• Asian Journal of Business Environment
• /
• v.1 no.1
• /
• pp.9-12
• /
• 2011

This study aimed to predict retail sales of local markets in Jinan city of China with the Huff model. Using the Huff Model, we examined whether the predicted retail sales of local markets may be different in Jinan, China, from the department stores, supermarkets, shopping centers/shopping malls, and home appliance stores. The probability that a customer shops at location depends upon the store size and the travel time factors calculated by the Huff Model. We found that the predictedretail sales of shopping malls have a greater value than others. People who live in a mid-sized city may have easier access to any stores within the city boundary than people in metropolitan areas. Therefore, people in a mid-sized city are more sensitive to store size, because a bigger store size means greater opportunities, incentivizing consumers to travel further to competing stores after passing by nearer, smaller stores. This study has some limitations. First, the data is somewhat restricted in that the subject stores do not represent all of the stores in Jinan. Second, we cannot compare the estimated market share of the stores and the actual sales data. It is further suggested in this study that more databases be developed throughout such East Asian countries as Korea and Japan and that a different parameter λ value in the Huff Model be utilized for mid-sized cities.

• Journal of Distribution Science
• /
• v.22 no.9
• /
• pp.119-128
• /
• 2024

Purpose: his study examines the effect of message valence on consumer perceptions of sales messages and salesperson evaluations in retail contexts. In contrast to previous studies on the negativity effect, it examines the positivity effect, which implies that the effect of positive information may outweigh that of negative information in certain situations. In addition, the current research examines how the content of the sales message influences consumers' perceptions of salespeople. Research design and methodology: The study presents an analytical model in which a potentially altruistic salesperson transmits quality information as a form of cheap talk. Several predictions were derived from the model and then empirically tested in two experiments. Results: When the sales message is about relatively less expensive products, positive information can be more credible and diagnostic than negative information. In addition, positive sales messages about the less expensive products signal the salesperson's benevolence. Conclusion: This paper is one of the few studies to predict and empirically test the positivity effect. It also contributes to the literature on trust in salespeople by showing that message valence influences buyers' perceptions of salespeople.

• Asia pacific journal of information systems
• /
• v.25 no.4
• /
• pp.763-783
• /
• 2015

Online customer reviews (i.e., electronic word-of-mouth) has gained considerable interest over the past years. However, a knowledge gap exists in explaining the mechanisms among the factors that determine the product sales in online retailing environment. To fill the gap, this study adopts a principal-agent perspective to investigate the effect of customer reviews and customer incentives on product sales in online retail stores. Two customer review factors (i.e., average review ratings and the number of reviews) and two customer incentive factors (i.e., price discounts and special shipping offers) are used to predict product sales in regression analysis. The sales ranking data collected from the video game titles at Amazon.com are used to analyze the direct effects of the four factors and the interaction effects between customer review and customer incentive factors to product sales. Result reveals that most relationships exist as hypothesized. The findings support both the direct and interaction effects of customer reviews and incentive factors on product sales. Based on the findings, discussions are provided with regard to the academic and practical contributions.

• Journal of Distribution Science
• /
• v.4 no.2
• /
• pp.5-20
• /
• 2006

Retail is called location business because it is one of the most important factors to estimate management of stores for retailers who are going to sell products directly to customers. Retailers' management achievements are shown in sale in general. Therefore, retailers tend to focus on ways to increase the numbers of customers in order to raise sales. First of all, in this research, I am going to examine the most fundamental models such as Reilly's retail gravitation, converse model, huff probability model and multiful losit model in selecting stores. Secondly, I am going to provide the process and analyzing ways to predict estimated sales amount with the previous theory model. Also I am going to predict estimated sales amount of the department store L which is located in D metorpolitan city. Lastly, I am going to argue about the problem of this research and the next research subject. Our main goal is to provide ways to complement and inspect sales estimation models, which can be used in fields after taking characters of high class structure of Korea into consideration on the base of previous researches. According to the result of the research, my conclusion is that if the process of analysis and changing factors are complemented, revise model, which can reflect reality of Korea, will be provided. Therefore, in the future study, we have to build up theory models to suit for our retail market through critic reviews about the existing high class structure of Korea.

• Fashion & Textile Research Journal
• /
• v.23 no.4
• /
• pp.480-490
• /
• 2021

Sales forecasting is crucial for many retail operations. For apparel retailers, accurate sales forecast for the next season is critical to properly manage inventory and plan their supply chains. The challenge in this increases because apparel products are always new for the next season, have numerous variations, short life cycles, long lead times, and seasonal trends. In this study, a sales forecasting model is proposed for apparel products using machine learning techniques. The sales data pertaining to outerwear items for four years were collected from a Korean sports brand and filtered with outliers. Subsequently, the data were standardized by removing the effects of exogenous variables. The sales patterns of outerwear items were clustered by applying K-means clustering, and outerwear attributes associated with the specific sales-pattern type were determined by using a decision tree classifier. Six types of sales pattern clusters were derived and classified using a hybrid model of clustering and decision tree algorithm, and finally, the relationship between outerwear attributes and sales patterns was revealed. Each sales pattern can be used to predict stock-keeping-unit-level sales based on item attributes.

• Industrial Engineering and Management Systems
• /
• v.15 no.4
• /
• pp.324-334
• /
• 2016

Manufacturers and retailers are interested in how prices, promotions, discounts and other marketing variables can influence the sales and shares of the products that they produce or sell. Therefore, many models have been developed to predict the brand share. Since the customer choice models are usually used to predict the market share, here we use hybrid model of Probabilistic Neural Network and Artificial Bee colony Algorithm (PNN-ABC) that we have introduced to model consumer choice to predict brand share. The evaluation process is carried out using the same data set that we have used for modeling individual consumer choices in a retail coffee market. Then, to show good performance of this model we compare it with Artificial Neural Network with one hidden layer, Artificial Neural Network with two hidden layer, Artificial Neural Network trained with genetic algorithms (ANN-GA), and Probabilistic Neural Network. The evaluated results show that the offered model is outperforms better than other previous models, so it can be use as an effective tool for modeling consumer choice and predicting market share.

• Journal of Information Technology Applications and Management
• /
• v.25 no.2
• /
• pp.73-90
• /
• 2018

Despite of increasing studies for product recommendation, the recommendation of product repurchase timing has not yet been studied actively. This study aims to propose deep neural network models usingsimple purchase history data to predict the repurchase timing of each customer and compare performances of the models from the perspective of prediction quality, including expected ROI of promotion, variability of precision and recall, and diversity of target selection for promotion. As an experiment result, a recurrent neural network (RNN) model showed higher promotion ROI and the smaller variability compared to MLP and other models. The proposed model can be used to develop a CRM system that can offer SMS or app-based promotionsto the customer at the right time. This model can also be used to increase sales for product repurchase businesses by balancing the level of ordersas well as inducing repurchases by customers.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.47 no.2
• /
• pp.155-167
• /
• 2024

Distribution and logistics industries contribute some of the biggest GDP(gross domestic product) in South Korea and the number of related companies are quarter of the total number of industries in the country. The number of retail tech companies are quickly increased due to the acceleration of the online and untact shopping trend. Furthermore, major distribution and logistics companies try to achieve integrated data management with the fulfillment process. In contrast, small and medium distribution companies still lack of the capacity and ability to develop digital innovation and smartization. Therefore, in this paper, a deep learning-based demand forecasting & recommendation model is proposed to improve business competitiveness. The proposed model is developed based on real sales transaction data to predict future demand for each product. The proposed model consists of six deep learning models, which are MLP(multi-layers perception), CNN(convolution neural network), RNN(recurrent neural network), LSTM(long short term memory), Conv1D-BiLSTM(convolution-long short term memory) for demand forecasting and collaborative filtering for the recommendation. Each model provides the best prediction result for each product and recommendation model can recommend best sales product among companies own sales list as well as competitor's item list. The proposed demand forecasting model is expected to improve the competitiveness of the small and medium-sized distribution and logistics industry.