A Study on the Factors Influencing Consumer Purchase Decision Under the Live-Streaming Sales Model

In recent years,with the rapid development and popularization of Internet information technology,many new media platforms have risen rapidly,and major e-commerce companies have begun to explore the mode of livestreaming.Especially during the COVID-19 pandemic,due to the lockdown,live-streaming has become an important means of economic development in many places.Owing to its remarkable characteristics of timeliness,entertainment,and interactivity,it has become the latest and trendiest sales mode of e-commerce channels,reflecting huge economic potential and commercial value.This article analyzes two models and their characteristics of live-streaming sales from a practical perspective.Based on this,it outlines consumer purchasing decisions and the factors that affect consumer purchasing decisions under the live-streaming sales model.Finally,it discusses targeted suggestions for using the live-streaming sales model to expand the consumer market,hoping to promote the healthy and steady development of the live-streaming sales industry.

UK manufacturers'sales decline 10%in Q12024

UK manufacturers experienced a challenging start to 2024,with sales in the first quarter(Q1)down 10 per cent on the previous quarter,according to a report by Unleashed.However,year-on-year growth showed a modest increase of 2 per cent,reflecting the Bank of England’s assessment of weak growth in the manufacturing sector.

The Impact of The Three-Dimensional Cultivation Model on the Development of the Suzhou Tea Industry:Estimating Value of Output Per Acre and Diversified Sales Models

This article explores the impact of the three-dimensional cultivation mode on the development of the Suzhou tea industry,focusing on the diversified estimation of the value of output per acre and sales mode.It introduces the history and traditional cultivation practices of tea in Suzhou,as well as the current challenges and problems faced by the industry.An in-depth analysis was conducted on the overview and improvement plans of the three-dimensional cultivation mode,covering relevant technical methods.Based on this analysis,the impact of the three-dimensional cultivation on the value of output per acre was studied and predicted.Its potential and advantages were explored and compared with the effectiveness of traditional cultivation models.Additionally,the impact of the three-dimensional cultivation mode on sales was analyzed,examining its market adaptability and competitiveness,as well as its advantages in expanding sales channels and market coverage.The study also focused on the promoting effect of diversified sales models on the Suzhou tea industry,including direct consumption market development,tea processing product development and promotion,and the integration of tea culture and the tourism industry.To ensure sustainable development,the article evaluates the environmental impact,economic feasibility,social benefits,and farmer benefits of the three-dimensional cultivation model.Finally,the prospects for the development of the Suzhou tea industry were discussed,and the positioning and response strategies of the threedimensional cultivation model were proposed.

Consumers'Purchase Intention of Geographical Indication Agricultural Products under the Background of Live-streaming Sales:An Exploratory Research Based on Grounded Theory

Exploring the mechanism for the formation of consumer purchase intentions of geographical indication agricultural products in the context of live-streaming sales can provide an important reference for brand marketing of geographical indication agricultural products.In this study,in-depth interviews were conducted with consumers of geographical indication agricultural products.Based on grounded theory,open coding,axial coding and selective coding were performed for interview text.Finally,21 concepts,7 subcategories and 3 main categories were obtained,and a model of the formation mechanism of the purchase intention of geographical indication agricultural products under the background of live-streaming sales was constructed,that is,"consumer cognition-consumer attitude-consumer behavior".Among them,consumer cognition includes two dimensions:the type of geographical indication agricultural products and the live-streaming appeal strategy,i.e.,the personal cognition of consumer and the promotion of live-streaming host's strategy.Consumer attitude is value perception of consumers,mainly including two dimensions of functional value and emotional value.Consumer behavior is the consumer's willingness to buy.It has been concluded that the types of geographical indication agricultural products interact with the live-streaming appeal strategies.Through the intermediary of consumers'value perception,consumers'purchase intention is generated.Among them,resource-oriented geographical indication agricultural products adopt rational live-streaming appeal strategies,which can enhance the consumer's perception of functional value,thereby promoting their purchase intention;and cultural and creative geographical indication agricultural products brands adopt perceptual live-streaming appeal strategies,which can enhance the emotional value perception of consumers,thereby promoting their purchase intention.

Clothing Sales Forecast Considering Weather Information: An Empirical Study in Brick-and-Mortar Stores by Machine-Learning

Reliable sales forecasts are important to the garment industry. In recent years, the global climate is warming, the weather changes frequently, and clothing sales are affected by weather fluctuations. The purpose of this study is to investigate whether weather data can improve the accuracy of product sales and to establish a corresponding clothing sales forecasting model. This model uses the basic attributes of clothing product data, historical sales data, and weather data. It is based on a random forest, XGB, and GBDT adopting a stacking strategy. We found that weather information is not useful for basic clothing sales forecasts, but it did improve the accuracy of seasonal clothing sales forecasts. The MSE of the dresses, down jackets, and shirts are reduced by 86.03%, 80.14%, and 41.49% on average. In addition, we found that the stacking strategy model outperformed the voting strategy model, with an average MSE reduction of 49.28%. Clothing managers can use this model to forecast their sales when they make sales plans based on weather information.

Product Specification Analysis for Modular Product Design Using Big Sales Data

Big data on product sales are an emerging resource for supporting modular product design to meet diversified customers’requirements of product specification combinations.To better facilitate decision-making of modular product design,correlations among specifications and components originated from customers’conscious and subconscious preferences can be investigated by using big data on product sales.This study proposes a framework and the associated methods for supporting modular product design decisions based on correlation analysis of product specifications and components using big sales data.The correlations of the product specifications are determined by analyzing the collected product sales data.By building the relations between the product components and specifications,a matrix for measuring the correlation among product components is formed for component clustering.Six rules for supporting the decision making of modular product design are proposed based on the frequency analysis of the specification values per component cluster.A case study of electric vehicles illustrates the application of the proposed method.

Optimization of Electricity Purchase and Sales Strategies of Electricity Retailers under the Condition of Limited Clean Energy Consumption

In the process of my country’s energy transition,the clean energy of hydropower,wind power and photovoltaic power generation has ushered in great development,but due to the randomness and volatility of its output,it has caused a certain waste of clean energy power generation resources.Regarding the purchase and sale of electricity by electricity retailers under the condition of limited clean energy consumption,this paper establishes a quantitative model of clean energy restricted electricity fromthe perspective of power system supply and demand balance.Then it analyzes the source-charge dual uncertain factors in the electricity retailer purchasing and selling scenarios in the mid-to long-term electricity market and the day-ahead market.Through the multi-scenario analysis method,the uncertain clean energy consumption and the user’s power demand are combined to form the electricity retailer’s electricity purchase and sales scene,and the typical scene is obtained by using the hierarchical clustering algorithm.This paper establishes a electricity retailer’s risk decisionmodel for purchasing and selling electricity in themid-and long-term market and reduce-abandonment market,and takes the maximum profit expectation of the electricity retailer frompurchasing and selling electricity as the objective function.At the same time,in themediumand longterm electricity market and the day-ahead market,the electricity retailer’s purchase cost,electricity sales income,deviation assessment cost and electricity purchase and sale risk are considered.The molecular results show that electricity retailers can obtain considerable profits in the reduce-abandonment market by optimizing their own electricity purchase and sales strategies,on the premise of balancing profits and risks.

Comparison of Sales Prediction in Conventional Insights and Machine Learning Perspective

With the integration of global economy development and the rapid growth of science knowledge and technology,the needs of people’s consumption are increasingly personalized and diversified.Such a market background makes sales forecasting become an indispensable part of enterprise management and development.The definition of the sales forecasting is that based on the past few years’sales situation,the enterprises through systematic sales forecasting models estimate of the quantity and amount of all or some specific sales products and services in a specific time in the future.Accurate sales forecasting can promote enterprises to do better in future revenue,and can also encourage enterprises to set and keep an efficient sales management team.This paper will analyze traditional sales forecasting methods and sales forecasting methods based on big data models related to the perspective of machine learning,and then compare them.The research shows that the two sales forecasting methods have their own advantages and disadvantages.In the future,enterprises can adopt the two sales forecasting methods in parallel to maximize the utilization advantage of sales forecasting for enterprises.

Did weekly economic index and volatility index impact US food sales during the first year of the pandemic?

We explore the impacts of economic and financial dislocations caused by COVID-19 pandemic shocks on food sales in the United States from January 2020 to January 2021.We use the US weekly economic index(WEI)to measure economic dislocations and the Chicago Board Options Exchange volatility index(VIX)to capture the broader stock market dislocations.We validate the NARDL model by testing a battery of models using the autoregressive distributed lags(ARDL)methodology(ARDL,NARDL,and QARDL specifications).Our study postulates that an increase in WEI has a significant negative long-term effect on food sales,whereas a decrease in WEI has no statistically significant(long-run)effect.Thus,policy responses that ignore asymmetric effects and hidden cointegration may fail to promote food security during pandemics.

Production and sales growth of China's knitting industry slows down,export hits a record high

In 2022,the international economic and trade environment is in turmoil,singed by the pandemic,while domestic sales are weak.In the face of complex and severe situation at home and abroad,China textile industry has shown strong resilience.In 2022,36,000 enterprises in China's textile industry achieved more than 5,200 billion yuan in main business revenue and 200 billion yuan in profit,with operating margin of 3.9 percent.In 2022,China's textile and apparel exports reached a new record high of 340.95 bilion US dollars,up 2.5 percent year-on-year.

NEV Sales Surge In April

Retail sales of new energy vehicles(NEVs)in China jumped 85.6 percent year on year in April,data from the China Passenger Car Association(CPCA)showed on 9 May.A total of 527,000 NEVs were sold in China in April,down 3.6 percent from March,according to the CPCA.NEV sales of major domestic brands accounted for 70.5 percent of the total NEV sales in the country,the data revealed.

Research on the Effect of R&D Investment Intensity and Sales Expense on the Performance of Biomedical Enterprises

Objective To explore the influence of new drug R&D investment and sales expense on the performance of biomedical enterprises.Methods The financial statements of 76 listed biomedical enterprises for 5 consecutive years were selected,and the data were modeled to study the effect of R&D investment and sales expense on the performance of biomedical enterprises by using financial indicators as tools and statistical methods of multiple linear regression.Results and Conclusion Under the premise that the weak related factors such as enterprise scale,life cycle and asset-liability ratio are set as unrelated variables,the R&D investment intensity of biomedical enterprises is negatively correlated with the current performance,which also shows that the R&D of biomedical enterprises has the characteristics of high risk.Besides,the influence of early R&D investment is delayed.However,the sales expense of leading biomedical enterprises with large scales have higher proportion.Meanwhile the greater sales expense of the same enterprise in different periods,the better the enterprise performance is.Biomedical enterprises should consider their own development stage to develop more patented drugs.Besides,they must formulate plans for allocating reasonable sales personnel and cost expense to ensure that enterprises can obtain better benefits.

The Contribution Margin due to a Limiting Factor in the Presence of Several Sales Options: Actuality Is Not Always As It Appears at the Beginning of the Analysis

The analysis of company data useful for economic decisions,if not interpreted in an overall view of the company situation,can lead to wrong conclusions.This is the case when a company has to choose between several sales options for one or more products in the presence of a limiting factor.The continuation of the investigation often denies the initial analysis.Not everything is as it appears,therefore,at the beginning of the deepening of the data useful for economic decisions.As it is well known,the choices of profitability concerning the planning of the sale of company products take place,at least in the majority of cases,through the determination of the contribution margin,i.e.the profitability margin connected to the individual goods/services sold by the companies(selling price net of variable costs).The contribution margin can be determined with four objectives:(1)Determination of the yield of the single product,net of variable costs only.In this case,the margin defines unitary,from net product yield to unitary contribution margin.(2)Determination of the return on total sales of an individual product,net of variable costs.In this hypothesis,reference is made to the first level(or gross)contribution margin.(3)Determination of the ability of the individual product to contribute to the coverage of fixed costs common to the company.This margin is determined net of special product variable and fixed costs.This aggregate is defined as a Level II(or semi-gross)margin.(4)Determination of the useful value in the planning choices in case of presence of scarce productive factors.In this case,it must identify the so-called unitary margin for low factor.Here we will only deal with the problem of the use of the contribution margin in the presence of rare factors.To complete the analysis,below are some very brief considerations regarding,respectively,the unitary,level I,and level II contribution margin in order to better understand where the problem of the most convenient choice of income is located in the event of the presence of rare production factors,especially in an environment characterized by a plurality of sales options.

Research on the Impact of Social Media Live-Streaming on Consumer Purchasing Behavior and Its Coping Strategies

Due to the rapid development of network technology and the popularity of live-streaming,more and more businesses and individuals are using live-streaming to promote their products,giving rise to a new marketing model of social media live-streaming.Based on the social media live-streaming market,businesses or enterprises focus on tapping into new consumer demand,and social media live-streaming marketing has gradually expanded into various fields.Based on the SOR theory,this paper discusses how to improve the coping strategies of consumer purchasing behavior through social media live-streaming by analyzing the representative factors affecting consumer purchasing behavior such as anchors,brands,and live-streaming content in social media live-streaming.The results of this study will contribute to a better understanding of the relationship between social media live-streaming marketing models and consumer purchasing behavior and will help to reveal the potentials and limitations of social media live-streaming,thus providing valuable ideas and scientific basis for social media live-streaming marketing strategies.

论基于SALES模型的销售技巧

所谓SALES模型是将英文单词SALES分解成See、Ask、Listen、Eat和Smile等5个单词,这5个单词又分别对应看、问、听、吃、笑等5种销售人员需要掌握的"待客"技巧,本文将以此为基础进行分析,阐述一下销售人员如何提高自身的业务素质,不断改善客情关系。

The Variance Analysis on Enterprise of Different Ways of Sales：Based on Analysis of Enterprise Tax Burden and Cash Flows

在目前竞争激烈的市场经济环境中,许多企业为了维持或扩大自己商品的市场份额,往往采取多种多样的销售方式,以达到促销的目的,而且随着经济的发展以及互联网时代的来临,销售形式也越来越多.基于企业税负与现金净流量的基础上对9 种销售方式从企业税负与现金净流量两个方面进行了分析与研究.研究发现税负高的销售方式,可能其现金净流量也比较高,因此企业在经营销售过程中,不能只考虑税负的问题,还要考虑企业现金净流量的问题,甚至是企业的整体发展问题以及顾客对销售方式的接受程度.

Sales Prediction and Product Recommendation Model Through User Behavior Analytics

The COVID-19 has brought us unprecedented difficulties and thousands of companies have closed down.The general public has responded to call of the government to stay at home.Offline retail stores have been severely affected.Therefore,in order to transform a traditional offline sales model to the B2C model and to improve the shopping experience,this study aims to utilize historical sales data for exploring,building sales prediction and recommendation models.A novel data science life-cycle and process model with Recency,Frequency,and Monetary(RFM)analysis method with the combination of various analytics algorithms are utilized in this study for sales prediction and product recommendation through user behavior analytics.RFM analysis method is utilized for segmenting customer levels in the company to identify the importance of each level.For the purchase prediction model,XGBoost and Random Forest machine learning algorithms are used to build prediction models and 5-fold Cross-Validation method is utilized to evaluate their.For the product recommendation model,the association rules theory and Apriori algorithm are used to complete basket analysis and recommend products according to the outcomes.Moreover,some suggestions are proposed for the marketing department according to the outcomes.Overall,the XGBoost model achieved better performance and better accuracy with F1-score around 0.789.The proposed recommendation model provides good recommendation results and sales combinations for improving sales and market responsiveness.Furthermore,it recommend specific products to new customers.This study offered a very practical and useful business transformation case that assists companies in similar situations to transform their business models.

Parameters Optimization Using Genetic Algorithms in Support Vector Regression for Sales Volume Forecasting

Budgeting planning plays an important role in coordinating activities in organizations. An accurate sales volume forecasting is the key to the entire budgeting process. All of the other parts of the master budget are dependent on the sales volume forecasting in some way. If the sales volume forecasting is sloppily done, then the rest of the budgeting process is largely a waste of time. Therefore, the sales volume forecasting process is a critical one for most businesses, and also a difficult area of management. Most of researches and companies use the statistical methods, regression analysis, or sophisticated computer simulations to analyze the sales volume forecasting. Recently, various prediction Artificial Intelligent (AI) techniques have been proposed in forecasting. Support Vector Regression (SVR) has been applied successfully to solve problems in numerous fields and proved to be a better prediction model. However, the select of appropriate SVR parameters is difficult. Therefore, to improve the accuracy of SVR, a hybrid intelligent support system based on evolutionary computation to solve the difficulties involved with the parameters selection is presented in this research. Genetic Algorithms (GAs) are used to optimize free parameters of SVR. The experimental results indicate that GA-SVR can achieve better forecasting accuracy and performance than traditional SVR and artificial neural network (ANN) prediction models in sales volume forecasting.

A New Type of Combination Forecasting Method Based on PLS——The Application of It in Cigarette Sales Forecasting

Cigarette market is a kind of monopoly market which is closed loop running, it depends on the plan mechanism to schedule producing, supplying and selling, but the “bullwhip effect” still exists. So it has a fundamental significance to do sales forecasting work. It needs to considerate the double trend characteristics, history sales data and other main factors that affect cigarette sales. This paper depends on the panel data of A province’s cigarette sales, first we established three single forecasting models, after getting the predicted value of these single models, then using the combination forecasting method which based on PLS to predict the province’s cigarette sales of the next year. The results show that the prediction accuracy is good, which could provide a certain reference to cigarette sales forecasting in A province.

Empirical Research on Relationship between Corporate Brand Value and Apparel Market Sales Revenue of Apparel Enterprise

Among the existing researches on brand value,few of them focused on the demands of the companies in the clothing industry to brand value. There were three innovation points as follows: first of all,two key demands of apparel companies to existing brand value were summarized through investigation; then,the concept of rapid brand value evaluation and multi-dimensional factors( MDFs) was presented; finally,the relationship between apparel corporate brand value and the sales revenue in apparel market with the sales revenue was proven by empirical research.During the process of empirical study,the sales revenue data of 66 apparel enterprises domestic and abroad were collected,and the regression analysis has been done on the relationship between the corporate brand value and the apparel market sales revenue of apparel enterprise. The empirical research showed that they were positively correlated to each other. This conclusion presented a method of judging brand value development trend quickly by single indicator.