• Fashion & Textile Research Journal
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• v.19 no.3
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• pp.289-295
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• 2017

This study aims to develop clothing sales forecast system using weather information. As the annual temperature variation affects changes in daily sales of seasonal clothes, sales period can be predicted growth, peak and decline period by changes of temperature. From this perspective, we analyzed the correlation between temperature and sales. Moving average method was applied in order to indicate long-term trend of temperature and sales changes. 7-day moving average temperature at the start/end points of the growth, peak, and decline period of S/S clothing sales was calculated as a reference temperature for sales forecast. According to the 2013 data analysis results, when 7-day moving average temperature value becomes $4^{\circ}C$ or higher, the growth period of S/S clothing sales starts. The peak period of S/S clothing sales starts at $17^{\circ}C$, up to the highest temperature. When temperature drops below $21^{\circ}C$ after the peak temperature, the decline period of S/S clothing sales is over. The reference temperature was applied to 2014 temperature data to forecast sales period. Through comparing the forecasted sales periods with the actual sales data, validity of the sales forecast system has been verified. Finally this study proposes 'clothing sales forecast system using weather information' as the method of clothing sales forecast.

• Journal of the Korean Data and Information Science Society
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• v.19 no.4
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• pp.1047-1058
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• 2008

Prediction of total sales in information security industry is considered. Exponential smoothing and spline smoothing is applied to the time series of annual sales data. Due to the different survey items of every year, we recollect the original survey data by some basic criterion and predict the sales to 2014. We show the total sales in infonnation security industry are increasing gradually by year.

• Journal of the Korean Society of Clothing and Textiles
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• v.46 no.5
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• pp.868-889
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• 2022

Fashion companies are using a big data approach as a key strategic analysis to predict and forecast sales. This study investigated the effectiveness of the past sales, web search volume, information amount, brand promotion, and the advertising endorser on the sales forecasting model. The study conducted the autoregressive distributed lag (ARDL) time series model using the internal and external social big data of a national fashion brand. Results indicated that the brand's past sales, search volume, promotion, and amount of advertising endorser information amount significantly affected the sales forecast, whereas the brand's advertising endorser search volume and information amount did not significantly influence the sales forecast. Moreover, the brand's promotion had the highest correlation with sales forecasting. This study adds to information-searching behavior theory by measuring consumers' brand involvement. Last, this study provides digital marketers with implications for developing profitable marketing strategies on the basis of consumers' interest in the brand and advertising endorser.

• The Journal of Information Systems
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• v.30 no.2
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• pp.105-126
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• 2021

Purpose The purpose of this study is to implement a optimal machine learning model about the cancellation prediction performance in car sales business. It is to apply the data set of accumulated contract, cancellation, and sales information in sales support system(SFA) which is commonly used for sales, customers and inventory management by imported car dealers, to several machine learning models and predict performance of cancellation. Design/methodology/approach This study extracts 29,073 contracts, cancellations, and sales data from 2015 to 2020 accumulated in the sales support system(SFA) for imported car dealers and uses the analysis program Python Jupiter notebook in order to perform data pre-processing, verification, and modeling that is applying and learning to Machine learning model after then the final result was predicted using new data. Findings This study confirmed that cancellation prediction is possible by applying car purchase contract information to machine learning models. It proved the possibility of developing and utilizing a generalized predictive model by using data of imported car sales system with machine learning technology. It can reduce and prevent the sales failure as caring the potential lost customer intensively and it lead to increase sales revenue by predicting the cancellation possibility of individual customers.

• Journal of the Korean Operations Research and Management Science Society
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• v.41 no.4
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• pp.55-73
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• 2016

Marketing activities play an important role in determining sales performances for B2B companies; however, prior research implies that the effect of marketing activities in the industrial market cannot be inferred from findings in the consumer market due to their differences such as types of customers or products. We further note that B2C companies (i.e. B2B client companies) serve individual consumers, and thus, B2B sales performance can be better understood as consumer market contexts are also taken into account. In this research, we study B2B marketing activities and B2C market contexts, and their effects on B2B sales performance. To this end, we focus on three factors : sales calls conducted by B2B companies, and market commercialization and social interactions in regions where B2C companies operate. Our empirical analyses provide the following results. First, B2B sales performance improves in proportion to sales calls. When sales calls serve as the means to provide product information, they help client companies understand product benefits and make purchases accordingly. Second, B2B sales performance increases as B2C markets become more commercialized, but the effect of sales calls on B2B sales declines. Commercialized markets are more attractive to individual consumers and thus, lead to greater sales in the consumer market. However, the role of sales calls as information sources weakens as B2C companies share product information themselves and develop expertise in commercialized markets. Finally, B2B sales are greater in urban markets compared to suburbs. However, the effect of sales calls on B2B sales increases in suburban markets compared to the urban counterpart. Cohesive social interactions in suburbs hinder information diffusion among B2C companies, which in turn strengthens the role of sales calls as information sources. We theoretically contribute to the B2B marketing literature and managerially suggest strategies to improve B2B sales performance.

• The Korean Journal of Community Living Science
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• v.24 no.3
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• pp.313-325
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• 2013

The purpose of this study is to investigate impulse buying and the searching for sources of information among males and females in their 20s by their utilization of sales promotion in an internet fashion shopping mall. The findings were as follows: First, there was a statistically significant difference in the use of sales promotion by age and gender. Second, two factors, such as a price-oriented utilization of sales promotion and a non price-oriented utilization of sales promotion were extracted from the analysis of sub factors of the utilization of sales promotion in an internet fashion shopping mall. Third, five factors, including affective impulse buying, provocative impulsive buying, situational impulse buying, reminder impulse buying, and pure impulsive buying, were extracted from the analysis of sub factors of impulse buying in an internet fashion shopping mall. Fourth, the utilization of sales promotions in an internet fashion shopping mall had statistically significant effects on the sub factors of impulse buying, provocative impulse buying, situational impulse buying, reminder impulse buying, and pure impulse buying. Fifth, the analysis of the correlation between the utilization of sales promotion and the searching for sources of information revealed that price-oriented utilization of sales promotion had a correlation with DM or text message, advice from friends or family, advice from a sales person, information from friends or colleagues, celebrity supporters on TV dramas or movies, and product commercials and information.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.649-651
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• 2021

In recent years, O2O services for real estate sales are widely distributed in web platforms and apps. This allows sellers, buyers, and real estate brokers to quickly and conveniently conduct real estate sales and charter contracts. However, in the O2O-based real estate sales information system, it wastes time and money for real estate buyers due to the posting of fake information, partial correction of the sales information, and intentional non-posting of the sales information. Therefore, we propose a method of detecting the false or not of real estate property information that can occur on the web platform, and design and implement a proposal system for this. To this end, we propose a method of detecting personal identity and property information based on DID, a distributed identity authentication protocol. The false real estate sales information detection system proposed by us can determine the existence of real estate sales information, partially correct the false sales information, or prove whether or not intentionally unpublished in three steps.

• Journal of Fashion Business
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• v.26 no.4
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• pp.52-63
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• 2022

As live commerce, mobile sales platforms based on real-time content and text are drawing attention as a new marketing channel. In particular, the fashion industry also using live commerce as a new fashion distribution channel, requiring marketing strategies to utilize it efficiently. This study attempted to verify the effect of information source, sales promotion, and impulse buying tendency characteristics on fashion live commerce purchase intention. The experimental design of this study was 2(characteristics of information source: expertise vs attractiveness) × 2(sales promotion type: value-added vs price discount) × 2(impulse buying tendency: high vs low) three-way mixed analysis of variance(ANOVA). A convenience sampling of 264 women in their 20s and 50s living in Seoul and the Gyeonggi area who had purchased products through Live Commerce was conducted. For the final analysis, 240 questionnaires were used. Data were analyzed by the SPSS 26 program and three-way ANOVA. Simple main effects analysis was conducted. The results of this study follow. First, there were statistically significant differences in purchase intention according to consumers' impulse buying tendencies and sales promotions. Second, information source and sales promotion showed statistically significant interaction effects on purchase intention. Lastly, information source, sales promotion, and impulse buying tendency showed significant three-way interaction effects on fashion live commerce purchase intention. Therefore, conducting appropriate marketing analysis can result in positive attitudes regarding live commerce products and substantive increases in sales.

• International Commerce and Information Review
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• v.10 no.2
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• pp.117-139
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• 2008

This study examines the determinants of trade show performance of export-oriented small and medium enterprises(SMEs). In this regard, the trade show performance is categorized into two dimensions, such as sales objective performance vs. non-sales objective performance Based on the analysis of 116 exporters participated in trade show held in DaeGu EXCO, it is found that the roles of the show organizers play more important roles in both the sales objective performance and the non-sales objective performance than the roles of the exhibitors do. It is also interesting to find that export experience has a negative influence on both the sales objective performance and the non-sales objective performance. These results suggest that trade show organizers should provide high service quality so as to enhance the trade show performance of exhibitors.

• Journal of the Korean Home Economics Association
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• v.41 no.4
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• pp.45-56
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• 2003

The purpose of this study was to segment consumers based on sales promotion orientation and examine the differences between the consumer segments on shopping behaviors and promotion usage behaviors. A total of 462 responses collected from a questionnaire survey to subjects aged over 20s were analyzed. Cluster analysis on sales promotion orientation identified four groups including rational group(21%), active group(28％), insensitive group(22.1%), and interest group(28.9%) of sales promotion. ANOVA revealed significant differences among the four groups on shopping behaviors(information seeking, store visit, and clothing purchase) and promotion usage behaviors(the usage level of sales promotion, impulse buying, brand switching, and store switching). The active and interest groups were more actively seeking information than the other two groups were. The active group was most affected by sales promotion showing the highest impulse buying and brand and store switching behaviors, and the interest group was most active on store visit and clothing purchase. The insensitive group was the least engaged in all the behaviors.