The Economics Behind Meituan’s Dynamic Pricing: How O2O Platforms Walk the Tightrope Between Profit and Consumer Welfare
Every day, hundreds of millions of Chinese consumers place orders on Meituan, yet few notice the subtle price mechanics at play: delivery fees differ between peak and off-peak hours for the same meal; the same restaurant may display different prices for new versus returning users; competing platforms in the same business district adjust their coupon offers in near real-time. These seemingly random price fluctuations are driven by a sophisticated dynamic pricing algorithm—and its inner logic is now being dissected by academia.
A paper published in SHS Web of Conferences (2024) by Jiajie Shao from Anhui University of Science and Technology, titled “Research on O2O Model Dynamic Pricing Strategy and Consumer Surplus Relationship Analysis and Promotion Strategy: Take Meituan as an Example,” systematically analyzes the relationship between dynamic pricing strategies and consumer surplus in O2O platforms. The paper’s value lies not in groundbreaking theory, but in its clear economic framework for deconstructing Meituan’s pricing logic—insights directly relevant to daily operational decisions for supply chain and platform economy practitioners.
Meituan’s Three-Dimensional Dynamic Pricing Engine
The paper deconstructs Meituan’s dynamic pricing into three dimensions, each corresponding to different data inputs and algorithmic logic. These engines don’t operate in isolation—driven by big data and AI, they coordinate in real-time to form a complex adaptive pricing system.
Dimension 1: Time-based supply-demand balancing. This is the most intuitive pricing logic—delivery fees during lunch peak (11:00–13:00) and dinner peak (17:30–19:30) are significantly higher than other periods. The paper notes that this temporal price differentiation is essentially a “demand management tool”: price signals guide price-sensitive consumers away from peak hours, smoothing the demand curve and reducing instantaneous pressure on the rider dispatch system. From a supply chain perspective, this mirrors airline revenue management and logistics peak surcharges—using price levers to allocate limited capacity across time. Meituan’s 2023 annual revenue reached ¥276.7 billion (up 26% YoY) with ¥13.4 billion in operating profit—profitability largely attributable to dynamic pricing’s efficient allocation of delivery capacity.
Dimension 2: User behavior-based personalized pricing. The most controversial yet technically sophisticated layer. By analyzing consumption frequency, historical order values, payment preferences, and coupon usage patterns, Meituan’s algorithm generates differentiated pricing and promotional offers for different users. Economically, this is third-degree price discrimination—segmenting markets by consumer characteristics. The core challenge is accurately estimating each user’s Willingness to Pay (WTP). Users with higher WTP receive fewer discounts. While this boosts platform profits, it has sparked widespread “big data price discrimination” debates in China. The paper argues that platforms must balance profit maximization with consumer trust—short-term profit gains may come at the cost of long-term user attrition.
Dimension 3: Competition-based real-time benchmarking. In the Meituan-Ele.me duopoly, competitor pricing directly influences pricing decisions. The paper describes a dynamic game: when Ele.me increases subsidies in a specific business district, Meituan’s algorithm responds within minutes—either matching discounts or differentiating on other dimensions (delivery speed, product variety). This real-time competitive sensing and strategy adjustment requires exceptional data collection, analysis, and execution capabilities. From a supply chain management perspective, this is retail “Competitive Price Monitoring” upgraded to a millisecond-level real-time version.
The Erosion and Creation of Consumer Surplus: Two Sides of One Coin
The paper’s most economically rigorous discussion focuses on consumer surplus—the difference between consumers’ maximum willingness to pay and the actual price paid. The paper identifies two opposing effects of dynamic pricing on consumer surplus, with the net outcome determining overall welfare impact.
Erosion effect: Personalized pricing inherently “extracts” consumer surplus by pushing prices as close as possible to each user’s WTP ceiling. In the extreme case of perfect price discrimination, consumer surplus approaches zero. The paper highlights the information asymmetry problem: platforms possess vastly more user data than users understand about pricing logic, placing consumers at a natural disadvantage. When this asymmetry is exploited, “big data price discrimination” emerges—loyal users paying more than new ones. The paper argues this not only damages consumer welfare but constitutes a trust risk that erodes the user base over time.
Creation effect: The flip side is that dynamic pricing also creates new consumer surplus. Off-peak low prices attract price-sensitive users who otherwise wouldn’t order, generating positive surplus. Coupons, spend-threshold discounts, and membership programs—while serving platform growth objectives—objectively increase total consumer surplus. Meituan’s 2023 data shows Flash Sales order volume grew over 40% YoY, with “Double 11” participating products up 123%—promotions that erode per-transaction surplus while creating larger total surplus through market expansion.
The key insight for supply chain pricing practitioners: optimal pricing doesn’t maximize per-unit profit, but finds the balance between “margin” and “market size” that maximizes total profit. In practice, dynamic pricing algorithms must simultaneously optimize two objective functions—short-term revenue and long-term user retention—that often conflict.
Limitations and Supply Chain Perspective Supplements
As with any academic work, this research has clear limitations practitioners should note. First, the paper relies primarily on qualitative analysis and case study methodology, lacking quantitative models and empirical data. Meituan’s pricing algorithm is a black box—the paper can only observe behavioral patterns from the outside without revealing internal weight parameters and optimization objectives. Second, the paper focuses on Meituan’s food delivery business, but Meituan has evolved into a super-platform spanning delivery, travel, flash sales, group buying, and ride-hailing—pricing logic differs significantly across businesses.
More importantly, the paper doesn’t adequately address dynamic pricing’s upstream and downstream transmission effects within the supply chain. In Meituan’s ecosystem, pricing decisions affect not only consumers but also merchants (commission rates, traffic allocation) and riders (delivery fees, workload intensity). When the platform raises delivery fees during peak hours, how is this additional revenue distributed among platform, merchants, and riders? When low-price promotions squeeze margins, what is the cost pass-through pathway? These supply chain dimensions are only superficially touched upon. Future research needs to examine pricing strategy within the entire O2O supply chain ecosystem, not just the platform-consumer binary relationship.
Implications for Supply Chain and Platform Economy Practitioners
1. Dynamic pricing is fundamentally supply chain capacity management. Whether it’s delivery rider capacity, airline seats, or truck space, the underlying logic is identical—using price signals to allocate limited capacity across time and space. For supply chain companies building pricing systems, Meituan’s three-dimensional framework (Time × User × Competition) provides a practical thinking model.
2. Data capability determines the ceiling of pricing precision. Meituan’s personalized pricing works because it possesses massive user behavioral data and real-time processing capability. Traditional supply chain companies with weaker data foundations should invest in data collection and analytics infrastructure before pursuing “dynamic pricing.” Dynamic pricing without data support is just random price changes that damage customer relationships.
3. Consumer trust is the long-term constraint on pricing strategy. The paper’s discussion of “big data price discrimination” reminds us that pricing strategies cannot optimize only for short-term profit. In increasingly transparent markets, pricing perceived as “unfair” spreads rapidly and triggers consumer backlash. Companies should establish clear “price fairness” constraints—for example, limiting price differentials between new and returning users, or providing transparent pricing logic explanations on user-facing interfaces.
4. Regulatory trends cannot be ignored. China’s Personal Information Protection Law and antitrust enforcement have created substantive constraints on platform pricing behavior. Since 2021, regulators have repeatedly addressed “exclusive dealing” and data-driven price discrimination. The integrity and information security concerns raised in the paper are evolving from “recommendations” to “hard constraints.” Supply chain and platform companies must incorporate compliance costs into their algorithmic constraints.
Conclusion: New Supply Chain Challenges in the Algorithmic Pricing Era
Meituan’s dynamic pricing system represents a microcosm of supply chain management in the platform economy era: pricing is no longer a marketing problem, but a data-driven supply chain optimization problem. It encompasses capacity allocation (rider fleet), demand management (temporal smoothing), inventory decisions (merchant preparation), and service levels (delivery timeliness)—all core supply chain concerns.
Shao’s paper, while approaching from an economics perspective, reveals a question that sits at the heart of supply chain management: how to balance multi-stakeholder interests (platform, merchants, riders, consumers) while maximizing efficiency within a platform ecosystem. As AI and big data continue to permeate, this challenge will extend from O2O delivery to logistics, retail, manufacturing, and every supply chain domain.
For supply chain practitioners, the key takeaway is not to replicate Meituan’s specific algorithms, but to understand the economic principles behind them: price is information, pricing is decision-making, and dynamic pricing is real-time supply chain optimization. Teams that master this mental framework will lead in the algorithmic pricing era.
