Sales Patterns Research Articles

Grouping Sales Levels Smartphone Of Offline Store Using BIRCH Clustering Algorithm

From 2020 to 2024, TM_Store and Jaya Com exhibited different sales patterns based on cluster analysis using the BIRCH algorithm. The background of this research is to provide strategic insights to both stores for improving their sales performance through data analysis. The sales data used includes brand, type, month, year, stock quantity, quantity sold, unit price, and total sales. The BIRCH method was chosen for its effectiveness in handling large datasets and providing accurate clustering results. The clustering results indicate a significant increase in the "Moderate" category, from 12 sales in 2020 to 354 sales in 2023. Meanwhile, the "Very High" category also saw an increase from 5 sales in 2020 to 97 sales in 2023, with sales in the "Very Low" category remaining high at 70 sales in 2023. On the other hand, Jaya Com was dominated by the "Very High" category, with a sharp increase from 25 sales in 2020 to 597 sales in 2023. The "High" category also showed significant growth, from 6 sales in 2020 to 98 sales in 2023. This data indicates that Jaya Com focuses on high-performance products, while TM_Store shows a more balanced distribution across various sales categories. Based on the analysis, Jaya Com had 1988 data points with 1984 cluster points, whereas TM_Store had 2012 data points with 1811 cluster points. Overall, the study concludes that the BIRCH algorithm can identify significant sales patterns in both stores, aiding in the development of more effective and efficient promotional strategies tailored to each sales category's performance.

Adoption of Chinese cars and the shift to electric vehicles: Early evidence from Norway

Chinese auto manufacturers have started exporting electric cars to developed markets. I study data from the world’s leading market for electric vehicles, Norway, to provide some early descriptive evidence on the patterns of Chinese-brand new car sales. Using a Bayesian multilevel regression model, I find evidence of local peer effects: A higher proportion of sales of a Chinese branded car in a postal code increases the probability of a new car sale being Chinese. I also hypothesize that municipalities with high penetrations of electric cars will be associated with higher penetrations of Chinese-branded new car sales. I do not find evidence for this.

Implementation of Data Mining using Aprirori Algorithm to Improve Sales Patterns of MSME Products

Implementation of Data Mining using Aprirori Algorithm to Improve Sales Patterns of MSME Products

Unlocking Inventory Efficiency: Harnessing Machine Learning for Sales Surge Prediction

Purpose: Sales forecasting plays a crucial role in inventory optimization for retail stores, especially during special events such as promotions, advertisements, holiday season, weather, social and economic situations etc. These events drive significant changes in customer buying patterns. Some of these events are captured in the current forecasting models as part of trend, seasonality, and cyclicality. But many times, unexpected local events such as extreme weather conditions, riots, and regional events such as marathons, concerts have a significant impact on sales surges which are usually not captured in the sales forecast. This leads to inventory being out of stock and store managers placing last-minute manual orders. By accurately predicting these sales pattern changes, retailers can make informed decisions, ensuring optimal inventory levels and maximizing profits. Methodology: In this paper, a data-driven solution was discussed that leverages machine learning models to predict sales pattern changes and surges during local events in a specific geographical location. Web scraping can be used to gather data on local events from a range of online sources, including Google News, local news channels, and social media platforms. By extracting pertinent details about upcoming events, it is possible to compile a thorough database of local happenings. Findings: Data Analysis of historical sales data mapped to its local events can provide insights on key department-categories where there is a surge in sales. Machine learning models can be used to analyze, experiment and train historical sales data in conjunction with the events data. Unique contribution to theory, practice and policy: Machine learning models would be trained to capture the complex relationships between different local events and their impact on sales. Therefore the study recommends using the machine learning approaches specified to consider various factors, such as event type, location, duration, and historical sales patterns, our models can effectively predict sales fluctuations during specific events.

Implementation of Data Mining to Determine Sales Patterns Using the Apriori Method

Research on the Implementation of Data Mining to Determine Sales Patterns Using the Apriori Method is an effort to understand and utilize sales data in making more informed and strategic business decisions. The main goal of this research is to extract hidden patterns from large sales data sets, which cannot be discovered by manual analysis alone. This research process is divided into several key stages, namely Data Selection, Preprocessing, Transformation, and Data Mining. The research results show that the Apriori method is effective in finding purchasing patterns. In terms of the frequency of 2 itemsets, the highest support value was found to be 1, which indicates that the combination of the two products is always purchased together in all transactions. For 3 itemsets and 4 itemsets, the high support value of 0.9 also indicates the existence of product combinations that are often purchased together. In terms of confidence, 2 itemsets show the highest value of 1.25, indicating that purchasing one product has a high tendency to be followed by purchasing other products. For 3 itemsets and 4 itemsets, the confidence values show a slightly lower trend but are still significant. Furthermore, lift analysis provides additional insight into the strength of association between itemsets, with 4 itemsets showing the highest lift value of 1.30, indicating the product combination has a very strong association compared to random expectations. This research confirms the potential of the Apriori method in finding valuable sales patterns, which can help companies make strategic decisions for increasing sales and customer satisfaction.

Revolutionizing Electric Vehicle Adoption: A Holistic Integration of Marketing Strategies and Analytical Insights

This study explores the synergies between marketing strategies, analytical insights, and consumer education in propelling electric vehicle (EV) adoption. We uncover intricate sales patterns in Turkey’s EV sales data using advanced statistical models such as ARIMA, SARIMA, and ETS. Our findings underscore the efficacy of aligning marketing strategies with analytical insights, demonstrating the significance of education in shaping positive consumer attitudes. Education-driven marketing emphasizing economic benefits, reduced emissions, and technological advancements is a potent catalyst in overcoming adoption barriers. Digital campaigns, experiential marketing, and sustainability messaging validated by our analysis, play pivotal roles in influencing consumer behavior. Strategic partnerships with energy companies address infrastructure challenges, while incentive-based marketing, personalized strategies, and after-sales support foster a sense of community and loyalty. This research contributes a holistic framework for marketers, policymakers, and stakeholders to navigate the evolving landscape of EV adoption successfully, providing actionable insights and paving the way for future research directions in sustainable transportation.

대마 매매 패턴 및 공범의 개입 시기에 대한 판결문 기반 범죄 스크립트 분석

대마 매매 패턴 및 공범의 개입 시기에 대한 판결문 기반 범죄 스크립트 분석

Optimization of Vegetable Restocking and Pricing Strategies for Innovating Supermarket Operations Utilizing a Combination of ARIMA, LSTM, and FP-Growth Algorithms

In the dynamic environment of fresh food supermarkets, managing the short shelf life and varying quality of vegetable products presents significant challenges. This study focuses on optimizing restocking and pricing strategies to maximize profits while accommodating the diverse and time-sensitive nature of vegetable sales. We analyze historical sales, pricing data, and loss rates of six vegetable categories in Supermarket A from 1 July 2020 to 30 June 2023. Using advanced data analysis techniques like K-means++ clustering, non-normal distribution assessments, Spearman correlation coefficients, and heat maps, we uncover significant correlations between vegetable categories and their sales patterns. The research further explores the implications of cost-plus pricing, revealing a notable relationship between pricing strategies and sales volumes. By employing Autoregressive Integrated Moving Average (ARIMA) and Long Short-Term Memory (LSTM) models, we forecast sales and determine optimal restocking volumes. Additionally, we use price elasticity theories and a comprehensive model to predict net profit changes, aiming to enhance profit margins by 47%. The study also addresses space constraints in supermarkets by proposing an effective assortment of salable items and individual product restocking plans, based on FP-Growth algorithm analysis and market demand. Our findings offer insightful strategies for sustainable and economic growth in the supermarket industry, demonstrating the impact of data-driven decision-making on operational efficiency and profitability.

Determining Sales Patterns of Beauty Products Using the Apriori Algorithm in Data Mining

Determining Sales Patterns of Beauty Products Using the Apriori Algorithm in Data Mining

Research on Seasonal Orderliness and Linear Correlation of Vegetable Sales Data

The key to the study of seasonal orderliness and linear correlation of vegetable sales data is to understand how sales patterns are affected by seasonal factors and the linear relationship between these patterns and other variables. Seasonal ordering reflects cyclical changes in sales data over time that are influenced by factors such as climate, traditional festivals, and consumption habits. For example, certain vegetables may sell better in the winter while others in the summer. By analysing historical sales data, time series models such as Autoregressive Moving Average (ARMA) can be used to forecast future sales trends, which in turn support inventory management and pricing strategies. In addition, linear correlation analysis helps to identify the relationship between sales volume and factors such as price, cost and market demand, as well as the interactions between different vegetables, including complementary and substitution effects. This information is crucial for supply chain optimisation, promotional campaign planning and risk management. With the development of big data and analytics, researchers can more accurately model and predict market behaviours to support strategic decision-making for vegetable producers and retailers. Research that integrates seasonal ordering and linear correlation will provide insights into the complex dynamics of the vegetable market and provide data to support market participants in developing strategies to adapt to the changing environment.

Optimizing the Sales Law and Replenishment Decision Analysis of Fresh Supermarket Products

In the field of fresh food supermarkets, due to the relatively limited shelf-life of vegetable products, their quality will gradually deteriorate with the sales time. Therefore, supermarkets need to perform daily rationing replenishment operations based on the historical sales data and demand of the products. This paper is based on the collected product information of vegetable categories, detailed vegetable sales flow data, wholesale prices of vegetable products, and recent vegetable product wastage rates. Data analysis and visualization techniques are used to analyze the distribution pattern of vegetable sales in each category and single product. Next, a functional relationship between total sales volume and cost-plus pricing of vegetable categories was constructed. Regression forecasts were used to simulate the future wholesale prices of vegetable categories.

Sales Analysis on Garment Industry with Datawarehouse and ETL Implementation on Star Schema

In the current industrial era, clothing companies face increasingly complex challenges in maintaining the quality and sales of their products. To overcome these obstacles, companies must have a good vision, mission, and decision-making capabilities supported by efficient data management. Data is becoming a valuable asset in Industry 4.0, pivotal in process management, increased productivity, and competitive advantage. This research explores the implementation of Data Warehouse (data warehouse) and ETL (Extraction, Transformation, and Load) processes on star schema for PT Golden Flower Tbk. A data warehouse is a container for storing company data, supporting decision-making, and providing business insights. Meanwhile, the ETL process ensures that the data entered into the warehouse is clean, structured, and ready for analysis. PTA garment manufacturing and exporter company, USPT Golden Flower Tbk, uses the Pentaho application to carry out the ETL process. Pentaho, a Business Intelligence tool, facilitates easy data processing and analysis. This research also explains the use of dimension tables and fact tables in star schemas, which form the basis for more in-depth data analysis. Through visualization and analysis using star schema, Mondrian, and schema workbench, companies can identify sales patterns in each branch and understand which products are most in demand by consumers. The results of the star schema, moMondrianand schema workbench provide a better understanding of the company's sales performance, enabling more informed decision-making and more effective strategies.

Comparative Analysis of Machine Learning Models for Accurate Retail Sales Demand Forecasting

This article compares sales forecasting models, LSTM and LGBM, using retail sales data from an American multinational company. The study employs a meticulous methodology, optimizing memory, performing feature engineering, and adjusting model parameters for both LSTM and LGBM. Evaluation metrics, including RMSE, MAE, WMAPE, and WRMSEE, demonstrate that LGBM consistently outperforms LSTM in capturing and predicting sales patterns. The analysis favors LGBM as the preferred model for retail sales demand forecasting, emphasizing the importance of model selection. This study contributes to practical machine learning applications in retail sales forecasting, highlighting LGBM as an effective choice.

Maximizing Supermarket Profits: Data-Driven Strategies for Pricing, Sales, and Forecasting

The actual circumstances of daily life are crucial for the purchasing and pricing strategies of supermarkets. Developing strategies based on these circumstances can assist businesses in ensuring profits and fostering win-win cooperation. This paper explores methods to maximize profit through purchasing and sales strategies. Initially, the relevant data for various categories of vegetables is integrated. Through histograms, their sales patterns are directly understood, highlighting the most popular vegetables. Upon analyzing each vegetable category, it becomes evident that their sales data do not conform to normal distributions. Therefore, Spearman correlation coefficients are calculated, revealing strong correlations between certain categories, such as aquatic roots and edible fungi. A line chart depicting the top ten selling vegetables indicates a noticeable periodicity. Traditional fitting methods struggle to adequately model the sales of each vegetable category and their relationship with cost-plus pricing. To address this, additional factors such as holidays, weeks, and months are incorporated using techniques like random forest regression. This approach yields cost-plus pricing dependence curves that better capture the relationship, while effectively managing noise. Regarding sales volume prediction, the original data displays significant volatility, necessitating the handling of outliers using the threshold method. For missing data, linear interpolation is employed to mitigate the impact of continuous missing values on prediction accuracy. Subsequently, Adam-optimized long short-term memory (LSTM) networks are utilized to forecast incoming quantities for the next seven days. By extrapolating from normal sales volume, market capacity is estimated, allowing for additional sales through discount strategies. This framework has the potential to increase original income by 1.1 times.

A Study on Sales Patterns for Vegetable Products in Retail Stores

A Study on Sales Patterns for Vegetable Products in Retail Stores

Research on the Sales Distribution Law of Vegetable Products Based on Linear Fitting

Firstly, the hourly sales volume changes of vegetable categories and individual products are analyzed from the time dimension, and frequency distribution histograms are used to describe the distribution patterns of vegetable categories and individual product sales volume. Then, Spearman correlation analysis is used to obtain the distribution patterns of individual product sales volume, and linear fitting is used to compare and analyze the continuous sales volume of the six major categories for three years using a bar chart, From the demand side perspective, there is often a certain correlation between the sales volume of vegetable products and time. This article analyzes the relationship between vegetable categories and individual product sales volume from the time dimension. At the same time, based on the distribution patterns of the six major categories, Spearman correlation analysis was used to obtain the distribution patterns of individual product sales volume. Linear fitting was performed, and the continuous sales volume of the six categories for three years was compared and analyzed using a bar chart to obtain the general monthly and seasonal patterns of vegetable sales. Then, descriptive statistical analysis is used to analyze the distribution patterns of vegetable categories and single item sales volume, and the correlation relationships between different categories or single items of vegetable products are obtained. Finally, a grey correlation analysis model is established to predict the distribution patterns and interrelationships of vegetable categories and single item sales volume based on this model.

Forecasting superstore fruit and vegetable sales based on ARIMA-LSTM

In the context of a fresh food supermarket, managing perishable vegetables, which have a notably limited shelf life, maintaining optimal stock levels becomes imperative for safeguarding profitability. This research paper intricately explores the analysis of numerous datasets using Power Query and Power BI, with a pivotal focus on devising a succinct coding system. To ensure data quality, outliers are meticulously eliminated using advanced techniques like linear interpolation and Z-Score processing. The ARIMA time series model is employed to predict the sales volume for each category in July, having been trained on data from June 2022 to June 2023, adeptly capturing historical sales patterns and emerging trends. Additionally, a parameter-tuned LSTM recurrent neural network, optimized with Adam's algorithm, is utilized to refine predictions, considering its inherent memory capabilities. A thoughtfully designed weighted average approach combines outputs from both models, strategically minimizing overfitting and enhancing prediction accuracy. The resultant research fortifies inventory management optimization, bolstering the supermarket's profitability while effectively meeting customer demands.

CHALLENGES OF ART MANAGEMENT AND THE MATERIALIZATION OF ARMENIAN JEWELRY ARTS IN THE REPUBLIC OF ARMENIA

The article aims to showcase the perspectives and news of Armenian jewelry art on international platforms. This research is pertinent as Armenian jewelry has significant relevance both in the past and the present. To achieve the objective, the following issues have been set: to observe the possible sales forms of contemporary Armenian jewelry arts in local and international markets, as an economic development boost. During the research, various methods were used to identify sales patterns. The main conclusion made within the research framework is that Armenia possesses a rich heritage of jewelry-making and creativity. This heritage is still relevant for today's Armenian craftsmen, serving as their method for combining the past with the present. By preserving traditional elements and using contemporary trends, Armenian craftsmen can present, display, and market their jewelry internationally. Ultimately, this will also ensure economic growth and stability.

Toward Enhanced Customer Transaction Insights: An Apriori Algorithm-based Analysis of Sales Patterns at University Industrial Corporation

Toward Enhanced Customer Transaction Insights: An Apriori Algorithm-based Analysis of Sales Patterns at University Industrial Corporation

Credit ability evaluation system of power selling enterprises based on big data

With the gradual relaxation of the electricity market access threshold, the development pattern of market-oriented electricity sales has brought great challenges to the power grid business development. It is of great significance to select the right electricity selling enterprises in the market-oriented electric power marketing mode to ensure the practical demand of electricity customers and the operating efficiency of power grid enterprises. First, the basic characteristics of power big data are analyzed. Secondly, it classifies and analyzes the important factors that affect the performance credit of electricity selling enterprises under the new electricity transformation environment. By normalizing different types of key index data and assigning index weights, a credit ability evaluation model of electricity selling enterprises based on matter-element extension-TOPSIS is established. Finally, taking eight electricity selling enterprises in a province as an example, the credit ability evaluation model is applied to analyze and calculate the performance ability of different electricity selling enterprises. The results prove the practicability of the evaluation system proposed in this paper.