I write about pricing strategy, applied economics, and the practical challenges
of translating quantitative analysis into business decisions. Posts draw
from my research and client advisory work across pricing optimization, marketplace economics, and experimentation.
Platform Commission Misalignment: When Seller-Optimal Pricing and Platform Incentives Diverge
May 2026 | Marketplace Economics | Industrial Organization
Most marketplace platforms earn commission on gross merchandise value, not seller profit. This creates a
structural bias toward volume over margin. In the Electronics category I analyzed, a 20% price increase is
seller-optimal, improving profit by 5.6% while reducing platform commission revenue by approximately 19%.
The result highlights a structural divergence between seller-optimal pricing and platform incentives. This
is not only a pricing problem, but an incentive design problem.
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The Lerner Index in Practice: When Elastic Demand Requires a Price Increase
May 2026 | Pricing Strategy
The Lerner Index says elastic demand means lower markups. A revenue-maximizing algorithm applied to an electronics
category with elasticity of -2.18 recommends a 40% price cut, projecting an 82.7% revenue increase. The Lerner rule
recommends a 20% price increase. Both are internally correct. They are optimizing different objective functions under
the same underlying demand structure. The key is what happens when you include the cost structure.
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When the Model Was Right and the Decision Was Wrong
May 2026 | Pricing Strategy
In one electronics category, the revenue-optimal recommendation produced an 82.7% increase in revenue
and a 144% reduction in profit at the same time. The elasticity estimate was correct. The optimization system was working
as designed. The problem was the objective function.
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When a Null Result Is the Answer: Aggregation Bias in Demand Estimation
May 2026 | Applied Econometrics
I estimated price elasticity at the bucket level and obtained a statistically insignificant result.
The model was not the problem. Aggregation masked the underlying demand signal through composition effects within product buckets.
Disaggregating back to the category level recovered economically meaningful elasticities, illustrating a classic unit-of-analysis
problem first documented by Tellis (1988) that still appears in modern e-commerce marketplaces.
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What a 3% Repeat Purchase Rate Teaches About Marketplace Loyalty
May 2026 | Marketplace Economics
In the marketplace I analyzed, 97% of customers made exactly one purchase.
That looks like a loyalty problem. It is not. The repeat rate reflects the aggregator model, not dissatisfaction.
Among returning customers, 53% purchased from a different category within 29 days. A sequential pattern that looks like
completing a room, not random browsing. The implication for recommendation systems is direct.
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Why Cost Uncertainty Drives Direction in Pricing Optimization
April 2026 | Pricing Strategy
Most pricing teams treat cost assumptions as background inputs and invest
heavily in demand modeling. My research suggests this sequence is backwards. A 5 percentage point
error in cost assumptions can reverse an optimal pricing recommendation entirely, while a 20 percent
elasticity error typically affects magnitude but not direction. The key distinction is whether
uncertainty crosses the breakeven cost threshold.
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The Revenue Maximization Trap: Why More Sales Can
Mean Less Profit
April 2026 | Pricing Strategy
A revenue-maximizing algorithm recommends
a 40% price decrease for an electronics category with elastic
demand, projecting an 82.7% revenue increase. At a 65% COGS
structure, that recommendation reduces profit by 144%. The
algorithm is technically correct on its own terms and
commercially destructive in practice. Here is why the
revenue-profit divergence happens and what to do about it.
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