Sales Strategy Research Articles

Development of a Time Series E-Commerce Sales Prediction Method for Short-Shelf-Life Products Using GRU-LightGBM

Effective sales prediction for e-commerce would assist retailers in developing accurate production and inventory control plans, which would further help them to reduce inventory costs and overdue losses. This paper develops a systematic method for e-commerce sales prediction, with a particular focus on predicting the sales of products with short shelf lives. The short-shelf-life product sales prediction problem is poorly addressed in the existing literature. Unlike products with long shelf lives, short-shelf-life products such as fresh milk exhibit significant fluctuations in sales volume and incur high inventory costs. Therefore, accurate prediction is crucial for short-shelf-life products. To solve these issues, a stacking method for prediction is developed based on the integration of GRU and LightGBM. The proposed method not only inherits the ability of the GRU model to capture timing features accurately but also acquires the ability of LightGBM to solve multivariable problems. A case study is applied to examine the accuracy and efficiency of the GRU-LightGBM model. Comparisons among other sales prediction methods such as ARIMA and SVR are also presented. The comparative results show that the GRU-LightGBM model is able to predict the sales of short-shelf-life products with higher accuracy and efficiency. The selected features of the GRU-LightGBM model are also useful due to their interpretability while developing sales strategies.

Results from Türkiye: Which Factors Drive Consumers to Buy Organic Food?

The trend towards organic foods as an alternative has recently increased. Several individual, environmental, and behavioural factors can affect this situation. This study was conducted with 1417 participants to examine the factors affecting attitudes, purchase intention (PI), and actual purchasing behaviour towards organic foods. Consequently, a two-part questionnaire was used to query participants' sociodemographic information and their attitudes and preferences towards organic foods. Data were analysed using multiple regression analysis, Pearson's correlation, and structural equation modelling. Our findings confirmed that health consciousness, the knowledge of organic foods, subjective norms, perceived price, values (health and safety), nutritional content, naturalness, availability, monetary barriers, risk barriers, and trust affect attitudes towards organic products. These findings indicate that increasing consumers' knowledge and awareness about organic foods, encouraging their consumption by society, accessibility them in the food market and making them affordable can affect the attitude towards these products. Furthermore, we determined the direct effect of the attitude on actual buying behaviour with the mediating role of PI. Additionally, we noted that marital status, employment status, disease diagnosis in the last 12 months, and the presence of a baby at home affect actual buying behaviour. In conclusion, they can help food marketers target consumers to their sociodemographic status and develop new sales strategies.

Pengaruh Strategi Penjualan, Kualitas Pelayanan Terhadap Kepuasan Pelanggan

A pharmacy is a place that carries out pharmaceutical activities which has an important role in obtaining information about drugs. In this case, pharmacists have the responsibility to create customer satisfaction. Customer satisfaction is the key to the success of a business. Different services will also have a very significant impact on a pharmacy. Apart from that, the company must also market its products by implementing the right sales strategy, because by implementing the right strategy the company will get the expected results. This research aims to determine the sales strategy, service quality and customer satisfaction at Kimia Farma Pharmacy, Bima City, which is located on Jl. Soekarno Hatta Paruga.

Implementasi Algoritma Naïve Bayes Terhadap Data Penjualan untuk Mengetahui Pola Pembelian Konsumen pada Kantin

Implementation of the Naïve Bayes algorithm on sales data to determine consumer buying patterns in canteens is an approach that seeks to overcome the limited understanding of consumer behavior in canteens. The goal of this approach is to analyze sales data and identify purchasing patterns to gain insight into consumer behavior, optimize sales strategies, and improve customer satisfaction. By applying the Naïve Bayes algorithm to sales data, this research seeks to identify factors that influence consumer behavior, such as price, convenience, and menu offerings, and provide insights that can be used to optimize menu offerings, pricing strategies and resource allocation. Additionally, the approach seeks to increase customer satisfaction and differentiate canteens from competitors by personalizing menu offerings and enhancing the overall customer experience. This research also aims to contribute to the field of consumer behavior research by applying the Naïve Bayes algorithm to canteen sales data, potentially providing insights that can be applied in other contexts. Overall, the application of the Naïve Bayes algorithm to canteen sales data can provide a data-driven approach to understanding consumer behavior and improving sales strategy and customer satisfaction.

Analysis of Purchasing Power and Purchasing Tendency for Modern Chinese Women

This paper analyzes the consumption strategies of today's women through different perspectives and delves into the reasons for the changes in women's purchasing power in different eras. Modern women are more independent, emphasize quality, pursue fashion and health, and are willing to shop through various channels and platforms. This makes Chinese women a necessary consumer force in the market. This paper analyzes sales strategies from both online and offline targeting women and comes up with targeted sales attraction methods to enhance women's purchasing power. With many emerging female consumer groups being formed, companies can develop sales strategies to target their products according to the behavioral characteristics of female consumers. This paper analyzes both online and offline strategies for women's consumption tendencies. It also shows women's sales strategies from the general and independent sales perspectives. Understanding women's needs and tendencies is essential for companies to succeed in the Chinese market. This paper conducts a comprehensive study based on this perspective.

Enhancing Orchid Inventory Management with K-Means Clustering: A Case Study in Sales Optimization

In the ornamental industry, particularly in orchid sales, manual inventory management has presented significant challenges. These challenges include excessive stock accumulation, financial pressure due to overstocks, and limited land resources to store overstocks. Utilizing information technology, data mining, specifically the K-Means Clustering algorithm, emerges as a solution. This research aims to optimize orchid inventory management by grouping products into three clusters: "highly sellable," "sellable," and "less sellable." Historical sales data was used as the main dataset, considering parameters such as sales volume, demand patterns, and seasonality. The K-Means Clustering algorithm categorized the orchid products, with 97 products in the "highly sellable" cluster, 68 products in the "sellable" cluster, and 22 products in the "less sellable" cluster. This analysis offers insights. Orchids that are "highly marketable" require higher stock levels to meet demand. "Sellable" varieties require standard stock levels, while "less sellable" types should maintain lower stock levels to avoid overstocking. In conclusion, using k-means clustering for orchid inventory management can optimize sales strategies. Orchids in high demand receive adequate stock, ensuring customer satisfaction. Effective stock allocation improves financial stability by overcoming the problem of overstock in the ornamental industry.

Taking BYD as an Example to Analyze New Energy Vehicles

With the development of the times, the environmental problems brought by fuel powered vehicles have gradually attracted people's attention, and new energy vehicles have also received increasing social attention. People seem to attach more importance to and want to understand new energy vehicles, and the technology and marketing strategies of new energy vehicles have gradually become more mature. This article will take BYD Automobile as an example to analyze and propose some suggestions for the sales strategy and supply chain management of new energy vehicles. The full text study found that both BYD and other new energy vehicle brands have their own methods to attract customers for consumption, and the sales of new energy vehicles are also getting better with the attention of society. The research significance of this article lies in identifying some problems encountered in the development of new energy vehicles and proposing solutions, as well as expectations for the future of new energy vehicles.

ANALYSIS OF THE INFLUENCE OF BUSINESS INTELLIGENCE ON BEVERAGE SALES KONNICHIWA COFFEE USING THE METHOD EQUIVALENCE CLASS TRANSFORMATION

Konnichiwa Coffee shop is one of the business beverage that sells various types of drinks. Determination of price discounts or product promotions sometimes doesn’t match what customers wants and needs. Another obstacle found was that there were no promotions for consumers who bought directly at Konnichiwa Coffee outlets. This causes less than optimal sales strategies and store promotion strategies. Determining menus that are often purchased simultaneously by consumers can be a reference for owners in determining promotional strategies. Therefore, this research was conducted to look for association patterns between menus that can implement business intelligence (BI) in the association rules method. One of the association rules algorithms is the ECLAT algorithm. The ECLAT algorithm is used because it is more efficient and faster in terms of time. The data used in this research were 214 products from 100 transactions with 26 types of drink menus. The resulting pattern refers to a minimum support value of 3% and a minimum confidence of 30%. This means that transaction data that has association patterns or that were purchased together is only 3% of the total transaction data with a confidence level of 30%. From the results obtained, the Java Latte, Kopi Latte and Sapporo Latte menus are the menus that are most often purchased together so they can be used as a marketing strategy for Konnichiwa Coffee.

The Competitive Strategy Analysis of China HeyTea

In the modern world, the new style of tea drink brands is getting a lot of attention and love.HeyTea is one of the more popular hot drink stores and has been very popular with consumers since it opened its doors. I will also examine the analysis of HeyTea’s competitive strategy in China. Analyze HeyTea’s sales strategy and its advantages. The thesis analyzes HeyTea, a competitive store brand of modern new tea drink brands, from different perspectives with the PEST model, SWOT model, and detailed analysis from the case study, respectively, from stores, advantages, marketing mode, etc. The PEST model introduces the current marketing status of the new tea drink brand in society and a detailed analysis of the brand in relation to HeyTea and the SWOT model analysis. The SWOT model analyzes the advantages, disadvantages, opportunities, and competitors of HeyTea stores. The SWOT model analyzes the strengths, weaknesses, opportunities, and competitors of HeyTea stores and analyzes HeyTea’s sales strategy to analyze how HeyTea has the upper hand in the new tea beverage brands and attracts more consumers. This paper will analyze the sales strategy of HeyTea step by step with the idea of new tea drink brands.

ATURAN ASOSIASI UNTUK REKOMENDASI STRATEGI PEMASARAN PRODUK KERAJINAN MENGGUNAKAN FREQUENT PATTERN GROWTH

Besides selling equipments and spareparts for two-wheeled vehicles, NTN Racing Store also sells handicraft products made from water hyacinth. Lately, NTN Racing Store facing challenges in selling its products, as evidence by the unpredictable monthly revenue. The reason is insufficiency of customer purchasing patterns understanding. The impacts are the declining of the marketing and sales performance for creative water hyacinth handicrafts products since 2020. The main purpose of this studi is to identify which products are purchase together by customers in one transaction when shopping for water hyacinth products. Market Basket Analysis is a rule that determines associations between various attributes and is a key application in the retail industry. This method is use to analyze past consumer purchasing behavior to determine items that are frequently purchase together. Its purpose is to uncover relationships between products purchased by customers in a single transaction. On the other hand, Frequent Pattern Growth is an alternative algorithm use to identify the most frequently occurring datasets within a collection of data. The testing results of 435 transactional data points from NTN Racing Store, using 1% minimum support value and 25% minimum confidence value, resulting four association rules within lift ratios above 1 which indicates significant relationships among itemsets. From these evaluation results, customer purchasing patterns can be identifying trough the association analysis rules. This information can assist the store owner in determining bundling packages to sell and to devise store sales strategies.

Perpetual or Subscription: Incumbent sales strategy with strategic consumers and social learning

This study explores the impact of social learning on sales strategies for incumbent firms facing new entrants in the digital software market. Consumers rely on peer reviews to assess product quality, making it difficult to evaluate new products. The study considers two pricing strategies: perpetual and subscription. Results suggest that monopolizing the market is not always beneficial for the incumbent. The best strategy depends on the strength of social learning effects, with a subscription strategy being more effective when social learning effects are strong, and a perpetual strategy being more effective when they are weak.

Financial Management Strategies in Traditional Coffee Shops on Efforts to Increase Sales, Makassar City, Indonesia

A type of qualitative research through a phenomenological approach, the results of the study show that the presence of traditional coffee sellers not only provides delicious drinks, but is also an inseparable part of daily life in many communities. Despite the challenges of competition and modernization, traditional coffee sellers remain a pillar of the local economy with unique economic behavior. With a commitment to product quality, innovation in sales strategies, and strong customer relationships, they are able to increase family income and create economic stability within the community. In addition, their role in creating additional economic opportunities and strengthening local economic infrastructure shows a significant impact on economic growth and social well-being at the community level. Therefore, traditional coffee sellers not only play the role of local entrepreneurs, but also as agents of change that are important in improving the quality of life of the community as a whole.

Research on the Selection of the most Popular Product Categories in TikTok based on Linear Regression

In the e-commerce industry, live streaming has become an increasingly popular way to promote and sell products. With the rise of social media platforms like Facebook, Instagram, and TikTok, more and more businesses are using live streaming to engage with their customers and boost sales. This study investigates the relationship between sales and the number of live viewers to find out the most popular partition for live sales of TikTok’s e-commerce by employing simple linear regression, deseasonalization, and variable transformation techniques to optimize the models. This study improves the accuracy and interpretability of regression models in the e-commerce industry, specifically focusing on the relationship between real-time viewership and Gross Merchandise Volume (GMV). The findings indicate that removing seasonality and applying log-to-log transformation provide more reliable and accurate models, ultimately helping businesses better understand and optimize their sales strategies.

Research on Customer Behavior Prediction Model for Cultural Industry Combined with Deep Learning

Abstract In recent years, as deep learning has demonstrated powerful characterization capabilities in the fields of speech, image, and text, researchers have begun to apply it to the field of prediction, i.e., predicting customer behaviors through current interaction records and features. This paper proposes a deep wandering-based customer behavior prediction model that combines deep learning techniques to forecast customer behavioral trends in the cultural industry. The model randomly wanders from the social network graph structure of the customer’s purchase of goods to generate a new behavioral sequence. We regard the user’s behavioral sequence as a word, and we pre-train all the behavioral sequence documents using the Word2vec algorithm model. The experimental comparison revealed that the model, which incorporates the depth-wandering technique, outperforms other models on the test set in terms of predictiveness. The website uses the deep wandering user behavior prediction model to forecast sales and adapts its sales strategy based on the customer’s behavior. 31% of customers were content with the books they bought from the website, while 52% were extremely content. By comparing the book sales before and after applying the model, it was found that the book sales increased significantly after adjusting the sales strategy, indicating that the customer behavior prediction model constructed in this paper can be used practically.

Factors affecting purchase intention and consumer behavior of gym equipment through live streaming

This study looks into why more people are choosing to shop through live streams rather than in traditional, physical stores. Despite the growing trend, there's not much information on why this shopping method is preferred. The goal of this research is to explore the reasons behind consumers' preference for live stream shopping and how it affects their intention to make future purchases. This study uses several theories to understand this behavior: the Use and gratification theory, which suggests people enjoy the entertainment and information provided by live streams; the source credibility theory, focusing on the presenter's attractiveness and trustworthiness; and the theory of planned behavior, which helps explain how people decide to buy something. The research focuses on online users earning more than RM1,000 who have bought fitness equipment during a live stream. A survey was shared through social media, gathering 393 responses. The analysis was done using linear regression. The results aim to help sellers understand what motivates buyers to complete a purchase, offering insights to develop better sales strategies. The findings also suggest how fitness equipment companies can use these theories to improve their online sales model.

Business decision-making of power generators in competitive electricity market

This paper presents a theoretical framework for the business decision-making process of the power generators as price takers when considering the participation of energy storage. The framework assesses rational valuation, optimal sales strategies, and hedging options for power plants with and without a gross sales constraint. The valuation and optimal sales strategy problems are analyzed using a risk-neutral pricing approach, dynamic programming principles, and the trinomial tree model suitable for the regime switching model. The formulation of a price risk hedging scheme flexible and widely used over-the-counter electricity derivative, the electricity contract for difference, as a tool for hedging electricity spot price risk. The minimum variance hedge ratio and its corresponding hedging efficiency formula are derived. In the section of numerical simulations, we first use the EM algorithm to calibrate the electricity spot model based on electricity spot price data of Nord Pool. Numerical simulations are then conducted on the operational decision-making of power generators under three different forms of energy storage. The results of the simulations provide a basis for power generators to evaluate the real-time value of power plants, to select optimal real-time power sales, and to determine the optimal timing of power plant transfer and storage methods.

ПСИХОЛОГИЧЕСКИ ОБОСНОВАННЫЙ И ЭКОНОМИЧЕСКИ ЭФФЕКТИВНЫЙ ГЕЙМДИЗАЙН: МЕТОДИКА РАЗРАБОТКИ МОБИЛЬНЫХ ИГР С ФОКУСОМ НА РАССЛАБЛЕНИЕ

The article is devoted to the development of a methodology for creating psychologically sound and cost-effective relaxing mobile games. The article emphasizes the need to create games that correspond to the player’s psychotype, which will ensure the economic efficiency and relevance of the project by retaining users, effectively attracting them and reproducing an exciting gameplay. The need to take into account the factors of the psychological impact of game elements on the player is noted, among which the most significant are design, motivation for the game, plot, game mechanics and others. The existence of connections between the player’s involvement in in-game processes, his immersion and response to neuromarketing, applied strategies and tricks to stimulate sales within the game is determined. The need for indirect influence on the consumer and abandonment of aggressive in-game sales strategies is highlighted, due to their negative impact on the degree of player immersion and relaxation. Some existing game mechanics aimed at engaging and relaxing users of mobile games are revealed. The leading role of such user-oriented mobile game design principles is clarified, such as: a clear understanding of the aspirations, characteristics and psychotype of players; organization of user-oriented development of plot and design, game mechanics; testing the gaming experience and collecting feedback; implementation of unobtrusive monetization strategies that exclude aggressive sales promotion; continuous improvement of the game with a focus on taking into account current user requests, trends and updating the target audience. The scientific novelty of the research is associated with the proposal of a methodology for developing relaxing games based on psychologically sound game design practices. The significance of the theoretical results of the work lies in determining the player’s motivation, describing ways to control the user’s attention, analyzing the psychotypes of players, and clarifying the game mechanics used in relaxing, cost-effective games. The practical significance of the study is determined by the prospects for applying the findings and methodology in the practice of developing mobile games that meet the principles of relaxation and sales promotion.

A Study on the Effect of Color Preference on Purchase Intention of Picture Books for 3-7 Year Old Children in China and South Korea

Abstract This paper adopts a linear regression model to study the relationship between picture book color and purchase intention. The research object is selected, the research data are obtained by distributing questionnaires, the correlation coefficient formula of the linear regression model is used to get the correlation of the research independent variables, and then the least squares method is used to conduct regression analysis of picture book color preference and purchase intention. The research data show that the R² of model 1 (control variable) is 0.015. The R² of model 2 (independent variable) is 0.625, which increases the degree of explanation by 61.00% compared with model 1. The regression coefficients of the independent variables are 0.214, 0.122, and 0.126, respectively. All of them reach a significant level, which indicates that there is a significant positive effect of the color preference of picture books of Chinese and Korean children between the ages of 3-7 on the willingness to purchase. The color preference of Chinese and Korean children’s picture books aged 3-7 has a significant positive effect on purchase intention, as indicated by this. The cross-border children’s book market can benefit from this study as a good sales strategy and suggestion to better promote children’s picture books.

The role of sales planning in the management system of a construction and manufacturing enterprise

The successful operation of construction and manufacturing companies requires the implementation of such a management function as sales planning, which allows, through certain mechanisms of goal setting and careful monitoring of their implementation, to achieve the desired development results for both individual organisations and the country's economy. The problem of improving the efficiency of planning the proper operation of construction enterprises is especially important for developing countries, which undoubtedly include Ukraine. Therefore, in order to improve their performance, our enterprises need to learn how to improve the work planning system, improve strategic and tactical development planning, and learn how to objectively assess the implementation of the measures taken. This paper develops a methodology for planning sales at a construction and manufacturing enterprise. Priority is given to identifying and thoroughly analysing the main indicators that affect the achievement of improvement of the sales planning system for an objective assessment. In the framework of this research, some selective target standards of the organisation's development were analysed in order to obtain all the necessary data for improving the overall development strategy of the enterprise. An active sales strategy was developed, which included certain changes in the structure and operation of the sales department, the introduction of the latest developments and the setting of certain tasks for the organisation's employees. The planned and actual expenses of the sales department were optimised. Particular attention is paid to motivating the work of commercial employees, which should combine the most effective methods of moral and material incentives. The article presents planned indicators of annual distribution of sales by managers, based on the analysis of sales of previous years and the specifics of the construction market. The indicator of total expenses of the sales department in relation to the planned indicator was introduced to enable a thorough control of performance results. The results of the presented study can be used to establish a sales planning system at construction and manufacturing enterprises.

ОСОБЛИВОСТІ ЦІНОВИХ СТРАТЕГІЙ ЯК ФАКТОРУ ПІДВИЩЕННЯ КОНКУРЕНТОСПРОМОЖНОСТІ ПІДПРИЄМСТВ В ГОТЕЛЬНІЙ ІНДУСТРІЇ

The article is devoted to the study of factors affecting the pricing process at hotel industry enterprises. In particular, these are the costs of the enterprise, the rate of profit, the level of demand, seasonality, geographical location, stardom, competition and others. Among these factors, more attention is paid to the study of the specifics of specific price strategies in the hotel industry, which serve as a factor in increasing the competitiveness of enterprises in this industry. The choice and implementation of a price strategy is one of the most important decisions of the management of a hotel enterprise, since profitability and competitiveness in the market depend on it. The list of such strategies includes: pricing strategy based on forecasting, differentiated pricing strategy, dynamic pricing strategy, discount strategy, instant sales strategy, and others. Each of these strategies has its own characteristics and is used by hotels depending on their specific goals, market conditions and customer segmentation. The development of a price strategy and price management tactics in the hotel sector is a constant iterative process. It is impossible to choose a strategy once and stay with it unchanged for a long period. It must constantly adapt to market conditions and situations. The chosen pricing strategy cannot be unambiguous, ideal or immutable. It should include a partial interweaving and combination of different strategies, taking into account the conditions of the external and internal environment. A combination and interchange of strategies is effective. The pricing strategy must be flexible and adapt to market changes. In this context, the strategy of elastic (flexible) price, which involves a quick response to fluctuations in market demand and supply, is of great importance. A correctly chosen pricing strategy can provide a hotel with increased competitiveness. The strategy of flexible prices should be recommended as a means of countering competitive pressure. The faster the hotel reacts to the actions of competitors, the greater the effect of its application. Overall, pricing strategy is a key element of success in the hospitality industry as it helps hotels to increase competitiveness, attract and retain guests, and increase revenue.