Customer Visit Segmentation Using Market Basket Data

Basket analytics is a powerful tool in the retail context for acquiring knowledge about consumer shopping habits and preferences. In this paper, we propose a clustering-based artifact that mines customer visit segments from basket sales data. We characterize a customer visit by the purchased product categories in the basket and identify the shopping intention or mission behind the visit e.g. ‘breakfast’ visit to purchase cereal, milk, bread, cheese etc. We demonstrate the utility of the artifact by applying it to a real case of a major fast-moving consumer goods (FMCG) retailer. Apart from its theoretical contribution, the proposed approach extracts knowledge that may support several decisions ranging from marketing campaigns per customer segment, redesign of a store’s layout to product recommendations

Analysis of Bankruptcy using Data Mining Approach

This study involves the development of neural network prediction model to predict the stage of bankruptcy of a company. A total of 367 data was attained from the Registrar of Business and Companies, Kuala Lumpur Stock Exchange (KLSE) and Bank Negara Malaysia (Central Bank of Malaysia). The data was then analyzed by considering the basic statistics, frequency and cross tabulation in order to get more information about the data. Initially, the data was classified using logistic regression.In addition, it was also trained using neural network in order to obtain the bankruptcy model. The findings show that the most suitable prediction model consist of 12 nodes of input , hidden layer 6 node and one output layer. The generalization performance of the selected model is100%. This methodology should be able to provide some new insight into the type of pattern that exists in the data. Thus, neural network has a great potential in supporting for predicting bankruptcy

Data Mining For Marketing

This paper gives a brief insight about data mining, its process and the various techniques used for it in the field of marketing. Data mining is the process of extracting hidden valuable information from the data in given data sets .In this paper cross industry standard procedure for data mining is explained along with the various techniques used for it. With growing volume of data every day, the need for data mining in marketing is also increasing day by day. It is a powerful technology to help companies focus on the most important information in their data warehouses. Data mining is actually the process of collecting data from different sources and then interpreting it and finally converting it into useful information which helps in increasing the revenue, curtailing costs thereby providing a competitive edge to the organisation DOI: 10.17762/ijritcc2321-8169.15032

A Methodology for Identifying Core Technologies Based on Technological Cross-Impact: Association Rule Mining and ANP Approach

There have been attempts to examine technological structure and linkage as technological impact. Cross-impact analysis (CIA) has been mainly employed with cross-impact index to identify core technologies. Cross-impact index, however, cannot successfully capture the overall relationship based on the impacts among technologies. Furthermore, it is a time-consuming task to calculate all cross-impact index especially based on patents without developing computer program. To address this limitation, this study suggests new approach to identify core technologies in technological cross-impact interrelationship. Specially, the approach applied data mining technique and multi-criteria decision making (MCDM) method to the co-classification information of registered patents. At first, technological cross-impact matrix is constructed with the confidence values by applying association rule mining (ARM) to the co-classification information of patents. Then, Analytic Hierarchical Process (ANP), one of MCDM methods, is employed to the constructed matrix for identifying core technologies from the perspectives of overall cross-impacts. A case study of telecommunication technology is conducted to illustrate the process of executing and utilizing the proposed approach. It is expected that suggested approach could help technology planners to formulate strategy and policy for technological innovation

CRM Strategies for A Small-Sized Online Shopping Mall Based on Association Rules and Sequential Patterns

Data mining has a tremendous contribution to the extraction of knowledge and information which have been hidden in a large volume of data. This study has proposed customer relationship management (CRM) strategies for a small-sized online shopping mall based on association rules and sequential patterns obtained by analyzing the transaction data of the shop. We first defined the VIP customer in terms of recency, frequency and monetary value. Then, we developed a model which classifies customers into VIP or non-VIP, using various techniques such as decision tree, artificial neural network and bagging with each of these as a base classifier. Last, we identified association rules and sequential patterns from the transactions of VIPs, and then these rules and patterns were utilized to propose CRM strategies for the online shopping mall

Association Rules in Data Mining: An Application on a Clothing and Accessory Specialty Store

Retailers provide important functions that increase the value of the products and services they sell to consumers. Retailers value creating functions are providing assortment of products and services: breaking bulk, holding inventory, and providing services. For a long time, retail store managers have been interested in learning about within and cross-category purchase behavior of their customers, since valuable insights for designing marketing and/or targeted cross-selling programs can be derived. Especially, parallel to the development of information processing and communication technologies, it has become possible to transfer customers shopping information into databases with the help of barcode technology. Data mining is the technique presenting significant and useful information using of lots of data. Association rule mining is realized by using market basket analysis to discover relationships among items purchased by customers in transaction databases. In this study, association rules were estimated by using market basket analysis and taking support, confidence and lift measures into consideration. In the process of analysis, by using of data belonging to the year of 2012 from a clothing and accessory specialty store operating in the province of Osmaniye, a set of data related to 42.390 sales transactions including 9.000 different product kinds in 35 different product categories (SKU) were used. Analyses were carried out with the help of SPSS Clementine packet program and hence 25.470 rules were determined

Penerapan Augmented Reality dalam Visualisasi Katalog Apartemen Berbasis Android

Apartemen menjadi hunian yang diminati masyarakat sehingga banyak dibangun oleh para pengembang properti. Pengembangan apartemen yang pesat menimbulkan persaingan dalam pemasaran, sehingga diperlukan cara pemasaran yang lebih inovatif untuk menarik pembeli. Penerapan teknologi augmented reality untuk membuat katalog apartemen menjadi lebih interaktif dapat menjadi solusi pemasaran apartemen yang inovatif. Penelitian yang dikembangkan berfokus pada penerapan Augmented Reality di katalog pemasaran apartemen dalam bentuk aplikasi berbasis Android. Denah apartemen pada katalog divisualisasikan melalui model 3-dimensi dengan penambahan informasi dan fitur-fitur yang interaktif. Aplikasi dikembangkan dengan marker dinamis. Aplikasi memiliki 3 scene yaitu Main Menu, About dan Panduan. Dua fitur utama pada aplikasi adalah tracking marker, yaitu fitur untuk mengenali marker dan rotate, fitur untuk memutar objek 3-dimensi secara 360 derajat. Aplikasi diujicobakan pada dua buah smartphone dengan spesifikasi berbeda. Hasil pengujian menunjukkan bahwa aplikasi telah berhasil mempermudah pengguna melihat visualisasi katalog tanpa mengunjungi apartemen asli. Kata Kunci: apartemen, katalog, augmented reality, Androi

Data Mining for Marketing

This paper gives a brief insight about data mining, its process and the various techniques used for it in the field of marketing. Data mining is the process of extracting hidden valuable information from the data in given data sets .In this paper cross industry standard procedure for data mining is explained along with the various techniques used for it. With growing volume of data every day, the need for data mining in marketing is also increasing day by day. It is a powerful technology to help companies focus on the most important information in their data warehouses. Data mining is actually the process of collecting data from different sources and then interpreting it and finally converting it into useful information which helps in increasing the revenue, curtailing costs thereby providing a competitive edge to the organisation

Measuring Customers Satisfaction of E-Commerce Sites Using Clustering Techniques: Case Study of Nyazco Website

Today the use of modern technologies in the daily life for satisfying the needs is unavoidable. Follow the news and searching through the internet has affected organizations to provide platform on the Internet for availability of information for the customers. With the development of e-commerce, online shopping plays an increasingly important role in people’s life. With the use of data mining technique prospect, managers of this site can analyze preferences and purchasing patterns of online customers in order to custom product recommendations. Data mining helps to provide services in accordance with customers’ requirements. The aim of this research is to identify the customers’ requirements in online shopping and cluster these customers based on independent attributes such as gender, product classification, recency, frequency and monetary. For this purpose, the data related to Nyazco website that is an e-commerce website with a variety of products, were examined as a case study in the period of 7 months. The authors of this paper will define four clusters by using k-means algorithm and RFM model by IBM SPSS Modeler 14.2 software. Customers in the third cluster and fourth cluster will be identified as the most important customers. Therefore, providing the demands of these customers should be prioritized

[[alternative]]Mining Demand Chain Knowledge for Collaboration Design and New Product Development

計畫編號：NSC94-2416-H032-001研究期間：200508~200607研究經費：396,000[[abstract]]一般而言，整個製造與商業的運作流程中，資訊流、金流及實體物流的傳遞，大多依 循供應鏈管理(Supply Chain Management)的模式，而上游製造商面對末端顧客需求的同 時，因為資訊流動的落差，所以必須加入本身對於該產品的經驗值來加以預測。相對 地，在供應鏈中越往上遊走，變異性越增大的現象就是所指的「長鞭效應(Bullwhip Effect)」(Dejonckheere et al., 2004) 。但是，隨著生活水準的提升以及製造能力的進步， 過去這種「樣少量多」的生產模式，正被「量少樣多」「求新求變」的商業模式所取代， 意謂者供應鏈的體系，無法完全滿足顧客在這方面的需求。因此以需求端為導向的生 產、製造、銷售、以及產品/設計開發的需求鏈管理 (Demand Chain Management)模式 因而應運而生(Willem et al., 2002)。我國自1920 年代起自行車產業即略具規模，同時 在政府刻意並大力輔導及協助下，1980 年代外銷量首次超越日本，奠定我國自行車產 業在全世界舉足輕重的角色。以巨大機械主力品牌「捷安特」為例，已在全球成為家 喻戶曉的自行車代名詞之一。巨大機械每年提撥大筆經費於研發團隊，在產品材質上 絞盡腦汁，並且在行銷通積極佈局。然而，銷售通路的顧客與產品知識是否充分反映 市場的需求？產品的設計與產品線的規劃，是否能夠將顧客與通路的知識結合？以及 產品在設計與開發的階段，能否將顧客與通路的知識，轉化成企業的知識資產，並在 新產品發展（New Product Development）時，能將這些知識運用在企業與需求端的協 同設計（Collaborative Design）？因此，本研究運用資料探勘 (Data Mining)的技術， 發掘自行車使用者(含同一家庭不同使用者)、產品(含同一家庭不同產品)、通路(含維 修點)、以及個案公司的產品開發知識，結合協同設計的概念，將使用者的需求與產品 的設計，轉化成產品與服務。同時，將顧客特質、地理因素、消費者偏好及市場區隔 等知識，設計成電子型錄以及提供通路行銷的紙本型錄，將新產品開發的知識，運用 於產品線設計(Product Line Design)以及產品創新(Product Innovation)。[[sponsorship]]行政院國家科學委員