Trends In Sales Research Articles

Machine Learning Sales Forecasting for Food Supplements in Pandemic Era

The Covid-19 pandemic has brought a lot of concerns about the operational and financial situation of businesses. Forecasting is crucial as it guides businesses through these critical points. Forecasting has become even more critical in the pandemic environment and therefore the necessity of using an accurate forecasting method has increased. Taking this into consideration, in this study, intelligent machine learning methods, namely; Grey Model (GM), Artificial Neural Network (ANN) and Support Vector Machine (SVM) are applied to make a short-term prediction of a food supplement, a product whose demand increased with the pandemic situation. Eighty-five percent of the historical data is used for training purposes and fifteen percent of the data is used for measuring accuracy. The accuracy of the models employed is improved with parameter optimization The accuracy performance indicator Mean Absolute Percentage Error (MAPE) showed that all methods give superior results when the historical data has an increasing sales trend. This study presents an important consideration for businesses and has a potential to be generalized for a business whose sales have an increasing trend not only because of the pandemic but also for any reason.

Consumer purchasing response to mandatory genetically engineered labeling

On January 1, 2022, mandatory genetically engineered (GE) food disclosure labeling became required nationwide in the United States. To date, the only mandatory GE labeling law implemented in the U.S. was Vermont’s Act 120. This article examines the consumer purchasing response to the implementation of Act 120 using store-level scanner data of food purchases. We measure the effects of Act 120 on the grocery store sales of (1) certain GE-labeled products, (2) non-GMO labeled products, and (3) organic products in Vermont. Using a difference-in-differences approach, we compare stores in Vermont to stores in control states before and after the law was passed, implemented, and repealed. We find that during Act 120′s one-month implementation period, sales of GE-labeled soup products decreased by 5.9%, sales of non-GMO labeled products increased by 2.5%, and sales of organic products increased by 1.7%. Areas with lower levels of education saw bigger increases in sales of non-GMO and organic products. Sales trends, however, reversed for all three product categories after Act 120 was repealed. The sales of GE-labeled soup products actually increased by 6% after the law was repealed in the medium term, suggesting improved attitudes towards GE products over time.

Unit Sales Trends of Hip Arthroplasty Procedures Performed in Ontario in 2020

In March 2020, an emergency was declared in the province of Ontario owing to the COVID-19 pandemic. From March 19 to May 26, all elective total hip arthroplasty (THA) and total knee arthroplasty (TKA) procedures were deferred. The purpose of this study was to review the unit sales data of THA and TKA procedures in Ontario over this time period to identify any trends in surgical activity. Unit sales data for all THA and TKA procedures performed in Ontario between January 2019 and December 2020 were reviewed. All orthopedic companies contributed to the data set. Femoral stems were considered an indicator of THA procedures, while primary femurs were considered an indicator of TKA procedures. Data were analyzed to determine trends in THA and TKA procedures during the deferral period and the relative change in THA and TKA activity following resumption. There was a 53% reduction in THA activity between March and May of 2020 when compared to the same interval in 2019. From June to September of 2019, THA sales activity was 87.3% that of TKA. From June to September of 2020, THA sales activity was 122.8% that of TKA. Provincial directives aimed at controlling the outbreak of COVID-19 contributed to a substantial reduction in THA and TKA activity in 2020. Compared to 2019, there was a disproportionate increase in THA compared to TKA activity in 2020 following resumption of surgical services. These data have implications in recovery planning for the surgical backlog of THA in Ontario.

Impact of COVID-19 on the Sales Trend of E-commerce In Malaysia

On March 2020, the Coronavirus pandemic spread globally, and the tendencies have been profoundly altered. This pandemic also made doing business extremely tough. As international borders tighten, people around the world are forced to stay in their home and shops are closed, businesses are now facing new challenges and difficulties. Many businesses are facing closure either temporarily or permanently, leaving workers with months of financial burden and anxieties. Consumer behaviour was forced to alter on a massive scale. People in isolation or under lockdown cannot go about their daily lives because local businesses have been ordered to close for safety reasons. This paper investigates the current e-commerce trends in this Covid-19 pandemic, as well as how e-commerce advancements may affect customer behaviour in the future and the growth of e-commerce due to Covid-19. E-commerce has become a substitute in this pandemic and e-retailers must provide quality products that usually customers buy in physical stores traditionally.

Improved sales time series predictions using deep neural networks with spatiotemporal dynamic pattern acquisition mechanism

The ability to predict product sales is invaluable for improving many of the routine decisions essential for the running of an enterprise. One significant challenge of sales prediction is that it is hard to dynamically capture changing dependent patterns along the sales time line, because sales are often influenced by complicated and changeable market environment. To address this issue, we model sales prediction as a task of multivariate time series (MTS) prediction, and propose a Spatiotemporal Dynamic Pattern Acquisition Mechanism (SDPA), which comprises four components, described below: (1) In the processing of input data: A Spatiotemporal Dynamic Kernel (SDK) component is designed for MTS to effectively capture different dependent correlation patterns during different time periods. (2) In terms of model design: A Simultaneous Regression (SR) component is proposed to dynamically detect stable correlations by using co-integration based dynamic programming over different time periods. (3) A novel Hierarchical Attention (HA) component is designed to incorporate SDK to detect spatiotemporal attention patterns from the captured dynamic correlations. (4) In the design of loss function, A Change Sensitive and Alignment component (DC) is proposed to provide more future information based on future trend correlations for better model training. The four components are incorporated into a unified framework by considering Homovariance Uncertainty (HU). This is referred to as SDPANet and contributes to model training and sales prediction. Extensive experiments were conducted on two real-world datasets: Galanz and Cainiao, and experimental results show that the proposed method achieves statistically significant improvements compared to the most state-of-the-art baselines, with average 41.5% reduction on RMAE, average 39.5% reduction on RRSE and average 46% improvement on CORR. Experiments are also conducted on two new datasets, which are Traffic and Exchange-Rate from other fields, to further verify the effectiveness of the proposed model. Case studies show that the model is capable of capturing dynamic changing patterns and of predicting future sales trends with greater accuracy.

Strategi Pertumbuhan Usaha Cafe Titik Nol

To develop a business or business to be successful, it depends on how to run the business according to the rules and pay attention to the changes that occur. Zero point cafe is a self-owned business, not a child of an entrepreneur or a branch of a medium or large business. Zero point cafe is managed by one of Hasanuddin University's undergraduate program students named Ajrullah Sumaji, this cafe is located on Jln Statistik, Tamalanrea Indah, Makassar City.This study aims to determine the business growth strategy and the factors that influence the Café Point Zero business. To achieve this goal, the researchers used data collection techniques through individual interviews. The data obtained from the research results are processed using qualitative descriptive data to find out what strategies are carried out or used by Point Zero Café in increasing its business growth and what factors influence the business growth strategy of Point Zero Café..The results showed that (1) the Point Zero café business growth strategy was carried out by making more use of social media as a tool to promote the cafe, fostering existing relationships with cafe employees to enliven or find regular cafe customers, providing the best service to all consumers, take advantage of technological developments in running a business. always trying to improve facilities to improve customer convenience, trying to maintain a consistent taste of the products sold. (2) the factors that influence the growth strategy of the Zero Point Café Business, namely internal factors and external factors. Internal factors include strengths and weaknesses. The strengths of Point Zero Café are the café's image, the strategic location of the café, the quality of the products offered, adequate advice and infrastructure, having regular customers, and perfecting innovative and creative products. Meanwhile, the weakness of this café is the lack of promotional activities, marketing network, and limited marketing personnel. External factors include opportunities and threats. Opportunities owned by the Cafe include (1) Hasanuddin University student growth rate (2) An increasing sales trend. While the threat for Point Zero Café is during the current covid 19 pandemic, of course this cafe is not operating properly.

Web 3.0 Digital Marketing in US Business Startups: A Literature Review

The study has adequately and thoroughly looked at the impact of web 3 digital marketing on startup businesses in United States as well as the history and evolution of web marketing. The study employed a case study research design to select the area of study. The secondary source of data that was analyzed revealed that web 3 digital marketing has had a significant impact on the overall performance of startup businesses in the United States. This is captured in the consistent increase in the profit of Flowcarbon, which grew from USD 150 million dollars in 2017 to USD 302 million dollars from 2017 to 2019. This shows approximately 50 percent increase in the company’s profit. The study also explored the sales trend of Bravado company. It was observed that the company’s sales have increased since 2017 till 2022. Based on the study’s analysis, the study concludes that web 3 digital marketing has overtime been a tonic for the success and growth of startup businesses in the United States and the world at large. Among other requisite recommendations, based on the conclusion of the study, it was recommended that businesses incorporate web 3 digital marketing strategies as this has become a prerequisite for accelerated business growth. Keywords: digital marketing, business start-ups, web 3.0. DOI: 10.7176/JIEA/12-1-05 Publication date: Juen 30th 2022

Machine Learning Approach for Big-Mart Sales Prediction Framework

The amounts of data predicted to increase at an exponential rate in the future. The modifications are essential to meet transaction speeds as well as the anticipated growth in data and customer behaviors. The information derived from prior data is extensively relied upon by the majority of companies. One of the primary goals of the suggested system is to identify a reliable sales trend prediction mechanism that is executed using machine learning techniques in order to maximize income. Sales forecasting advises managers about how to manage a company's employees, working capital and assets. It's a requirement for strategic planning and decision-making in the corporate world. Reasonable forecasts enable the company to increase market growth while increasing revenue generating. Operations, marketing, sales, production, and finance all use sales predictions as inputs in their decision-making processes. The concept of sales data and sales forecast has been examined in the suggested system. Machine learning algorithms such as GLL (Generalized Linear Model), GBT (Gradient Boosted Trees), and Decision Trees were used to develop the model, and the optimum model for prediction was established based on the results analysis. A best-fit prediction model for anticipating sales trends is offered based on a performance review. The effectiveness and accuracy of the prediction and forecasting approaches used are discussed in the findings. The Gradient Boost Algorithm has been demonstrated to be the best fit model for forecasting and predicting future sales. The sales projection is done using Gradient Boosted Trees, which predicts which product will be sold in what quantity in the future.

International trends in prescription opioid sales among developed and developing economies, and the impact of the COVID-19 pandemic: A cross-sectional analysis of 66 countries.

PurposeWe sought to compare trends in opioid purchasing between developed and developing economies to understand patterns of opioid consumption, and how they were impacted by the COVID‐19 pandemic.MethodsWe conducted a retrospective cross‐sectional study of retail pharmacy opioid sales from 66 jurisdictions between July 2014 and August 2020. We measured monthly population‐adjusted rate of opioid units purchased, stratified by development group and country, and used interventional time series analysis to assess the impact of the COVID‐19 pandemic on rates of opioid purchasing among developed and developing economies separately.ResultsRates of opioid purchasing were generally higher among developed economies, although trends differed considerably by development group. Rates of opioid purchasing declined 23.8% (95% confidence interval [CI] −34.7% to 3.6%) in the 5 years prior to the pandemic in developed economies, but rose 15.2% (95% CI 4.6%–35.6%) among developing economies. In March 2020 there was a short‐term increase in the rate of opioid purchases in both developing (10.9 units/1000 population increase; p < 0.0001) and developed (145.5 units/1000 population; p < 0.0001) economies, which was followed immediately by reduced opioid purchasing of a similar scale in April–May 2020 (−14.8 and −171.8 units/1000 population in developing and developed economies, respectively; p < 0.0001).ConclusionThe COVID‐19 pandemic led to disruptions in opioid purchasing around the world; although the specific impacts varied both between and among developed and developing economies. With global variation in opioid use, there is a need to monitor these trajectories to ensure the safety of opioid use, and adequate access to pain management globally.

High Utility Item-Set Mining From Retail Market Data Stream With Various Discount Strategies

High Utility Item-set Mining (HUIM) is the futuristic remodel version of Frequent Item Mining (FIM). It has the ability to discover customer purchase trends in the retail market. By using that knowledge, retailers can incorporate innovative schemes (discounts, cross-marketing, seasonal sales offers,... etc) to enhance profit. Even though many HUIM algorithms are proposed to detect profitable patterns, most of them cannot be applied to all kinds of retail market data sets due to certain assumptions. The first assumption is that the items always produce a positive profit. But in reality, even though overall profit could be positive, some of the items make negative profit. The second one is they are developed for static transactional data. Those are useful to take decisions at some intervals like quarterly, half-yearly, yearly. But, to take decisions at any time by analyzing the present sales trend, it is required to process the data stream.

India\u2019s ban on antimicrobial fixed-dose combinations: winning the battle, losing the war?

Background and objectivesIndia, the country with the largest market availability of antimicrobial fixed-dose combinations (FDCs), banned certain antimicrobial FDCs in September 2018. Our objective was to examine the impact of Government ban on the sales of antimicrobial FDCs.MethodsThe sales patterns of 14 of the 26 banned antimicrobial FDCs were analyzed using monthly private sector drug sales data from IQVIA (a comprehensive and nationally representative drug sales database) between January 2018 and December 2019. We carried out descriptive analyses to evaluate the trend in sales over time for banned and non-banned antimicrobial FDCs using cumulative sales volumes.ResultsOverall, the cumulative sales volume of banned antimicrobial FDCs declined by 75% between January and September 2018 and the same months of 2019, although some banned FDCs continued to be available in significant volumes. The effectiveness of the ban was offset by several pathways. First, the sales of combinations containing moieties belonging to the same drug-classes as the antimicrobials in the banned FDCs increased after the ban. Second, while certain formulations of particular combinations were banned, the sales of other non-banned formulation of these combinations increased. Third, in some cases, products containing new non-antimicrobial components added to the banned combinations remained available.Interpretation and conclusionsWhile sales of the banned antimicrobial FDCs decreased in 2019, we identified several mechanisms that counterbalanced the ban, including implementation failure, rising sales of congeners, and products with additional non-antimicrobial components.

Sales Estimator Using Machine Learning Algorithm

Today incorporationofestimationanddecision analysis is required by analytical systems. Predictingthe item sales is a very tough challenge so the business organization depends on the predictions of sales trends. Accuracy obtained in sales predictions serves as a boon to business organizations. Machinelearning algorithms are efficient and effective for extractinghidden information from a huge dataset to enhance efficiency and accuracy of sales prediction. The detailed study and analysis of predictive models is used to performfuture sales predictions. Theproblem with traditional prediction systems is that they are tough to deal with the enormous dataset inorder to providethe accurate prediction. To overcome these challenges various machine learning techniques and algorithms can be used. On the basis of their performance, the model which gives more accuracy will be used to predict forecasting trends. The results obtained are encapsulated collectively in terms of accuracy, reliability and efficiency of the techniques that are used for forecasting and prediction.

Alcohol consumption and closed borders - how COVID-19 restrictions have impacted alcohol sales and consumption in Europe

BackgroundThe closing of bars, restaurants and international borders during the COVID-19 pandemic led to significant changes in alcohol availability. This study provides a first systematic overview of the monthly development of alcohol sales in Europe during the pandemic in order to determine the effect of closed borders on the sales and consumption of alcohol.MethodsThe study covers 72 months from January 2015 to December 2020 in 14 countries from northern, central and western Europe with excise revenue data for beer, spirits, wine separately and summed, converted into litres of pure alcohol per capita 15+ as a proxy for alcohol sales. March–December 2020 is seen as the pandemic period. The analyses consist of (1) descriptive trends of sales before and during the pandemic, (2) assessment of the pandemic impact on sales by time-series analyses and (3) case studies of countries and a region with substantial cross-border inflow or outflow of alcohol.ResultsThe result shows an overall reduction in alcohol sales with 3.6% during the pandemic. Nevertheless, the results differ based on the level of cross-border purchasing flows pre-pandemic, as countries with high cross-border inflow saw an increase in domestic sales as the pandemic hit. Norway, for example, saw a 23% increase in domestic sales during the pandemic period March–December 2020 compared to the same period in 2019.ConclusionThe closing of intra-European borders had a significant redistributing effect on alcohol sales. While noting sales increases, cross-border inflow countries generally saw a decrease in total amount of alcohol consumed per capita as not all cross-border purchases were replaced by domestic sales. This has important policy implications as large volumes of cross-border inflow of alcohol can negatively affect excise revenue as well as public health outcomes.The methodology can be used to further explore the reliance of different purchasing streams in a domestic alcohol market.

Trends in treatments for erectile dysfunction in Chile between 2010 and 2020 with special focus on penile prostheses.

Evidence regarding demand trends for erectile dysfunction (ED) treatments are scarce in South America. This study aims to evaluate trends in ED treatments in Chile over a 10-year period (2010- 2020) and estimate the potential number of candidates for penile prosthesis. Sales trends of pharmacological treatments and penile prosthesis were obtained from market studies. The potential number of candidates for penile prosthesis implantation was calculated by crossing epidemiological data with previously reported ED prevalence, proportion of sexually active men, percentage of men seeking medical assistance for ED, and the proportion of patients who are non-responders to ED oral drug therapies Results: In the 10-year studied period, the Chilean male population older than 50 years increased 34.7%, with an average annual variation (AAV) of 3.4%. For the same period, the sales of oral drug therapies for ED increased by 71.3% (AAV 6.2%), the sales of intracavernosal vasoactive agents (ICVA) decreased by 0.4% (AAV -0.2%), and penile prosthesis sales increased by 113% (AAV 6.7%). We estimated that only 0.05% of sexually active men older than 50 years old with ED who sought medical assistance finally had a penile prosthesis implanted to manage their condition. Demand for ED oral drug therapies significantly increased in Chile during the last decade, while ICVA remained stable. The annual rate of penile prosthesis implantation increased. However, the gap between the potential penile prosthesis candidates and the actual number of devices implanted is suspected to remain extremely high.

TREND SEMI AVERAGE AND LEAST SQUARE IN FORECASTING YAMAHA MOTORCYCLE SALES

This study compared the Semi Average and Least Square methods to determine the sales trend of Yamaha motorcycles in obtaining the best method for predicting motorcycle sales at CV Surya Prima Pelaihari. Mean Absolute Percentage Error (MAPE) was used to determine the accuracy of the Semi Average and Least Square methods in predicting the sales of CV Surya Prima Pelaihari motorbikes. Semi Average method was based on the MAPE value of 43.96%. The Least Square method has a MAPE value of 31.89%. The comparison of MAPE values shows that the Least Square method provides better predicting results because of the lower MAPE value. Therefore, the Least Square method was used to predict sales at CV Surya Prima Pelaihari. A more accurate output can be obtained than the Semi Average method.

Long-term trend of electric vehicle sales in Thailand

Abstract This research study aims at examining the long-term trend of EV sales in Thailand, utilising the system dynamics (SD) modelling approach. This approach is commonly used to model complex systems with causal relationships among key factors within the system. The developed SD model consists of five key factors affecting electric vehicle (EV) sales, namely, the environment, economy, charging infrastructure, government support, and battery maintenance. The simulation results show the increase in EV sales by ten times in the next 20 years with implementation plans related to the five key factors. The government support factor is the most important in enhancing EV sales in the short term. Several government support plans should be initiated to attract more EV consumers, such as subsidies and tax reductions. The environment and charging infrastructure factors are crucial to increasing EV sales in the long term. The enforcement of the CO2 tax and the provision of charging stations all around the country should be established to achieve a sustainable EV market in the long term. This research study contributes to the Thai government and automotive industry to better understand the complex relationships among key factors affecting EV sales. The related sectors may use the study results to plan for EV campaigns to promote the use of EVs and achieve a sustainable EV market.

The Impact of a Pricing Manipulation Intervention on Food Sales in a Small Community Grocery Store in Baltimore City

ABSTRACT This study tested a pricing manipulation strategy in a small grocery store to assess the impact on food sales. The prices of 35 healthy foods were decreased over time while simultaneously increasing the prices of 45 unhealthy foods. Trends in sales were examined across each manipulation period. Total sales of healthy foods remained relatively stable over time, indicating that total quantity sold increased. In contrast, total sales of unhealthy foods generally declined during the study period, indicating that total quantity sold decreased. In this store, we found that healthy and unhealthy foods may be sensitive to pricing manipulation strategies.

Primena softverskih alata u procesu selekcije IT proizvoda

When it comes to making of certain decisions everyday/routine or specific, which often imply planning, programming, selection of the most favorable alternative, financing or realization of business task, it is mostly based on the data analysis. Criteria which are imposed on that occasion are diverse and sometimes mutually opposed. Problems posed to the decision maker are often unstructured so there is no mathematical algorithm for solving them, and often there is a question of objectivity in choosing the optimal solution.The subject of this work is the use of Excel Solver in finding the optimal choice of products in the company which is engaged in trade of computer equipment, considering sales trends, available storage and human capacities as well as the financial limitations. The fundamentals of linear programming are presented with the concrete example of sales of the desktop computers, laptop computers and tablet appliances, and their implementation presents the base of the use of the Excel Solver.

Who's Next: Rising Star Prediction via Diffusion of User Interest in Social Networks

Finding items with potential to increase sales is of great importance in online market. We propose to study this novel and practical problem: rising star prediction. We call these potential items Rising Star, which implies their ability to rise from low-turnover items to bestsellers in the future. Rising stars can be used to help with unfair recommendation in e-commerce platform, balance supply and demand to benefit the retailers and allocate marketing resources rationally. Although the study of rising star can bring great benefits, it also poses challenges to us. The sales trend of rising star fluctuates sharply in the short-term and exhibits more contingency caused by some external events (e.g., COVID-19 caused increasing purchase of the face mask) than other items, which cannot be solved by existing sales prediction methods. To address above challenges, in this paper, we observe that the presence of rising stars is closely correlated with the early diffusion of user interest in social networks, which is validated in the case of Taocode (an intermediary that diffuses user interest in Taobao). Thus, we propose a novel framework, RiseNet, to incorporate the user interest diffusion process with the item dynamic features to effectively predict rising stars. Specifically, we adopt a coupled mechanism to capture the dynamic interplay between items and user interest, and a special designed GNN based framework to quantify user interest. Our experimental results on large-scale real-world datasets provided by Taobao demonstrate the effectiveness of our proposed framework.

Impacts of COVID-19 on Tourism in Bar Harbor, Maine

This paper examines the effects of COVID-19 on tourism in Bar Harbor, Maine. The analysis considers four channels by which the pandemic affected Bar Harbor: (1) reduction in overnight visitors, (2) decrease in Acadia National Park users, (3) cancellation of the 2020 cruise ship season, and (4) a national effect related to trends in overall US restaurant sales. The relative impacts of these four factors on Bar Harbor restaurant sales varied widely by month: the reduction in overnight tourists explains 40 percent of the overall decline; the national effect is associated with 35 percent of Bar Harbor’s decline; the cancellation of all cruise ship visits in 2020 explains 11 percent; and the reduction in the number of Acadia National Park users is responsible for 14 percent of the decline in restaurant sales in 2020. Knowing more about the channels by which COVID-19 affected hospitality sales in Bar Harbor can help state and local officials with recovery efforts and support tourism along the Maine coast.