Using the Apriori Algorithm to Identify Purchase Patterns for Enhancing Sales in Personal Shopper Services

Due to the increase of retail sales in Egypt and all over the world, came the importance for the managers of supermarkets to develop marketing strategy to maximize their profits, by getting rid of inactive products. Product bundling is one of the most important marketing strategies used to get rid of stock by making integrated bundles of inactive products and demanded products with discount prices. We can do that through our recommendation system and also increase customers' faith by keeping up with their purchase habits changes in low prices. In this study, first association rules are applied to find the best-integrated bundles with optimal suggested bundle size according to customer habits. Second testing these resulting product bundles to eliminate bundles didn't contain Products aim to get rid of them. Finally the given suggested bundles’ elements replaced with stagnant products that are the same kind of product in the bundle but with another trade. During that study algorithms (Apriori and FP-growth) were studied. Although the two algorithms give strong association rules, and the results were so near. But FP-Growth algorithm was more efficiency as Apriori algorithm caused problems with minimum support parameter as, in a small system transaction, big minimum support did not work using it. Also, it makes the memory PC faces memory hangs when minimum support was very low. On the other hand, all of this didn't appear with FP-Growth algorithm, and it was faster in dealing data.

Culture Coffee MSME is one of the MSMEs engaged in the culinary field and is experiencing business competition. A marketing strategy is needed with the right decision-making process so that the business can survive and excel. UMKM Culture Coffee uses a point of sales application to accommodate the transaction process and record transactions. Historical customer data can be processed into a basis for decision making for marketing strategies that effectively increase sales. However, the transaction data has not been used optimally. There is a need to analyze historical customer data that can generate information to form marketing strategies. Market Basket Analysis (MBA) is one of the methods in data mining used in knowing products that tend to be purchased together by customers known as Association Rule. Association rules produce products in the form of packages or bundling which are used as marketing strategies. The marketing strategy obtained is supported by data visualization which contains information from the data. Apriori algorithm is used to generate association rules. The result of this research is an association rule on the historical data of MSME Culture Coffee customer purchases. Based on these rules, recommendations for selling menu packages to customers can be given. The purpose of this research is to find customer purchasing patterns which are used as the basis for decision making in determining menu sales. The results showed 2 product packages, namely, nuggets and french fries with sausages and french fries with a support and confidence value of 12.5% and 37.6% with 10.8% and 29% respectively. The results of this study can be used as a basis for the sales and marketing strategy of Culture Coffee MSMEs to increase business revenue.

Objectives: To investigate the Modified Inverse Weibull Distribution (MIWD) to determine its effectiveness in modelling real customer transaction data, which is often skewed and exhibits heavy-tailed characteristics. Methods: Parameters of the model were estimated using the Maximum Likelihood Estimation (MLE) technique. The model's performance was evaluated using goodness-of-fit measures, such as the log-likelihood (ℓ), Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), and Kolmogorov–Smirnov (K–S) statistic. The probability density function (PDF), cumulative distribution function (CDF), hazard function, and survival function were derived analytically and visualized with Python to assess model behavior and data alignment. Findings: The MIWD demonstrated a better fit for the customer transaction data than its sub-models and other competing distributions. The estimated probability density function (PDF), cumulative distribution function (CDF), hazard, and survival functions highlighted the model's adaptability in representing both skewness and heavy-tailed characteristics in actual transaction data. Novelty: The main contribution of this study is the empirical validation of the four-parameter MIWD using actual customer transaction data, demonstrating its superiority over established sub-models based on various quantitative criteria. The findings provide compelling evidence that the MIWD is a robust and adaptable alternative for modelling skewed, heavy-tailed transaction and lifetime data, with practical applications in customer behavior analysis, reliability engineering, and risk modelling. Keywords: Modified Inverse Weibull Distribution, Maximum Likelihood Estimation, Customer transaction data, Parameters, Rayleigh distribution

Madiun is one of the regencies in East Java province. Known as the city of Pecel it has excellent potential in the culinary field. The culinary industry is one of the economic activities for the people of Madiun. Marketing mix is an essential tool for companies to achieve the right goals. This is important to do to encourage the success of marketing activities. One of them is implementing marketing communications for Lesung Kresek restaurant. This application is to find out the increase in sales in Madiun City. This study aims to determine the marketing communication strategy of Lesung Kresek restaurant to increase sales. This research method uses a qualitative approach using observation, in-depth interviews, and documentation to obtain valid data. To test the validity of the data, this study was conducted by comparing and combining the data that had been received. The results of this study show that the marketing communication strategy applied by Lesung Kresek restaurant uses the 7P marketing mix concept, including product, price, place, promotion, people, and physical evidence. Based on the marketing mix, five marketing strategies are going well. While the two marketing mixes, such as places and processes, do not run optimally in running them. But in terms of location, it is very supportive in restaurant marketing.

This research project focuses on the design and development of a web-based Point of Sale (POS) application that incorporates advanced analytical techniques, specifically Market Basket Analysis with the Apriori algorithm and the Association Rule method. The primary objective of this web-based POS system is to empower retailers in managing their sales transactions efficiently and gaining valuable insights into customer purchasing behavior. The web-based POS application is multifaceted, offering features such as sales transaction recording, product inventory management, and customer data tracking. What sets it apart is the integration of the Apriori algorithm and Association Rule method, which enable the system to analyze and understand customer purchasing patterns. It identifies strong product associations and establishes rules that support intelligent decision-making for businesses. The advantages of Market Basket Analysis are substantial. Retailers can identify relevant purchase patterns, such as frequently co-purchased products or cross-selling opportunities. This information can be used to enhance marketing strategies, optimize product placement in stores, and create bundled product offerings, ultimately boosting sales and revenue. By analyzing transaction data and recognizing patterns, retailers can streamline their operations, minimize wastage, and allocate resources more effectively. In summary, this research project showcases the transformative potential of integrating Market Basket Analysis, the Apriori algorithm, and the Association Rule method into web-based POS systems. By doing so, retailers can enhance operational efficiency, boost sales, and improve customer satisfaction, ultimately leading to more successful and competitive businesses in the retail sector.

A retail store faces significant challenges in crafting effective sales strategies, particularly in designing promotional product bundles. To address this, the store leverages transaction data to analyze customer purchasing patterns, aiming to uncover products frequently bought together. This study employs data mining techniques, specifically the Apriori algorithm, to identify co-purchasing behaviors using 49,316 transaction records collected from January to June 2024. After thorough data cleaning and transformation, the Apriori algorithm identified 877 itemsets, spanning from frequent 1-itemsets to 4-itemsets. By setting a minimum support threshold of 0.003, the analysis narrowed down to 343 significant itemsets, including 325 frequent 1-itemsets and 18 frequent 2-itemsets, which served as the basis for generating association rules. Initially, 36 association rules were derived, highlighting various product relationships. To focus on impactful insights, the rules were filtered using a minimum confidence level of 0.5, yielding 3 highly relevant rules with lift ratios exceeding 1, indicating strong associations between antecedent and consequent products. These insights enable the store to design targeted promotional bundles, optimize product placement, and enhance overall sales performance. Additionally, this study demonstrates how data-driven strategies can provide a competitive edge by aligning with customer purchasing behaviors. To ensure continuous improvement, a Python-based system was developed, empowering the store to independently analyze transaction data and refine sales strategies in real time, adapting to evolving purchasing patterns as the dataset grows.

The demand for staple products that vary among customers makes it necessary for the store to determine how the marketing strategy should be. Data mining are known as KDD (Knowledge Discovery in Database) is to digging up valuable knowledge from the data. Research purpose is to identify the right marketing strategy to sales the goods. The marketing strategy is took by analyze how much consumers demand for basic needs. The algorithms used in this research are FP (Frequent Pattern)-Growth and A-priori Algorithm. Finding combinations patterns between itemset using the Association Rule. FP-Growth algorithm is an algorithm that been used to determining a set of data in a data set that often appears on the frequency of the itemset. the KDD stages study are data cleansing, data integration, data selection, data transformation, data mining, pattern evaluation and knowledge presentation. the Testing used Rapidminer software with a minimum confidence value of 0.6 and a minimum support of 0.45. FP-Growth algorithm obtained 5 rule conclusions while Apriori Algorithm obtained 3 rule conclusions. The FP-Growth algorithm make a better decision rules than a priori algorithms in determining of marketing strategies, because it produces more decisions on how the goods sold.

Honey bee cultivation has long been known to people in Indonesia. Honey is an agricultural product that has high benefits and economic value. Indonesia's fertile natural conditions allow the growth of various types of plants which can be used as a source of food for bees. Kele-kele is one of the forest resources that has the potential to be developed. This is because Kele-kele's food sources are abundant, almost all plants that produce flowers can be used as a food source, whether they come from forest plants, agricultural plants or plantation plants. Many people in Balangan already cultivate kele-kele as a honey producer, but there is still a lack of knowledge and understanding about marketing management as one of the spearheads of business. With the emergence of many similar businesses, competition is becoming increasingly fierce. To face competition and improve business, competitive position analysis and marketing strategies are very necessary. So that Trigona beekeepers are able to know market growth and relative market share. The analysis used is BCG Analysis (Boston Consulting Group). SWOT analysis is used to analyze internal factors so that the company's strengths and weaknesses are known. External factors to find out and analyze the opportunities and threats/challenges faced by the company. From the results of this analysis, the most relevant strategy can be determined to be used to face competition, increase sales, so as to increase company profits in the long term.

Pharmacy sales transaction data contain valuable information on customer purchasing patterns; however, in practice, such data are often used merely as operational records, making relationships between purchased drugs difficult to identify. This study analyzes drug purchasing patterns using the Apriori and FP-Growth algorithms based on sales transaction data from Apotek Gadi Lamba Condet for the period January to June 2025. The transaction data were processed through data cleaning, drug name standardization, and transformation into transaction format, resulting in 7,038 transactions with 1,495 drug items. Association rule mining was performed using a minimum support of 0.01 and a minimum confidence of 0.17. The results show that the Apriori and FP-Growth algorithms generate ten identical association rules with the same support, confidence, and lift values, and all rules have lift values greater than one. Paracetamol 500 MG emerges as the most frequently involved drug in the association rules. These findings demonstrate that, for medium-scale pharmacy transaction datasets, Apriori and FP-Growth have equivalent capability in identifying drug purchasing patterns, with the primary difference lying in computational efficiency rather than the quality of the generated patterns.

The current government policy is directed towards strengthening the MSME sector to achieve sustainable economic growth. The Ecoprint MSME in Lampung faces several challenges in maintaining the continuity of its business, including a decline in sales, limited capital and raw materials, as well as the unstable demand for ecoprint products, especially those that are still relatively new. This instability makes it difficult for MSMEs to manage inventory, which hinders the production process. The research method to address the issue of applying Business Intelligence in the strategy for developing superior products in ecoprint MSMEs using the Apriori Algorithm can be approached through Business Intelligence methods. The data used in this study is sales transaction data from June to August 2024. The stages of applying Business Intelligence include Justification, Planning, Business Analysis, Design, and Construction. By applying Business Intelligence and the Apriori Algorithm, MSMEs can compete in the digital era and achieve sustainable success. Based on testing results using Rapidminer, three association rules were obtained: Dress, Hijab with a support of 57% and confidence of 100%; Hijab, Dress with a support of 57% and confidence of 80%; and Hijab, Accessories with a support of 57% and confidence of 80%. The Apriori algorithm is capable of identifying consumer purchase patterns, allowing MSMEs to determine the most in-demand products and adjust their production strategy accordingly. The application of the Apriori algorithm on transaction data has resulted in various unique association rules. The key difference between these rules lies in the combinations of related products. Further analysis shows that the performance of the Apriori algorithm is optimal when using a minimum support value of 50% and a minimum confidence of 80%.

The creative sector in Indonesia has great potential for economic growth, but many Micro, Small, and Medium Enterprises (MSMEs) face challenges in adopting product innovation for international expansion. This study aims to analyse the role of product innovation, marketing strategy, and collaboration with creative industries in supporting the international expansion of MSMEs. The method used is Structural Equation Modeling (SEM) analysis with AMOS, involving 600 registered MSMEs in the creative sector. The results showed that product innovation, effective marketing strategies, and collaboration have a significant influence on the ability of MSMEs to enter the global market. The findings imply that an increased focus on innovation and the development of adaptive marketing strategies, as well as building strong collaboration with stakeholders, are critical to the successful international expansion of MSMEs in Indonesia.

Analysis of Transaction Database Using Apriori Algorithm. This study examines the use of the Apriori algorithm for analysing transaction databases. The Apriori algorithm is a fundamental technique in data mining that allows for the efficient discovery of frequent patterns and association rules in large datasets. The Apriori algorithm employs a two-step approach. Initially, it identifies frequent items in the database based on a user-defined minimum support threshold. Subsequently, it generates association rules that describe relationships between these frequent items based on metrics such as confidence and lift. This paper provides an in-depth explanation of the Apriori algorithm, emphasizing its strengths and limitations. Additionally, it presents various applications of the Apriori algorithm in real-world scenarios, such as shopping cart analysis, cross-selling and upselling, and customer segmentation. The significance of this study lies in its comprehensive analysis of the Apriori algorithm and its practical relevance in diverse data mining tasks. It serves as a valuable resource for researchers, practitioners, and anyone seeking to understand the fundamentals and applications of association rule mining. Keywords: Datamining, OLTP, Apriori algorithm, Item sets and Market Basket Analysis

Marketing strategy positioning

For the consumer market, finding valuable customers is the first priority and is assumed to assist companies in obtaining more profit. If we could discover critical products that are related with valuable customers, then it will lead to better marketing strategy to fulfill those essential customers. It will also assist companies in business development. This study selects real retail transaction data via the recency, frequency, and monetary (RFM) analysis and adopts the K-means algorithm to obtain results. Moreover, the Apriori algorithm with minimum support and skewness criteria is used to filter and find critical products. In this research, we found a novel methodology through setting the minimum support and skewness criteria and utilized the Apriori algorithm to identify 31 single critical products and 60 critical combinations (two products). This study assist companies in finding critical products and important customers, which is expected to provide an appropriate customer marketing strategy.

The research aims to overcome the problem of managing customer data, room data, and transaction data manually at Hotel Fergael. The management of customer data, room data and transaction data that are processed manually, is overcome by building a Front Office Information System for hotel room reservations at Hotel Fergael. The results of the Front Office Information System research for hotel room reservations at the Fergael Hotel can be done quickly, effectively and efficiently at the receptionist's work time, making reports can also assist managers in seeing reservations that have or have occurred at the Fergael Hotel. The system is designed in the form of Visual Basic and uses a MySQL database. With this research, the receptionist can expedite the activities that run at the Fergael Hotel, especially room input, customer input, and room reservations that occur at the Fergael Hotel Pematangsiantar. Keywords: Information System, Visual Basic, MySQL, Reservation, Hotel