The Application of Product Marketing Strategy Using an Association Rule Mining Apriori Method (original) (raw)
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Market Basket Analysis using Apriori Algorithm
International Journal of Innovative Research in Computer Science & Technology
A Technique that check for dependency for one Data item to another is Association Rule which is an old Data mining approach. Which is used to identify the next product that might interest a customer. The Apriori Algorithm is applied in this for mining frequent products sets and relevant Association rule. With this algorithm we can use this for up-sell and also in cross-sell to show the Association rule with the help of the algorithm. These methods are widely used in global companies, so for the good understanding the companies used the methods to remain up to date that what customers demands with which products. The results helps the big retailers to identify a trend for customers buying patterns, which is very helpful information for the retailers to plan their big business operations.
A Technical Analysis of Market Basket by using Association Rule Mining and Apriori Algorithm
2017
Data mining refers to extracting knowledge from large amount of data. Market basket analysis is a data mining technique to discover associations between datasets. Association rule mining identifies relationship between a large set of data items. When large quantity of data is constantly obtained and stored in databases, several industries is becoming concerned in mining association rules from their databases. For example, the detection of interesting association relationships between large quantities of business transaction data can help in catalog design, cross-marketing and various businesses decision making processes. A typical example of association rule mining is market basket analysis. This method examines customer buying patterns by identifying associations among various items that customers place in their shopping baskets. The identification of such associations can assist retailers expand marketing strategies by gaining insight into which items are frequently purchased by c...
Sales Level Analysis Using the Association Method with the Apriori Algorithm
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The company does not yet know the pattern of consumer purchases because so far, the sales transaction data has not been used correctly and does not have a unique method to determine consumer buying patterns. To overcome the problems on the company, this research was done to reprocess sales transaction data for 2018-2019 using data mining techniques with association methods and apriori algorithms. RapidMiner is a supporting application used to find association rules derived from transaction data. Processed transaction data using the Knowledge Discovery in Database (KDD) approach. Thus, the company can determine consumer habits in buying goods derived from sales transaction data for 2018-2019. The results of this study are that in 2018, nine association rules were obtained, of which the best were CT G-246 ⇒ CT G-250 and CT G-250 ⇒ CT G-246. In 2019, nineteen association rules were obtained, of which the best were PN 0441, SK 0175 ⇒ SK 0530 and SK 0175, SK 0283 ⇒ SK 0530. From the best...
Utilizing Apriori Data Mining Techniques on Sales Transactions
Webology, 2022
The establishment of a marketing strategy is important for every business actor in the competitive world of business. Business operators must be able to develop sound marketing strategies to influence the attractiveness of consumers and to buy interest in the products provided so that the enterprise they operate can compete and have a market share and to maximize sales sales. To implement marketing strategies, references are required so that promotions can reach the right target, for example by seeking similarities between items. By using data mining techniques, these studies apply the a priori approach to the promotion of customer product recommendations by association rules on product sales transaction datasets to aid in the formation of applications between product items. The dataset represents a sample of sales of products for 2020. The application used for analyzing is RapidMiner, where a support value of > 20% and confidence of > 60% is determined. Each product package promoted is made up of 2 products from the calculation results. The two best rules that have value confidence is combined with 2 items (Cre1→Cre2), (Cre1→Cre12) and (Cre9→Cre10). Based on the minimum support and confidence values that have been set, the results of the a priori method can produce association rules that can be used as a reference in product promotion and decision support in providing product recommendations to consumers.
Data Mining in Market Basket Transaction: An Association Rule Mining Approach
Data is one of the valuable resources for organization, and database management systems are gradually becoming ubiquitous in many small and medium scale companies. Although, some of the benefits of database management systems have been explored, however, many companies have not been able to exploit the advantages of gaining business intelligence from their databases. This has led to inadequate business decision making based on the data contained in the databases.
Market Basket Analysis on Transaction Data Using the Apriori Algorithm
Jurnal TAM (Technology Acceptance Model)
This research aims to get information about the relationship between sales patterns carried out by CV. Dian Abadi Jaya workshop by using APRIORI algorithms through transaction data sets carried out by customers. The subject of research is a record of shopping cart transactions made by customers, namely vehicle parts sales transactions and vehicle repair service transactions. The data collection techniques used are interviews and documentation. The criteria used in this research are a minimum of frequent itemset of 20 transactions with support criteria of 1,7%, confidence value of 40% and lift ratio value above 1. The results of the research have produced 9 sales pattern relationships with the highest confidence of 100%. The results that have been obtained are expected to help the CV. Dian Abadi Jaya workshop in making a decision for the next sale.
Journal of Physics: Conference Series, 2019
The development of the food and beverage culinary industry is growing very rapidly. Making food and beverage business owners, especially restaurants, have to make the right decision to stay in a very strong competition, restaurant owners must be ready to always innovate and remain to be able to meet consumer needs through products that can attract customers and determine strategies promotions that can boost sales. Stored transaction data has information that can be extracted by data mining techniques, for example knowing the pattern of sales in purchases by consumers. Information about sales patterns can be used by O! Fish restaurants to create more potential promotional strategies to boost sales by referring to items (menus) that are often purchased together.. To be able to find out the purchase patterns by consumers simultaneously, knowing what products are often purchased simultaneously can be used data mining techniques using a priori algorithms. A priori algorithm is used to generate association rules. Information about the association's rules in purchasing items (menus) by consumers can be used by O! Fish restaurants to create more potential promotional strategies to boost sales by referring to a combination of items that are often purchased simultaneously. Later the results of this study are in the form of a website-based application to analyze purchasing patterns (item association rules) by consumers where the purchase pattern can be used as recommendations in determining the promotion development strategy for O! Fish restaurants.
International Journal of Engineering Research and Technology (IJERT), 2020
https://www.ijert.org/association-rule-mining-using-apriori-algorithm-for-extracting-product-sales-patterns-in-groceries https://www.ijert.org/research/association-rule-mining-using-apriori-algorithm-for-extracting-product-sales-patterns-in-groceries-IJERTCONV8IS03007.pdf Association Rule Mining is used for finding the patterns, associations and relationships in dataset. The rule is used for identifying the frequently occurs in item set. It helps to retailers to identify relationships among the items that people buy together frequently. It involves machine learning models to analyze the dataset for predicting patterns and co-occurrence. Many algorithms are used to generate the association rules. In this paper, we implemented apriori algorithm using R tool.
Basket Market Analysis Using R-Based Apriori Algorithm to Find Information from Sales Data
INTERNATIONAL JOURNAL ENGINEERING AND APPLIED TECHNOLOGY (IJEAT)
Market Basket Analysis is a data mining technique that is used to determine which products a customer will buy simultaneously by analyzing a list of customer transactions. By knowing these products, an e-commerce system can create or develop a customer profile system and can determine its own customer catalog layout. This journal discusses data mining techniques, with association rules that can help check customer buying behavior and increase sales. The result can provide reference prices for cross selling, designing promotions and placing merchandise in stores increasing sales
Determination of Sales Data Patterns Using the Association Rules Apriori Method
2020
In competition in the business world, it is necessary to find the right strategy that can be used in sales optimization. Factors that influence the needs of market analysis is the level of frequency of consumers in buying an item. Because it is needed a solution to find sales patterns with the website to be more effective and efficient. The required data is taken from sales transaction data for a certain period and processed to produce association rules for goods and transactions. Besides being able to look for patterns that often appear among many transactions, this can make it easier for companies to increase sales turnover. The making of this application uses HTML as web page development, PHP as website development, and MySQL as database management. In the testing phase, this application starts from the login to get the results of the association analysis going well. Then from the conclusion of the application made with this application the manager can add more stock of goods to the product with the highest itemset, while for the lowest itemset marketing can be done by providing a package or discount for the purchase of these items.