account-research
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\"Design bundle pricing strategies using pure bundling, mixed bundling, and consumer surplus analysis. Use this skill when the user needs to set prices for product bundles, determine whether bundling increases profit, or analyze unbundling opportunities — even if they say
$ npx -y skills add charlieviettq/awesome-agent-skill --skill algo-price-bundle --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/algo-price-bundleContext preview
The summary Claude sees to decide when to auto-load this skill.
\"Design bundle pricing strategies using pure bundling, mixed bundling, and consumer surplus analysis. Use this skill when the user needs to set prices for product bundles, determine whether bundling increases profit, or analyze unbundling opportunities — even if they say
name: "\"algo-price-bundle\"" description: "\"Design bundle pricing strategies using pure bundling, mixed bundling, and consumer surplus analysis. Use this skill when the user needs to set prices for product bundles, determine whether bundling increases profit, or analyze unbundling opportunities — even if they say 'should we bundle these products', 'bundle pricing', or 'package deal pricing'.\"." allowed-tools: Read, Glob, Grep
Bundle pricing sells multiple products together at a combined price, extracting consumer surplus by averaging valuations across products. Works when customers have heterogeneous, negatively correlated valuations. Three types: pure bundling (bundle only), mixed bundling (bundle + individual), unbundling.
**Trigger conditions:**
**When NOT to use:**
IRON LAW: Bundling Increases Profit ONLY With NEGATIVELY CORRELATED Valuations If ALL customers value the same items highly, bundling adds no surplus. Bundling works when: Customer A values Product 1 high + Product 2 low, while Customer B values Product 1 low + Product 2 high. The bundle price captures both at a middle price neither would pay for their low-value item alone.
Collect: individual product valuations (or willingness to pay) per customer segment. Compute correlation of valuations across products. **Gate:** Valuation data available, correlation is negative or mixed.
1. Compute optimal individual prices: maximize Σ(revenue per product) 2. Compute optimal bundle price: find price that maximizes bundle revenue given joint valuation distribution 3. Compare: pure bundling revenue, mixed bundling revenue, individual pricing revenue 4. Mixed bundling: set bundle price < sum of individual prices; discount = bundle incentive
Check: mixed bundling should weakly dominate both pure bundling and individual pricing (Adams & Yellen, 1976). If not, review valuation assumptions. **Gate:** Mixed bundling profit ≥ max(pure bundling, individual pricing).
Return optimal pricing strategy with profit projections.
{
"recommendation": "mixed_bundling",
"prices": {"product_a": 299, "product_b": 199, "bundle_ab": 399},
"profit_comparison": {"individual": 45000, "pure_bundle": 48000, "mixed_bundle": 52000},
"metadata": {"segments": 3, "valuation_correlation": -0.35}
}**Input:** Product A (WTP: Seg1=$80, Seg2=$30), Product B (WTP: Seg1=$30, Seg2=$70). Each segment has 100 customers. **Expected:** Individual optimal: A=$80, B=$70, revenue=$15K. Bundle at $100: both segments buy, revenue=$20K. Bundling wins.
| Input | Expected | Why | |-------|----------|-----| | Perfectly positive correlation | Individual pricing wins | All customers value both high or both low | | One product is free good | Bundle = premium + free | Common in software (free trial + paid add-on) | | 10+ products in bundle | Mixed bundling complex | Too many combinations — use tiered bundles |
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