An Extended RFM Model for Customer Behaviour and Demographic Analysis in Retail Industry

Background: Customer segmentation has become one of the most innovative ways which help businesses adopt appropriate marketing campaigns and reach targeted customers. The RFM model and machine learning combination have been widely applied in various areas. Motivations: With the rapid increase of transactional data, the RFM model can accurately segment customers and provide deeper insights into customers’ purchasing behaviour. However, the traditional RFM model is limited to 3 variables, Recency, Frequency and Monetary, without revealing segments based on demographic features. Meanwhile, the contribution of demographic characteristics to marketing strategies is extremely important. Methods/Approach: The article proposed an extended RFMD model (D-Demographic) with a combination of behavioural and demographic variables. Customer segmentation can be performed effectively using the RFMD model, K-Means, and K-Prototype algorithms. Results: The extended model is applied to the retail dataset, and the experimental result shows 5 clusters with different features. The effectiveness of the new model is measured by the Adjusted Rand Index and Adjusted Mutual Information. Furthermore, we use Cohort analysis to analyse customer retention rates and recommend marketing strategies for each segment. Conclusions: According to the evaluation, the proposed RMFD model was deployed with stable results created by two clustering algorithms. Businesses can apply this model to deeply understand customer behaviour with their demographics and launch efficient campaigns

Post-processing of association rules.

In this paper, we situate and motivate the need for a post-processing phase to the association rule mining algorithm when plugged into the knowledge discovery in databases process. Major research effort has already been devoted to optimising the initially proposed mining algorithms. When it comes to effectively extrapolating the most interesting knowledge nuggets from the standard output of these algorithms, one is faced with an extreme challenge, since it is not uncommon to be confronted with a vast amount of association rules after running the algorithms. The sheer multitude of generated rules often clouds the perception of the interpreters. Rightful assessment of the usefulness of the generated output introduces the need to effectively deal with different forms of data redundancy and data being plainly uninteresting. In order to do so, we will give a tentative overview of some of the main post-processing tasks, taking into account the efforts that have already been reported in the literature.

Towards an Integrative Framework for Predicting SME Cluster’s Innovative Capability: the Case of SUAME Magazine Cluster Ghana

The paper clarifies the concept of innovative capability by identifying five measurable dimensions constituting it. An empirical framework is constructed from related configurations integrating transactional, social, and knowledge based networks determining innovative capability. The framework is tested using Hierarchical and Standard multiple regression techniques to predict Small and Medium Enterprises (SME) clusters innovative capability in a leading West African cluster. The study has contributed to the debates on entrepreneurship, innovation, and geographical clustering of SME‟s. It has also provided a practical framework guiding policy makers inclined to improving innovative capability in Afric

Student Engagement in Aviation Moocs: Identifying Subgroups and Their Differences

The purpose of this study was to expand the current understanding of learner engagement in aviation-related Massive Open Online Courses (MOOCs) through cluster analysis. MOOCs, regarded for their low- or no-cost educational content, often attract thousands of students who are free to engage with the provided content to the extent of their choosing. As online training for pilots, flight attendants, mechanics, and small unmanned aerial system operators continues to expand, understanding how learners engage in optional aviation-focused, online course material may help inform course design and instruction in the aviation industry. In this study, Moore’s theory of transactional distance, which posits psychological or communicative distance can impede learning and success, was used as a descriptive framework for analysis. Archived learning analytics datasets from two 2018 iterations of the same small unmanned aerial systems MOOC were cluster-analyzed (N = 1,032 and N = 4,037). The enrolled students included individuals worldwide; some were affiliated with the host institution, but most were not. The data sets were cluster analyzed separately to categorize participants into common subpopulations based on discussion post pages viewed and posts written, video pages viewed, and quiz grades. Subgroup differences were examined in days of activity and record of completion. Pre- and postcourse survey data provided additional variables for analysis of subgroup differences in demographics (age, geographic location, education level, employment in the aviation industry) and learning goals. Analysis of engagement variables revealed three significantly different subgroups for each MOOC. Engagement patterns were similar between MOOCs for the most and least engaged groups, but differences were noted in the middle groups; MOOC 1’s middle group had a broader interest in optional content (both in discussions and videos); whereas MOOC 2’s middle group had a narrower interest in optional discussions. Mandatory items (Mandatory Discussion or Quizzes) were the best predictors in classifying subgroups for both MOOCs. Significant associations were found between subgroups and education levels, days of activity, and total quiz scores. This study addressed two known problems: a lack of information on student engagement in aviation-related MOOCs, and more broadly, a growing imperative to examine learners who utilize MOOCs but do not complete them. This study served as an important first step for course developers and instructors who aim to meet the diverse needs of the aviation-education community

Integrating defeasible argumentation and machine learning techniques : Preliminary report

The field of machine learning (ML) is concerned with the question of how to construct algorithms that automatically improve with experience. In recent years many successful ML applications have been developed, such as datamining programs, information-filtering systems, etc. Although ML algorithms allow the detection and extraction of interesting patterns of data for several kinds of problems, most of these algorithms are based on quantitative reasoning, as they rely on training data in order to infer so-called target functions. In the last years defeasible argumentation has proven to be a sound setting to formalize common-sense qualitative reasoning. This approach can be combined with other inference techniques, such as those provided by machine learning theory. In this paper we outline different alternatives for combining defeasible argumentation and machine learning techniques. We suggest how different aspects of a generic argumentbased framework can be integrated with other ML-based approaches.Eje: Inteligencia artificialRed de Universidades con Carreras en Informática (RedUNCI

Exploring fish purchasing behaviour using data analytics

Nas últimas décadas têm ocorrido mudanças significativas no setor do retalho resultantes da globalização, do aumento de competitividade e da transformação do comportamento de compra do consumidor. Esta mudança de paradigma também se aplica ao setor do peixe fresco, que tem sido alvo do interesse de investigadores internacionais por razões políticas e económicas. Tendo em conta este ambiente competitivo, que valoriza a qualidade e o serviço fornecido ao consumidor assente em custos aceitáveis, é necessário a adoção de estratégias focadas no cliente. Esta dissertação está integrada no projeto ValorMar, que nasceu do compromisso de um conjunto alargado de entidades, desde empresas até centros de investigação posicionados pela relevância da economia marítima na cadeia de valor do pescado. Assim, esta dissertação irá tentar compreender relações que se revelem críticas para a tomada de decisão dos consumidores no momento de compra de peixe fresco. Para tal, irão ser usados dados transacionais e técnicas de data mining adequadas ao problema.A metodologia proposta por esta dissertação tem como objetivo não só a identificação de clientes recorrendo a técnicas de segmentação, mas também uma análise ao carrinho de compras de um cliente de peixe fresco. Estas análises aos dados irão mostrar que a extração de conhecimento de grandes bases de dados permite melhorar as decisões estratégicas das empresas e a sua relação com os clientes.In the last decades there have been significant changes in the retail sector resulting from globalization, the increased competitiveness and transformation on consumer's purchasing behaviour. This paradigm shift also applies to the fish sector, that has been capturing the interest of researchers internationally for political and economic reasons. Taking this competitive environment into account, which values the quality and the service given to the customer based on acceptable costs, it is necessary to adopt customer focused strategies.This thesis is integrated in the ValorMar's project, which was born from the commitment of a broad spectrum of entities, from companies to research centers, positioned by the relevance of the sea economy in the fishery value chain. Thus, this dissertation will try to understand critical relations for the decision making of customers when buying fresh fish.For this, transactional data and data mining techniques appropriate to the problem will be used.The methodology proposed by this thesis aims not only to identify customers using clustering techniques, but also to analyze the market basket of a fresh fish customer. These data analyzis will show that the knowledge extraction from large databases allows to improve the companies strategic decisions and their relationship with customers