchas

• Cost-plus is a fallback, not a strategy: Cost-plus only makes sense when you can't measure value or differentiate. In most cases, value-based pricing captures more margin.
• Penetration pricing requires a plan to raise prices: If you start low, you need a clear path to profitability. "We'll raise prices later" without a mechanism (switching costs, network effects) is wishful thinking.
• Discounts are addictive: Frequent discounts train customers to wait for sales. Use selectively and time-limit them.
• B2B vs B2C psychology differs: B2B buyers evaluate ROI rationally (though with organizational politics). B2C buyers are more susceptible to psychological pricing. Calibrate techniques to the buyer.
• Free is not a price — it's a category change: Moving from paid to free (or vice versa) changes the product category in the customer's mind. The shift from "paid product" to "free with ads" is a complete repositioning.

References

• For Van Westendorp and Gabor-Granger methodology details, see references/price-sensitivity.md
• For SaaS-specific pricing models, see references/saas-pricing.md

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Supporting file: examples/sample_scenario.md

Example: 台灣健身 App 從免費轉付費定價策略

Scenario

公司： FitFlow（台灣新創，2023年成立） 情境： FitFlow 是一款針對台灣用戶的居家健身 App，目前擁有 12 萬名免費用戶。 創辦人 Amy Chen 準備在 2026 年 Q2 推出付費版，來自投資人的壓力要求 2026 年底 前達到 MRR NT$500 萬。

用戶對話：

"我們有 12 萬個免費用戶，想開始收費。競品 MindBody 一個月要 NT$590，但我覺得 我們的用戶不會付那麼多。我打算定 NT$99/月，應該夠低了吧？"

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Analysis

Step 1：三個定價錨點

成本底線（Cost Floor）

→ 成本底線：NT$35/用戶/月（付費版）

競爭對手參考（Competitor Reference）

→ 市場區間：NT$168–599，主流付費點落在 NT$200–350

顧客天花板（Customer Ceiling）

Amy 手上有一份 800 人問卷（Van Westendorp 四問法）：

→ 可接受區間：NT$59–450，最佳定價帶 NT$199–280

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Step 2：選擇定價方式

Amy 原本想定 NT$99，但三個錨點顯示問題：

• NT$99 已低於顧客「太便宜會起疑」的底線 NT$59 邊緣
• 競品 Keep 定 NT$168，NT$99 會被市場解讀為「次等品」
• 更關鍵：Iron Law 違反 — NT$99 會將 FitFlow 永久定位為折扣 App，未來漲價困難

選定策略：Freemium + 價值導向定價（Value-Based）

FitFlow 的核心差異化：AI 個人化課表 + 本土教練影片（非國際化通用內容）。 這是可量化的感知價值，應用價值定向定價，而非跟隨競品或成本加成。

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Step 3：方案設計（誘餌效應）

設計三層方案，讓 Pro 方案 成為明顯最佳選擇：

心理定價技巧：

• Charm pricing：NT$199、NT$399（非 200/400）
• 誘餌效應：Elite NT$399 讓 Pro NT$199 顯得非常划算
• 年繳折扣：Pro 年繳 NT$1,788（相當於 NT$149/月，省 25%）→ 提升 LTV

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Step 4：敏感度分析

假設付費轉換率情境（現有 12 萬免費用戶）：

*年繳用戶佔 40% 時，月均 ARPU 略升（現金流）

→ 結論：NT$199 定價正確，但 NT$500 萬 MRR 目標需要靠新用戶獲取（MAU 成長）， 不是靠降價。Amy 原本的 NT$99 方案即使全部轉換，MRR 上限也只有 NT$1,188,000。

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Result

# Pricing Strategy: FitFlow 付費版（2026 Q2 上市）

## Three Anchors
- Cost floor: NT$35/用戶/月（主機 + 影片儲存 + 客服）
- Competitor reference: NT$168–599（主流帶 NT$199–350）
- Customer ceiling: NT$280（Van Westendorp 最佳定價帶上限）

## Recommended Approach
**Value-Based + Decoy Effect（三層 Freemium）**
FitFlow 的 AI 個人化是可感知的差異化價值，不應用成本加成或跟競品削價。

## Price Points
- Free：NT$0（保留免費用戶基礎，維持網路效應）
- Pro：NT$199/月（目標方案）；年繳 NT$1,788
- Elite：NT$399/月（錨點 + 高端教練服務）

預期 Pro 毛利：(199 - 35) / 199 = **82%**
定位訊號：「專業但親民的本土健身夥伴」，非折扣品牌

## Psychological Techniques Applied
- Charm pricing：199、399（感知低於整數）
- Decoy effect：Elite NT$399 讓 Pro 顯得划算
- 年繳折扣：NT$1,788（鎖定用戶、提升 LTV）

## Sensitivity Analysis
| Price Point | Est. 轉換率 | 付費用戶 | MRR | 風險 |
|------------|------------|---------|-----|------|
| NT$99（Amy 原提案） | 15% | 18,000 | NT$1,782,000 | 品牌定位損傷，難以回頭 |
| NT$199（建議） | 5–8% | 6,000–9,600 | NT$1.4–2.4M | 需搭配用戶成長計畫 |
| NT$299 | 3% | 3,600 | NT$1,076,000 | 轉換率太低，天花板受限 |

## Monitoring Plan
- 審查頻率：每月（上線後前三個月每兩週）
- 關鍵指標：Free→Pro 轉換率、Pro→Elite 升級率、年繳比例、Churn rate
- 警示線：月 Churn > 8% → 調查是否為價格問題或功能缺口

對 Amy 的一句話建議： NT$99 不是「夠低」——它是一個會損傷品牌的訊號，而且即使 100% 轉換也達不到 投資人目標。NT$199 配合積極的用戶成長才是正確路徑。

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Supporting file: references/price-sensitivity.md

Price Sensitivity Analysis Methods

Three validated research methods for measuring customer willingness-to-pay before committing to a price point.

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Van Westendorp Price Sensitivity Meter (PSM)

What It Measures

PSM identifies an acceptable price range and an optimal price point by asking customers four questions at four psychological thresholds.

The Four Questions

Ask each respondent about your specific product:

1. Too cheap — "At what price would this product be so cheap that you'd question its quality?"
2. Cheap/bargain — "At what price would this be a bargain — great value for the money?"
3. Expensive/getting pricey — "At what price would this start to feel expensive, though you might still buy it?"
4. Too expensive — "At what price would this be too expensive — you definitely wouldn't buy it?"

Analysis: The Four Cumulative Curves

Plot cumulative % of respondents at each price on the x-axis:

The Four Intersection Points

Acceptable Price Range: LAP to UAP
Target: OPP if maximizing adoption; IDP if matching market perception

Worked Example: SaaS project management tool for Taiwan SMBs

Survey of 80 respondents. After collecting all four thresholds per respondent, count cumulative % at each NT$ price point:

Reading the intersections:

• Too Cheap ∩ Too Expensive → ~NT$249 (OPP)
• Cheap ∩ Expensive → ~NT$295 (IDP)
• Too Cheap ∩ Expensive → ~NT$199 (LAP)
• Cheap ∩ Too Expensive → ~NT$365 (UAP)

Interpretation: The acceptable range is NT$199–NT$365. A price of NT$299 sits close to the IDP — customers perceive it as "normal market price." The OPP at NT$249 minimizes resistance but may sacrifice margin relative to what customers would pay.

Decision: The SKILL.md example chose NT$299 for the Pro tier — within range, above OPP, close to IDP. Consistent.

Sample Size

PSM Limitations

• Measures stated preference, not revealed preference (actual purchase behavior)
• Respondents imagine buying; real WTP is often lower
• Does not model volume — no demand curve output
• Less reliable for products respondents have never bought before

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Gabor-Granger Method

What It Measures

Gabor-Granger builds a demand curve by measuring purchase intent at multiple price points. Output: estimated revenue-maximizing price.

Protocol

Show each respondent a single price (or a descending sequence), then ask:

"At NT$X per month, how likely would you be to subscribe?"
Scale: Definitely would / Probably would / Probably not / Definitely not

Assign binary purchase intent: "Definitely would" + "Probably would" = buyer

Two design variants:

Sequential descending is standard; start 30–50% above your expected ceiling.

Analysis: Demand Schedule

Revenue Index Calculation

Revenue Index = Price × % Would Buy

Revenue-maximizing price: NT$249–NT$299 range. NT$299 is defensible given IRON LAW — it maintains premium positioning while staying near peak revenue.

Price Elasticity from Gabor-Granger

Point Elasticity = (ΔQ / Q) / (ΔP / P)

Between NT$249 and NT$299:

ΔQ = (52% − 64%) / 64% = −18.75%
ΔP = (299 − 249) / 249 = +20.08%

Elasticity = −18.75% / 20.08% = −0.93

Elasticity of −0.93 means inelastic in this range — a 20% price increase reduces demand only 19%. This supports pricing at NT$299 over NT$249.

Between NT$399 and NT$499:

ΔQ = (18% − 31%) / 31% = −41.9%
ΔP = (499 − 399) / 399 = +25.1%

Elasticity = −41.9% / 25.1% = −1.67

Elasticity of −1.67 means elastic above NT$399 — demand drops faster than price rises. Avoid pricing above NT$399.

Gabor-Granger Limitations

• Hypothetical bias: stated intent overestimates actual purchase 30–50% (rule of thumb: treat "Probably would" as only 50% likely)
• No quality signal: unlike PSM, does not capture "too cheap" concerns
• Sequential anchoring: in descending sequences, respondents who refuse NT$499 are primed to see NT$299 as reasonable

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A/B Price Testing (Revealed Preference)

When to Use

Use when you have live traffic and can randomize price exposure. This is the only method measuring actual willingness to pay (not stated intent).

Minimum Viable Setup

Control:   Price = NT$249, shown to 50% of eligible visitors
Treatment: Price = NT$299, shown to 50% of eligible visitors

Measure: Conversion rate (sign-ups / unique visitors)
Run duration: Until statistical significance (see below)

Required Sample Size

For a two-sample proportion test:

n = 2 × (Z_α/2 + Z_β)² × p̄(1−p̄) / (p1−p2)²

Where:
  Z_α/2 = 1.96  (α = 0.05, two-tailed)
  Z_β   = 0.84  (power = 80%)
  p̄     = average conversion rate across both groups
  p1, p2 = expected conversion rates per group

Example: Baseline conversion 5%, expect NT$299 reduces it to 4%

p̄ = (5% + 4%) / 2 = 4.5%
(p1 − p2)² = (0.05 − 0.04)² = 0.0001

n = 2 × (1.96 + 0.84)² × 0.045 × 0.955 / 0.0001
n = 2 × 7.84 × 0.04298 / 0.0001
n ≈ 6,739 per group

You need ~6,700 visitors per variant — ~13,400 total — before reading results.

Revenue per Visitor Comparison

Do not optimize for conversion rate alone. Compare revenue per visitor:

RPV = Price × Conversion Rate

Control:   NT$249 × 5.0% = NT$12.45 / visitor
Treatment: NT$299 × 4.2% = NT$12.56 / visitor  ← +0.9% RPV despite lower conversion

Even though conversion dropped, revenue per visitor is higher at NT$299. This is the correct metric.

A/B Testing Pitfalls

• Peeking: checking results before reaching required n inflates false positive rate. Commit to the sample size before starting.
• Segment leakage: if the same customer sees both prices (different sessions, shared devices), results are contaminated. Use user-ID randomization, not session-ID.
• Temporal confounds: do not run over a promotional period, major holiday, or competitor campaign.
• Legal / ethical exposure: in some markets (EU consumer law, Taiwan Consumer Protection Act), showing different prices to different users for the same product requires disclosure. Confirm with legal before running.

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Choosing a Method

Sequencing when budget allows:

1. PSM → find acceptable range and OPP (early concept stage)
2. Gabor-Granger → build demand curve within that range (pre-launch)
3. A/B test → validate with real transactions (post-launch)

Each method anchors the next: PSM narrows the range, Gabor-Granger identifies the revenue-optimal point, A/B test confirms with revealed behavior.

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Quick Reference: Formulas

Price Elasticity of Demand:
  E = (% change in quantity) / (% change in price)
  E < −1  → elastic   (price-sensitive; revenue falls when price rises)
  E > −1  → inelastic (price-insensitive; revenue rises when price rises)
  E = −1  → unit elastic (revenue unchanged)

Revenue Index (Gabor-Granger):
  RI = Price × Purchase Intent %

Revenue per Visitor (A/B):
  RPV = Price × Conversion Rate

Sample Size (A/B, two-proportion):
  n per group = 2(Z_α/2 + Z_β)² × p̄(1−p̄) / (p1−p2)²
  Shortcut: use n≈16,000 / (p1−p2)² for α=0.05, power=80%, p̄≈0.05

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Supporting file: references/saas-pricing.md

SaaS Pricing Models

SaaS pricing differs from one-time product pricing in one fundamental way: the customer pays repeatedly, so the price must justify renewal at every billing cycle, not just at the moment of purchase. This shifts the pricing question from "what is this worth?" to "what is this worth every month?"

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The Five SaaS Pricing Models

These are not mutually exclusive. Most mature SaaS products combine them — e.g., tiered plans where each tier has a per-seat price up to a seat cap, then usage-based overages.

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Model Selection Decision Framework

Is your product's value tied to a countable resource (API calls, storage, messages)?
  YES → Usage-based is honest and aligns incentives. Consider hybrid (flat base + usage overage).
  NO ↓

Do your customers have very different usage levels (1-seat freelancer vs 200-seat enterprise)?
  YES → Per-seat or tiered. Flat-rate will either overprice small customers or underprice large ones.
  NO ↓

Is viral/word-of-mouth adoption important to your growth model?
  YES → Freemium. Calculate whether you can afford the free tier (see Freemium Math below).
  NO → Tiered with a clear feature ladder.

Per-Seat vs Usage-Based: The Core Tradeoff

Per-seat aligns with how buyers budget (headcount is a known quantity). It creates a ceiling on expansion revenue per account — you can only earn more by adding seats.

Usage-based aligns revenue with value delivery. It expands naturally as customers grow. But it creates revenue volatility: a customer who reduces usage next month costs you MRR without churning. Forecasting is harder.

Rule of thumb: if your product's value is primarily about enabling a person (communication, project management, writing), go per-seat. If it's primarily about processing something (data, transactions, API calls), go usage-based.

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Tier Architecture: Designing the Feature Ladder

The goal of a tier structure is to route each customer segment to the tier that maximizes their perceived value while capturing a fair share of it.

The Three-Tier Template

Most SaaS products use three named tiers. The names themselves communicate positioning:

Starter / Basic / Free     ←  Acquisition tier (low or zero cost)
Pro / Growth / Standard    ←  Primary revenue tier (target 60–70% of paying customers)
Business / Team / Plus     ←  Expansion tier (upsell target)
Enterprise                 ←  Optional fourth tier, sold not bought

Feature Gating Principles

Gate by outcome, not by feature count. Customers shouldn't feel punished — they should feel that the higher tier solves a bigger problem.

The limit gate pattern: Starter gets X of something; Pro gets unlimited. Customers feel the limit naturally as they grow.

Example — project management SaaS:

Starter  NT$99/user/month   → 5 active projects, no automation
Pro      NT$299/user/month  → Unlimited projects, automation, reporting
Business NT$499/user/month  → Everything in Pro + SSO, admin controls, audit log

Applying the Decoy Effect (from parent SKILL.md)

The decoy tier is usually Starter — priced close enough to Pro that upgrading feels obvious, but limited enough that power users feel genuinely constrained.

The Business tier serves as a price anchor for Pro: NT$299 feels reasonable when NT$499 exists. This is why Enterprise pricing is often not shown publicly — showing an extreme anchor on the pricing page cheapens lower tiers.

Decoy test: Ask "would a reasonable new customer look at Starter, intend to stay on Starter, but then feel nudged to upgrade within 30-90 days?" If yes, the decoy is working. If Starter is good enough for most customers forever, you've mis-tiered.

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Freemium Math

Freemium is not "free pricing" — it's a customer acquisition channel. The economics work only if the cost to serve free users is covered by the conversion revenue.

Freemium Unit Economics Formula

Freemium CAC = (Cost to serve N free users for 1 month) / (N × conversion rate)

Compare this to your blended CAC from paid channels.

Worked example:

• Monthly server + support cost per free user: NT$15
• Free users in cohort: 1,000
• Conversion rate (free → paid within 90 days): 4%
• Average paid plan: NT$299/month

Monthly cost to serve 1,000 free users = NT$15,000
Converted users = 1,000 × 4% = 40
Freemium CAC = NT$15,000 / 40 = NT$375 per converted customer

If your paid acquisition CAC (Google Ads, sales) is NT$2,000, freemium at NT$375 CAC is excellent. If it's NT$200 (strong SEO, viral loop), freemium may not be worth the infrastructure cost.

Conversion Rate Benchmarks

Industry averages are noisy and product-specific, but rough guidance:

Warning: A 1% conversion rate is not a failure if free users are also a referral engine. Model the referral-adjusted CAC.

The Freemium Trap

If free tier is too generous, customers have no reason to upgrade. Common signs:

• Free users stay on free for 12+ months without hitting limits
• Support volume from free users exceeds revenue contribution
• Paid features are "nice to have" but free tier covers 80% of use cases

Fix: tighten the free tier limits or deepen the paid feature differentiation — do not lower the paid price as the first response.

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Expansion Revenue: The SaaS Pricing Multiplier

One-time product pricing ends at the sale. SaaS pricing compounds through expansion revenue — additional revenue from existing customers via upsell, cross-sell, and seat growth.

Net Revenue Retention (NRR)

NRR measures whether your revenue base is growing or shrinking from existing customers alone, independent of new customer acquisition.

NRR = (MRR start + Expansion MRR − Contraction MRR − Churned MRR) / MRR start × 100%

Why this matters for pricing design: a pricing model that enables NRR > 100% means the company can grow revenue even with zero new customers. This is only possible if the pricing architecture expands with customer success — per-seat grows as teams hire; usage-based grows as customers process more; tiered grows as customers hit limits and upgrade.

Designing for Expansion

Build expansion triggers into the pricing structure before launch:

The worst outcome is a customer who grows significantly (more users, more usage, more revenue for them) but pays you the same flat fee. Value-based pricing (from parent SKILL.md) is the solution: tie the price to the metric that scales with customer success.

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SaaS Price Sensitivity: Willingness to Pay by Segment

B2B SaaS buyers think in ROI, not sticker price. The question they're asking is not "is NT$299 cheap?" but "does this save us more than NT$299/month per user?"

The Value Metric Frame

For each customer segment, identify the value metric: the unit of outcome your product delivers.

When you can quantify the value metric, you can calculate the value-to-price ratio (V/P). Target V/P ratio of 10:1 for B2B SaaS: if the product saves NT$3,000/user/month, NT$299/user/month is credible. NT$1,500/user/month may stall sales even if technically justified.

SMB vs Enterprise Pricing

Do not use the same pricing page for both. SMBs self-serve and need transparent pricing with instant credit card checkout. Enterprise needs custom quotes, procurement processes, and negotiation room.

Typical structure:

SMB tiers (NT$X–NT$Y/user/month):  published, self-serve
Enterprise:                         "Contact sales" — custom contract, annual commitment

Enterprise deals typically carry a 30–60% premium over the equivalent per-seat cost on the public pricing page, justified by SLA, onboarding, custom integrations, and security review.

Do not publish Enterprise pricing. The moment you anchor Enterprise at NT$999/user, you've created a ceiling expectation. Sales needs room to price NT$1,500–NT$2,000/user for large, complex accounts.

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Annual vs Monthly Billing: The Cash Flow Decision

Always offer annual billing with a discount. The standard discount is 15–20% (equivalent to 2 free months).

Annual plan value = Monthly × 12 × (1 − discount)
Example: NT$299/month × 12 × 0.83 = NT$2,978/year (vs NT$3,588 monthly)

Why this matters for pricing design:

1. Cash flow: annual billing provides capital upfront
2. Churn reduction: annual customers churn at 1/4 the rate of monthly customers (they have to make an active renewal decision once, not twelve times)
3. Commit signal: annual customers are more invested; they onboard more thoroughly

Make annual the default selection on the pricing page, with monthly as the alternative. The psychological default drives a meaningful % of customers to annual without reading the fine print.

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Worked Example: Pricing a B2B SaaS for Taiwan SMBs

Product: AI meeting notes tool for Taiwanese SMBs
Market: Companies with 5–100 employees, Chinese-speaking users

Step 1: Three Anchors

Cost floor: NT$80/user/month
  (AI inference cost + storage + customer support allocation)

Competitor reference:
  Otter.ai: ~NT$450/user/month (USD pricing, converted)
  Fireflies.ai: ~NT$380/user/month
  Local alternative: none at feature parity

Customer ceiling: NT$600/user/month
  (Based on 25 SMB interviews: 1 meeting/day × 20 min saved × NT$300/hr wage = NT$1,800/user/month value)
  (Target V/P ratio 3:1 for SMB — they're more price-sensitive than enterprise)

Step 2: Tier Design

Starter   Free
  → 3 meetings/month, 30-min limit
  → Freemium acquisition channel

Pro       NT$249/user/month (annual: NT$2,490/year)
  → Unlimited meetings, full transcripts, action item extraction
  → Primary revenue tier

Team      NT$399/user/month (annual: NT$3,990/year)
  → Pro + team analytics, shared workspace, admin controls
  → Upsell for managers / ops teams

Decoy check: Starter is genuinely limited (3 meetings/month is enough for a trial, not a workflow). Pro at NT$249 is well below the NT$380–450 competitor range, creating a clear value signal. Team at NT$399 looks affordable next to competitors' base pricing.

Step 3: Freemium Economics Check

Cost to serve 1 free user/month: NT$80 (at cost floor)
Target conversion rate: 6% (meeting tool category, product-led growth)
Conversion timeline: 90 days

For a cohort of 500 free users:
  Monthly cost = 500 × NT$80 = NT$40,000
  90-day total cost = NT$120,000
  Converted = 500 × 6% = 30 users
  Freemium CAC = NT$120,000 / 30 = NT$4,000

Acceptable if LTV > NT$4,000 × 3 = NT$12,000
  Pro plan annual = NT$2,490; need ~5 year retention to justify
  → Freemium may not work at NT$80/user cost floor unless conversion exceeds 10%
  → Consider limiting free tier to reduce serving cost (e.g., 1 meeting/week, lower quality transcript)

This math is the reason many SaaS products degrade free tier over time — not anticompetitive behavior, but unit economics forcing a rebalance.

Step 4: NRR Design

Expansion levers built into pricing:
  - Adding team members → more seats → linear MRR growth
  - Team plan upgrade when manager joins → seat × price increase
  - Annual upgrade (15% discount but 12× upfront commitment)

Target NRR: 105%
  Achieved if: average account grows from 2.5 seats to 2.8 seats within 12 months
  (This is a modest assumption — 0.3 additional users per account over a year)

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Common SaaS Pricing Mistakes

Setting per-seat price below cost floor after applying team discounts. If you offer "50% off for teams of 10+", model the new effective per-seat price against your cost structure. A NT$299 plan discounted 50% for 10 seats = NT$1,495/month. At NT$80/user cost floor, cost is NT$800/month. Margin holds. But if cost floor is NT$160/user (larger AI inference), cost = NT$1,600 > revenue.

Annual discount too deep. 30–40% annual discount is common in hyper-competitive markets but destroys ARR predictability. 15–20% is standard; 25% maximum unless you have evidence it materially improves conversion rate.

Free tier that cannibalizes paid. If free users can export data, integrate with third-party tools, or invite unlimited collaborators, there's no forcing function to upgrade. Gate these specifically on paid tiers.

Charging per seat for a product used by one person on behalf of many. A social media scheduling tool used by one marketing manager for a 50-person company should not be priced per employee — it should be priced per workspace or per connected social account (usage metric).

Ignoring price-tier cognitive load. More than 4 public tiers creates analysis paralysis. If you need more segments, hide them behind "Compare all plans" or use an interactive pricing calculator rather than a static table.