Customer Purchase Patterns Research Articles

Analisis Pola Pembelian Pelanggan Menggunakan Algoritma Squeezer, Apriori dan FP-Growth Pada Toko Bangunan

To compete with other stores, store owners need to design various strategies, one of which is understanding customer purchase patterns. This article examines the Squeezer algorithm and compares the performance of the Apriori and FP-Growth algorithms in forming customer purchase association patterns that can be used as a reference for store owners in planning sales strategies. The data mining process was carried out using Association Rules and Clustering methods. A total of 1256 sales transaction data samples were analyzed to understand the association patterns produced by each method. Based on the test results with a minimum support of 0.2 and a confidence of 0.6, the Apriori algorithm produced 194 association rules with a total rule strength of 1.16. Meanwhile, the FP-Growth algorithm produced 52 association rules with the same total rule strength of 1.16. The Clustering Method resulted in 7 clusters with a similarity value of 0.06322. After comparison, the FP-Growth algorithm proved to have better performance in generating association rules compared to the Apriori algorithm.

Market Basket Analysis

Abstract: Market Basket analysis is a technique applied by retailers to understand customer’s shopping behaviour from their stores. The result of the effective analysis may improve supplier’s profitability, quality of service and customer satisfaction. The purpose of this project is to make use of anonymized data on customers’ transactional orders to focus on descriptive analysis on the customer purchase patterns, items which are bought together and units that are highly purchased from the store to facilitate reordering and maintaining adequate product stock. Market Basket Analysis is an important aspect of a retail organization's analytical framework for deciding where products should be placed and developing sales promotions for various segments of consumers to increase customer loyalty and, as a result, benefit. Market Basket Analysis is a data mining technique that can be used in various fields, such as marketing and etc. The frequent itemsets are mined from the database using the Apriori algorithm and then the association rules are generated. The project will assist supermarket managers in determining the relationship between the items that their customers purchase.

Web-Based POS with Apriori Market Basket Analysis

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.

Exploring the Depths of Market Basket Analysis: A Comprehensive Guide to Transaction Analysis with FP-Growth and Apriori Algorithms

This research investigates the role of data science in understanding customer behavior and enhancing sales, focusing specifically on the application of Apriori and FP-Growth Algorithms at a retail store, Deli Point, in Labuan Bajo. It illuminates the impact of 'rubbish data' on transactional data analysis, emphasizing the need for robust data cleaning procedures to ensure accurate results. Utilizing the faster FP-Growth Algorithm, the study effectively analyzed customer purchasing patterns to identify optimal product combinations for sales improvement. It discovered that 'parsley local' and 'mint flores' items had the highest support with a value of 0.036, indicating that strategic placement of these items together could enhance sales. The rule between chicken leg bone, orange sunkist, and chicken breast boneless was found to have a high confidence value and a lift value higher than 1, implying a higher potential for these items to be sold when positioned near each other. This study contributes to understanding consumer behavior and provides insights for enhancing sales and competitiveness in the retail industry. An association rule involving 'chicken leg bone’, 'orange sunkist', and 'chicken breast boneless' demonstrated high confidence and a lift value above one, suggesting significant sales potential when these items are grouped together. This study not only contributes valuable insights into retail consumer behavior and effective product placement strategies but also underscores the transformative role of data science in optimizing sales and boosting competitiveness in the retail sector.

Optimizing Predictive Accuracy: A Study of K-Medoids and Backpropagation for MPX2 Oil Sales Forecasting

This study assesses the use of K-Medoids and Backpropagation methods for predicting MPX2 Oil sales in the automotive workshop industry, crucial for meeting customer demands and refining sales strategies. Using transaction data from 2022 to 2023, the research involved normalizing and processing this data with these algorithms to forecast stock levels, focusing on accuracy measures like Mean Absolute Deviation (MAD) and Mean Squared Error (MSE). K-Medoids aided in identifying customer purchase patterns through clustering, while Backpropagation effectively predicted sales trends, improving accuracy with training. These findings offer valuable insights into MPX2 Oil's sales dynamics, enabling the company to enhance marketing, transaction management, and inventory strategies.

Pendampingan Planogram untuk Pemilik Ritel

Retail owners need to utilize transaction data for customer buying patterns. By knowing the pattern of customer purchases, retail owners can adjust the layout of the store and the layout of goods on the display rack or a planogram to increase product visibility which refers to increased sales. Not many retail owners are aware of the benefits of using a good planogram and how to design it, so it is essential to provide training related to planograms to the public so they can know how to create planograms properly and correctly. Community service will be conducted from July to October 2022 in Sleman, Bantul, and Temanggung Regency and its surroundings, with interactive mentoring methods with material delivery and questions and answers. Submission of the material contains definitions, benefits, and illustrations of the application of the planogram in the retail industry, as well as the stages of planning the planogram. With this community service, it is hoped that retail business owners who participate in the mentoring can utilize transaction data at each retailer to develop a planogram based on customer purchasing patterns, thereby increasing sales and customer satisfaction.

IMPLEMENTASI ALGORITMA APRIORI DALAM MENENTUKAN POLA PEMBELI CUSTOMER

CV. Syahfira Bakery and Cake is a business engaged in the production of cakes, pastries and bread. The more a business develops, the transactions that occur daily also increase. However, sometimes the sales transaction data is not fully utilized properly by administrative staff and company owners. In fact, if it is processed and used by administrative staff and supervised by business owners, then sales data will have a major influence on product sales in the future. Data processing can be done using data mining techniques, these data sets can produce new information, one of which is customer purchasing patterns. Syahfira Bakery and Cake is a cake, sponge and bread shop that has not fully utilized data for determining sales business strategies. With these problems, it is necessary to use transaction data to be processed with the Apriori algorithm so that it can provide new knowledge that can be utilized by shop owners. Based on the research that has been done, from the 20 processed transaction data, results are obtained that meet a minimum support of 30% and a minimum confidence of 70% and one rule is obtained, namely if a customer buys chocolate jam products, he will buy plain bread

Analisis dan Implementasi Market Basket Analysis (MBA) Menggunakan Algoritma Apriori dengan Dukungan Visualisasi 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.

Influence of Social Media on Consumers' Online Purchasing Habits During: The COVID-19 Pandemic in Pakistan

Currently, businesses located all over the world are adjusting to a new standard of operation. Customers are encouraged to make their purchases of necessities through the favored e-commerce platform of the organization. For the purpose of marketing web-based enterprises, websites such as Facebook, Twitter, Instagram, and Pinterest are utilized. The purpose of the study was to investigate how the COVID-19 epidemic altered the purchase patterns of Pakistani customers shopping online, with a particular emphasis on the role played by social media. The study utilized a quantitative research model and a descriptive research technique in its data collection and analysis. In order to obtain responses from 400 Pakistani clients, an online questionnaire employing the "purposive sampling" method was used. When looking at the data and putting the hypothesis to the test, PLS-SEM analysis was utilized. During the COVID-19 pandemic in Pakistan, a PLS-SEM study indicated that advertising tools, online reviews, and endorsements from celebrities had a favorable and significant effect on how individuals bought products online. According to the findings of this study, online businesses can improve their use of social media for marketing and advertising by following a number of particular measures. Customers have a greater propensity to make purchases via social media platforms when they come across favorable evaluations and recommendations from celebrities.

IMPLEMENTATION OF MARKET BASKET ANALYSIS WITH APRIORI ALGORITHM IN MINIMARKET

The rapid growth of the retail business has an impact on increasing the economic growth of the community. The retail business has high profit potential in areas that have a large population such as Indonesia. A retail business that is popular among the public is a modern market retail business or convenience store. With the rapid growth, it gives a tendency between convenience stores to compete. By designing a marketing strategy is one of the efforts to win the competition in supermarkets. Management needs to understand the purchase behavior made by customers, this action is useful to find out the products that customers are popularly buying. Association algorithm is a form of algorithm in the field of data mining that serves to provide correlation between one item and another. there are several popular algorithms in applying association algorithms one of which is the a priori algorithm created by Agrawal and Srikant in 1994. To support the understanding of customer purchase patterns, it is necessary to implement market basket analysis that has the ability to recognize pattern patterns from transaction data in a convenience store. Performance in market basket analysis also needs to be tested to handle a lot of transaction data, considering that the recording of sales transaction data continues to run over time. The implementation carried out using flask is one of the implementations that is relevant to technological developments, this implementation results in a relatively short data speed with the factor that the magnitude of transaction data is middle to lower, which is 14,963 transaction data.

Different Coping Patterns among US Graduate and Undergraduate Students during COVID-19 Pandemic: A Machine Learning Approach.

COVID-19 caused unprecedented disruptions to regular university operations worldwide. Dealing with 100% virtual classrooms and suspension of essential in-person activities resulted in significant stress and anxiety for students coping with isolation, fear, and uncertainties in their academic careers. In this study, we applied a machine learning approach to identify distinct coping patterns between graduate and undergraduate students when facing these challenges. We based our study on a large proprietary dataset collected from 517 students in US professional institutions during an early peak of the pandemic. In particular, we cast our problem under the association rule mining (ARM) framework by introducing a new method to transform survey data into market basket items and customer transactions in which students’ behavioral patterns were analogous to customer purchase patterns. Our experimental results suggested that graduate and undergraduate students adopted different ways of coping that could be attributed to their different maturity levels and lifestyles. Our findings can further serve as a focus of attention (FOA) tool to facilitate customized advising or counseling to address the unique challenges associated with each group that may warrant differentiated interventions.

Assessing an on-site customer profiling and hyper-personalization system prototype based on a deep learning approach

• This article examines the perceptions of early adopters of an AI-based on-site customer profiling and hyper-personalization system (OSCPHPS). • The article employs a qualitative comparative analysis to outline the causal recipes clustered in three categories: facial expression recognition, automatic age and gender recognition and in-store customer tracking, enabling a high intention to test OSCPHPS prototype. • The findings offer strategic guidance to marketing managers of retail stores in order to capture the great hyper-personalization opportunities created by AI technologies. • We prove that deep algorithms and neural convolutional networks are highly valuable in analyzing customer profiles and offering hyper-personalized products and services to customers. The development of artificial intelligence (AI) technologies is proceeding fast across many fields. Based on a deep learning approach, we propose a prototype of an on-site customer profiling and hyper-personalization system (OSCPHPS) targeted at marketing professionals. We propose an AI platform to create customer profiles during their physical presence in stores. The idea of the OSCPHPS prototype is to automatically detect and gather customer data directly from the store, essentially completing customer profiles containing gender, age, personality, emotions, and products they interacted with or bought, irrespective of where they are in the store. Each buying operation could generate an anonymous customer profile. Therefore, for every product sold, the system will track multiple customer-generated profiles of the people who bought that product. These kinds of data offer endless further possibilities for the business. Through a configurational study conducted via fsQCA methodology, we assessed the interest in the OSCPHPS prototype on the part of marketing managers of clothes & fashion stores located in different European countries. Based on these live generated profiles, we could further enhance the OSCPHPS system by adding support for customer segmentation, strategic product campaigns, live product recommendation, analysis of emotions toward a product or a category of products, sales forecasts, and personalized store space enhancements based on augmented reality, customer exploratory statistics and customer purchasing patterns.

Data Mining Model for Predicting Customer Purchase Behavior in E-Commerce Context

Nowadays e-commerce environment plays an important role to exchange commodity knowledge between consumers commonly with others. Accurately predicting customer purchase patterns in the e-commerce market is one of the critical applications of data mining. In order to achieve high profit in e-commerce, the relationship between customer and merchandise are very important. Moreover, many e-commerce websites increase rapidly and instantly and competition has become just a mouse-click away. That is why the importance of staying in the business, and improving the profit needs to accurately predict purchase behavior and target their customers with personalized services according to their preferences. In this paper, a data mining model has been proposed to enhance the accuracy of predicting and to find association rules for frequent item sets. Also, K-means clustering algorithm has been used to reduce the size of the dataset in order to enhance the runtime for the proposed model. The proposed model has used four different classifiers which are C4.5, J48, CS-MC4, and MLR. Also, Apriori algorithm to provide recommendations for items based on previous purchases. The proposed model has been tested on Northwind trader’s dataset and the results archives accuracy equal 95.2% when the number of clusters were 8.

Discovering Customer Purchase Patterns in Product Communities: An Empirical Study on Co-Purchase Behavior in an Online Marketplace

Marketplace platforms gather and store data on each activity of their users to analyze their customer purchase behavior helping to improve marketing activities such as product placement, cross-selling, or customer retention. Market basket analysis (MBA) has remained a valuable data mining technique for decades for marketers and researchers. It discovers the relationship between two products that are frequently purchased together using association rules. One of the issues with this method is its strict focus on binary relationships, which prevents it from examining the product relationships from a broader perspective. The researchers presented several methods to address this issue by building a network of products (co-purchase networks) and analyzing them with network analysis techniques for purposes such as product recommendation and customer segmentation. This research aims at segmenting products based on customers’ purchase patterns. We discover the patterns using the Stochastic Block Modeling (SBM) community detection technique. This statistically principled method groups the products into communities based on their connection patterns. Examining the discovered communities, we segment the products and label them according to their roles in the network by calculating the network characteristics. The SBM results showed that the network exhibits a community structure having a total of 309 product communities, 17 of which have high betweenness values indicating that the member products play a bridge role in the network. Additionally, the algorithm discovers communities enclosing products with high eigenvector centralities signaling that they are a focal point in the network topology. In terms of business implications, segmenting products according to their role in the system helps managers with their marketing efforts for cross-selling, product placement, and product recommendation.

Design and Development of Efficient Cost Saving Algorithms for Guiding Customer Purchasing Patterns in Modern Consumerism Scenario Using Fuzzy Logic System

This research deals with the development of a new architecture. The system deals with self-scanning and self-checkout of the consumer products. Here we have made an effort to take the system is supported by an app. The database is maintained in the local server and both mobile and the local host are connected to the same network. As a result, the user can use all the features of this system and send and receive the data which is required by the user. This framework takes care of taxations such as GST. The concept involves designing and implementation of a cost saving algorithms by using fuzzy logic system by using python programming for guiding customer purchasing patterns, retrieval and faster method of accessibility with respect to different patterns.

Analysis of the Application of Customer Purchase Mining Data on Paint Sales Using Apriori Algorithm (Case Study: PT Indowarna Cemerlang Indonesia)

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 will later help companies make business decisions. PT Indowarna Cemerlang Indonesia is a company engaged in the paint trade, where the main activity is selling various wall paints, oil / wood paints, NC paints (car paints), epoxy paints (floor paints), depo-proof (anti leaked). PT Indowarna Cemerlang Indonesia does not reuse sales transaction data resulting from its sales activities. This data is only used as a reference for making sales reports and as an archive only, causing accumulation of data and unknown paint brands that are often sold or those that are of interest to customers. Therefore, the author takes the title application of data mining analysis of customer purchase patterns in paint sales using a priori algorithm. By doing this research, it is expected to provide results in the form of information that can be useful for related parties and can design sales strategies to increase company turnover.

The Purchase and Attitude Patterns of Handloom Customers A Gender Based Cross Sectional Study

The todays customers got familiarized to the low-cost clothing with latest designs and offered available widely in the stores. In such a situation, it has almost become a big challenge for the handloom products, which are traditional, comparably highly-priced, limited designs and less durable. Furthermore, the middlemen/channels are making profits with duplication of the handloom products. Nearly half of the customers can’t differentiate handloom products. This made the situation more disastrous for the handloom sector and the weavers. Hence, a study has been conducted to answer the questions: How much the customers are aware of handlooms? How the customers feel, opine and purchase handloom products by considering the four p’s (Product, Price, Place and Promotion)? and detecting the problems of handloom customers regarding the sector. this paper answers these questions.

Creating the best first impression: Designing online product photos to increase sales

Effectively displaying goods in search results is valuable for B2C merchants to earn clicks from consumers and even increase sales. Taking down jackets and trousers sold on Tmall—China's largest B2C e-commerce platform—as the example, this paper collects data about three factors that influence consumers' first impression on their search results: the price of a product, the quantity of historical reviews, and a photo of the product. Among these factors, previous research shows that a product photo contains four attributes: brand logo, promotional information, street scenes, and model display. Focusing on these attributes, we apply a decision tree to explore customer purchasing patterns, which allows us to further investigate the influence of product photo attributes on sales volume by using a hierarchical regression model. Our research finds that among the products from the list of their search results, customers prefer those with many good historical reviews and low prices. In addition, gender that differentiates men's from women's clothing has a moderating effect on the relationship between photo attributes and product sales. The purchase decision of consumers shopping for men's clothing is susceptible to the influence of the product photo. Furthermore, different from the traditional view which shows that brands can reduce perceived risks and increase sales, this study finds that men's clothing sales are negatively affected by brand logo attribute in product photos. Finally, using models cannot significantly increase product sales among consumers shopping for either men's or women's clothing.

Iterative Approach to Calculating the Order Fill Rate Under Purchase Dependence

For the business environment under purchase dependence that characterizes customer purchase patterns observed in such areas as marketing, manufacturing systems, and distribution systems, this paper proposes a new approximate approach to calculate the order fill rate, probability of filling an entire customer order immediately from the shelf. The new approximate approach divides customer orders into item orders and calculates fill rates of all order types to approximate the order fill rate. In order to avoid the curse of dimensionality and prevent the solution from diverging for the large instances, we develop the greed iterative search algorithm (GISA) based on the Gauss-Seidel method. Through the computational analysis that compares the GISA with the simulation, we demonstrate that the GISA is a dependable algorithm to derive the stationary joint distribution of on-hand inventories in the type-K pure system. In addition, we present some managerial insights.

A Multi Attribute Value Theory approach to rank association rules for leveraging better business decision making

Market Basket Analysis or Association Rule Mining (ARM) is an approach to discover the purchase patterns of the customers by extracting and analyzing the basket of items which sell together. Businesses are also keen to discover those rules which can generate more profits. These profitable purchase patterns of customers once identified can lead to better product assortment decision making for businesses. Better Product Assortment decisions can surely be a competitive advantage for businesses in terms of customer satisfaction and profit generation. The concepts of support and confidence in the association rules help to extract the rules with frequent and reliable co-occurrences of the items in customers’ purchases. The profitable rules can be assessed using the domain-related measures such as item set value and the cross-selling profit associated with the association rules. We propose a ranking mechanism to combine the different criteria of Confidence, Support, Item Set Value and Cross-Selling Profit to get an overall interestingness measure; using Multi-Attribute Value Theory (MAVT) approach; which in turn uses DIVIZ as the implementation tool. These association rules can be used as a leverage for the marketing activities like cross-selling promotions, shelf placement etc. and other crucial decisions like product assortment selection.