Actual Sales Research Articles

Impact of mindfulness-based programs on reducing stress and enhancing academic performance in college students

Utilizing strategic marketing techniques that are especially created for obtaining qualified leads and attracting them into your sales funnel is the foundation of social media lead generation. Social media sales lead generation enables businesses to find potential customers. More significantly, these leads serve as a conduit to potential clients, enabling businesses to approach them with special deals and win their business. It speaks of the actions made by companies on social media platforms to find new leads. Leads being sent into the sales funnel, the leads being qualified via various channels (chatbots, email, social media, phone, meeting, or sales call), and finally the prospects being turned into actual sales. Building relationships with prospective customers, even if they are not immediately looking to purchase a good or service, is the goal of lead nurturing. They might have subscribed to your newsletter or followed you on social media, but they might not be prepared to make a purchase just yet. The last phase of social media leads is conversion. It speaks to the process of persuading potential customers to make judgments about their purchases. In this study, a sample size of 310 company managers in different types of companies based out of Pune was utilized. To streamline data collection, a simple random probability sampling method was employed, and respondents were drawn from diverse descriptive profiles. Descriptive Statistics, One way Anova and Independent sample t test were used to analyze the data collected from the respondents. In conclusion, there are many interesting potentials for social media and lead generation in the future. Businesses now have a wealth of chances to create leads and expand their businesses through social media thanks to the development of AI, the expansion of voice and visual search, the concentration on video, the advent of social commerce, and the greater usage of influencer marketing.

An Applied Group-centric Approach for Empowering Banana Farmer's Agripreneurial Culture

Agripreneurship is a dynamic strategy which enables farmers to generate sustainable income and thereby providing economic benefits to farmers. In the pursuit of sustainable agriculture, agripreneurship emerges as a dynamic strategy, empowering farmers to cultivate not only crops but also a reliable stream of income. This study explores the transformative impact of agripreneurship ventures on banana farming in Chirakkara Panchayat, Kerala. Through participatory action and learning, a heptalogical model was developed, addressing identified constraints in three phases: constraint identification, intervention, and market analysis. The study engaged 10 farmers with a minimum of three years of experience, strategically chosen from Krishi Bhavan clusters and VFPCK groups. The intervention focused on value addition, introducing products like banana jam and pseudostem squash. Results indicate a positive shift in the economic landscape, with the sale of 355 bottles of squash and 260 bottles of jam generating a profit of Rs. 11,219 and a favorable Benefit-to-Cost ratio of 1.36 achieved in a short sales period (13 weeks). Market analysis demonstrated weekly, monthly, and cumulative sales trends. Simulation scenarios based on actual sales and a 20% increase in quantity, cost, and returns projected promising outcomes. Simulation based on the actual sales yielded a net return of Rs. 1,34,628 per year, while the simulation with increased parameters suggested potential net returns of Rs. 1,61,554 per year, emphasizing the agripreneurial success potential. The agripreneurial model, aligned with the heptalogical model of entrepreneurship, proved relevant in areas where banana prices are low. The success in Chirakkara inspired confidence in the farming community, fostering diversification and product expansion based on seasonal inputs. In conclusion, the study highlights the significant impact of participatory action and learning in transforming banana farming. The success achieved in Chirakkara provides a scalable blueprint for the broader implementation of agripreneurial models, showcasing the potential for holistic economic development and improved livelihoods for banana farmers.

РЕТРОСПЕКТИВНИЙ АНАЛІЗ ВАРТОСТІ ПОХИБКИ ПРОГНОЗУ ДЛЯ ПОБУДОВИ БАЛАНСУЮЧИХ ГРУП ВИРОБНИКІВ З ВІДНОВЛЮВАНИХ ДЖЕРЕЛ ЕНЕРГІЇ

The significant increase in the installed capacity of power plants with renewable energy sources and the imbalance of the financial system of the wholesale electricity market of Ukraine prompted the Ministry of Energy to develop an alter-native support mechanism for RES producers. The introduction of a feed-in tariff (FIP), which compensates for the difference between the actual sale price of electricity and the "green" tariff, can help producers receive more money immediately after the electricity is released. However, studies have shown that exiting a balancing group without form-ing a new one can lead to increased costs associated with forecasting error. It is important for manufacturers to form independent balancing groups to compensate for negative consequences. The findings of the article show that there is no single optimal balancing group for all manufacturers, but some groups are often repeated. Switching to a separate balancing group can have a significant economic effect for the manufacturer, reducing the cost of forecasting error compared to being solely responsible for the imbalance. However, the balancing group determined by the method of retrospective calculation of the cost of the forecast error is not stable in the long term. Ref. 8, fig. 2, tab. 3 Key words: renewable sources, electricity market, short-term forecasting, forecast interval, deep learning neural net-works, cost estimation.

The Effect of Posted Prices on Auction Prices: An Empirical Investigation of a Multichannel B2B Market

Although multichannel sales strategies have become common due to the use of advanced information technologies, how one trading mechanism can influence the outcome of another, especially in the B2B market, remains largely underexplored. This paper investigates the effect of price and quantity information from an online posted-price presales channel on the performance of the century-old sequential Dutch auction system. Sellers can control the price paid and make a proportion of their stock available in auction presales. Anything left after presales is sold via auctions. Our analysis of nearly 1.5 million flower lots reveals a positive effect with higher auction prices and total revenue for lots listed in presales than for lots that are not. The result holds even for lots with no actual sales in the presales, indicating that buyers pay close attention to the additional information from the posted-price presales channel. By teasing out the information effect of presales prices and presales quantity on auction prices, we evaluate a number of pricing strategies. The results suggest that selling at a high price in presales is still more beneficial than selling more by discounting prices.

Taking the MOBA (Multiplayer Online Battle Arena) Game "League of Legends" as a Study Case

By 2020, the actual sales of China's game market reached 296.513 billion yuan, an increase of 17.826 billion yuan over the previous year, a year-on-year increase of 6.40%. Officially, in 2021, games and e-sports were written into the "14th Five-Year Plan" cultural industry plan, and in the people, more and more e-sports schools have sprung up, and more parents have begun to understand and accept the existence of games. As a hot topic in recent years, the game industry should receive more attention, so this article takes the design of characters in video games as the starting point to make some research on how games develop and maintain customers, hoping that everyone can trigger more thinking about the game industry.

Managing presales with two payments and return policy when consumers are time‐inconsistent

We consider a firm using presales with two payments—including an upfront deposit and a postponed arrear—to sell a product to time‐inconsistent consumers under various return policies. With the passage of time, consumers decide whether to preorder the product by paying the deposit, whether to settle the balance by paying the arrear by the due date, and, when returns are allowed, whether to return a fully paid product for a refund after its fitness is revealed. Canceling a preorder before settling the balance or failing to settle the balance on time causes a deposit payer to lose deposit. Consumers use quasi‐hyperbolic discounting to weigh intertemporal utilities and exhibit time inconsistency when making purchase decisions. Our results indicate that, as consumers' time inconsistency weakens, the firm manages the deposit and the arrear to induce no, some, and all deposit payers, in that sequence, to settle the balance. It has mixed effects on the profit of the firm and consumer surplus but always causes social welfare to improve since actual sales increase. Regarding the choice of return policy, the firm prefers full returns while consumers prefer no returns when consumers are time‐inconsistent; they share similar preferences when consumers are time‐consistent. Moreover, we find that consumers' imbalance (caused by time‐inconsistent preference) is less likely to occur as consumers are more sophisticated, as the presales period shortens, as the product value increases, or as the product is more likely to be a good fit. In addition, allowing a quick refund for fully paid but not yet delivered products can completely deter imbalance, while this option harms the firm and may or may not benefit consumers. All this yields novel insights into managing presales with two payments and returns when consumers are time‐inconsistent.

Consumer in Sustainable Textiles: A Scientometric Review

In the realm of textiles, numerous brands are endeavouring to embrace sustainable development and prioritize consumer orientation. Nevertheless, during the actual sales process, sustainability is frequently communicated to consumers through the medium of sustainable fabrics, often failing to elicit profound resonance among them. This study uses scientometry to conduct a comprehensive and quantitative analysis of the current status of the textile industry based on consumers, aiming to explore the current situation and development direction between consumers and the sustainable behaviour of the textile industry. From 2014 to 2023, the Scopus database included 1010 articles related to the clothing field, based on user-oriented research. The analysis results of this study are as follows: 50 research keywords appear in the knowledge base; 8 current research areas; 10 research hotspots; 10 important knowledge references in this field; Practitioners have a better understanding of the current status and trends of sustainable development in the clothing field under user guidance. The research results provide suggestions for the sustainable development of the textile industry, promote effective communication between the textile industry and consumers on a sustainable level, and help improve the consumer stickiness and sustainable development of the textile industry.

An Analysis about the Pure Electric Vehicle Sales Prediction Based on the BP Neural Network

With the increasing demand for pure electric vehicles among Chinese residents, being able to accurately forecast the future sales volume of pure electric vehicles has become a hot topic of research in today’s society. In this paper, the sales volume of pure electric vehicles from January 2020 to December 2021 have been collected. And based on the monthly sales volume of 24 months, the BP neural network model has been used to test the data, and the sales volume of pure electric vehicles from January to December 2021 has been predicted with the trained model. Comparing its prediction results with the actual sales volume, it has been found that the BP neural network model can predict the future sales volume of electric vehicles well; and based on the prediction results, it can provide some useful references for the production planning and the raw material procurement.

Estimation of Sales Decline Risk Based on COVID-19 as a Model

Owing to the coronavirus disease 2019 (COVID-19), many companies experienced a sharp and significant decline in sales, resulting in a major crisis. This study proposes a method for estimating the risk of a sudden and significant decline in sales to tackle the aforementioned issue. The method is based on the distributional characteristics calculated from historical data of companies mainly in the transportation industry, which are vulnerable to disasters, using the damage caused by COVID-19 as a lesson. Furthermore, we conduct an empirical analysis using the proposed stochastic model for the case of the All Nippon Airways Co., Ltd. (ANA), a major Japanese airline company. The results are as follows: (1) the sales change rates are normally distributed before COVID-19, but inclusion of post-COVID-19 data produced asymmetric distribution of sales change rates; (2) the proposed statistic is log-normally distributed (including post-COVID-19 data) for a time interval of two or three years; (3) the probability of actual sales decline was estimated to be between 0.1% and 1.6% in fiscal year (FY) 2020 and FY2021 post-COVID-19; (4) the estimated risk of future sales decline is well-grounded in light of past actual values.

Average booking curves draw exponential functions

The booking curve time series in perishable asset industries, including hotels, has been studied to manage a demand-supply condition or revenue management (RM). However, due to changing times, e.g., economy and technology, many RM practitioners have put their efforts into catching on to peoples’ booking pattern shifts, representing macroscopic changes in booking curves. We investigate macroscopic aspects of booking curves with actual sales data across six properties in the hotel and car-rental industries for two years, considering the difference in the economic environment characterized before and during the COVID-19 epidemic. We explain a new cross-industry and cross-economic-environment universal statistical law: average booking curves draw exponential functions (the ABCDEF law). We provide a basis for the ABCDEF law from three perspectives; data validation, modeling in the statistical physics framework, and empirical justification for the causality of the model. The ABCDEF law derives informative statistics to quantitatively measure peoples’ buying behavior even in time or society changes, which is expected to contribute to management in various industries.

Responsibility of Land Deed Officials in the Making of Sale and Purchase Deeds with Improper Transactions

Before a Land Deed Official make a land sale and purchase deed so that a transfer of rights over land and buildings can arise, he/she must carry out tax payment services by the parties. This is sometimes different from the reality, in which in making a sale and purchase deed the Land Deed Official commits an act of manipulating the tax price to be less than the actual sale and purchase price. The research method used in this legal research is normative. The data used in this legal research is secondary data. The results of research in writing this law are that there are still Land Deed Making Officials in making sale and purchase deeds who openly suggest reducing the value of land transactions to as small as possible for service users with the aim of ensuring that the tax costs paid by the parties are not large. Therefore, the Land Deed Official must bear the sanctions in the form of administrative sanctions, civil sanctions and criminal sanctions.Keywords: Tax Violations, Land Deed Official, Land Sale and Purchase Deed.

Using consumer preference data in forecasting demand in apparel retailing

PurposeThis study was carried out to analyze the importance of consumer preference data in forecasting demand in apparel retailing.MethodologyTo collect preference data, 729 hypothetical stock keeping units (SKU) were derived using a full factorial design, from a combination of six attributes and three levels each. From the hypothetical SKU's, 63 practical SKU's were selected for further analysis. Two hundred two responses were collected from a store intercept survey. Respondents' utility scores for all 63 SKUs were calculated using conjoint analysis. In estimating aggregate demand, to allow for consumer substitution and to make the SKU available when a consumer wishes to buy more than one item in the same SKU, top three highly preferred SKU's utility scores of each individual were selected and classified using a decision tree and was aggregated. A choice rule was modeled to include substitution; by applying this choice rule, aggregate demand was estimated.FindingsThe respondents' utility scores were calculated. The value of Kendall's tau is 0.88, the value of Pearson's R is 0.98 and internal predictive validity using Kendall's tau is 1.00, and this shows the high quality of data obtained. The proposed model was used to estimate the demand for 63 SKUs. The demand was estimated at 6.04 per cent for the SKU cotton, regular style, half sleeve, medium priced, private label. The proposed model for estimating demand using consumer preference data gave better estimates close to actual sales than expert opinion data. The Spearman's rank correlation between actual sales and consumer preference data is 0.338 and is significant at 5 per cent level. The Spearman's rank correlation between actual sales and expert opinion is −0.059, and there is no significant relation between expert opinion data and actual sales. Thus, consumer preference model proves to be better in estimating demand than expert opinion data.Research implicationsThere has been a considerable amount of work done in choice-based models. There is a lot of scope in working in deterministic models.Practical implicationThe proposed consumer preference-based demand estimation model can be beneficial to the apparel retailers in increasing their profit by reducing stock-out and overstocking situations. Though conjoint analysis is used in demand estimation in other industries, it is not used in apparel for demand estimations and can be greater use in its simplest form.Originality/valueThis research is the first one to model consumer preferences-based data to estimate demand in apparel. This research was practically tested in an apparel retail store. It is original.

The analysis of the range of antihistamines at the pharmaceutical market of Ukraine

Aim. To analyze the range of antihistamines (AH) at the pharmaceutical market of Ukraine.
 Materials and methods. The analysis of the range of drugs was carried out in accordance with the State Register of Medicines of Ukraine as of February 2023, the weekly “Apteka” to determine the data of actual sales of AH. Statistical, frequency and graphic methods were used in the analysis. The information was processed using Microsoft Office Excel. 
 Results. As a result of the frequency analysis, it was found that the pharmaceutical market of Ukraine as of February 2023 of AН for systematic use was represented by 170 trade names (TN) and 135 international non-proprietary names (INN). The leaders by INN were Desloratadine (34 TN) and Levocetirizine (26 TN). According to the State Register of Medicines of Ukraine as of February 2023, there were different drug forms, namely: tablets, solutions, drops, syrups, dragees and granules for the preparation of oral suspension. The largest share was AH in the form of tablets (70.59 %), solutions, drops, and syrups accounted for almost the same percentage (10 %, 9,41 %, 8,82 %), and 2 % of AH were registered in the form of dragees. The ratio of AH producing countries at the pharmaceutical market of Ukraine by TN was also determined. It was found that among the dosage forms presented, 62 % were of foreign production. AH of domestic production were 38 %; moreover, the largest market share (19 %) was produced by LLC Pharmaceutical Company “Zdorovye”. According to the results of the official data of weekly “Apteka” on the actual consumption of AH, it was found that AH of the second generation were the most popular both by INN and TN. It is due to their pharmacokinetic properties and the optimal price/effectiveness ratio.
 Conclusions. Based on the above, we can conclude that the pharmaceutical market of Ukraine is represented by a wide range of AH in various forms of production and various manufacturers. This leads to a wide choice of drugs of this group among the population, pharmaceutical and medical professionals.

A Dynamic Export Product Sales Forecasting Model Based on Controllable Relevance Big Data for Cross-Border E-Commerce

Abstract To improve the prediction accuracy of export product sales, this paper constructs a dynamic export product sales prediction model based on controlled relevance big data for cross-border e-commerce to improve sales prediction’s scalability and dynamic evolution. Based on the traditional prediction model, a big data controllable clustering algorithm is used to divide the data into several macro-clusters by data dimensions to determine the number of class clusters and the location of centroids. The K-mean algorithm is used to estimate and categorize the indicators affecting the prediction online, to dig out the key factors affecting the prediction of export product sales, and to establish a dynamic prediction model. The analysis results show that the plausibility measure of the proposed model is 21.9, and the error coefficient is 5.1, which are the smaller values in the reference interval. The average prediction error ratio is 2.25%, the average confidence level is 93.05%, and the error efficiency between predicted sales and actual sales is only 0.98%. Thus, the prediction model proposed in this paper improves the prediction effect of export product sales and has high practical value.

Seize market opportunities: market segmentation, profile and monitoring through user-generated content

PurposeThe purpose of the study is to propose a normative approach for market segmentation, profile and monitoring using computing and information technology to analyze User-Generated Content (UGC).Design/methodology/approachThe specific steps include performing a structural analysis of the UGC and extracting the base variables and values from it, generating a consumer characteristics matrix for segmenting process, and finally describing the segments' preferences, regional and dynamic characteristics. The authors verify the feasibility of the method with publicly available data. The external validity of the method is also tested through questionnaires and product regional sales data.FindingsThe authors apply the proposed methodology to analyze 53,526 UGCs in the New Energy Vehicle (NEV) market and classify consumers into four segments: Brand-Value Suitors (32%), Rational Consumers (21%), High-Quality Fanciers (26%) and Utility-driven Consumers (21%). The authors describe four segments' preferences, dynamic changes over the past six years and regional characteristics among China's top five sales cities. Then, the authors verify the external validity of the methodology through a questionnaire survey and actual NEV sales in China.Practical implicationsThe proposed method enables companies to utilize computing and information technology to understand the market structure and grasp the dynamic trends of market segments, which assists them in developing R&D and marketing plans.Originality/valueThis study contributes to the research on UGC-based universal market segmentation methods. In addition, the proposed UGC structural analysis algorithm implements a more fine-grained data analysis.

Boosting eCommerce sales with livestreaming in B2B marketplace: A perspective on live streamers’ competencies

This study identifies and tests how the competencies of live streamers affect the B2B livestreaming sales performance, with a particular focus on the moderating role of product type and product competitiveness. By analyzing a fine-grained livestreaming event dataset consisting of 915 brand firms from a B2B livestreaming platform and matching them with actual sales transactions, the results show that the cognitive and social competencies of live streamers have a positive effect on the sales performance, while emotional competence has a negative effect. More importantly, this study provides strong evidence that the effects of live streamers' competencies on online sales are contingent on product type and competitiveness. Specifically, live streamers' cognitive and emotional competencies have a more salient impact on experience product sales than search products. On the other hand, product competitiveness moderates the interaction effect between product type and live streamer competencies.

부동산 집합투자기구의 자산 매각시점에 영향을 미치는 요인분석

This study analyzed the factors affecting the successful sale of real estate by private equity funds. In particular, it was examined through a comparison between the group in which the real estate, which is an operating property, was successfully sold and the group that was not. Previous previous studies focused on the correlation between major economic indicators and the return of real estate funds for the entire duration of real estate funds. However, this study is meaningful in that it attempted to analyze the rate of change in major economic indicators during the actual sale period. As a result of the analysis, it was significantly confirmed that the successful sale of private equity funds was a negative relationship with the level of corporate bond issuance rates, and a negative relationship with the increase in the industrial production index and the bank loan delinquency rate. On the other hand, the housing sales index in Seoul was limited, but it was confirmed that it was a positive relationship. Through this relationship analysis, collective investment businesses that operate private equity real estate funds can devise a timing of sale that can maximize sales profits. In addition, investors are expected to be meaningful to stakeholders, such as being able to use it as a decision-making indicator for the timing of the sale of property by real estate funds.

Evaluating and Predicting the Long-Term Impact of the COVID-19 Pandemic on Manufacturing Sales within South Africa

Manufacturing sales forecasting is crucial for business survival in the competitive and volatile modern market. The COVID-19 pandemic has had a significant negative impact on the demand and revenue of firms globally due to disruptions in supply chains. However, the effect of the pandemic on manufacturing sales in South Africa (SA) has not been quantified. The progress of the country’s manufacturing sector’s recovery after the pandemic remains unknown or unquantified. This paper uses a Box–Jenkins approach to time series analysis to produce long-term forecasts/projections of potential manufacturing sales, thereby quantifying the effects of the pandemic shock when the projections are compared with actual manufacturing sales. The Box–Jenkins approach is chosen because of its credibility and ability to produce accurate forecasts. Long-term projections enable organisations to plan ahead and make informed decisions, develop successful recovery plans, and navigate through similar economic shocks in the future, thereby ensuring long-term business survival and sustainability of the manufacturing sector. The SARIMA (0,1,1)(0,1,1)12 model best fits the SA manufacturing sales data according to the Akaike information criterion (AIC) and Bayesian information criterion (BIC), as well as the root mean square error (RMSE) and the mean absolute error (MAE). The results indicate that SA’s manufacturing sector was negatively impacted by the COVID-19 pandemic from about April 2020, but by November 2020 manufacturing sales had recovered to levels similar to projected levels had the COVID-19 pandemic not occurred. Long-term forecasts indicate that SA manufacturing sales will continue to increase. The manufacturing sector continues to grow, leading to increased employment opportunities and a boost to the gross domestic product (GDP).

Big Market Delusion: The Case of Electric Vehicle Stocks

The hallmark of a big market delusion is when the stock prices of all the firms in an evolving industry rise together, even though they are in direct competition with one another and with established firms in the industry. Investors become so enthusiastic about the market that they price each firm as if it will be a major success story. As a result, the aggregate value of the industry exhibits a fallacy of composition in which the sum of the parts (the market values of the individual firms) exceeds any reasonable estimate of the value of the business. The result is that prices become unsustainable. Over time, as data on actual sales and earnings become available, stock prices of the vast majority, if not all, of the companies in the “big market” decline. The case of the market for light duty electric vehicles provides a perfect illustration of the rise and fall of a big market delusion.

The use of janus kinase inhibitors anti-interleukin therapy in the Russian Federation with COVID-19: pharmacoepidemiological study

The objective of this study was to assess the volume of consumption of anti-interleukins and janus kinase inhibitors in the period 2018–2021, used as well as preventive pathogenetic therapy of COVID-19.Materials and methods. Based on actual sales data in the segments of public procurement (including regional and federal preferential drug provision) and retail sales segment in the period 2018–2021, obtained from the IQVIA database. All data was recalculated into the number of established daily doses (DDDs), with the calculation of pharmacoepidemiological indicators «Incidence of prescribing drugs», «Cumulative risk of prescribing drugs», as well as «Prescribing prevalence per year» for tocilizumab, olokizumab, levilimab, sarilumab, kanakinumab, anakinra, baricitinib, tofacitinib and upadacitinib.Results and discussion. The growth of total sales volumes was demonstrated in all market segments, but mostly in the segment of regional purchases, primarily related to the purchase of these groups of medicines for COVID-19 therapy in the period 2020–2021. It was demonstrated that the increase in the number of cases, accompanied by the expansion of prescribing preventive pathogenetic therapy, led to a twofold increase in the number of new cases of prescribing of janus kinase inhibitors and an increase in this indicator for anti-interleukins by 1.5 times, taking into account the estimated number of patients with moderate and severe COVID-19. The cumulative risk of prescribing these classes of drugs increased proportionally: for JAK inhibitors from 14 to 32%, and for anti-IL drugs from 38 to 69%. Calculations showed that the proportion of people over 18 years of age in the Russian Federation who received at least one dose of janus kinase inhibitors and anti-interleukins increased many times, in 1000 times and 500 times.Conclusions. Taking into account the expansion of the use of these groups of medicines, careful monitoring of information about their safety is required.