Store Profitability Research Articles

The role of geographic market definition in analysis of grocery retailing

AbstractWe examine how estimates of household food demand elasticities and store profit margins vary with alternative geographic market extents using structural models of household store choice and retailer competition. Our consumer store choice model is novel, simultaneously accounting for the heterogeneity of store choice sets, households' travel distance to stores, and their store‐specific shopping basket prices. We estimate the models using a unique combination of datasets on grocery purchases. We find that the geographic market extent is positively associated with household demand elasticity and negatively associated with store profit margins. The maximum market extent at which changes in demand elasticities become statistically insignificant varies by retailers, ranging between 10 and 16 km. These findings are robust to alternative assumptions of store competition. Our results imply that overlooking the locality of retail competition can result in overestimating the magnitudes of household demand elasticities while underestimating store profit margins, characterizing a relatively more competitive market.

Research on Pricing and Replenishment Strategies of Supermarkets Based on PSO-LSTM- Transformer Models

The supermarket's role in ensuring market supply and enhancing livelihoods prompted the implementation of a new PSO-LSTM-Transformer hybrid model in this study. This amalgamation combines Particle Swarm Optimization (PSO), Long Short-Term Memory (LSTM), and Transformer to forecast commodity prices and inventory needs. By combining these methods, the research aimed to satisfy market requirements while maximizing grocery store profits and improving market operations sustainability.Particle Swarm Optimization (PSO) enhances prediction accuracy by effectively exploring the vast solution space for optimal solutions. Long Short-Term Memory (LSTM), which is known for capturing long-term data dependencies, enhances the comprehension and forecasting of market trends. Furthermore, the Transformer model enhances the forecasting process by capturing intricate patterns and relationships in market data through its attention mechanism.The research concludes that the PSO-LSTM-Transformer model effectively predicts commodity pricing and replenishment needs, thereby aiding supermarkets in making informed decisions to balance market demands and optimize revenue. The findings of the study contribute to the promotion of forecasting methods within the supermarket sector, facilitating efficient management of the market and sustainable economic growth.

The Impact of Private Label Introduction on Assortment, Prices, and Profits of Retailers*

We study how introducing private‐label brands (PLs) affects retail prices and profits, accounting for assortment adjustments of national brands (NBs). We employ an event‐study framework and scanner data on the US beef market. When a PL is added to the low‐priced market segment, we find that retail stores further differentiate NBs from the PL and remove same‐segment NBs. When a PL is added to the high‐priced segment, however, NB assortment changes are limited. PL introduction and PL‐driven NB assortment changes impose small price effects on NB, but strongly cannibalize NB demand and steer consumers toward PLs, likely increasing store profits.

Retail Markups and Discount-Store Entry

“Hard discounters” are retail formats that set retail food prices even lower than existing discount formats, such as Walmart and Target. Offering limited assortments and focusing on store-brands, these formats promise to change the competitive landscape of food retailing. In this paper, we study the effect of entry of one hard-discount format on markups earned by existing retail stores, focusing on several important grocery markets across the Eastern U.S. Focusing on establishment-level profitability, we estimate store-level markups using the production-side approach of De Loecker and Warzynski (Am Econ Rev 102(6):2437–2471). We find that hard-discounter entry reduced markups for incumbment retailers by 7.3% relative to markups in non-entry markets. These results indicate that the net effect of hard-discounter entry reduces the overall level of store profitability—despite the somewhat higher sales realized by incumbent retailers.

Machine-Learning-Based Approach for Anonymous Online Customer Purchase Intentions Using Clickstream Data

Since online shopping has become an important way for consumers to make purchases, consumers have signed up to e-commerce platforms to shop online. However, retailers are beginning to realise the critical role of predicting anonymous consumer purchase intent to improve purchase conversion rates and store profitability. Therefore, this study aims to investigate the prediction of anonymous consumer purchase intent. This research presents a machine learning model (MBT-POP) for predicting customer purchase behaviour based on multi-behavioural trendiness (MBT) and product popularity (POP) using 33,339,730 clicks generated from 445,336 sessions of real e-commerce customers. The results show that the MBT-POP model can effectively predict the purchase behaviour of anonymous customers (F1 = 0.9031), and it achieves the best prediction result with a sliding window of 2 days. Compared to existing studies, the MBT-POP model not only improves the model performance, but also compresses the number of days required for accurate prediction. The present research has argued that product trendiness and popularity can significantly improve the predictive performance of the customer purchase behaviour model and can play an important role in predicting the purchase behaviour of anonymous customers.

Implementasi Algoritma Monte Carlo Untuk Memprediksi Permintaan Aksesoris Mobil

Toko ADS Variasi dan AC Mobil is a shop engaged in the sale of car accessories, repairing and replacing car air conditioning spare parts in Kota Palembang, Toko ADS Variasi dan AC Mobil faces challenges in predicting the demand for the right car accessories to sell so that there is no excess or shortage of stock items. So that a method is needed that can be implemented into the inventory system and sales and purchase records. In this research, the author uses the Monte Carlo Algorithm which will generate predictions of demand for car accessories by considering variations in factors that affect demand, taking historical data on car accessories sales from the relevant period, then conducting Monte Carlo simulations using the data as input. The simulation will be repeated as many times as needed and provide results in the form of probability distributions of demand for car accessories that may occur. By taking the average value of the probability distribution, an accurate demand prediction can be generated. The result obtained from this research is a more accurate prediction of demand for car accessories based on the latest historical data so that stores can determine the right amount of stock and avoid overstocking or understocking. In addition, by considering the factors that affect demand, stores can plan more effective marketing strategies and increase store profits

Food Donations, Retail Operations, and Retail Pricing

Problem definition: For grocery retailers, managing perishable food that is nearing expiry is a major challenge. Donating food to food banks is socially responsible, as it improves local communities and reduces waste generation. It also diverts food to a secondary, quality-differentiated market. Academic/practical relevance: In this paper, we quantify the economic impacts of this secondary market for food by examining donation and pricing behaviors for competing retailers. Methodology: We use a structural model of price-discriminating oligopoly retailers to study the effect of food donations on store and category-level demand and equilibrium prices. Empirically, we estimate the food donation effect using a unique data set of food donations and sales for several categories of perishable foods across two major retail chains that compete in the same market. Results: The competitive effects of food donations follow from the price-discrimination logic. First, food donations create a direct demand effect. Donations raise the average quality of products on display, shifting demand curves inward and rotating them clockwise (e.g., more inelastic). Second, food donations create a market enhancement effect, softening price competition and raising equilibrium prices among competing retailers. Managerial implications: Food donations increase food prices and store profits, tying the socially responsible option to an economic benefit. This study contributes a new type of reuse operation to the literature on closed-loop supply chains.Funding: Funding from the Agriculture and Food Research Initiative (National Institute for Food and Agriculture, U.S. Department of Agriculture) [Grant 2020-67021-31377] is acknowledged.Supplemental Material: The online supplement is available at https://doi.org/10.1287/msom.2022.1185 .

Анализ и моделирование траекторий поведения пользователей онлайн-сервисов с использованием платформы RETENTIONEERING

The active development and widespread distribution of online stores and sales sites sets the task for marketers and IT specialists to analyze the results of sites and customer behavior to maximize store profits and predict the development of online sales. The article provides an analysis of the purchasing activity of Internet users of various categories in various periods, the postulates of online trading are formulated. To comprehensively analyze the impact of a variety of factors, predict the demand for goods, form additional recommendations and special support for purchases, the authors offer an integrated approach based on taking into account traditional components, and using the latest tools and software products for analyzing the behavior of online buyers. As a system of analysis, the Retentioneering platform is considered as the most representative and meets the modern requirements for business intelligence systems. The use of platform tools simplifies the processing and analysis of event flows, user behavior trajectories, user classification, allows you to create logical connections and functions for machine learning when predicting a user's category and behavior, as well as the likelihood of a target event - making a purchase based on previously collected data on user behavior. Based on a set of statistics on the behavior of online store buyers, the article discusses such tools for analyzing the Retentioneering platform as an interactive graph of visualization of the behavior trajectory, the matrix of steps and transitions, conversion funnels, clustering the behavior of vectoring user trajectories. Clustering methods use multivariate space convolution algorithms. The UMAP and t-SNE algorithms are considered as dimensionality reduction methods. The main stages and formulas of implementation of convolution algorithms are given, their advantages and disadvantages are considered. These algorithms simplify the process of finding global minima, and improve the quality of rendering. The described algorithms and methods allow you to analyze the behavior of visitors to the online store, combine users into clusters with a similar behavior strategy according to various target features, identify the most pressing problems and bottlenecks of the network platform.

Modelling retail inventory pricing policies under service level and promotional efforts during COVID-19

Most supply chain and production systems faced multiple manufacturing and delivery challenges during COVID-19 and transformed their supply chain for improved customer service. These challenges are mostly related to stocking and managing the inventory flow throughout the supply chain (from manufacturer to end consumer). Due to the COVID-19 travel and movement restrictions, inventory reorganisation is necessary for fulfilling consumer demand with adequate service facilities. The safety and serviceability of inventory consumption is the primary concern of many retail grocery stores and consumers. Maintaining the supply of groceries items during and post COVID-19 time without disruption is a real operational and policy challenge. Therefore, this research tries to solve an inventory pricing mechanism and retailer's profit under the optimal service level and retailers' promotional efforts. The proposed optimisation model is validated in the grocery retail sector. The grocery retail market situation is modelled when the demand for the grocery product (which may be essential items) and selling price depending on the investment in item promotional effort and consumer serviceability. The retail grocery store's investment in the product promotional efforts, such as awareness of the item availability and no-contact delivery which, may attract consumers. Therefore, the proposed inventory consumption is modelled with an optimisation problem to maximise the store profit with the optimal investment in promotional activities and service facilities to the consumers and maintain an optimal replenishment cycle. The optimisation model is tested with three different cases (no investment in promotional efforts, no investment in service facility, and investment in both) of investment to maximise the retailer's profit and stock availability. The optimality results depicted that investment in promotional efforts and service facility givens higher profit to the retailer. The proposed optimisation model's policy implications would help grocery retail store managers to develop operational strategies for maximising profit with the optimal service level and promotional efforts.

Coping emotional discomfort at retail checkout: Potential distractions and implications

Retail customers often wait to complete their purchases during the checkout process. Prior research suggests that long checkout lines and service delays negatively affect customers’ evaluation of store services. The present study investigates the potential customer and in-store distractions and their implication for emotional discomfort due to crowding stress. This study employed a cross-sectional research design and surveyed 385 respondents visiting the target retail outlets in Bengaluru, India. Correlation analysis explored the relationship between self-distraction, in-store distractions, and emotional discomfort. The study found that self-distraction negatively correlates with discomfort while in the queue (r = –0.119) and discomfort during the billing (r = –0.119). In contrast, in-store distractions (r = –0.161) and video displays near the checkout area (r = 0.116) effectively reduce emotional discomfort while in the queue. Additionally, point-of-purchase (POP) display (r = –0.265) and availability of refreshments near the billing counter (r = –0.175) are effective in reducing emotional discomfort during the billing. This study thus offers viable and affordable methods of improving the customer’s waiting experience while contributing to store profits.

The Profitability of Purchase Limits During Shortages

Pandemics, natural disasters (e.g., hurricanes, droughts, fires), strikes, piracy, and other events can unexpectedly disrupt supply or spike demand, creating shortages. Unlike ordinary stockouts caused by store-specific inventory policies, shortages involve the entire supply chain. One tool for managing shortages is imposing purchase limits. Purchase limits restrict the quantity each shopper can purchase of the scarce product (e.g., gasoline, toilet paper, sanitizers, meat, batteries), possibly increasing availability to other shoppers. Although altruistic stores might use purchase limits for egalitarian goals (e.g., reducing hoarding, waste, panic buying, arbitrage, unfair distribution), the authors find that profit-maximizing stores can use purchase limits to increase profits during shortages. These findings suggest that stores’ price-and-limit strategies depend on shortage severity, store size, competition, and seasonality. For moderate shortages, large multiproduct stores, where average shopping basket sizes are large, should maintain low prices and impose limits, whereas small stores should increase prices and not impose limits. For severe shortages, by contrast, large stores should keep low prices but not impose limits, whereas small stores should increase prices and impose limits. Generally, large stores benefit from increased future store traffic when they impose limits. Interestingly, purchase limits can improve both store profits and, with lower prices, consumer surplus.

Perancangan Sistem Informasi Penjualan Online Berbasis Web Di Pixel Computer Prembun

Pixel Prembun Computer is computer shop that sells laptops, computers and computer spare parts located in the Prembun, Kebumen, Central Java However, in business practice, the store has not used online media in all aspects of the store. Another problem in addition to looking for data on the availability of goods is making reports which is quite slow and time consuming, and what the author highlights the most is low capability of store coverage in attracting consumers which affects to the store profit. Because of the problems above, the author concludes that an online sales information system design is needed. In making this Research Paper, The Author intends to conduct research on online sales information systems at Pixel Computer Prembun, with limitations on the presentation of sales information including product type information with order information facilities, payment confirmations and product checks that author applied with PHP programming language with MySQL database. Based on the results of trials conducted in all the existing menus can run well and the resulting information is effective and accurate. Keywords: Design, Information System, Online Sales, Web

Online Shopper Intention Analysis Using Conventional Machine Learning And Deep Neural Network Classification Algorithm

The use of e-commerce throughout the world in recent years is very rapid. The continuous increase in sales shows that e-commerce has huge market potential. Store profits are derived from the process of assessing data to identify and classify online shopper intentions. The process of assessing the data uses conventional machine learning algorithms and deep neural networks. Comparison of algorithms in this study using the python programming language by knowing the value of Accuracy, F1-Score, Precision, Recall, and ROC AUC. The test results show that the accuracy of the deep neural network algorithm is 98.48%, the F1 score is 95.06%, precision is 97.36%, recall is 96.81% and AUC is 96.81%. So, based on this research, deep neural network data mining techniques can be an effective algorithm for online shopper intention data sets with cross-validation folds of 10, six hidden layer decoder-encoder variations, relu-sigmoid activation function, adagrad optimizer, and learning rate of 0.01 and no dropout. The value of this deep neural network algorithm is quite dominant compared to conventional machine learning algorithms and related research.

Development of a System Dynamics Model to Guide Retail Food Store Policies in Baltimore City

Policy interventions to improve food access and address the obesity epidemic among disadvantaged populations are becoming more common throughout the United States. In Baltimore MD, corner stores are a frequently used source of food for low-income populations, but these stores often do not provide a range of affordable healthy foods. This research study aimed to assist city policy makers as they considered implementing a Staple Food Ordinance (SFO) that would require small stores to provide a range and depth of stock of healthy foods. A System Dynamics (SD) model was built to simulate the complex Baltimore food environment and produce optimal values for key decision variables in SFO planning. A web-based application was created for users to access this model to optimize future SFOs, and to test out different options. Four versions of potential SFOs were simulated using this application and the advantages and drawbacks of each SFO are discussed based on the simulation results. These simulations show that a well-designed SFO has the potential to reduce staple food costs, increase corner store profits, reduce food waste, and expand the market for heathy staple foods.

Quality of product, service, and delivery affect consumer perceptions in determining online store ratings

This study is a quantitative study that aims to determine and analyze whether 3 quality perspectives namely product quality, service quality and delivery quality affect consumer perceptions in rating online stores. The purpose of this study is to find out how consumers rate online stores from three points of view, namely the point of view of product quality, delivery quality, and from the point of view of service quality. This study is located in Surabaya and took a sample of 70 respondents with the provisions of doing online shopping as much as 5 times in the last 1 month. After fulfilling the validity and reliability test, then doing hypothesis testing using the F test and t-test. The results showed that 3 quality perspectives, namely product quality, service quality, and delivery quality either partially or simultaneously had a significant effect on consumer perceptions in determining online store ratings. In addition to building a store image, giving a high rating from consumers will also increase sales turnover which will have an impact on increasing online store profits.

Genetic Algorithm for the Retailers’ Shelf Space Allocation Profit Maximization Problem

This paper discusses the problem of retailers’ profit maximization regarding displaying products on the planogram shelves, which may have different dimensions in each store but allocate the same product sets. We develop a mathematical model and a genetic algorithm for solving the shelf space allocation problem with the criteria of retailers’ profit maximization. The implemented program executes in a reasonable time. The quality of the genetic algorithm has been evaluated using the CPLEX solver. We determine four groups of constraints for the products that should be allocated on a shelf: shelf constraints, shelf type constraints, product constraints, and virtual segment constraints. The validity of the developed genetic algorithm has been checked on 25 retailing test cases. Computational results prove that the proposed approach allows for obtaining efficient results in short running time, and the developed complex shelf space allocation model, which considers multiple attributes of a shelf, segment, and product, as well as product capping and nesting allocation rule, is of high practical relevance. The proposed approach allows retailers to receive higher store profits with regard to the actual merchandising rules.

ANALISIS AKUNTANSI PERSEDIAAN PRODUK RUSAK PADA TOKO ROSMART SUKARAJA KECAMATAN CURUP TIMUR

Abstrack-The problem of product damage is an important problem in the company. The bad influence that occurs in the company against this damaged product is in obtaining profits / profits.The purpose of this study was to analyze the causes of product damage, record and calculate the inventory of damaged products and analyze efforts to reduce damaged products at the Rosmart Sukaraja Store for five months, from January to May 2020. This research was clarified using descriptive quantitative research, to determine management of damaged or expired products at Rosmart Stores and inventory records for damaged products.
 From the results of this study indicate that at the Rosmart Store, for damaged products that cannot be returned to the distributor, the damaged product is discarded immediately, and recorded as the cost of the damaged product and automatically reduces the store's profit in selling the beverage product.

A model and solution approach for store-wide shelf space allocation

Store space is limited and one of the most costly resources of retailers. Retailers have to apportion available store space among the individual product categories of a store and therefore assign a certain share of shelf space to each category. Assigning more shelf space to one category requires reducing the number of shelves for another category as total space is limited. Reducing available shelf space in turn decreases assortment size and lessens the presentation quantity of products and vice versa. Both affect the demand of products and ultimately the profitability of the entire category such that the profit contribution of a category depends on its shelf size. This interrelation between category sizes and store profits needs to be taken into account for the shelf space assignment to categories and the space allocation for individual products.We introduce a store-wide shelf space model that optimizes shelf space assignment for categories based on the profit contribution of the corresponding product allocations. We decompose the problem into two hierarchically interlinked subproblems and show that the solution approach suggested works efficiently and provides solutions that are applicable to large problems in retail practice. In a case study with a major European retailer, we show that profits at stores can be improved by 3.2% using our approach. Further, we use simulated data to generalize the findings and derive managerial insights.

Online selling through O2O platform or on your own? Strategic implications for local Brick-and-Mortar stores

Local brick-and-mortar (B&M) retail stores have traditionally served customers only in a proximal geographic area with their existing operations from a physical store. Emerging online-to-offline (O2O) third-party platforms allow the local B&M stores to increase their consumer base by offering online ordering and delivery services (which we refer to as platform mode). To implement online retailing, a store can also choose to build all the online retail and delivery capabilities by itself (self-building mode), or outsource order-taking to the third-party platform but provide self-built delivery service to customers (mixed mode). We analytically investigate when a local B&M store should adopt an O2O strategy, and the conditions under which each of the three modes is optimal. We show that a store should implement the O2O strategy only when the market is partially covered, or when the market is fully covered and the store's delivery cost is sufficiently low. We fully characterize the optimal mode of operations based on the delivery cost threshold. Interestingly, we find that the self-building and mixed modes dominate the platform mode when the store offers uniform pricing for both online and B&M store customers. We also propose a novel cooperation mechanism that can improve both the platform's and the local B&M store's profits. Additionally, with the proposed cooperation mechanism, the platform mode dominates the mixed mode.

A multi-category demand model incorporating inter-product proximity

Grocery stores typically contain hundreds of product categories. Physically arranging these product categories among a limited number of store aisles and shelves is a challenging task. We propose a multi-category demand model that considers physical locations of product categories to be important determinants of consumers’ purchase incidence decisions in these categories at the store, and examine two issues: (1) to what extent does store layout influence shopping basket decisions? (2) how should the store layout be re-designed to achieve optimal profit? Our analyses suggest the retailer can significantly increase store profitability by re-organizing retail layout configurations.