• Asian Journal of Business Environment
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• 제1권1호
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• pp.9-12
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• 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.

• 유통과학연구
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• 제22권9호
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• pp.119-128
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• 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
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• 제25권4호
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• pp.763-783
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• 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.

• 유통과학연구
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• 제4권2호
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• pp.5-20
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• 2006

소매업은 '입지산업'이라고 한다. 왜냐하면 소비자를 직접 판매대상으로 하는 소매업자에 있어서 입지는 점포의 경영성과를 좌우하는 소매믹스 중 가장 중요한 요인이기 때문이다. 소매업자의 경영성과는 일반적으로 매출액으로 나타낼 수 있다. 따라서 소매업자는 매출액을 올리기 위해서 고객 수를 늘릴 수 있는 방안에 집중하게 된다. 본 연구에서는 먼저, 점포선택에 관한 가장 기본적인 모델인 라일리의 소매인력모델, 콘버스 모델, 허프확률모델, 다항로짓모델을 검토하고자 한다. 다음에는, 기존의 이론모델을 이용하여 대형 백화점의 예상매출액을 추정하는 분석방법과 절차를 제시하고, 사례시설인 D광역시 L백화점의 출점에 따른 예상매출액을 추정하고자 한다. 마지막으로 본 연구의 문제점과 향후의 연구과제에 대해서 논의 하고자 한다. 본 연구는 선행연구들을 토대로 한국의 상권구조 특성을 감안하여 유통업 현장에서 통용될 수 있는 매출 변수들이 보완된다면 한국적 현실을 반영할 수 있는 수정모델의 제시가 가능한 것으로 분석되었다. 따라서 앞으로의 연구에서는 기존의 상권분석모델에 대한 비판적인 검토를 통하여 우리나라의 소매시장에 적합한 이론모델을 구축해야 할 것이다.

• 한국의류산업학회지
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• 제23권4호
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• pp.480-490
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• 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
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• 제15권4호
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• pp.324-334
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• 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
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• 제25권2호
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• pp.73-90
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• 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.

• 산업경영시스템학회지
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• 제47권2호
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• pp.155-167
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• 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.