Artist collaborations in music have been on the rise, and they tend to produce commercially and critically successful songs. We seek to uncover the effect of these collaborative projects on career trajectories and identify the factors that lift an artist's profile in the short and long term. We develop a theory of collaboration based on the transfer of capital between the collaborating artists that facilitates spillovers across time. To validate the theory, we use weekly radio plays of individual songs across 25 European countries between the years 2011 to 2018, together with a multiattribute Spotify data set of songs and Hofstede's cultural dimensions in relation to artist origins. We create pairs of similar artists who released a collaboration and a solo song in the same week and measure the impact of collaborations based on the difference-in-differences methodology. We find that releasing a collaboration song, in comparison with a solo song, increases the number of plays of an artist in the future by +4.6%. This lift can be broken down into +9.6% for the current song and +7.7% for subsequently released songs, whereas past songs are unaffected. The effect is moderated by the difference in economic, social, and cultural capitals and is significantly larger when one's partner has higher economic and social capital or is highly dissimilar along the cultural dimension. Our theoretical and empirical exploration of such strategic alliances uncovers several underlying mechanisms at play in the success of these pairings and can serve as the basis for future work targeted at prescriptive contributions.

Integrating inventory and assortment planning decisions is a challenging task that requires comparing the value of demand expansion through broader choice for consumers with the value of higher in-stock availability. We develop a stockout-based substitution model for trading off these values in a setting with inventory replenishment, a feature missing in the literature. Using the closed form solution for the single-product case, we develop an accurate approximation for the multiproduct case. This approximated formulation allows us to optimize inventory decisions by solving a fractional integer program with a fixed point equation constraint. When products have equal margins, we solve the integer program exactly by bisection over a one dimensional parameter. In contrast, when products have different margins, we propose a fractional relaxation that we can also solve by bisection and that results in near-optimal solutions. Overall, our approach provides solutions within 0.1% of the optimal policy and finds the optimal solution in 80% of the random instances we generate.

Problem definition: Marketplace platforms such as Amazon or Farfetch provide a convenient meeting point between customers and suppliers and have become an important element of e-commerce. This sales channel is particularly interesting for suppliers that sell seasonal goods under a tight time frame because they provide expanded reach to potential customers even though it entails lower margins. In this dyadic relationship, a supplier needs to optimize when to share inventory with the platform, and the platform needs to set the right commission structure during the season. Academic/practical relevance: We characterize supplier participation into the platform in a dynamic setting and link it to inventory levels, demand rates, time left in the season, and commission structure. This directly drives the commission structure decision made by the platform. We, thus, provide a framework to evaluate platform commission fee policies, taking into account supplier responses. Methodology: We use an optimal control framework with limited inventory supply and a stochastic demand process. We study the conditions under which the supplier accepts participation and use the platform as a sales channel. We also study the optimal commission structure that the platform should employ and the supplier procurement response. Results: We find that suppliers only participate if inventory is high relative to the time left to sell the items. As a result, the platform can only offer limited supply at the beginning of the season. Given this behavior, we find that the platform and the system are always better off with flexible pricing via fully dynamic commissions, which hurts the supplier the most (better off with less flexible commission fees). Interestingly, when the inventory decision is contingent on the platform pricing policy, the platform often finds it beneficial to commit to a static fee to incentivize the supplier to stock up, highlighting that inability to commit to fixed commissions may destroy value through double marginalization effects. Managerial implications: Our work suggests that short-term profit for the platform is maximized with fully dynamic commission fees at the expense of supplier profit. If inventory is endogenous, suppliers can retaliate by reducing their commitment at the start of the season. Despite the increased revenue obtained with the fully dynamic commission fee, the lost sales from the inventory drop incentivize the platform to opt for supplier-friendly commission fees, which are better for long-term profit.

The weather has been identified as an important driver of demand and constitutes a major risk for retailers, especially in goods for which usage is affected by weather conditions, such as soft drinks or fashion apparel. Specifically, weather variations change the propensity to visit the point of sales, because travel cost is affected by weather conditions; and they impact differently different product categories, because the reference utility in the mind of the consumer is affected by current weather. We empirically study these two impact dimensions at a large fashion apparel retailer. We find that rain has a large effect on footfall, increasing it in shopping mall stores and decreasing it in street stores, which suggest that it is a first-order factor for channel choice. Temperature has a milder effect on footfall. In contrast, temperature has a large impact on conversion, increasing sales of the "appropriate" categories: summer items are sold more under positive temperature shocks, and winter items less. Finally, although theory suggests that the weather should have a moderating effect on price sensitivity, we find that it is unaffected by the weather. (c) 2020 Elsevier B.V. All rights reserved.

In this paper, we study the problem of network discovery and influence propagation, and propose an integrated approach for the analysis of lead-lag synchronization in multiple choices. Network models for the processes by which decisions propagate through social interaction have been studied before, but only a few consider unknown structures of interacting agents. In fact, while individual choices are typically observed, inferring individual influences - who influences who - from sequences of dynamic choices requires strong modeling assumptions on the cross-section dependencies of the observed panels. We propose a class of parametric models which extends the vector autoregression to the case of pairwise influences between individual choices over multiple items and supports the analysis of influence propagation. After uncovering a collection of theoretical properties (conditional moments, parameter sensitivity, identifiability and estimation), we provide an economic application to music broadcasting, where a set of songs are diffused over radio stations; we infer station-to-station influences based on the proposed methodology and assess the propagation effect of initial launching stations to maximize songs diffusion. Both on the theoretical and empirical sides, the proposed approach connects fields which are traditionally treated as separated areas: the problem of network discovery and the one of influence propagation.

Lenient return policies enable consumers to return or exchange products they are unsatisfied with, which boosts sales. Unfortunately, they also increase retailer costs. We develop a search framework where consumers sequentially learn about products' true value and evaluate whether to keep, exchange, or return them. Our formulation results in a tractable attraction demand model that can be used for optimization. We show that when pricing is not a decision, the assortment problem does not have a simple structure, but we provide an approximation algorithm to solve it. When prices and assortment can be controlled, the optimization becomes tractable: product prices can either be set so that potential return costs are added to the product price, be reduced to ensure that consumers choose to evaluate them after an exchange, or be set so high so that the items are effectively excluded from the assortment. We find that when prices and assortment can be jointly optimized, assortment size always increases when consumers pay a higher share of the return cost. Finally, retailers prefer to pass all return costs on to the consumers, which not only improves social welfare but also can raise consumer surplus.

Retailing consists of all the activities associated with the selling of goods to the final consumer. In this article, we review the research on retail operations published in Manufacturing & Service Operations Research (M&SOM) since 1999. We then discuss the current retail landscape and the new research directions it offers, in which M&SOM can play a prominent role.

Flash sales retailers organize online campaigns where products are sold for a short period of time at a deep discount. The demand in these events is very uncertain, but clickstream data can potentially help retailers with detailed information about the shopping process, thereby allowing them to manage such risks. For this purpose, we build a predictive model for shoppers' sequential decisions about visiting a campaign, obtaining product information and placing a purchase, which we validate using a large data set from a leading flash sales firm. The proposed hierarchical approach mirrors the different stages of the shopping funnel and allows for a direct decomposition of its main sources of variation, from customers arrival to products purchase. We identify life-cycle dynamics and heterogeneity across campaigns and products as the main sources of variation: these allow us to provide the best predictions from a statistical standpoint, which outperform machine learning alternatives in out-of-sample accuracy. Our model thus enables flash sales retailers to learn about the performance of new products in a few hours and to update prices so as to better match supply and demand forecast and improve profits. We simulate our forecasting and optimization procedures on several campaigns including thousands of products and show that our model can successfully separate popular and unpopular products and lift revenues significantly.