Please or Squeeze? Brand performance implications of constrained and unconstrained multi-item promotions

Existing research on price promotions typically investigates the effects of single-product promotions. However, brand managers often promote multiple items simultaneously to bolster the performance of a complete brand line. This paper investigates the brand performance implications of such multi-item promotions. We distinguish between two types of multi-item promotions: traditional, unconstrained multi-item promotions that grant a discount irrespective of the size or composition of the purchase set, and constrained multi-item promotions that require consumers to purchase specific quantities (self-bundling) or even predetermined combinations (fixed bundling) of promoted items. Using a normative consumer decision model, the authors analyze the relative effectiveness of these promotions in terms of unit sales and revenue impact and show that promotion performance is moderated by consumers’ preference for variety. The authors find that unconstrained multi-item promotions outperform single-item promotions, especially in markets without variety-seeking. In markets with variety seeking, unconstrained multi-item promotions are not as effective in stealing extra sales from competition because consumers prefer to spread their purchases across all items, including competitive ones. However, in those markets, self-bundling promotions are particularly effective because they force consumers to adopt larger amounts of the promoted items at the expense of competitors. Finally, in neither type of market does fixed bundling substantially improve promotion performance.     

Dynamic pricing of multiple home delivery options

Online grocers accept delivery bookings and have to deliver groceries to consumers’ residences. Grocery stores operate on very thin margins. Therefore, a critical question that an online grocery store needs to address is the cost of home delivery operations. In this paper, we develop a Markov decision process-based pricing model that recognizes the need to balance utilization of delivery capacity by the grocer and the need to have the goods delivered at the most convenient time for the customer. The model dynamically adjusts delivery prices as customers arrive and make choices. The optimal prices have the following properties. First, the optimal prices are such that the online grocer gains the same expected payoff in the remaining booking horizon, regardless of the delivery option independently chosen by a consumer. Second, with unit order sizes, delivery prices can increase due to dynamic substitution effects as there is less time left in the booking horizon.     

一种电子商务环境下耐用品的动态定价模型与策略

针对电子商务环境下消费者对价格歧视的抗拒问题,以及耐用品生命周期长、产品需求依赖于时间、价格等特点,提出了一种动态定价模型与策略。该模型通过构造转移概率矩阵,推导出在线消费者浏览到耐用品的不同价格状态下的概率,接着根据消费者多阶段效用函数分析消费者的购买决策行为,进而给出零售商利润达到最大化时的最优定价策略集合。为了验证模型与策略的有效性,通过数值模拟实验,分析模型主要参数变化对最优定价策略的影响。研究发现当效用折扣因子越高,零售商应该降低促销频率和高价格并且提高低价格,从而诱导高端消费者在高价格购买产品。折扣效用因子大小还决定了网上零售商是否要隐藏自己的促销概率。     

基于促销和延保的渠道入侵与订货时序策略

针对单个制造商和单个零售商组成的二级供应链,制造商一方面考虑是否开拓电子商务市场而建立直销渠道与零售商展开渠道竞争,另一方面为刺激市场需求开展促销活动并对所有消费者提供有偿延保服务。分别针对先后订货和同时订货两种情形,刻画了不同入侵成本下制造商的最优入侵、促销、定价以及销售策略,并分析了制造商渠道入侵策略对零售商订货决策和盈利能力的影响。研究表明：满足入侵成本阈值条件时,渠道入侵有利于改善制造商利润;同时订货会降低制造商的入侵动机和零售商利润;在延保服务价格敏感系数和促销成本系数满足相关约束时,零售商能够从制造商渠道入侵中攫取更多利润,实现供应链节点企业的互利共赢;无论订货时序如何,渠道入侵总会提高促销努力水平和延保服务价格。     

不确定需求下的网络代销平台返利优化决策

在电商与信息化时代, 网络代销平台在产品促销方面的优势越来越明显。与折扣促销不同, 返利促销需要消费者先全款购买, 再事后兑现。本文构建了一个制造商和一个网络代销平台组成的销售系统, 考虑产品需求为可加式随机且同时受价格和返利促销影响, 在Stackelberg博弈框架下研究了制造商的定价与库存策略以及网络代销平台的返利优化决策。研究发现, 当消费者对返利的敏感程度逐渐增强时, 网络代销平台会更倾向于向消费者提供更多的返利, 此时代销平台与制造商实现共赢; 而当消费者的返利兑现率逐渐增大时, 返利平台会降低返利促销的强度, 此时会导致制造商的利益受损。     

Entry of online presale of fresh produce: A competitive analysis

The fresh produce market is one of the last unexplored spaces for e-commerce and has attracted the entrance of many e-commerce firms in recent years; these firms are likely to influence the traditional fresh produce sales channels. The online presale of fresh produce can lower the circulation loss rate while traditional sales channels suffer from a huge waste in the circulation. In this paper, we study the online presale model of fresh produce from a competitive perspective. We examine the pricing and order decisions of one online grocery and one physical store. By examining the physical store with and without the online grocery, we investigate the impact of the entry of an online grocery on the physical store. We first identify two pricing strategies, penetration-pricing and skimming-pricing, for the physical store and two pricing strategies for the online grocery, and then identify the conditions under which pricing equilibrium will occur. We find that the store does not necessarily alter its pricing strategy after the online grocery enters the market. When the physical store uses the penetration-pricing strategy to compete with the online grocery for Internet shoppers, it may fail to achieve that goal if the online grocery's delivery cost is low and if the Internet shoppers account for a relatively low proportion of shoppers. Alternatively, the physical store may fail if the online grocery's delivery cost is not high and if the circulation loss rate of fresh produce is high.     

Fighting strategies in a market with counterfeits

Counterfeiting is a widely spread phenomenon and has seen rapid growth in recent years. In this paper, we adopt the standard vertical differentiation model and allow consumers the choices of purchasing an authentic product, purchasing a counterfeit, or not buying. We focus on how non-deceptive counterfeits, which consumers know at time of purchase that the products are counterfeits with certainty, affect the price, market share and profitability of brand name products. We also consider the strategies for brand name companies to fight counterfeiting. We compare different fighting strategies in a market with one brand name product and its counterfeit, and derive equilibrium fighting strategies in a market with two competing brand name products and a counterfeit under general conditions.     

Pricing to accelerate demand learning in dynamic assortment planning for perishable products

Retailers, from fashion stores to grocery stores, have to decide what range of products to offer, i.e., their product assortment. Frequent introduction of new products, a recent business trend, makes predicting demand more difficult, which in turn complicates assortment planning. We propose and study a stochastic dynamic programming model for simultaneously making assortment and pricing decisions which incorporates demand learning using Bayesian updates. We show analytically that it is profitable for the retailer to use price reductions early in the sales season to accelerate demand learning. A computational study demonstrates the benefits of such a policy and provides managerial insights that may help improve a retailer’s profitability.     

Prices, promotions, and channel profitability: Was the conventional wisdom mistaken?

Because of the lack of empirical evidence supporting the shift of economic power from manufacturers to retailers, it has been claimed that the conventional wisdom that retailers benefit more from the use of consumer promotions was mistaken. This paper assesses this claim and examines how two different pricing approaches during manufacturers’ instant price promotions targeted at consumers impact on channel profits in a bilateral monopoly. We found that manufacturers should only offer rebates when they keep their prior-to-promotion wholesale prices unchanged. Consistent with the conventional wisdom, retailers do indeed take the lion’s share of the promotion incremental profits. Surprisingly, however, we found that in the largest part of the parameter space, manufacturers still earn more total channel profits than retailers over time. The theoretical and managerial implications of these findings are discussed.     

Integrated sales and operations planning with multiple products: Jointly optimizing the number and timing of promotions and production decisions

This paper presents a modelling framework for sales and operations planning (S&OP) that considers the integration of price promotion and production planning for multiple products. Such a modelling framework takes into account the potential competition and cannibalization between products, as well as the allocation of shared production resources. The demand model that we adopt combines purchase incidence, consumer choice and purchase quantity in a sequential framework to obtain the dynamics and heterogeneity of consumer response to promotions. Due to large problem sizes, we develop a heuristic approach for solving the resulting joint optimization problem. The results of our numerical study show interesting findings on the optimal number and timing of promotions that take into account the mutual dependence of marketing and production related factors.     

考虑策略型消费者和退货的零售商降价促销决策研究

基于零售商降价促销问题,引入策略型消费者,考虑到异质性消费者有可能对商品不满意,构建两期决策模型,旨在从退货和价格路径优化两方面提高零售商利润。研究给出(不)允许退货时,零售商面对策略型消费者的定价建议,指出零售商制定价格要在一定程度上参考商品类型。订货量相同时,给出策略型消费者降低零售商的期望利润的条件;面对短视型或者策略型消费者时,允许退货可在特定条件下提升零售商利润。消费者退货成本越高,对策略型消费者消极影响的抑制作用越明显,零售商的利润增长越显著。最后,通过数值算例分析了在两种退货决策以及不同退货成本下产品类型对零售商定价的影响,以及退货措施对策略型消费者消极影响的作用。     

Trade deals and/or on-package coupons

The selection of either a pull or a push price promotion has mainly been investigated in contexts where manufacturers offer deals to consumers at the time of purchase or offer trade deals to retailers. This paper extends this framework to where manufacturers can offer either trade deals or rebate-like promotions to consumers such as on-pack coupons that stimulate the first and second purchases or a combination of the two promotion vehicles. It is demonstrated that the decision to implement either of the three promotion options critically depends, among other factors, on the percentage of first-time buyers who redeem their coupons at the second purchase. Particularly, a necessary condition to simultaneously offer both a trade deal and coupons is to have a positive coupon redemption rate. When possible, manufacturers prefer on-pack coupons over trade deals to take advantage of slippage and to further increase the overall demand via coupon-induced repeat purchase. Manufacturers are more likely to take the lion’s share of channel profits.     

碳交易规制下信息不对称供应链的减排和低碳推广博弈研究

论文在碳交易规制下,研究单一制造商和双零售商组成的供应链减排与低碳推广决策以及零售商对制造商的成本信息分享问题,分析了零售商对制造商分享成本信息的条件,及不同情形下的制造商减排和零售商低碳推广策略。研究发现:零售商的最优低碳推广水平只与自身相关参数有关,零售商只有在自身低碳推广效率足够高时才会与制造商分享信息;制造商最优减排量随消费者低碳意识、碳交易价格、零售商低碳推广效率及其不确定性增大而提高,随零售商之间竞争程度提高而降低;无论零售商是否对制造商分享成本信息,供应链成员的最优利润随零售商之间竞争程度提高而增加,随消费者低碳意识、碳交易价格提高而提高;零售商低碳推广效率的不确定性越大则制造商的最优利润越低,零售商的最优利润越高;仅一家零售商分享成本信息时,制造商无法通过转移支付使另一家零售商与其分享信息。     

Airline pricing and fare product differentiation: A new theoretical framework

A basic premise in the development of yield management has been that the differentiated fare products offered by airlines are targeted to distinct segments of the total demand for air travel in a market, each of which compete for space on a fixed capacity aircraft. Such representations of differential pricing assume that the airline can segment its demand perfectly and without cost to consumers, and further, ignore the dependence of the demand for a given fare product on the price levels and characteristics of the other available fare products. In this paper, we introduce a new ‘generalised cost’ model of fare product differentiation that incorporates the relationships between available airline fare products as well as the cost incurred by consumers of accepting more restrictions. We extend the model to incorporate the diversion of passengers to lower-priced fare products as a result of their ability to meet the additional restrictions imposed by airlines, and then demonstrate how seat inventory control can be used to induce diverting passengers to ‘sell up’ to higher-priced fare products by applying booking limits. An example of how the model can be used for joint fare product price level optimisation is presented, along with a numerical example that illustrates the extent to which the conventional model of price discrimination over-estimates passenger demand and, in turn, total airline revenues.     

Goal programming model for grocery shelf space allocation

The highly competitive grocery retail industry has annual sales of roughly half a trillion dollars in the US. While gross margins average about 28% of sales, net profits after taxes are only 1% industry-wide, causing retailers to continually search for operational improvements that increase profitability and improve customer service. One important decision that affects both of these goals is how to allocate shelf space to different products.     

A mixed integer nonlinear optimization model for gas sale company

In this paper the authors propose an optimisation model, called OMoGaS (Optimisation Modelling for Gas Seller), to assist companies dealing with gas retail commercialisation. The model takes into account the limits on price imposed by law on small consumers as well as the gas company policies in order to explore the commercial consequences of different policies. The GAMS framework is used for the optimisation of the defined MINLP model where the profit function is based on the number of contracts with the final consumers, on the tipology of consumers and on the cost supported to meet the final demand while the constraints include information on a maximum daily gas consumption, on yearly maximum and minimum comsumption in order to avoid penalties and on consumption profiles. A case study is presented.     

Promoting impulse buying by allocating retail shelf space to grouped product categories

This paper addresses a problem where a retailer seeks to optimize store-wide shelf-space allocation in order to maximize the visibility of products to consumers and consequently stimulate impulse buying. We consider a setting where the retailer, because of product affinities or the retailer’s historical practice, has pre-clustered product categories into groups each of which must be assigned to a shelf. On the basis of its location in the store layout, each shelf is partitioned into contiguous shelf segments having different anticipated customer traffic densities. The retailer seeks to assign each group of product categories to a shelf, to determine the relative location of product categories within their assigned shelf, and to specify their allocated total shelf space within given lower/upper bounds. We propose a 0–1 integer programme that takes into account expected customer traffic densities within the store, groups of product categories, their relative profitability, and the desirability to keep certain product groups in the same aisle, with the objective of maximizing the impulse buying profit. The proposed model is grounded in a preprocessing scheme that explores feasible assignments of subsets of product groups to available aisles by iteratively solving an Open image in new window -hard subproblem and is numerically observed to greatly outperform an alternative mixed-integer programming formulation. We demonstrate the usefulness of and the enhanced tractability achieved by the proposed approach using a case study motivated by a grocery store in New England and a variety of simulated problem instances.     

The Challenges of Scanner Data

Data capture at point of sale, using laser scanning technology, is now commonplace in retail outlets in developed economies. The volume of data collected is enormous (a single UK grocery superstore carries over 15 000 different lines, each with a unique 13 digit barcode and will sell 600 000 items per week). It has been recognized that, where point of sale data can be translated into meaningful information, there is scope for efficiency savings and exploitable competitive advantage. In UK grocery retailing the uses of scanner data have concentrated on operational aspects such as improved ordering systems and staff scheduling. There have, however, been many recent studies, predominantly in the USA, which have examined, using scanner data, the relationship between marketing variables (price, display, advertising, coupons, and other promotions) and customer purchasing behaviour. This paper presents an overview of these studies and identifies the opportunities for operational researchers to further the understanding of such relationships through modelling and data handling.     

Optimal pricing and inventory policies: Centralized and decentralized decision making

The paper studies an optimal control problem of pricing and inventory replenishment in a system with serial inventories. Consumer demand for a specific product at a retail outlet depends on price as well as the in-store stock of the product. The hypothesis is that for certain consumer products, a large volume of displayed goods leads consumers to buy more than if the stock is small. In addition to the stock that is on display in the store, there is an inventory of the product in a central warehouse. First we consider a setup in which management of the two stocks is decentralized such that pricing decisions are made by the store manager who also decides on the level of in-store inventory. The warehouse manager makes the replenishment decisions concerning the stock in the warehouse. Next we study the problem where stock management and pricing decisions are centralized. Optimal trajectories for inventories, replenishment rates, and retail price are derived by using phase diagrams and a formal synthesizing procedure.     

Optimal promotion planning—depth and frequency—for a two-stage supply chain under Markov switching demand

In considering the retailer–supplier supply chain, this paper analyzes how a retailer reasonably decides both the depth and frequency of the price discount promotion including or excluding a supplier’s inventory decision. Assuming that the promotion frequency used by the retailer is probabilistic, we model a promotion-inventory decision under an AR(1) demand with a Markov switching promotion regime. After obtaining the optimal promotion plan, our analysis also considers the behavior of the optimal promotion decision; the retailer’s price format selection, either an Every-Day-Low-Price policy (EDLP) or a Promotion policy (HiLo); and the impact of information sharing of promotion status on the system’s performance. Our results suggest that a retailer tends to overpromote if inventory cost is excluded in its promotion decision, that increasing the market share is a preferable action for both the retailer and the supplier, that total margin and price-elasticity play an important role in selecting the price format, and that the profitability for a supplier of sharing promotion information depends on the transition probabilities of the Markov switching regime.