Retail Revenue Research Articles

Exploring passenger and flight characteristics' impacts on airport retail income: Evidence from Incheon International Airport

This study examines passenger and flight characteristics' impacts on airport retail income using transaction data from Incheon Airport (ICN) duty-free. Two indicators, per-flight expenditure and buyer proportion, were introduced to measure the impacts on retail revenue. This study employed the female passenger ratio per flight and the domestic passenger ratio as passenger characteristics. Flight type (LCC or not), transit passenger proportion, and flight delay were introduced as flight characteristics. Unfavorable departure gates and disadvantageous terminal locations were presented as location characteristics. Based on the multiple regression model, this study analyzed the effect of each factor on airport retail revenue. The research findings provide managerial implications to airport operators. It is financially advantageous to avoid allocating flights with many female passengers to favorable departure gates for accessing retail shops. A high frequency of purchases of Korean passengers does not lead to a significant sales amount because of the regulations. Transit passengers are low-yield customers for airlines and airport operators. Flight delay increases the per-flight expenditure and the buyer percentage to a certain level, while excessive delays can reduce the purchase amount. Also, this study identified that the impact of assigning flights to disadvantageous locations varies depending on the type of flight based on the expansion model using an interaction term. The location has a significant influence on passenger shopping behavior. Since FSC passengers have a solid impulsive buying tendency, airport operators should avoid giving FSCs to unfavorable gates. Although ICN kept operation policies to assign LCC flights to a terminal disadvantageous for shopping, this study presents that the commercial revenue contribution of LCCs is not weaker than FSCs when controlled a terminal location. Therefore, it is necessary to reconsider assigning LCCs to a terminal unfavorable for shopping without evaluating purchasing power.

Inter-organizational contract control of advertising strategies in the supply chain

<p style='text-indent:20px;'>Advertising has a crucial impact on a product's goodwill. To further improve a product's goodwill and make more profit, member firms in the supply chain use various contracts to coordinate the channel. Considering the dynamic effect of advertising, this paper studies a two-level supply chain consisting of one manufacturer and one retailer. The two members focus on maximizing their profits through advertising and pricing strategies under two types of contracts: the wholesale price contract and the consignment contract. The Stackelberg differential game is introduced, and the optimal advertising effort, wholesale and retail pricing strategies in the two situations are studied. Numerical examples and sensitivity analyses are conducted to explore the models further. The results show that the retailer's revenue proportion and the product's goodwill according to consumers significantly affect the strategies and the contract choice of the partner firms in the supply chain. A proportion of too high or too low revenue may lead to a contract selection conflict between the two partner firms. However, when consumers care more about the product's goodwill, this contract selection conflict can be weakened.</p>

Design of an environmental contract under trade credits and carbon emission reduction

<p style='text-indent:20px;'>Most of the previous literatures proposed a single coordination contract to increase the total profit of the supply chain, while this paper focuses on how to design environmental contracts to increase economic and environmental performance in the context of sustainable development. This paper designs the environmental contract based on cap-and-trade mechanism and trade credits which has rarely been studied before, especially the impact of trade credit on environmental performance. We consider a green supply chain, assuming that the demand rate is linear with retail prices, joint carbon emission reduction efforts and trade credit. Two models, a decentralized one and a centralized one, are compared; four contracts are proposed. Via numerous examples and sensitivity analysis, we gain some insight into how to select supply chain contracts to better improve environmental performance. The results reveal that the manufacturer sharing the retailer's revenue and cost contract obtains the highest profit. While revenue sharing contract between both parties is the optimal environmental contract, but it is difficult to increase the profit of supply chain. Furthermore, it is found that trade credit works well in protecting the environment and plays a significant role in achieving coordination.</p>

Privacy-Preserving Dynamic Personalized Pricing with Demand Learning

The prevalence of e-commerce has made customers’ detailed personal information readily accessible to retailers, and this information has been widely used in pricing decisions. When using personalized information, the question of how to protect the privacy of such information becomes a critical issue in practice. In this paper, we consider a dynamic pricing problem over T time periods with an unknown demand function of posted price and personalized information. At each time t, the retailer observes an arriving customer’s personal information and offers a price. The customer then makes the purchase decision, which will be utilized by the retailer to learn the underlying demand function. There is potentially a serious privacy concern during this process: a third-party agent might infer the personalized information and purchase decisions from price changes in the pricing system. Using the fundamental framework of differential privacy from computer science, we develop a privacy-preserving dynamic pricing policy, which tries to maximize the retailer revenue while avoiding information leakage of individual customer’s information and purchasing decisions. To this end, we first introduce a notion of anticipating [Formula: see text]-differential privacy that is tailored to the dynamic pricing problem. Our policy achieves both the privacy guarantee and the performance guarantee in terms of regret. Roughly speaking, for d-dimensional personalized information, our algorithm achieves the expected regret at the order of [Formula: see text] when the customers’ information is adversarially chosen. For stochastic personalized information, the regret bound can be further improved to [Formula: see text]. This paper was accepted by J. George Shanthikumar, big data analytics.

An Association Rule of Market Basket Analysis to Optimize Retail Revenue

An Association Rule of Market Basket Analysis to Optimize Retail Revenue

Structural and regional characteristics of the world retail trade leaders

The state of development of world retail trade through the prism of the TOP 250 retailers functioning is considered in the article. As part of the study the main performance indicators of Top 250 retailers for 2013-2020 have been analyzed. The world's leading retail trade networks have been performing positive dynamics of their development and a slight increase in the values of indicators. This might take place due to the stabilization of the level of globalization of retail trade as the result of active development of e-commerce and m-commerce. Retailers were also affected by such negative macroeconomic factors as slow economic growth in major developed economies, high levels of debt in emerging countries, deflation or low inflation in rich countries, a protectionist backlash against globalization, troubled credit markets in some countries, and worsening demographics in many countries. The size of retail revenue in 2020 helped us define five groups of trade networks. In addition, we determined the share of Top 250 companies by region/country in 2013-2019 and their revenue. The companies from Europe and North America have shown leading positions in the structure of Top-250 retailers in terms of retail revenue share in 2013-2019. The analysis of the features of expansion policy manifestation by trade structures, depending on the region of their origin, have been conducted. The European trade networks, which in 2019 were present on average in 18.3 countries has demonstrated the largest internationalization. The structure of Top 250 companies by product sector is considered as well. FMCG companies were dominated in the structure of the Top 250 retailers. Special attention has also paid to determining the place of European retailers in the Top 250 in the framework of the European model of retail development. This model assumes an active presence of trade networks in the markets of foreign countries.

Dynamic pricing of perishable food as a sustainable business model

PurposeThe purpose of this paper is to (1) investigate the effect of freshness on consumers' willingness to pay, (2) derive static and dynamic pricing strategies and (3) compare the effect of these pricing strategies on a retailer's revenue and food waste. This investigation helps to reveal the potentials of dynamic pricing strategies for building more sustainable business models.Design/methodology/approachThe authors conduct an online experiment to measure consumers' willingness to pay for fresh and three-days’ old strawberries. The impact of freshness on willingness to pay is analysed using univariate tests and regression analysis. Pricing strategies are compared using a Monte Carlo simulation.FindingsThe results of this study show that freshness largely determines consumers' willingness to pay and price sensitivity. This renders dynamic pricing a promising strategy from an economic point of view. The results of the simulation study show that food waste can be reduced by up to 53.6% with a dynamic pricing instead of a static pricing strategy in the case that there are as many consumers as strawberry packages in the inventory. Revenue can be increased by up to 10% compared to a static pricing strategy based on fresh strawberries.Practical implicationsThis study suggests that food retailers can improve their revenue when switching from static to dynamic pricing. Furthermore, in most cases, food retailers can reduce food waste with a dynamic instead of a static-pricing strategy, which might help to improve their image through a more sustainable business model and attract additional consumers.Originality/valueThis study is the first to analyse the possibility of using food freshness to design a dynamic pricing strategy and to analyse the impact of such a pricing strategy on both, a retailer's revenue and a retailer's food waste.

An Empirical Examination of the Effect of COVID-19 Travel Restrictions on Canadians' Cross-Border Travel and Canadian Retailers.

The coronavirus disease 2019 (COVID-19) pandemic has been devastating for many Canadian retailers. In this article, we estimate the offsetting positive effects of decreased international travel by Canadians on retail revenues. We use data from 1991 to 2021 on Canadians’ travel to the United States to estimate a model of cross-border travel and establish community-level counterfactual staying rates had the pandemic not occurred. Combined with actual staying rates and elasticities of retailers’ revenues with respect to staying rates, we estimate offsetting revenue gains due to the fall in cross-border travel. Our results suggest that, on average, the border closure generated a 1.49 percent offsetting gain in revenues for small Canadian retailers located within 150 kilometres of the border. We document variation across communities and sub-sectors, with estimates ranging from 0 to 125 percent. Retailers located in less-affluent communities near US shopping opportunities, and those operating in sub-sectors catering to travellers, experienced the largest gains.

Revenue characterisation with Singular Spectrum Analysis

ABSTRACT In this work, a method to characterise the daily sales revenue for an online store is presented. Daily sales revenue is a time series. The developed characterisation identifies the major sources of variation in the time series. Such a characterisation can be used for purposes such as developing structural forecasting models and extracting insights that can be leveraged for business and operations planning. In this work, this characterisation is developed using a technique called Singular Spectrum Analysis. Achieving good results with Singular Spectrum Analysis requires the judicious selection of an algorithm parameter called the window length. A framework to select this parameter is provided. Literature survey revealed that applications of Singular Spectrum Analysis to business data are limited. To the best of found knowledge from the literature survey, Singular Spectrum Analysis has not been applied to retail revenue stream analysis.

Impact of mobile operator consolidation on unit prices

We evaluate the impact of mobile operator mergers on the unit price of data and voice by using country-level observations on retail revenue for data, cellular data traffic, retail revenue for voice, and outgoing voice minutes. Using a difference-in-differences estimation strategy, we estimate the effect of operator merger by comparing the difference between the non-merging countries and the merging countries before and after the introduction of the operator merger. In accordance with the theoretical prediction provided in this paper, we empirically find that mergers tend to decrease the data unit price and increase the voice unit price.

The Power of Opaque Products in Pricing

We study the power of selling opaque products, that is, products where a feature (such as color) is hidden from the customer until after purchase. Opaque products, which are sold with a price discount, have emerged as a powerful vehicle to increase revenue for many online retailers and service providers that offer horizontally differentiated items. In the opaque selling models we consider, all of the items are sold at a single common price alongside opaque products that may correspond to various subsets of the items. We consider two types of customers, risk-neutral ones, who assume they will receive a truly random item of the opaque product, and pessimistic ones, who assume they will receive their least favorite item of the opaque product. We benchmark opaque selling against two common selling strategies: discriminatory pricing, where one explicitly charges different prices for each item, and single pricing, where a single price is charged for all the items. We give a sharp characterization of when opaque selling outperforms discriminatory pricing; namely, this result holds for situations where all customers are pessimistic or the item valuations are supported on two points. In the latter case, we also show that opaque selling with just one opaque product guarantees at least 71.9% of the revenue from discriminatory pricing. We then provide upper bounds on the potential revenue increase from opaque selling strategies over single pricing and describe cases where the increase can be significantly more than that of discriminatory pricing. Finally, we provide pricing algorithms and conduct an extensive numerical study to assess the power of opaque selling for a variety valuation distributions and model extensions. This paper was accepted by Gabriel Weintraub, revenue management and market analytics.

How Big Data Collected Via Point of Sale Devices in Textile Stores in Spain Resulted in Effective Online Advertising Targeting

<p class="0abstract"><strong>—</strong> In these days of online commerce, we need to know the real behavior of consumers in physical stores: the points of sale must anticipate the purchasing decisions of consumers in order to be able to offer the best buying experience as well as tailor the marketing variables to the specific needs of each consumer. This is where retail intelligence emerges, especially in the textile industry, as a potential technology that makes use of extremely large data sets (“big data”) to engage potential customers better in order to increase company sales. The objective of this study is to show how big data can be effectively leveraged for direct and clear commercial purposes in textile stores. The development of research is based on the analysis of the application of systematic observation of consumer behavior in three main streets in Spain known for textile retail stores and interpreting their differences. The results show that data collected through various point-of-sale devices have a significant influence on retail revenue. The differences between commercial areas and the relative attractiveness of the textile trade in different cities are also borne out by the results. The main conclusions point to the need to improve the profitability of textile fashion stores on the back of promotional tactics that focus on the number of estimated customers and the possibilities of selling to them. All of the aforesaid have a significant influence on how advertising planning is carried out for retail stores.</p>

Last mile non-delivery: consumer investment in last mile infrastructure

ABSTRACT Consumers execute routine last mile logistics in most brick-and-mortar supply chains. The online version of last mile logistics varies greatly, but still suffers from substantial challenges – one of these being theft, specifically porch piracy. In fact, each day more than 1.7 Million packages are stolen or go missing each day in the United States. In this paper, we look at a consumer’s decision, to buy or not to buy a drop box for online package delivery. We view this decision as an act of co-creation, something routine in both online and traditional retailing, the consumer co-creation of the supply chain. The purpose of this research is to examine the feasibility of using drop boxes, an alternative delivery system, to reduce retailer’s financial and reputation costs and increase consumer confidence in direct ordering. We determine that such alternative infrastructures signal a potential increase in revenues for online retailers. We also show that consumers are willing to pay for such drop boxes even as they co-create a variety of supply chains.

Determinants of airport retail revenue: a review of literature

ABSTRACTAs the airport retail industry continues to grow due to increasing travel demands, airport operators are increasingly developing their retail revenue potential to ensure financial viability. This study aims to provide a review of airport retail literature and identify the salient factors associated with passenger shopping behaviour. The paper presents a review of contemporary airport retail literature, covering a total of 50 studies from 1998 to 2018. The review identified 26 factors, which could broadly be grouped into five categories: airport/operator related; passengers' demographic related; passengers’ travel related; passengers’ psychological related and passengers’ resources related. In addition to providing a summary of the statistically significant factors across studies, the review identifies and discusses potential approaches for future research. These include the importance of considering both “to spend or not” decision and how much to spend and how most empirical studies focussed on the former; the gap in empirical analysis on the impact of airport terminal design layout on retail performance; and the gap in application of general consumer shopping behavioural models to airport retail problems. The paper concludes with the suggestion that we can build on the existing studies to develop a hybrid approach to solve several of the identified gaps simultaneously.

Investigating the effects of demand flexibility on electricity retailers’ business through a tri‐level optimisation model

The investigation of the effects of demand flexibility on the pricing strategies and the profits of electricity retailers has recently emerged as a highly interesting research area. However, the state-of-the-art, bi-level optimisation modelling approach makes the unrealistic assumption that retailers treat wholesale market prices as exogenous, fixed parameters. This study proposes a tri-level optimisation model, which drops this assumption and represents the wholesale market clearing process endogenously, thus capturing the realistic implications of a retailer's pricing strategies and the resulting demand response on the wholesale market prices. The scope of the examined case studies is three-fold. First of all, they demonstrate the interactions between the retailer, the flexible consumers and the wholesale market and analyse the fundamental effects of the consumers’ time-shifting flexibility on the retailer's revenue from the consumers, its cost in the wholesale market and its overall profit. Furthermore, they analyse how these effects of demand flexibility depend on the retailer's relative size in the market and the strictness of the regulatory framework. Finally, they highlight the added value of the proposed tri-level model by comparing its outcomes against the state-of-the-art bi-level modelling approach.

Fundamental basket size patterns and their relation to retailer performance

This study uses a sample of approximately 60,000 US households to document fundamental shopping basket size patterns across a range of retail types, and examines them in relation to retailer performance metrics (unit sales and dollar revenue). Specifically, this research addresses two main questions: 1) how do shopping basket metrics (mean and median number of items, the distribution of one, two, three … n items) differ by retail type, and 2) how does the Pareto ratio (sales generated by the heaviest 20%, and lightest 80% of buyers) apply to shopping baskets. The results show that basket size patterns in retailers are predictable. Shoppers purchase more items on average in retailers that offer a greater variety of items, and the distribution of basket sizes follows the Poisson lognormal model. The results also show that the largest 20% of shopping baskets on average generate 50% of unit sales, and 40% of dollar revenue. These results set additional benchmarks of the patterns that can be expected when looking at data on a basket-level. This research offers implications to practitioners by showing the importance of small and large baskets for retailer revenue and growth, which can guide more informed decision making to better manage their stores and brands.

Effects of Minimum Unit Pricing for Alcohol in South Africa Across Different Drinker Groups and Wealth Quintiles: A Modelling Study

Background: Previous research in high income countries has found Minimum Unit Pricing (MUP) for alcohol to be an effective policy for reducing alcohol-related harm. However, these results may not translate well to South Africa, notable for high levels of abstinence, binge drinking, unrecorded alcohol and alcohol-related injury and infectious disease. Methods: We built an epidemiological policy appraisal model using secondary data to estimate the effects of MUP across sex, drinker groups and wealth quintiles. Stakeholder interviews and workshops informed model development and ensured policy relevance. Model outcomes include individual consumption, individual spend, tax revenue, retail revenue and group health harms (HIV, intentional injury, road injury, liver cirrhosis and breast cancer). Findings: We estimate that a MUP of R10 per standard drink (15ml of pure alcohol) would lead to an immediate reduction in consumption of 5.5%. The absolute reduction in standard drinks per drinker per year is greatest for heavy drinkers, followed by moderate drinkers and then binge drinkers (-97.76, -25.48, -17.16).A R10 MUP generates 23,149 lives saved and 1,009,477 cases averted across the five health harms over 20 years. Poorer drinkers would see greater impacts from the policy (consumption: -7.9% in poorest, -3.7% in richest quintile). The bottom three wealth quintiles accrue 76% of the deaths averted and 74% of the cases averted. Interpretation: Our model estimates minimum pricing would reduce alcohol consumption in South Africa, improving health outcomes whilst raising retail and tax revenue. Consumption and harm reductions would be greater in poorer compared to richer groups. Funding Statement: This work was supported by the Wellcome Trust Doctoral Training Centre in Public Health Economics and Decision Science [108903/Z/19/Z] and the University of Sheffield. Also the South African Medical Research Council. Declaration of Interests: We declare no competing interests. Ethics Approval Statement: Ethical approval for engaging with stakeholders was granted by the South African Medical Research Council (Protocol ID: EC005-4/2019) and the School of Health and Related Research at the University of Sheffield, UK (Reference Number: 023357). All data for the model came from secondary sources and was managed according to an approved information governance plan.

Proposed business strategy for improving retail business (Case study: PT. Angkasa Pura Solusi)

The number of flights and passenger frequencies has decreased significantly, which is an impact of the airline's tariff policy which sets prices close to the upper limit for each class. Based on data from the Indonesian Central Bureau of Statistics (BPS) it was stated that the movement of passengers in January 2019 decreased by 6.7 million people or decreased by 16.07% compared to December 2018 and the Soekarno-Hatta Airport in Tangerang experienced the largest decline of 23.31 %. This condition will certainly threaten the company's efforts to achieve its predetermined targets, namely one of the revenue targets of 11.4T and of course aero revenue as the biggest contributor becomes threatened and the assumption of non-aero income with a composition of 35% of revenue aero. Related to this, that in order to increase non-aero income there are opportunities that are very possible by developing support areas in the subsidiaries. Subsidiaries are very potential due to the flexibility of the portfolios owned by the subsidiaries and profits that can be directly consolidated into the group. This research to analyze current business of PT Angkasa Pura Solusi, by analyzing external and internal conditions in order to increase the portion of non-cash income in the corporate income structure. Then, it is expected to formulate appropriate strategy for PT Angkasa Pura Solusi based on Analysis in order to increase the portion of non-captive income in the APS revenue structure and the final step is to develop action plan from proposed corporate strategy in order to increase retail revenue by implementing business model strategies. In order to reach the goals from the aims, the author use business model canvas and marjeting mix to create the strategy. And to strengthen the quality of the strategy, the author started to analize the conditions using the TOWS Matrix. Finally, the conclusion is offered based on the analysis result.

Privacy-Preserving Dynamic Personalized Pricing with Demand Learning

The prevalence of e-commerce has made detailed customers’ personal information readily accessible to retailers, and this information has been widely used in pricing decisions. When involving personalized information, how to protect the privacy of such information becomes a critical issue in practice. In this paper, we consider a dynamic pricing problem over T time periods with an unknown demand function of posted price and personalized information. At each time t, the retailer observes an arriving customer’s personal information and offers a price. The customer then makes the purchase decision, which will be utilized by the retailer to learn the underlying demand function. There is potentially a serious privacy concern during this process: a third party agent might infer the personalized information and purchase decisions from price changes from the pricing system. Using the fundamental framework of differential privacy from computer science, we develop a privacy-preserving dynamic pricing policy, which tries to maximize the retailer revenue while avoiding information leakage of individual customer’s information and purchasing decisions. To this end, we first introduce a notion of anticipating (e,δ)-differential privacy that is tailored to dynamic pricing problem. Our policy achieves both the privacy guarantee and the performance guarantee in terms of regret. Roughly speaking, for d-dimensional personalized information, our algorithm achieves the expected regret at the O(d^1.5 sqrt(T)/eps), when the customers’ information is adversarially chosen. For stochastic personalized information, the regret bound can be further improved to O(d sqrt(T) + d^2/eps^2).

Value of Promotions with Delayed Incentives: An Empirical Investigation of Gift Card Promotions

A gift card promotion provides customers an incentive to spend more than an expenditure level on regularly priced (as opposed to discounted) products, by rewarding customers with a gift card to be redeemed against a future purchase. This type of promotion is widely used by luxury fashion, department, and consumer electronic stores. In this paper, we empirically test and quantify the causal effects of gift card promotion on the retailers' revenue and the mechanisms through which it impacts customers' purchase behavior. To do so, we collaborate with a major U.S.-based fashion retailer that regularly runs gift card promotions on its online channel by targeting its customers through promotion emails. We utilize our partner retailer's targeting policy to run a regression discontinuity design analysis. We find that gift card promotion increases retailer's revenue by up to 113%. The magnitude of this effect, however, reduces with the customers' recency of purchase from the retailer's website and becomes insignificant for customers who made their last online purchase more than 13 months ago. We also find that frequent purchasers, who are likely to have lower cost of redeeming a gift card, are more likely to participate in the promotion. Using our estimations, we attribute $554K to $1.14M (i.e., 18.50% to 38.14%) of the retailer's $2.99M total online channel revenue during promotion days to gift card promotion, on average. We find that the gift card promotion email increases sales even among customers who do not participate in the promotion. Specifically, advertising the gift card promotion through an email increases customers' preference for the retailer but has no significant informative or reminder effects. We attribute 70 to 89% of the promotion's total impact to this advertisement effect. We also run a propensity score matching analysis and find that customers who redeem their earned gift cards increase their expenditures (beyond the face value of the gift card) by 23.3%, on average. Therefore, we show that redemption of gift cards induces additional expenditure and, hence, can be profitable for the retailer offsetting any promotional costs incurred.