/pricing-and-wtp

name: pricing-and-wtp
description: Models willingness to pay using Van Westendorp price sensitivity analysis, desire-premium multipliers, category benchmarks, and market_insights monetization signals. Recommends pricing model, freemium conversion estimate, and annual/monthly strategy.

<!-- version: 0.2.0 | outputs: memory/ideas/<slug>/pricing.json -->

Skill: pricing-and-wtp

Purpose

Determine what users would actually pay, not what the developer hopes to charge. Anchor WTP to pain intensity, desire strength, competitive pricing, and real monetization signals from market_insights — never to the cost of building. Pricing is the single decision with the highest leverage on unit economics: a $1/mo difference at 1,000 users is $12K/year.

Input

• Idea slug
• `memory/ideas/<slug>/idea.md` (app concept, key features)
• `memory/ideas/<slug>/user_extraction.json` (pain intensity)
• `memory/ideas/<slug>/competitors.json` (competitive pricing, pricing models in use)
• `memory/ideas/<slug>/desire_scores.json` (desire strength, primary/secondary drivers)
• `memory/market_insights/<niche>-*-<YYYY>-<MM>.md` (trend data — **use all available platform files**)

Using Market Insights

Trend analysis files provide monetization reality-checks that desk research alone cannot:

| Field | How it informs pricing | |---|---| | `monetization_evidence` | The strongest signal. If competitors are visibly making money (in-app purchases mentioned in reviews, subscription pricing in App Store listings, sponsored content), the market has validated WTP. If monetization_evidence is empty across all platforms, WTP is unproven — discount aspirational price and flag risk. | | Platform narratives — Reddit | Users discuss pricing directly: "I'd pay for X if...", "Y is overpriced because...", "$Z/mo is too much for just..." These are raw WTP signals. Extract any price mentions. | | Platform narratives — App Store | Review complaints about pricing reveal the ceiling: "Great app but not worth $X/mo." Review praise about pricing reveals the floor: "Amazing value at $X." | | Platform narratives — TikTok | Creator recommendations often mention price as part of the hook ("this FREE app...", "worth every penny of $X"). Signals whether freemium or premium positioning resonates with the audience. | | `trend_velocity` | Rising-fast markets can support premium pricing (early adopters pay more). Declining markets face price pressure (users comparison-shop harder). |

Pricing Models

| Model | Best for | Risk | Indie sweet spot | |---|---|---|---| | **Freemium → subscription** | Habit-forming apps with daily use, feature-gated value | Conversion friction; most users stay free forever | $3–$8/mo or $20–$50/yr | | **Subscription only (paywall)** | High-value, immediately obvious ROI, professional tools | High churn risk; must prove value fast | $5–$15/mo | | **One-time purchase** | Utilities, tools with clear one-time value, privacy-focused apps | No recurring revenue; must rely on new users | $3–$10 | | **Consumables / credits** | Variable usage (AI generations, exports, premium content) | Unpredictable revenue; usage may decline | $1–$5 per credit pack | | **Freemium + consumables** | Apps where core is free but power usage costs (AI, storage) | Complex to balance free vs. paid tiers | Free tier + $2–$10 packs | | **Lifetime deal** | Launch phase only — use for early adopter cash and reviews | Destroys LTV; never use as primary model | $20–$60 (3–5× annual price) | | **Tip jar / patronage** | Content-focused, community-driven, or open-source-adjacent | Very low conversion; unreliable revenue | $1–$5 tips, rare |

Model selection criteria

| If the app... | Recommended model | |---|---| | Has daily usage with progressively unlocked value | Freemium → subscription | | Delivers immediate, obvious ROI in one session | Subscription only or one-time purchase | | Produces output that varies in volume (AI, exports) | Freemium + consumables | | Is a simple utility used occasionally | One-time purchase | | Requires trust before users see value (health, finance) | Extended free trial → subscription | | Competes with free alternatives that are "good enough" | Freemium (generous free tier) → subscription for power features |

Process

Step 1 — Establish competitive pricing range

From `competitors.json`, extract pricing data for all direct competitors: 1. List each competitor's pricing model and price point. 2. Compute the range: `competitive_min` (cheapest) and `competitive_max` (most expensive). 3. Identify the **modal price** — the price point most competitors converge on. This is the market's revealed equilibrium. 4. Note any competitor using a different model (e.g., all use subscription but one uses one-time purchase) — this is a potential positioning gap.

If `competitors.json` has limited pricing data, supplement with market_insights monetization_evidence and App Store narrative pricing mentions.

Step 2 — Van Westendorp Price Sensitivity Analysis

The Van Westendorp model uses four price perception thresholds to identify the acceptable price range. Since we can't survey users directly, we estimate each threshold from available data:

The four thresholds

| Threshold | Question (conceptual) | How to estimate | |---|---|---| | **Too cheap** (floor) | Below this price, users suspect low quality | Half the cheapest competitor's price. If app is free, this is $0 (no floor). | | **Cheap but acceptable** (low) | Feels like a good deal | Competitive minimum price. Discount slightly if the app offers less than the cheapest competitor. | | **Getting expensive** (target) | Fair price — users would consider it but think carefully | Competitive modal price, adjusted by desire multiplier (Step 3). | | **Too expensive** (ceiling) | Above this, users won't consider it regardless of value | Competitive maximum price × 1.2. Cap at the "pain threshold" — the maximum users pay for similar apps in the category. |

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