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Journal articles on the topic 'SALES PATTERN'

Author: Grafiati

Published: 11 September 2023

Last updated: 31 July 2025

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1

Pramita, Made Dinda Pradnya, Made Sudarma, and Ida Bagus Alit Swamardika. "Analysis of Sales Pattern Determination System and Drug Stock Recommendation." Jurnal Ilmu Komputer 12, no. 2 (2019): 53. http://dx.doi.org/10.24843/jik.2019.v12.i02.p04.

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The tight competition in the pharmacy industry, requires pharmacy owners to develop strategies in increasing drug sales. One of the strategies carried out is to analyze patterns of drug sales and determine drug stock recommendations based on sales transaction data. Based on this, an application was built to determine the pattern of drug sales and drug stock recommendations by using a modified Apriori Algorithm and Triple Exponential Smoothing Method. Apriori algorithm modification is used to overcome the problem of large amounts of sales transaction data, thus minimizing the time in the databa

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2

Chelsey Monica Teo and Sonja Andarini. "Pengaruh Konflik Boikot terhadap Penjualan Carnation Evaporasi di Q4 2023 pada Bidang HORECA di Batam." El-Mal: Jurnal Kajian Ekonomi & Bisnis Islam 5, no. 5 (2024): 3674–78. http://dx.doi.org/10.47467/elmal.v5i5.1781.

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The purpose of this study is to analyse the effect of boycott conflict on evaporated carnation sales using sales data patterns, identify the relationship between boycott activities and evaporated carnation sales at PT Nestle Indonesia and cause an increase in sales during this boycott period. The research method used is descriptive quantitative analysis through purpose sampling technique. The results showed that the data pattern of evaporated carnation sales during the 4th quarter of 2023 for the horeca sector has a seasonal pattern that depends on major holidays in Indonesia.

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Boby, Solikhun, and Zulia Almaida Siregar. "Analisis Pola Penjualan Produk Makanan dan Minuman Menggunakan Algoritma Apriori." Journal of Informatics Management and Information Technology 2, no. 2 (2022): 65–72. http://dx.doi.org/10.47065/jimat.v2i2.161.

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Sales are seen as a unit of business parameters that are very vital and valuable for business people to manage the business they are running, especially in the cafe business. Cafe Aksara as one of the business actors in the culinary world has a lot of demand in the sale and supply of goods on certain days due to the dynamic nature of visitor patterns and makes business people have to be wiser in setting strategies for the combination pattern of sales of food and beverage products found in Indonesia. Aksara cafe to attract customers. In a process of determining the sales pattern strategy at Caf

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Murlidharan, Vijayalakshmi, and Bernard Menezes. "Frequent pattern mining-based sales forecasting." OPSEARCH 50, no. 4 (2013): 455–74. http://dx.doi.org/10.1007/s12597-012-0119-9.

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Weng, Cheng-Hsiung, and Cheng-Kui Huang. "Discovering Specific Sales Patterns Among Different Market Segments." International Journal of Data Warehousing and Mining 16, no. 3 (2020): 37–59. http://dx.doi.org/10.4018/ijdwm.2020070103.

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Formulating different marketing strategies to apply to various market segments is a noteworthy undertaking for marketing managers. Accordingly, marketing managers should identify sales patterns among different market segments. The study initially applies the concept of recency–frequency–monetary (RFM) scores to segment transaction datasets into several sub-datasets (market segments) and discovers RFM itemsets from these market segments. In addition, three sales features (unique, common, and particular sales patterns) are defined to identify various sales patterns in this study. In particular,

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Fauzi, Cholid, and Aly Dzulfikar. "Implementation of Product Sales Forecast Using Artificial Neural Network Method." IJISTECH (International Journal of Information System & Technology) 5, no. 2 (2021): 153. http://dx.doi.org/10.30645/ijistech.v5i2.126.

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Product sales forecasting is used by companies to estimate or predict future sales levels using sales data in the previous year. The Artificial Neural Network Backpropagation Algorithm can forecast the sales of goods for the next period for each item in the company. The forecasting process begins by determining the variables needed in the network pattern, and then the established network pattern continued in the network training process using the backpropagation algorithm. After carrying out the network training process, the researcher comparisons with several network patterns formed. This res

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Kim, Jonghyuk, Hyunwoo Hwangbo, Sung Jun Kim, and Soyean Kim. "Location-Based Tracking Data and Customer Movement Pattern Analysis Using for Sustainable Fashion Business." Sustainability 11, no. 22 (2019): 6209. http://dx.doi.org/10.3390/su11226209.

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Retailers need accurate movement pattern analysis of human-tracking data to maximize the space performance of their stores and to improve the sustainability of their business. However, researchers struggle to precisely measure customers’ movement patterns and their relationships with sales. In this research, we adopt indoor positioning technology, including wireless sensor devices and fingerprinting techniques, to track customers’ movement patterns in a fashion retail store over four months. Specifically, we conducted three field experiments in three different timeframes. In each experiment, w

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Seethapathy, Kavitha. "Unlocking Inventory Efficiency: Harnessing Machine Learning for Sales Surge Prediction." International Journal of Supply Chain and Logistics 8, no. 1 (2024): 57–66. http://dx.doi.org/10.47941/ijscl.1863.

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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 no

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Raharjo, Adi, Nur Ichsan Utama, and Muharman Lubis. "Using Supervised Machine Learning to Predict Sales in Marketplaces: Case study Predicting Sales of Padimas Bread in Marketplaces in Indonesia." Informatics Management, Engineering and Information System Journal 1, no. 2 (2024): 139–46. http://dx.doi.org/10.56447/imeisj.v1i2.264.

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This project intends to apply supervised machine learning to anticipate sales of Padimas bread in marketplaces in Indonesia, with an emphasis on evaluating sales data to gain more profits and predict future income. Data from the Shopee, Tokopedia, and TikTok markets in 2023 was analyzed, employing techniques like exploratory sales data analysis and machine learning. The analysis findings encompass the top-selling products, the highest sales figures, regions with the most substantial sales, overall market sales, sales patterns, and revenue forecasts. The primary discoveries encompass the widesp

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-, Rino, and Maman Novian. "Analysis of the Application of Customer Purchase Mining Data on Paint Sales Using Apriori Algorithm (Case Study: PT Indowarna Cemerlang Indonesia)." bit-Tech 2, no. 3 (2020): 131–40. http://dx.doi.org/10.32877/bt.v2i3.161.

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Sales transaction data is one thing that can be used for making business decisions. Most sales transaction data is not reused, and is only stored as an archive and only used for making a sales report. Paint sales data is one science that can be applied in cases like this. Sales transactions that are not utilized properly can be extracted and reprocessed into useful information using data mining techniques. Using one of the data mining methods, namely the a priori algorithm, sales transaction data can be reprocessed so that it can produce a consumer buying pattern. This consumer buying pattern

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Muhammad Aldi Zarkashy, Ismarmiaty Ismarmiaty, and Ria Rismayati. "ANALISA POLA PEMBELIAN PRODUK DENGAN MENGGUNAKAN METODE FP-GROWTH PADA TOKO HERON." Journal of Information Systems Management and Digital Business 1, no. 2 (2024): 107–15. http://dx.doi.org/10.59407/jismdb.v1i2.312.

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Technological developments have had an impact on all activities including retail business. Utilization of managed data can provide information that can be used to improve the quality and effectiveness of buying and selling business activities. One of the patterns analyzed in the retail business sector is the pattern of product sales linkages using association algorithms in data mining using the FP-Growth algorithm. The aim of this research is to find association patterns in purchasing behavior at Heron stores to help make policies in better product inventory management and also increase sales

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Fey, Ferry Putrawansyah. "Application of the Apriori Algorithm to Purchase Patterns." Indonesian Journal of Computer Science 12, no. 2 (2023): 553–61. http://dx.doi.org/10.33022/ijcs.v12i2.3105.

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The purpose of this research is to produce an Apriori Algorithm application system to increase sales turnover at Viona stores. The problem faced by the Viona store is that the Viona store has decreased turnover in the midst of business competition because it has not been able to optimally analyze the products that are often purchased and the combination of purchases by consumers so that sales seem monotonous and do not have a business strategy to attract customers. sales that can attract consumers. One way is to make sales with sales packages at lower prices. It must have a good pattern and an

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Ali Ridla, Muhammad, Fajriyanto, and Misbahul Marzuqi. "Implementasi Algoritma Apriori untuk Menentukan Pola Transaksi Penjualan Berbasis Web." JTIM : Jurnal Teknologi Informasi dan Multimedia 5, no. 3 (2023): 196–207. http://dx.doi.org/10.35746/jtim.v5i3.399.

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The Apriori algorithm is an algorithm that is well known for searching frequent itemsets using the association rule technique. The calculation of the Apriori algorithm uses minimal support and minimal confidence to determine the limit for calculating goods. The a priori algorithm functions to determine the pattern of sales of goods that are often purchased together by customers. The history of sales transactions owned by a store can be calculated for its frequent itemset pattern by using an a priori algorithm so that customers can find patterns of items that are often purchased simultaneously

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Wisda, Wisda, and Mashud Mashud. "Designing an Application for Analyzing Consumer Spending Patterns Using the Frequent Pattern Growth Algorithm." Jurnal Penelitian Pos dan Informatika 9, no. 2 (2019): 151. http://dx.doi.org/10.17933/jppi.2019.090206.

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<p class="JGI-AbstractIsi">In this modern era, the market has been growing rapidly which can be seen from the navel shopping that is lined up in the hearts of big cities such as supermarkets, grocery stores and others that are provided to meet people's needs for primary goods that are always needed at all times. One of them is Giant Express Tamalanrea, a supermarket in the city of Makassar that serves the sale of household goods and general needs. With the use of customer data analysis to determine the customers' purchasing patterns, Giant Express can optimize the collation of goods, by

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Wisda, Wisda, and Mashud Mashud. "Designing an Application for Analyzing Consumer Spending Patterns Using the Frequent Pattern Growth Algorithm." Jurnal Penelitian Pos dan Informatika 9, no. 2 (2019): 151–59. http://dx.doi.org/10.17933/jppi.v9i2.285.

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In this modern era, the market has been growing rapidly which can be seen from the navel shopping that is lined up in the hearts of big cities such as supermarkets, grocery stores and others that are provided to meet people's needs for primary goods that are always needed at all times. One of them is Giant Express Tamalanrea, a supermarket in the city of Makassar that serves the sale of household goods and general needs. With the use of customer data analysis to determine the customers' purchasing patterns, Giant Express can optimize the collation of goods, by positioning goods at closer shelv

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Yuliana, Lingga. "Dampak Kondisi Pandemi di Indonesia Terhadap Trend Penjualan (Studi Kasus pada PD. Sumber Jaya Aluminium)." JRB-Jurnal Riset Bisnis 4, no. 1 (2020): 27–38. http://dx.doi.org/10.35814/jrb.v4i1.1480.

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The purpose of this study was to analyze the sales conditions at PD. Sumber Jaya Alunimium, through the sales data pattern, identified the relationship between PSBB and the sales impact of PD. Sumber Jaya Aluminum, as well as factors from the decline in sales due to the Pandemic conditions. The research method used is descriptive quantitative analysis through purposive sampling technique with Analytic Network Process (ANP). The results showed that the sales data pattern of PD. Sumber Jaya Aluminum during the period December 2019 to April 2020 has a sales trend that tends to decline. Based on p

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17

Patil, Kirti S., and Sandip S. Patil. "Sequential Pattern Mining Using Algorithm." Asian Journal of Computer Science and Technology 2, no. 1 (2013): 19–21. http://dx.doi.org/10.51983/ajcst-2013.2.1.1715.

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The concept of Sequential Pattern Mining was first introduced by Rakesh Agrawal and Ramakrishnan Srikant in the year 1995. Sequential Patterns are used to discover sequential sub-sequences among large amount of sequential data. In web usage mining, sequential patterns are exploited to find sequential navigation patterns that appear in users’ sessions sequentially. The information obtained from sequential pattern mining can be used in marketing, medical records, sales analysis, and so on. In this paper, a new algorithm is proposed; it combines the Apriori algorithm and FP-tree structure which p

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18

Hayati Ifroh, Riza, Iwan M.Ramdan, Vivi Filia Elvira, Rahmi Susanti, Reny Noviasty, and Ika Wulan Sari. "Cigarette Sales Promotion Pattern and Smoking Behavior of Sellers in Mulawarman University, Samarinda." Jurnal Ilmu Kesehatan Masyarakat 10, no. 3 (2019): 153–62. http://dx.doi.org/10.26553/jikm.2019.10.3.153-162.

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Mulawarman University have the largest number of students in Kalimantan (37,000). This amount has the potential to be smokers supported by the non-realization of non-smoking areas in all faculties and the high circulation of cigarettes through the mobilization of street vendors and retail franchises. The purpose of this study was to find out the dominant factors influencing cigarette sales in the environment of street vendors in Mulawarman University. The design of this study is quantitative research to analyze the correlation between cigarette sales figures, types or brands of cigarettes, att

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Ahmad Syaeful Ma’arief, Rudi Kurniawan, and Saeful Anwar. "Improvement of Fashion Product Sales Association Model in the Largest Store on Melgit Official Lazada with the Frequent Pattern Growth Algorithm." Journal of Artificial Intelligence and Engineering Applications (JAIEA) 4, no. 2 (2025): 1557–461. https://doi.org/10.59934/jaiea.v4i2.926.

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In this digital era, it is increasingly easier for people to shop. E-commerce or Marketplace is a communication technology in the scope of business that seeks to maximize information from large transaction data to create relevant product recommendation models. The Melgit Official store on Lazada is one of the stores with the most sales of fashion products. To understand consumer buying patterns and improve sales strategies, data analysis is needed that can uncover associations between products that are frequently purchased together. One of the algorithms that can be used to find this associati

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Aryanti, Dessy, and Johan Setiawan. "Visualisasi Data Penjualan dan Produksi PT Nitto Alam Indonesia Periode 2014-2018." Ultima InfoSys 9, no. 2 (2019): 86–91. http://dx.doi.org/10.31937/si.v9i2.991.

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PT Nitto Alam Indonesia is a Manufacturing company engaged in screw manufacturing services. The company has a total of 134,252 rows sales and production data, but the data has never been analyzed so that the information is still not fully explored. This research proposes to make a visualization in the form of a dashboard containing sales and production data at PT Nitto Alam Indonesia in 2014 – 2018. It will be shown by using visual data mining (VDM) method with Tableau Software tools. The purpose of this study was to assist PT Nitto Alam Indonesia in analyzing sales and production data to find

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21

Chitre, Vidya. "Big Mart Sales Analysis." International Journal of Innovative Technology and Exploring Engineering 11, no. 5 (2022): 8–11. http://dx.doi.org/10.35940/ijitee.c9833.0411522.

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In the modern era of reaching new lengths of advancement, every company and enterprise are working on their customer demands as well as their inventory management. The models used by them help them predict future demands by understanding the pattern from old sales records. Lately, everyone is abandoning the traditional prediction models for sales forecasting as it takes a prolonged amount of time to get the expected results. Therefore now the retailers keep track of their sales record in the form of a data set, which comprises price tag, outlet types, outlet location, item visibility, item out

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Vidya, Chitre, Mahishi Shruti, Mhatre Sharvari, and Bhagwat Shreya. "Big Mart Sales Analysis." International Journal of Innovative Technology and Exploring Engineering (IJITEE) 11, no. 5 (2022): 8–11. https://doi.org/10.35940/ijitee.C9833.0411522.

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<strong>Abstract:</strong> In the modern era of reaching new lengths of advancement, every company and enterprise are working on their customer demands as well as their inventory management. The models used by them help them predict future demands by understanding the pattern from old sales records. Lately, everyone is abandoning the traditional prediction models for sales forecasting as it takes a prolonged amount of time to get the expected results. Therefore now the retailers keep track of their sales record in the form of a data set, which comprises price tag, outlet types, outlet location

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Riikonen, Antti, Timo Smura, and Juuso Töyli. "Price and Sales Volume Patterns of Mobile Handsets and Technologies." International Journal of Business Data Communications and Networking 11, no. 2 (2015): 22–39. http://dx.doi.org/10.4018/ijbdcn.2015070102.

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This article provides empirical evidence on the price and unit sales volume patterns of mobile handsets and mobile technologies, using data on the Finnish market. The prices and sales are studied on product category, product model, and product feature levels. The results show how the dynamic of prices and sales changed after the proliferation of smartphones. Otherwise, the dynamics seem to be relatively systematic supporting the use of simple assumptions in practical estimations. The median price of handset models decreases linearly, from 89% of the introduction price at peak sales in the fift

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Intan Sari, Yani Maulita, and Lina Arliana Nur Kadim. "Pengelompokan UMKM Kota Binjai Menggunakan Metode Clustering K-Means Untuk Mengidentifikasi Pola Perkembangan Bisnis." Bridge : Jurnal publikasi Sistem Informasi dan Telekomunikasi 2, no. 3 (2024): 198–206. http://dx.doi.org/10.62951/bridge.v2i3.148.

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Grouping is a process or activity to develop a system that is more organized and easy to understand, making it easier to analyze, identify or manage data and can also be used to explore information so that it becomes new knowledge for anyone who wants to obtain it. and in this case the information we want to explore is about MSME data in Binjai City. Namely, it is difficult to know how to identify existing business development patterns, whether they are not yet developed, less developed, already developed, and very developed. Offline and online promotions have not been optimal in increasing th

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Sang, Yuxin, Peiyan Li, and Ziliang Yin. "Vegetable Merchandise Analysis from a Quarterly Sales Data Perspective." Highlights in Business, Economics and Management 33 (May 9, 2024): 402–9. http://dx.doi.org/10.54097/8e7wb791.

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In the field of fresh vegetable retailing, the category diversity and time-sensitive characteristics of commodities pose unique challenges to inventory management and pricing decisions. Comprehensively analyzing the distribution pattern of sales volume of different categories and individual items of vegetable commodities and their interrelationships is of great significance to retailers' scientific replenishment and pricing decisions. By categorizing vegetable sales every quarter, a comprehensive analysis is carried out on a category and individual item basis. Visualization was used to show th

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Chae, Jin Mie, and Eun Hie Kim. "Sales Pattern and Related Product Attributes of T-shirts." Journal of the Korean Society of Clothing and Textiles 44, no. 06 (2020): 1053–69. http://dx.doi.org/10.5850/jksct.2020.44.6.1053.

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Kwon, Gae Eun, Dong Woo Ko, and Sang-Uk Jung. "Usage pattern of sales promotion in the Korean market." Pressacademia 4, no. 3 (2017): 296–302. http://dx.doi.org/10.17261/pressacademia.2017.707.

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Ahn, Jin Sook, and So Young Sohn. "Customer pattern search for after-sales service in manufacturing." Expert Systems with Applications 36, no. 3 (2009): 5371–75. http://dx.doi.org/10.1016/j.eswa.2008.06.061.

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Gaddam, Sandhya Rani, Sarada Jayan, Pentakota Ravi, and Bilal Alatas. "Data-driven sales optimization with regression and chaotic pattern search." PeerJ Computer Science 10 (June 25, 2024): e2144. http://dx.doi.org/10.7717/peerj-cs.2144.

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Lead generation is the process of gaining potential customers’ interest to increase future sales, and it is an essential part of many businesses’ (amusement parks, theme parks, clubs, etc.) sales processes as their membership is more expensive. The main objective of these businesses is to increase the count of customers. By generating sales leads, a club/park can find leads who have already expressed interest in its products and services and access their audience potential, allowing them to focus on future marketing and sales efforts on those leads that are more likely to convert. The current

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Zhang, Dingfeng, Yuhang She, Limei Liu, Rongjie Ouyang, and Jinpeng Ye. "Research on the correlation and distribution law of commodities." Highlights in Business, Economics and Management 33 (May 9, 2024): 9–17. http://dx.doi.org/10.54097/x8wpp523.

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In the fresh supermarket, the supermarket will generally make replenishment plans according to the historical sales and demand of each commodity. There is a certain correlation between the sales volume of different commodities. It is crucial to deeply understand the distribution law and correlation relationship of commodity sales to optimize the replenishment strategy. In this paper, Spearman correlation coefficient analysis and entropy weight method-TOPSIS model are constructed to analyze the distribution law and correlation relationship of vegetable products and single product sales volume.

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Purba, Tigor Novanda, and Diky Firdaus. "DETERMINATION FOR CONSUMER PATTERNS IN BEVERAGE PRODUCT SALES USING THE FREQUENT PATTERN GROWTH ALGORITHM." IJISCS (International Journal of Information System and Computer Science) 5, no. 2 (2021): 84. http://dx.doi.org/10.56327/ijiscs.v5i2.982.

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The culinary business is now increasingly developing and competition is increasing, so it requires a strategy to market the products to be sold. In the business sector, the results of the implementation of FP-Growth algorithm data mining can help business people find opportunities from consumption trends so that culinary business people can find out what types of products currently have the highest rating in the community so that managers can provide menu recommendations so they can increase sales turnover. The data required is a certain period of transaction data which is analyzed to produce

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Setyoko, Hanum Taru, Ahmad Rofiqul Muslikh, and Viry Puspaning Ramadhan`. "Analisis komparatif metode dekomposisi aditif dan multiplikatif dalam memprediksi penjualan pada industri fashion." Journal of Information System and Application Development 3, no. 1 (2025): 21–30. https://doi.org/10.26905/jisad.v3i1.15395.

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Sales forecasting is very important, especially for businesses engaged in the fashion sector to make strategic decisions. This study aims to compare the additive and multiplicative decomposition methods in forecasting the sales of couple prayer mats at Elora Fashion. The dataset used consists of monthly sales data from January 2021 to September 2024. Through decomposition methods, the analysis was conducted to observe changes in trends, seasonal components, cycles, and random variations. The trend analysis indicated a rising sales pattern. The highest seasonal index occurred in June, while the

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Wang, Junyi, and Derek T. Robinson. "Assessing the Relative and Combined Effects of Network, Demographic, and Suitability Patterns on Retail Store Sales." Land 12, no. 2 (2023): 489. http://dx.doi.org/10.3390/land12020489.

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Despite challenges associated with acquiring proprietary sales data, there exists a wealth of literature using different types of data (e.g., spending, demographic, geographic) to understand or represent different drivers of retail store sales. We contribute to the spatial analysis of drivers of retail store sales by analyzing the relative influence of road networks, demographic, and suitability variables on retail store sales within the home-improvement sector. Results demonstrate that the inclusion of variables describing the road network pattern is more influential in predicting store sales

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Clark, Stephen D., Becky Shute, Victoria Jenneson, Tim Rains, Mark Birkin, and Michelle A. Morris. "Dietary Patterns Derived from UK Supermarket Transaction Data with Nutrient and Socioeconomic Profiles." Nutrients 13, no. 5 (2021): 1481. http://dx.doi.org/10.3390/nu13051481.

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Poor diet is a leading cause of death in the United Kingdom (UK) and around the world. Methods to collect quality dietary information at scale for population research are time consuming, expensive and biased. Novel data sources offer potential to overcome these challenges and better understand population dietary patterns. In this research we will use 12 months of supermarket sales transaction data, from 2016, for primary shoppers residing in the Yorkshire and Humber region of the UK (n = 299,260), to identify dietary patterns and profile these according to their nutrient composition and the so

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Damanik, Florida Nirma Sanny, Andrew Sagita, Harianto -, and Andy Syaputra. "Aplikasi Pengenalan Pola Pembelian Konsumen Menggunakan Kombinasi Algoritma FP-Growth Dan ECLAT Method (FEM)." Jurnal SIFO Mikroskil 19, no. 2 (2018): 1–12. http://dx.doi.org/10.55601/jsm.v19i2.553.

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Sales data stored in enterprise databases are usually stored as archives or documentation. In the case of retail companies, data mining science can be used to extract new information from sales database, ie consumer purchase pattern analysis. The algorithm that can be used to analyze consumer purchase pattern is FEM algorithm using combination of Frequent Pattern Growth (FP-Growth) and Eclat algorithm. The construction of FP-Tree tree structure is done by using FP-Growth algorithm, while the process of extraction of items purchased (frequent itemset) is done by using Eclat algorithm. The appli

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Wijaya, Ardi, Muhammad Rifqo, A. R. Walad Mahfuzhi, and Prayoga Putra. "Sales Transaction Analysis at Barokah Minimarket With the Implementation of the Apriori Algorithm." Jurnal Komputer, Informasi dan Teknologi 5, no. 1 (2025): 12. https://doi.org/10.53697/jkomitek.v5i1.2287.

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Minimarket Barokah is a minimarket located in Rafflesia Hospital, Bengkulu City. Minimarket Barokah is owned by the Rafflesia Hospital Foundation Bengkulu. The sales focus or target market targeted by Minimarket Barokah is visitors from Rafflesia Hospital, families of patients, and employees of the hospital. Data mining, often also called knowledge discovery in database (KDD), is an activity that includes collecting, using historical data to find regularities, patterns or relationships in large data sets. The output of data mining can be used to improve future decision making. Apriori algorith

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Yue, Xiaoli, Yang Wang, Yabo Zhao, and Hong’ou Zhang. "Spatial Pattern of Housing Sales Vacancy in Guangzhou’s Urban District, China." Journal of World Architecture 5, no. 6 (2021): 47–51. http://dx.doi.org/10.26689/jwa.v5i6.2774.

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Housing vacancy can reflect the destocking degree of the real estate market. Based on the data of 57 opened residential quarters (46,622 units) from 2015 to 2018, this paper constructs a calculation formula of the sales vacancy rate and then analyzes the spatial pattern in Guangzhou’s urban district. The results show that there is obvious differentiation in the spatial pattern of housing sales vacancy in Guangzhou’s urban district, showing a higher spatial pattern in the old area and urban district and a lower spatial pattern in the core area. Subdistricts with high vacancy rates are mainly lo

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Ismarmiaty, Ismarmiaty, and Ria Rismayati. "Product Sales Promotion Recommendation Strategy with Purchase Pattern Analysis FP-Growth Algorithm." Sinkron 8, no. 1 (2023): 202–11. http://dx.doi.org/10.33395/sinkron.v8i1.11898.

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The development of retail business technology is related to the need for management to meet customer demands by using technology. To help make effective sales strategic decisions, it is necessary to optimize the use of information technology on existing sales transaction data. The transaction database that has been stored as a company archive asset can be used for processing information that is useful in increasing product sales and promotions. This study aims to provide an analysis related to the product sales pattern of PT. X in Sumbawa Besar city. PT. X is a retail company that sells distri

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Hardi, Nila, Jordy Lasmana Putra, and Tika Adilah M. "Implemetasi Data Mining Menggunakan Algoritma Apriori Dalam Menentukan Pola Penjualan Carton Box." Journal of Information System Research (JOSH) 5, no. 4 (2024): 1472–78. https://doi.org/10.47065/josh.v5i4.5646.

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Good company managers must be able to examine the sales patterns that exist in the company. Some companies have shortcomings, including the problem of stock of goods that do not match the number of goods sold. This certainly affects the level of sales. The existence of sales activities every day, sales transaction data will continue to grow, causing greater data storage. Sales transaction data is only used as an archive without being put to good use. Basically the data set has very useful information. In data mining there are several algorithms or methods that can be done, one of which is the

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Farah Ayu Mufida, Nurafni Eltivia, and Nur Indah Riwajanti. "Time Series Forecasting of Nickel Sales in Nickel Mining Companies Listed on Indonesia Stock Exchange (IDX)." eCo-Fin 6, no. 2 (2024): 133–42. http://dx.doi.org/10.32877/ef.v6i2.1110.

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This research aims to analyze nickel sales forecasting using time series forecasting with the help of Microsoft Excel and then compare the pattern between Nickel Mining Companies listed on IDX (Indonesia Stock Exchange). This research uses a quantitative descriptive study with a forecasting method. The data used is secondary data, which is sales data contained in the financial statements of nickel mining companies listed on the IDX (Indonesia Stock Exchange) from 2015-2023. There are a total of 43 data. The results of this study show that the highest sales forecast from PT Aneka Tambang Tbk's

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Wang, Jiangning. "A Study on Pricing and Replenishment Decision of Vegetables in Fresh Superstores Based on Time Series Analysis." Highlights in Science, Engineering and Technology 76 (December 31, 2023): 690–97. http://dx.doi.org/10.54097/wmdhhw18.

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This paper focuses on an in-depth study of the vegetable pricing and replenishment decision problem in fresh produce superstores. Using data preprocessing and multiple mathematical models, the sales and correlation laws between different categories and single products of vegetable products are analyzed. Firstly, the cyclical pattern of sales is revealed through the analysis of historical sales data; secondly, the pricing and replenishment strategies are optimized by using multiple linear regression and time series models; finally, an optimization model is constructed to predict future sales an

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Ismarmiaty, Ismarmiaty, and Ria Rismayati. "Purchase Pattern Analysis with FP-Growth Algorithm for Product Sales Promotion Recommendation Strategies." Jurnal Teknologi Informasi dan Pendidikan 15, no. 1 (2022): 132–42. http://dx.doi.org/10.24036/jtip.v15i1.499.

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The development of retail business technology is related to the need for management to meet customer demands by using technology. To help make effective sales strategic decisions, it is necessary to optimize the use of information technology on existing sales transaction data. The transaction database that has been stored as a company archive asset can be used for processing information that is useful in increasing product sales and promotions. This study aims to provide an analysis related to the product sales pattern of PT. X in Sumbawa Besar city. PT. X is a retail company that sells distri

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SUZUKI, Akinori, Kenichi WATANABE, Hitoshi SHIMOII, Osamu AKITA, and Yuichi AKIMOTO. "Sales Pattern of Alcoholic Beverages in each Prefecture of Japan." JOURNAL OF THE SOCIETY OF BREWING,JAPAN 80, no. 7 (1985): 485–89. http://dx.doi.org/10.6013/jbrewsocjapan1915.80.485.

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Shah, Maulik, Nirali Shah, Anviksha Shetty, Darshan Shah, and Pradnya Gotmare. "A Comparative Study of Pattern Recognition Algorithms on Sales Data." International Journal of Computer Applications 141, no. 1 (2016): 38–41. http://dx.doi.org/10.5120/ijca2016909463.

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Anwar, Badrul, Ambiyar Ambiyar, and Fadhilah Fadhilah. "Application of the FP-Growth Method to Determine Drug Sales Patterns." Sinkron 8, no. 1 (2023): 405–14. http://dx.doi.org/10.33395/sinkron.v8i1.12004.

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Pharmacies are shops that sell and mix medicines based on doctors’ prescriptions and trade medical goods. Apart from being a business actor, the pharmacy also plays a role in providing health services that are easily accessible to the public. The problem that often occurs in pharmacies selling drugs is that they are less than optimal in service to consumers. The habit of consumers buying more than one type of drug makes pharmacy staff slow in providing the drug due to the inaccurate layout of the drug. The FP-Growth method in Data Mining is a method that can provide a solution in determining d

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Yu, Li, and Zai Fang Zhang. "Trend Analysis of Product Function Using Sequential Pattern Mining." Applied Mechanics and Materials 519-520 (February 2014): 736–40. http://dx.doi.org/10.4028/www.scientific.net/amm.519-520.736.

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During the early stage of product design, it is important for design engineers to decide the most appropriate functions for various customers. To facilitate this time consuming task, sequential pattern mining is applied to uncover the useful patterns in historical database. The mined sequential patterns can reflect the dynamic change of product functions, which can help design engineers find the most suitable product functions for customers. Based on the historical sales transactions of computer, a case study is conducted to illustrate the proposed method.

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Kusnadi, Yahdi, and Muhamad Auliya Ahsan. "Pemilihan Strategi Penjualan Obat Apotik Antar Menggunakan Algoritma A Priori." Jurnal Teknologi Informatika dan Komputer 6, no. 2 (2020): 74–83. http://dx.doi.org/10.37012/jtik.v6i2.213.

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Business competitors are required to think of a sales strategy to attract the attention of buyers, especially the amount of business competition that can increase sales. There are many ways used by a company to boost sales, even similar ways have been followed by other companies as competitors. Business competitors, especially Inter Pharmacies, are required to think creatively to increase sales. Using a sales database and assisted with algorithm A Priori data mining companies will know the pattern of selling goods and can determine the determination of the provision of goods and the right stoc

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Ciszewski, Robert L., and Philip D. Harvey. "The effect of price increases on contraceptive sales in Bangladesh." Journal of Biosocial Science 26, no. 1 (1994): 25–35. http://dx.doi.org/10.1017/s0021932000021039.

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SummaryIn April 1990, the prices of five brands of contraceptives in the Bangladesh social marketing project were increased, by an average of 60%. The impact on condom sales was immediate and severe, with sales for the following 12 months dropping by 46% from the average during the preceding 12 months. The effect on oral contraceptive sales was less dramatic: average sales in the year following the increases dropped slightly despite a previously established pattern of rapidly rising sales. There appears no reasonable combination of events other than the price increase itself to explain most of

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Utami, Fadhila Putri, and Arief Jananto. "Implementation of the Association Rule Method using Apriori Algorithm to Recognize The Purchase Pattern of Pharmacy Drugs “XYZ”." CESS (Journal of Computer Engineering, System and Science) 8, no. 1 (2023): 34. http://dx.doi.org/10.24114/cess.v8i1.40377.

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XYZ Pharmacy is a Special Health Service Point for employees and retirees of the XYZ company. This pharmacy carries out the process of buying and selling drugs by providing various types of drugs. The number of sales transactions in each day, resulting in sales data will increase over time. If the data is left alone, the pile of data will only become archives that are not utilized. By carrying out the data mining process, this data can be used to produce information that can be used to increase sales transactions at XYZ Pharmacy. The method used in this study is the Association Rule which func

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Yanti, Roaida, Prita Nurkhalisa Maradjabessy, Qurtubi Qurtubi, and Ira Promasanti Rachmadewi. "Determining the retail sales strategies using association rule mining." International Journal of Advances in Applied Sciences 13, no. 3 (2024): 530. http://dx.doi.org/10.11591/ijaas.v13.i3.pp530-538.

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Competitive competition in the retail industry requires retailers to maintain improvements and formulate accurate strategies to maintain their competitiveness. A small number of daily visitors visit retail store Y if compared to other retail stores, which leads to decreased store revenue due to the small number of products sold. Therefore, it is crucial to formulate the right business strategy to increase sales by utilizing customer shopping behavior derived from transaction data. The method used is association rule mining (ARM) with a frequent pattern growth (FP-growth) algorithm to determine

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