『The Platform Economy with Fexingo: Marketplaces, Networks, and Multi-Sided Businesses』のカバーアート

The Platform Economy with Fexingo: Marketplaces, Networks, and Multi-Sided Businesses

The Platform Economy with Fexingo: Marketplaces, Networks, and Multi-Sided Businesses

著者： Fexingo
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Lucas and Luna explore the architecture of multi-sided platforms—the marketplaces, networks, and digital ecosystems that reshape industries. Each episode dissects a single platform business, from its fee structures and network effects to the competitive moats and regulatory pressures that define its trajectory. Lucas brings the numbers: take rates, liquidity ratios, contribution margins. Luna pushes on the human and strategic trade-offs: why a marketplace chooses to subsidize one side, how a network solves the cold-start problem, when a platform risks tipping into a monopoly. They draw on real cases—Uber's surge pricing, Airbnb's host guarantee, Etsy's niche positioning—to ground every abstraction in a named company and a measurable outcome. This show is for operators, investors, and strategists who need to understand why some platforms win while others vanish. No hot takes, no hype—just the mechanics of matching supply with demand at scale. What happens when the marketplace becomes the market? #PlatformEconomy #Marketplaces #NetworkEffects #MultiSidedBusinesses #DigitalMarketplaces #TwoSidedMarkets #Liquidity #TakeRate #ColdStart #Moat #Regulation #Uber #Airbnb #Etsy #Business #FexingoBusiness #BusinessPodcast #Technology Keep every episode free: buymeacoffee.com/fexingo© 2026 Fexingo. All rights reserved. 経済学
エピソード
  • How Marketplaces Weaponize Reputation

    How Marketplaces Weaponize Reputation
    2026/09/10
    Marketplaces like Airbnb and Uber have long relied on two-way rating systems to enforce quality, but this mechanism is now being flipped into a strategic weapon. This episode explores how platforms use asymmetric reputation data to lock in suppliers while protecting consumer-facing brands from liability. We examine the structural shift where reviews are no longer just feedback loops but tools for supply-side discipline, often leaving individual providers vulnerable to algorithmic shadow-banning or de-ranking without clear recourse. By September 2026, the regulatory gaze on these opaque ranking systems has intensified, forcing platforms to balance transparency with competitive secrecy. #PlatformEconomy #ReputationSystems #TwoWayRatings #Airbnb #Uber #AlgorithmicRanking #SupplySideDiscipline #DigitalTrust #GigEconomy #NetworkEffects #AsymmetricData #ConsumerProtection #FexingoBusiness #BusinessPodcast #TechPolicy #MarketplaceDesign #SupplierRights #DigitalGovernance Keep every episode free: buymeacoffee.com/fexingo
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    10 分
  • How Marketplaces Turn Idle Assets Into Revenue

    How Marketplaces Turn Idle Assets Into Revenue
    2026/09/09
    Marketplaces like Airbnb, Uber, and Turo have mastered a specific economic trick: turning idle capacity into high-margin revenue. This episode examines the unit economics of asset-light models, focusing on how platforms monetize underutilized resources without taking ownership. We break down the math behind dynamic utilization rates, the hidden costs of customer acquisition in saturated markets, and why the next wave of marketplace growth isn't about adding more users, but squeezing more value from existing ones. With concrete examples from the travel and gig sectors, we explore whether this model is sustainable or if it hits a hard ceiling. #PlatformEconomy #MarketplaceStrategy #AssetLightBusiness #IdleCapacity #UnitEconomics #AirbnbCaseStudy #UberGrowth #DynamicPricing #NetworkEffects #FexingoBusiness #BusinessPodcast #TechInvesting #StartupMetrics #RevenueModels #OperationalEfficiency #ConsumerTech #GigEconomy #ShareEconomy Keep every episode free: buymeacoffee.com/fexingo
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    10 分
  • How Marketplaces Use Dynamic Pricing Algorithms

    How Marketplaces Use Dynamic Pricing Algorithms
    2026/09/08
    We break down the mechanics of dynamic pricing in digital marketplaces, using Uber and Airbnb as primary case studies. Lucas and Luna explore how algorithms adjust prices in real-time based on demand, supply constraints, and user behavior. We look at the specific data points that trigger price hikes, the consumer backlash against perceived unfairness, and the regulatory scrutiny facing platforms like Lyft and DoorDash. This episode examines whether algorithmic pricing creates efficiency or exploits information asymmetry, offering a clear view into the invisible hands that set your ride fare or rental cost. #DynamicPricing #MarketplaceEconomics #Uber #Airbnb #AlgorithmicBias #PlatformBusiness #RevenueManagement #ConsumerProtection #TechPolicy #DigitalEconomies #SupplyAndDemand #FexingoBusiness #BusinessPodcast #LucasAndLuna #PlatformStrategy #DataPrivacy #PriceTransparency #MarketEfficiency Keep every episode free: buymeacoffee.com/fexingo
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    15 分
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