Profiling clients in the tourism sector using fuzzy linguistic models based on 2-tuples

This work has been funded by the Spanish State Research Agency through the project PID2019-103880RB-I00 / AEI / 10.13039/501100011033.Customer segmentation is a key piece of a company's business strategy. This paper presents a segmentation of the online users of tourism platforms through the recency, frequency and helpfulness of the users. 2-tuples model is applied to these variables to be more precise without loss of information. In addition, the functionality of the proposal made by the authors is verified through a use case in which TripAdvisor opinioners are segmented in reference to the experience lived in hotels and tourist accommodation.Spanish Government PID2019-103880RB-I00 / AEI / 10.13039/50110001103

An RFM Model Customizable to Product Catalogues and Marketing Criteria Using Fuzzy Linguistic Models: Case Study of a Retail Business

In the field of strategic marketing, the recency, frequency and monetary (RFM) variables model has been applied for years to determine how solid a database is in terms of spending and customer activity. Retailers almost never obtain data related to their customers beyond their purchase history, and if they do, the information is often out of date. This work presents a new method, based on the fuzzy linguistic 2-tuple model and the definition of product hierarchies, which provides a linguistic interpretability giving business meaning and improving the precision of conventional models. The fuzzy linguistic 2-tuple RFM model, adapted by the product hierarchy thanks to the analytical hierarchical process (AHP), is revealed to be a useful tool for including business criteria, product catalogues and customer insights in the definition of commercial strategies. The result of our method is a complete customer segmentation that enriches the clusters obtained with the traditional fuzzy linguistic 2-tuple RFM model and offers a clear view of customers’ preferences and possible actions to define cross- and up-selling strategies. A real case study based on a worldwide leader in home decoration was developed to guide, step by step, other researchers and marketers. The model was built using the only information that retailers always have: customers’ purchase ticket details

A product-centric data mining algorithm for targeted promotions

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Targeted promotions in retail are becoming increasingly popular, particularly in the UK grocery retail sector where competition is stiff and consumers remain price sensitive. Given this, a targeted promotion algorithm is proposed to enhance the effectiveness of promotions by retailers. The algorithm leverages a mathematical model for optimizing items to target and fuzzy c-means clustering for finding the best customers to target. Tests using simulations with real life consumer scanner panel data from the UK grocery retailer sector shows that the algorithm performs well in finding the best items and customers to target whilst eliminating "false positives" (targeting customers who do not buy a product) and reducing "false negatives" (not targeting customers who could buy). The algorithm also shows better performance when compared to a similar published framework, particularly in handling "false positives" and "false negatives". The paper concludes by discussing managerial and research implications, and highlights applications of the model to other fields

Gestión estratégica de atención al cliente mediante modelos lingüísticos difusos, modelos de decisión y machine learning. Aplicación práctica en entornos B2C y B2B

En el último decenio hemos asistido a lo que se conoce como 4ª Revolución Industrial. Entre los factores que han contribuido a este cambio están entre otros la capacidad de almacenamiento y procesamiento de información, además de la velocidad delas comunicaciones. Desde el punto de vista del consumidor, esta irrupción digital está transformando la forma en la que los usuarios se relacionan con las marcas. Y en este sentido, cada segmento de población, cada cliente define su propio mapa de viaje (en inglés Customer Journey Map) en su relación con la marca (Lemon & Verhoef, 2016), creando de esta forma una mayor comprensión de la experiencia del cliente y de su viaje a través de sus distintas interacciones a través de cualquier canal. Por otro lado, atraer a los clientes es importante, pero más aún es poder retenerlos, las estrategias de marketing están focalizadas en este proceso de retención de los clientes más rentables. Todo esto ha supuesto para las empresas un importante cambio de paradigma, pasando de una aproximación centrada en el producto a otra donde el cliente pasa a ser el centro de su estrategia, en inglés Customer Centric. Conceptos como Transformación Digital e Industria 4.0 (Oztemel & Gursev, 2020), entre otros aspectos tienen en cuenta este empoderamiento del consumidor, y esta aproximación centrada en el cliente (V. Kumar & Reinartz, 2012). En este modelo, cobra especial relevancia la comunicación bidireccional entre cliente y marca, donde el marketing y el servicio de atención al cliente personalizados son fundamentales en la definición de una estrategia de reconocimiento de marca y de fidelización, logrando una mayor retención de clientes y en consecuencia mayor rentabilidad para la empresa..

Comparación de técnicas de minería de datos para descubrir información relevante de ventas de una Mype comercial

Perú aplicó Inteligencia Artificial (IA) en empresas de envergadura, constituyó apalancamiento para la productividad y se estimó que impulsaría un crecimiento de hasta 6% en PBI al 2028. Muchos emprendimientos no fueron sostenibles ante la falta de herramientas tecnológicas como minería de datos. Se han realizado soluciones de negocio en tecnología de información e inteligencia artificial, para superar incidencias en fraude electrónico, toma de decisiones, soluciones para ventas y otros que no han sido suficiente por alto costo que representan, para las Medianas y Pequeñas Empresas MYPE´s acceder a tales herramientas tecnológicas, para fortalecimiento de capacidades. En ese sentido se desarrolló un método que permita conocer que técnicas de minería de datos existentes, proporcionan mejor desempeño, para descubrir información relevante de ventas que permita apuntalar sus objetivos de negocio y proporcione confiabilidad y eficiencia. Este método comprendió elegir una MYPE comercial en virtud al área de influencia de la Universidad señor de Sipán que proporcionó los datos y se construyó un data set al cual se le aplicó normalización de variables de entrada haciendo uso de la técnica de escalado de variables de Min y Max, procesándose 5,522 registros y a éstos se les aplicó las técnicas de minería seleccionadas por su eficiencia y rendimiento concorde a la investigación de las bases de datos Ieeexplore, Scopus y Science Direct. Posteriormente haciendo uso de librerías contenidas en la suite Anaconda Navigator, junto a Python como herramienta de programación y Jupyter como editor, se logró resultados que evidencian que regresión logística es la técnica eficiente en tanto que las demás no ofrecen óptimos resultados en indicadores tiempo de respuesta y precisión; concluyendo que la técnica de clasificación en lo concerniente a regresión logística es la más eficiente con un promedio de tiempo de respuesta de 0.0620 segundos, nivel de precisión (P) de 99.93%, consumo de CPU 4.6 Gb; consumo de memoria de 6.13; error cuadrático medio (ECM) de 0.00090 y desviación absoluta media (MAD) 0.000898.TesisInfraestructura, Tecnología y Medio Ambient

BIG DATA IN MARKETING & RETAILING

Data is increasingly being created, stored, analyzed, and applied. Big Data is becoming an everyday phrase that appears in popular media and people’s daily conversations. This paper provides a framework to define Big Data from technical and business perspectives, to present its enormous value in different fields, to share its applications in marketing and retailing, market segmentation, targeting and positioning as well in developing marketing mix. We also provide some real life industry examples, to shed light on the challenges in harnessing the potential of Big Data, and to discuss its future. Big Data will separate the winners from the losers in the business field in the future. The leading companies in the Big Data field, such as Google, Amazon, and Wal-Mart, will continue to build their competitive advantage, both in marketing and other areas, by acting on the insights developed from Big Data analysis

Cost-sensitive deep neural network ensemble for class imbalance problem

In data mining, classification is a task to build a model which classifies data into a given set of categories. Most classification algorithms assume the class distribution of data to be roughly balanced. In real-life applications such as direct marketing, fraud detection and churn prediction, class imbalance problem usually occurs. Class imbalance problem is referred to the issue that the number of examples belonging to a class is significantly greater than those of the others. When training a standard classifier with class imbalance data, the classifier is usually biased toward majority class. However, minority class is the class of interest and more significant than the majority class. In the literature, existing methods such as data-level, algorithmic-level and cost-sensitive learning have been proposed to address this problem. The experiments discussed in these studies were usually conducted on relatively small data sets or even on artificial data. The performance of the methods on modern real-life data sets, which are more complicated, is unclear. In this research, we study the background and some of the state-of-the-art approaches which handle class imbalance problem. We also propose two costsensitive methods to address class imbalance problem, namely Cost-Sensitive Deep Neural Network (CSDNN) and Cost-Sensitive Deep Neural Network Ensemble (CSDE). CSDNN is a deep neural network based on Stacked Denoising Autoencoders (SDAE). We propose CSDNN by incorporating cost information of majority and minority class into the cost function of SDAE to make it costsensitive. Another proposed method, CSDE, is an ensemble learning version of CSDNN which is proposed to improve the generalization performance on class imbalance problem. In the first step, a deep neural network based on SDAE is created for layer-wise feature extraction. Next, we perform Bagging’s resampling procedure with undersampling to split training data into a number of bootstrap samples. In the third step, we apply a layer-wise feature extraction method to extract new feature samples from each of the hidden layer(s) of the SDAE. Lastly, the ensemble learning is performed by using each of the new feature samples to train a CSDNN classifier with random cost vector. Experiments are conducted to compare the proposed methods with the existing methods. We examine their performance on real-life data sets in business domains. The results show that the proposed methods obtain promising results in handling class imbalance problem and also outperform all the other compared methods. There are three major contributions to this work. First, we proposed CSDNN method in which misclassification costs are considered in training process. Second, we incorporate random undersampling with layer-wise feature extraction to perform ensemble learning. Third, this is the first work that conducts experiments on class imbalance problem using large real-life data sets in different business domains ranging from direct marketing, churn prediction, credit scoring, fraud detection to fake review detection

Classification of linked historical data to calculate customer lifetime value

Most organisations employ customer relationship management systems to provide a strategic advantage over their competitors. One aspect of this is applying a customer lifetime value to each client which effectively forms a fine-grained ranking of every customer in their database. This is used to focus marketing and sales budgets and in turn, generate a more optimised and targeted spend. The problem is that it requires a full customer history for every client and this rarely exists. In effect, there is a large gap between the available information in application databases and the types of datasets required to calculate customer lifetime values. This gap prevents any meaningful calculation of customer lifetime values. In this research, we present a methodology to close this gap, by using a record linkage methodology to create a holistic customer record for each client. At this point, the remaining gaps in data are filled by our imputation algorithms, a process which then facilitates the calculation of values for each customer. The final step, evaluating our methodology, is achieved using a clustering approach to classify customers so that the customer lifetime value scores can be validated against the clusters in which they reside

23rd Recent Advances in Retailing & Services Science Conference, July 11-14, 2016, Edinburgh, Scotland:book of abstracts

This book includes the (edited) abstracts of the papers that will be presented at the 23rd Recent Advancesin Retailing and Services Science Conference, at the Carlton/Hilton hotel, Edinburgh, Scotland, July 11-16, 2016.The aim of the conference is to bring together an international and multidisciplinary audience working ondifferent topics in retailing and consumer behavior research. Both completed work and work in progresswill be presented. This is reflected in the kind of papers that have been accepted for presentation