product bundling strategy

This study investigates the impact of mixed bundling and price-matching guarantees (PMGs) on competitive equilibrium in a duopoly market for complementary goods. We develop a non-cooperative game-theoretic model between two retailers, where one offers mixed bundling while the other provides only pure bundling, and characterize heterogeneous consumer purchasing behavior through equilibrium analysis and comparative statics. Our findings show that, whenever equilibrium exists, mixed bundling strictly dominates the unbundled benchmark. The adoption of a PMG hinges on a trade-off between the loss in loyal-customer profits and the gain from strategic demand acquisition, a balance jointly moderated by demand elasticity and the degree of product complementarity. This work thus uncovers the competitive advantage of mixed bundling and elucidates its strategic interaction with PMG policies.

This study addresses the design of optimal bundling mechanisms for complementary goods in multidimensional type spaces. To tackle the pricing and allocation challenges arising from complementarities, the authors develop a multidimensional screening framework grounded in duality theory and employ geometric methods to characterize combinatorial preferences, recasting mechanism optimality as a coverage problem over “essential directions.” The key innovation is a “core–periphery” menu structure, governed by two critical thresholds: when a buyer’s valuation exceeds the lower threshold, the full bundle must be offered; above the higher threshold, the optimal mechanism consists of a fixed core bundle augmented with optional add-ons that cannot be sold separately. An “inclusivity” condition ensures the higher threshold remains finite, preventing exclusion of high-valuation buyers and thereby guaranteeing both completeness and optimality of the mechanism.

This study investigates how online platforms jointly leverage three strategic instruments—pricing (commissions and transaction prices), matching (recommendation and search mechanisms), and bundling (product assortment)—to simultaneously enhance platform revenue and improve overall market welfare. By developing a game-theoretic model of multi-sided interactions and integrating equilibrium analysis with mechanism design theory, the paper systematically uncovers the mechanisms through which the interplay of these levers shapes participant behavior, transaction structures, and value distribution. The findings elucidate the intrinsic coupling among key platform design dimensions and offer theoretical foundations for governance strategies that balance efficiency and fairness in digital markets.

This paper studies the combinatorial allocation problem for multiple-unit, indivisible complementary goods, aiming to reconcile sellers’ packaging cost preferences with fairness, transparency, and linearity of equilibrium prices. Methodologically, it introduces a novel graph-structured incremental packaging cost model, where packaging cost is defined as the marginal increment of a good bundle over a graph—enabling, for the first time, the unified existence of anonymous and package-linear Walrasian equilibria. Theoretically, it establishes necessary and sufficient conditions for Walrasian equilibrium existence, along with several verifiable sufficient conditions. Algorithmically, it develops a computationally tractable, transparent equilibrium computation framework grounded in linear programming and dual pricing analysis, which simultaneously satisfies two fairness criteria: (i) allocation concentration preferences (reflecting packaging cost structure) and (ii) inter-buyer pricing fairness. The framework ensures both economic interpretability and implementability in practical multi-unit complementary markets.

This paper investigates how a multi-product monopolist can leverage increasingly precise buyer valuation data—under private valuation information—to improve pricing mechanism efficiency. Methodologically, it integrates tools from information design, Bayesian mechanism design, and multidimensional screening theory to establish a quantitative framework linking data richness to revenue convergence rates. The key contribution is the first rigorous proof that, as data precision grows, pure bundling not only strictly dominates separate sales but also converges to the first-best revenue at the same optimal (first-order) rate as the overall optimal mechanism; in contrast, separate sales achieve only a suboptimal convergence rate. This result overturns the conventional wisdom that simple mechanisms are inherently limited in performance, providing foundational theoretical support for data-driven pricing in multi-dimensional settings.

This paper addresses the dynamic pricing problem in appointment-based services featuring multi-tiered, multi-temporal, and multi-window substitutable options (e.g., varying time slots, prices, and capacity levels). We propose a unified framework integrating hierarchical discrete choice modeling with dynamic pricing. Our approach innovatively employs decision trees to drive interpretable market segmentation and segment-specific parametric choice models—explicitly incorporating reference price effects. We further design an efficient heuristic algorithm for scalable pricing optimization. An A/B test conducted on an Amazon business line demonstrated a 19% improvement in core metrics; the solution was fully deployed starting Q4 2023, enabling rapid iteration of new services. To our knowledge, this is the first work to jointly integrate interpretable segmentation, behavior-aware choice modeling, and scalable pricing optimization—significantly enhancing demand forecasting accuracy and revenue performance in complex appointment settings.

This study investigates how a seller can optimally bundle advertising with a product to maximize revenue when buyers possess two-dimensional private information—namely, their valuation for the product and their tolerance for advertising. Building on the duality approach of Daskalakis et al., the authors introduce a transformed measure μ that depends on advertising revenue k, enabling the derivation of interpretable and tractable sufficient conditions in the two-dimensional type space. These conditions delineate the optimality boundaries among three pricing mechanisms: Good-Only, Ad-Tiered, and Single-Bundle. The analysis reveals clear comparative statics with respect to k: for low advertising revenue, ads are excluded; for moderate k, the seller segments users by type; and for high k, all buyers are offered a bundled package. This work thus establishes the pivotal role of advertising revenue levels in shaping the optimal selling strategy.

This study addresses the inefficiencies in pricing and resource allocation arising from complementarities under “No Assembly” constraints in combinatorial double auctions. The authors propose a constrained combinatorial buyer-bid double auction model that incorporates stability and price impact conditions, enabling bundled submarkets to inherit the competitive discipline of single-item large markets, thereby achieving efficient price discovery and eliminating strategic underbidding. Theoretically, in the two-good case, bundle bidding introduces no first-order strategic distortion, and complementarity mitigates frictions due to limited market size; clearing prices converge to competitive levels and track common values. Simulations reveal that welfare losses stem primarily from the “No Assembly” constraint rather than strategic behavior, are negligible in moderately thick markets, and further diminish as complementarity strengthens.

This study investigates the sequential interaction between pricing and inventory decisions in digital retail competition, focusing on a price-then-inventory setting where demand uncertainty and strategic uncertainty induce behavioral biases. Using a combination of game-theoretic modeling and controlled laboratory experiments, it tests theoretical predictions against observed human behavior. Results reveal three key deviations: (1) retailers’ pricing decisions exhibit strong reference-price dependence while neglecting demand volatility; (2) inventory choices display systematic “pull-to-center” bias; and (3) pricing and inventory decisions are severely decoupled, with markedly lower sensitivity to profit margins and demand uncertainty than predicted by equilibrium theory. This work is the first to systematically identify and quantify these two critical behavioral biases—reference-price anchoring and pull-to-center—in a sequential operations game. It demonstrates that such biases substantially distort competitive equilibria, offering novel empirical evidence and theoretical refinements for digital platform governance and retailer operational optimization.