In an increasingly globalized market, the relationship between the customer and the brand goes beyond the purchasing process. It is very important to understand the customer, to know their needs and to propose actions to increase the value of the brand for them. In the literature, there are several models capable of determining and segmenting customers according to variables dependent on the purchasing process. However, we have not found any study that applies to the business case of classifying customers according to their relationship with the contact centre. In this paper, we establish a working model that allows us to define the value of the customer in the process of interaction with the contact centre, so that we can propose actions both in the sales phase and during the post-sales service, so that the value and perception of the brand is increased. In this model, we propose using the value of recency, frequency, importance and duration of customer interactions with the post-sales service, thus obtaining a ranking, and grouping of customers to help establish personalized communication strategies. We have verified this model by presenting a business case applied to the telecom sector.

The literature related to Artificial Intelligence (AI) models and customer churn prediction is extensive and rich in Business to Customer (B2C) environments; however, research in Business to Business (B2B) environments is not sufficiently addressed. Customer churn in the business environment and more so in a B2B context is critical, as the impact on turnover is generally greater than in B2C environments. On the other hand, the data used in the context of this paper point to the importance of the relationship between customer and brand through the Contact Center. Therefore, the recency, frequency, importance and duration (RFID) model used to obtain the customer’s assessment from the point of view of their interactions with the Contact Center is a novelty and an additional source of information to traditional models based on purchase transactions, recency, frequency, and monetary (RFM). The objective of this work consists of the design of a methodological process that contributes to analyzing the explainability of AI algorithm predictions, Explainable Artificial Intelligence (XAI), for which we analyze the binary target variable abandonment in a B2B environment, considering the relationships that the partner (customer) has with the Contact Center, and focusing on a business software distribution company. The model can be generalized to any environment in which classification or regression algorithms are required.

In the digital era, companies have experienced a progressive change in their business models, which has led them to develop greater agility to adapt to changing environments, and the necessity to properly manage the group decision making in these companies is patent. This paper shows how fuzzy models are utilized in group decision making. In this context, one of the most important group decisions to be adopted is how to manage the digital transformation of the company, drawing up the best possible roadmap. To achieve this goal, this paper proposes a new methodology improvement of group decision making based on a fuzzy 2-tuple linguistic model and the analytic hierarchy process (AHP) method. The suggested methodology proposes the calculation of the digital maturity level (DML) of companies based on five of the most cited and agreed upon criteria in the existing literature. The methodology proposed in this paper was tested and validated for the business case of Spanish SMEs through three different clusters to derive global recommendations tailored to each specific cluster and company segments, using a sample of 1428 companies. The main fundings reveal that the digital maturity level directly impacts on the size of the company and its digital mindset in the sense of driving change management processes. As future works, authors recommend extending the model to any industry using the proposed methodology and evaluate disruptive technologies such as artificial intelligence (AI) in supporting the digital transformation of SMEs.

In the literature, the Information Technology Infrastructure Library (ITIL) methodology recommends determining the priority of incident resolution based on the impact and urgency of interactions. The RFID model, based on the parameters of Recency, Frequency, Importance and Duration in the resolution of incidents, provides an individual assessment and a clustering of customers based on these factors. We can improve the traditional concept of waiting queues for customer service management by using a procedure that adds to the evaluation provided by RFID such additional factors as Impact, Urgency and Emotional character of each interaction. If we also include aspects such as Waiting Time and Contact Center Workload, we have a procedure that allows prioritizing interactions between the customer and the Contact Center dynamically and in real time. In this paper we propose to apply a model of unification of heterogeneous information in 2-tuple linguistic evaluations, to obtain a global evaluation of each interaction by applying the Analytic Hierarchy Process (AHP), and in this way be able to have a dynamic process of prioritization of interactions.

Due to the irruption of new technologies in cities such as mobile applications, geographic infor-mation systems, Big Data, Internet of Things (IoT) or Artificial Intelligence (AI), new approaches to citizen management are being developed with the aim of adapting citizen services to this new en-vironment. These new services can enable city governments and businesses to offer their citizens a truly immersive experience that facilitates their day-to-day lives and ultimately improves their quality of life. In this sense, it is important to emphasize that all investments in infrastructure and technological developments in smart cities will be wasted if the citizens for whom they have been created eventually do not use them for whatever reason. To avoid these kinds of problems, the citizen's level of adaptation to the technologies should be evaluated. However, although much has been studied about new technological developments, studies to validate the actual impact and user acceptance of these technological models are much more limited. This paper tries to fill this gap presenting a new model of recommender system based in the most cited and used model in the scientific and academic literature: the Technology Acceptance Model (TAM) and using the most cited and agreed upon criteria in the existing literature. To accomplish the objective, this study in-troduces a novel recommender system that utilizes a fuzzy 2-tuple linguistic model in conjunction with the analytic hierarchy process (AHP) method to prioritize and personalize the relationship between tourists and smart cities. The methodology proposed in this paper was tested and validated in a case of study through different clusters to derive global recommendations tailored to each specific cluster. The main findings reveal that the use of technology is closely linked to the ability to enjoy personalized experiences in the realm of Smart Cities and Smart Tourism. As future works, authors recommend extending the recommender system model to any cluster of tourists using the proposed methodology and evaluate also other kind of disruptive technologies such as artificial intelligence (AI) in supporting the citizens.