Indonesian pharmacy retailer segmentation using recency frequency monetary-location model and ant K-means algorithm

We proposed an approach of retailer segmentation using a hybrid swarm intelligence algorithm and recency frequency monetary (RFM)-location model to develop a tailored marketing strategy for a pharmacy industry distribution company. We used sales data and plug it into MATLAB to implement ant clustering algorithm and K-means, then the results were analyzed using RFM-location model to calculate each clusters’ customer lifetime value (CLV). The algorithm generated 13 clusters of retailers based on provided data with a total of 1,138 retailers. Then, using RFM-location, some clusters were combined due to identical characteristics, the final clusters amounted to 8 clusters with unique characteristics. The findings can inform the decision-making process of the company, especially in prioritizing retailer segments and developing a tailored marketing strategy. We used a hybrid algorithm by leveraging the advantage of swarm intelligence and the power of K-means to cluster the retailers, then we further added value to the generated clusters by analyzing it using RFM-location model and CLV. However, location as a variable may not be relevant in smaller countries or developed countries, because the shipping cost may not be a problem. 

Study about customer segmentation and application in a real case

The hospitality industry generates a huge variety of data that grows by the day, becoming incrinsingly difficult to analyse this data manually in order to build a good data model. A thorough understanding of current customer profiles enables better resource allocation and leads to better definition of product and market development strategies. Dividing customers into similar groups to help develop more objective and focused marketing messages for each of the segments. Thus, in the present dissertation methods of classification and segmentation of existing data in the literature review are studied. Then, a real case study is presented, using data from Property Management Systems of eight Portuguese hotels, four city hotels and four resort hotels. This data set consists of fortyone attributes but, after selection of the most predictive variables, only a subset of attributes is used for data modeling. Next, the classification and segmentation methods studied in the literature review are applied for extracting the relevant information. The results are analyzed and discussed to understand their suitability to study the particular characteristics of hotel reservations.O setor de hospitalidade gera uma enorme variedade de dados que crescem a cada dia, tornando-se fisicamente impossível analisar esses dados manualmente a fim de construir um bom modelo de dados. Um profundo entendimento dos perfis dos atuais clientes permite uma melhor alocação de recursos e leva a uma melhor definição das estratégias de desenvolvimento de produtos e mercados. A divisão dos clientes em grupos semelhantes para ajudar a desenvolver mensagens de marketing mais objetivas e focadas para cada um dos seus segmentos. Desse modo na presente dissertação são estudados métodos de classificação e segmentação de dados existentes na revisão da literatura. De seguida, procede-se à apresentação de um estudo de um caso real, usando dados pertencentes a Sistemas de Gestão de Propriedade de oito hotéis portugueses, quatro hóteis de cidade e quatro hóteis de resort, este conjunto de dados é composto por quarenta e um atributos, mas, após uma selecção das variáveis com maior poder preditivo, apenas um subconjunto de atributos é utilizado para a modelação dos dados. Em seguida, são aplicados os métodos de classificação e segmentação estudados na revisão de literatura de modo a extrair informação relevante. Os resultados são analisados e discutidos para entender sua adequação ao estudo das características particulares das reservas de hotéis

Segmentation Analysis of Students in X Course with RFM Model and Clustering

In the business world, the competition to maintain and obtain more customers has become tougher. The presence of new players entering the market is driven by the developments of internet and advertisement. The X guitar course is an institution engaged in the field of non-formal education services. The customers are the course student that has made the payment transaction. The map of customer segmentation is one of the most important components in finding the main needs of each customer. Know the main needs of each customer is expected to increase the customer’s loyalty. Customer segmentation can be done by using the clustering method through a data mining approach in the form of RFM (Recency, Frequency and Monetary) Models. Recency is the data of the last payment transaction date. Frequency shows the number of course payment transactions. Monetary comes from the nominal amount of the transaction. RFM data is combined with the Fuzzy Gustafson-Kessel and K-Means clustering method to produce output in the form of k-clusters of customer. The formed segment is expected to represent the need of customers that vary by using validation process with the Global Silhouette Index. The customer population of the course is 225 students. It has been concluded that the RFM score for each subject by using 3 FGK clusters is the optimum cluster model with the largest Silhouette Index, which is 0.523. This research is expected to provide an in-depth analysis of customer segmentation for X guitar course

The Business Insight Index – Evaluating Customer Insights through Hybrid Models

Customer segmentation and target analysis are two essential tasks when identifying a company’s customers. To perform these tasks, this thesis develops and applies hybrid data-mining models, integrating clustering and decision trees. The hybrid models are applied to the life-logging camera company Narrative, in order to gain insights into their customer data. From previous research, we found that these hybrid models lacked means for evaluating the amount of insights proposed to decision makers. For this reason, we created, tested, and validated a new evaluation measure – the Description Tree Index. Through experiments on five separate datasets, we conclude that the measure enables decision makers to evaluate the insights gained through the hybrid model. In each case, the index generates the best results for the expected number of segments. We then integrated the Description Tree Index with existing evaluation models to form a Business Insight Index. This index evaluates customer segmentation and target analysis from both a business and data-mining perspective. By applying the index to the Narrative data, we found four customer segments to present the most insights.Att öka förståelsen av sina kunder har utvecklats till en allt viktigare uppgift för dagens företag. För att få bättre kunduppfattning utvecklas i detta examensarbete ett nytt mått som bedömer kundinformation. Ett allt vanligare problem för affärsverksamheter är att försäljnings- och marknadsföringskostnader ökar. Detta beror på att dagens kunder har alltmer olikartade köpbeteenden. För att kunna prioritera de mest värdeskapande kunderna krävs numera att en segmentering genomförs. Att segmentera kunder innebär att dela upp en marknad i mindre delar utefter olika kundegenskaper, exempelvis ålder, kön eller inkomstnivå. Att segmentera kunder har dock blivit en allt mer komplex uppgift, då informationen om kundernas egenskaper och beteende ökat lavinartat de senaste åren. Bara på det sociala mediet Facebook laddas över 500 terabyte data upp varje dag. Med hjälp av data mining kan segmentering och prioritering utföras även på stora mängder data. Data mining består av verktyg och tekniker för att hitta mönster, samband och trender i data. Dessa insikter kan sedan utnyttjas av beslutsfattare för att skapa konkurrensfördelar. The Business Insight Index För att kunna utvärdera kundinsikterna skapas i examensarbetet ett nytt mått – Business Insight Index (BII). Detta mått kan användas för att avgöra om en kundsegmentering är mer kvalitativ än annan. Genom att utvärdera mängden information som görs tillgänglig till beslutsfattare kan måttet förbättra kundsegmenteringsprocessen. BII uppvisar goda resultat vid test på fem datafiler där segmenteringen är känd sedan tidigare. För varje datafil genererar måttet bäst resultat för de förväntade segmenten. Traditionella evalueringsmetoder håller inte måttet Vanligtvis utvärderas segmentering inom data mining genom att mäta hur inbördes lika segmenten är i förhållande till hur olika de är sinsemellan. Dessa mått tar dock inte hänsyn till mängden information som förmedlas till säljare och marknadsförare. Bättre kundinformation kan på sikt leda till konkurrensfördelar och ökad försäljning. Därför är det viktigt att dels utvärdera segmentens kvalitativa egenskaper, men även till vilken grad dessa kan förstås och kommuniceras. Narrative För att hjälpa företaget Narrative att segmentera sina kunder utnyttjas BII. Narrative är ett Linköpingsbaserat företag som marknadsför lifelogging-kameror. Var 30:e sekund tar dessa bilder, vilka kan laddas upp till företagets servrar. Kunder kan sedan komma åt korten via företagets mobil-app. Genom att dela in företagets kunder i segment och sedan utvärdera dessa, får Narrative information om vilka värdedrivare kunderna ser i produkten. Är exempelvis hög bildkvalitet viktigare än anpassningsmöjligheter till sociala medier? Eller är bildfrekvensen den viktigaste faktorn? Då företaget identifierar kamerans värdedrivare kan produkten utvecklas och marknadsföras till de olika segmenten. Integrering av segmentering och beslutsträd Genom att analysera segmenten i beslutsträd framkommer vilka egenskaper som är utmärkande för kunderna. Beslutsträdet förutsäger vilka värden som kommer krävas för att en kund ska placeras i ett specifikt segment. Detta verktyg är fördelaktigt då det möjliggör en visualisering av kunderna som enkelt kan förstås av och förklaras för beslutsfattare

Improving Organizational Decision Making Using a SAF-T based Business Intelligence System

Today, companies need to quickly adapt to business changes and react to customers\u27 tendencies and market demands in an unpredictable environment. In this field, the analytical systems represent an important asset that each company should have and use. Data Warehousing Systems (DWS) support companies\u27 analytical needs, however, the development and integration of the data systems is a critical part. Due to specificities of the involved data, each DWS is unique, which compromises the use of reusable components or even the use of pre-built solutions. In this paper, we propose a standard skeleton for a DWS based on Portuguese Audit Tax documents (SAF-T (PT)). These documents represent a standardized procedure for every Portuguese company, providing the necessary data about billing, accounting, and taxation. Thus, they can provide the foundations to use them as a standard data representation to create a DWS that can be posteriorly explored by analytical techniques to generate useful insights

RFM analysis optimized

In the classic recency, frequency and monetary approach to market segmentation, i.e. RFM analysis, given a time frame, customers are clustered together into an arbitrary number of segments according to their most recent day of purchase, the number of purchases and the monetary value of their purchases. In this work we show how the choice of the number of segments and the time frame used in the RFM segmentation process can be optimized to maximize the result of direct marketing campaigns. We also indicate how RFM analysis can be extended to accommodate new dimensions of customer behavior and how the extended RFM analysis can be optimized. Furthermore, we discuss the implications of the optimized and extended RFM approach to market segmentation for direct marketing and business strategies

Interaction of descriptive and predictive analytics with product networks: The case of Sam's club

Due to the fact that there are massive amounts of available data all around the world, big data analytics has become an extremely important phenomenon in many disciplines. As the data grow, the need for businesses to achieve more reliable and accurate data-driven management decisions and to create value with big data applications grows as well. That is the reason why big data analytics becomes a primary tech priority today. In this thesis, initially we used a two-stage clustering algorithms in the customer segmentation setting. After the clustering stage, the customer lifetime value (CLV) of clusters were calculated based on the purchasing behaviors of the customers in order to reveal managerial insights and develop marketing strategies for each segment. At the second stage, we used HITS algorithm in product network analysis to achieve valuable insights from generated patterns, with the aim of discovering cross-selling e ects, identifying recurring purchasing patterns, and trigger products within the networks. This is important for practitioners in real-life application in terms of emphasizing the relatively important transactions by ranking them with corresponding item sets. From practical point of view, we foresee that our proposed methodology is adaptable and applicable to other similar businesses throughout the world, providing a road map for the potential application