Determinants of Tourism Product Development in Southeast Ethiopia: Marketing Perspectives

Tourism has been given much attention in developing countries like Ethiopia. In this regard, tourism product development played a great role in achieving sustainable developmental goals. The study aims to examine the determinants of tourism product development in southeast Ethiopia. The research employed a mixed research approach, and descriptive and explanatory research design was used. Both secondary and primary data sources were in place to obtain the relevant data. A total of 398 samples were employed to collect the data. A convenience sampling technique was employed to select domestic tourists and walk-in guests and purposive sampling was applied to select marketing managers of hotels, lodges, boat associations, park administration, resorts, restaurants, nightclubs, and guest houses. The qualitative data was analyzed through thematic analysis and the quantitative data was analyzed using descriptive and inferential statistics by computing SPSS. The study revealed that tourism product development is affected by marketing mixes of which promotion was the major factor of tourism product development and price was the only tool inversely related to tourism product development. The brand image also positively determined tourism product development. The results showed that the exercise of tourism product development strategies among tourism businesses was low. The main implication drawn from the study is that the tourism businesses have to develop new customer profiles or segmenting in customers in their specified characteristics like gender, level of income, and age. Furthermore, the need to invest in research and development of the current market to develop new tourism products is of great importance.

Prediction of Domestic Hot Water Temperature in a District Heating Network

With the fourth generation of district heating networks in sight, opportunities are rising for better services and optimized planning of energy production. Indeed, the more intensive data collection is expected to allow for load prediction, customer profiling, etc. In this context, our work aims at a better understanding of customer profiles from the captured data. Given the variety of households, such profiles are difficult to capture. This study explores the possibility to predict domestic hot water (DHW) usage. Such prediction is made challenging due to the presence of two components in the signal, the first one bound to the physical properties of the DHW distribution system, the second one bound to the human patterns related to DHW consumption. Our contributions include (1) the analysis of recurrent neural network architectures based on GRU, (2) the inclusion of state-based labels inferred in an unsupervised way to simulate domain knowledge, (3) the comparison of different features. Results show that the physical contribution in the signal can be forecasted successfully across households. On the contrary, the stochastic “human” component is harder to predict and would need further research, either by improving the modelling or by including alternate signals.

Laporan Keuangan UMKM Katering Mbok Dade dan Pengaruhnya Pada Akses Kredit Perbankan Era Pandemi Covid-19

This study purpose to determine the effect of SMEs financial reports on access to credit that can be received by SMEs entrepreneurs in the Covid 19 pandemic. This study uses primary data obtained from direct observations with catering owners Mbok Dade. The analysis technique used is observation and literature study. The results of the study indicate that the SMEs Financial Statements can be one of the supporting factors in obtaining access to bank credit. Other factors can be in the form of prospective customer profiles and credit guarantees.

New fuzzy RFMD model. A method to identify new customer profiles due to an increase in online activity. Case study of a retail business

Gaining customer loyalty has become one of the main objectives of all companies. Retailers, especially the online ones, have the advantage of knowing their customers’ historical purchase data, which provides them with an understanding of the customers’ buying patterns. A widely-used tool in strategic marketing and customer loyalty is segmentation based on the traditional Recency, Frequency and Monetary (RFM) model. Subsequently, the fuzzy RFM model proved to be an improvement on the traditional RFM model. There has been a change in the retail customer profile, with the growth of a new cluster, the “One-Shot Customer”, new customers that buy from a retailer just once and never come back. In response to this change, the fuzzy RFM model has been modified to include a new dimension capturing Length or Duration. This study presents the new fuzzy RFMD model (Recency, Frequency, Monetary and Duration model), which can be used to better identify that new, large group of customers. The paper also provides a case study based on an e-commerce clothing retailer. Its customer database was segmented using the k-means algorithm and the Isolation Forest algorithm was applied to identify and correctly treat possible anomalies. The Customer Lifetime Value and the weights for the RFMD attributes were calculated by applying the Analytic Hierarchy Process (AHP) model. Results reveal the improvement that the weighted fuzzy RFMD model offers to retailers, enabling them to detect the One-Shot Customers and thus optimize their strategic marketing plans.

Data mining approach investigates Western-style restaurant hospitality management in Taiwan

Purpose In terms of service hospitality, recent discussions of value-in-use from the perspective of service-dominant logic have focused on the customer’s determination of value and control of the value creation process. The purpose of this paper is to extend these discussions by exploring the value creation process in the Western-style restaurant in Taiwan, which is developed value-in-eat creation for restaurants. In Taiwan, Western-style restaurants are as popular as Chinese restaurants because of globalization and cultural integration. However, to local restaurateurs and managers, managing a Western-style restaurant in terms of localization and hospitality on value-in-eat creation presents both academic and practical issues. Thus, this paper aims to investigate Western-style restaurant hospitality management alternatives on the value-in-eat creation process in Taiwan using a data mining approach. Design/methodology/approach Based on a market survey, a total of 1,187 questionnaires was incorporated into a database. The questionnaire design is divided into 7 parts with 35 items. All questions are designed as nominal and ordinal (not the Likert scale) scales. Data mining approach, including cluster analysis and association rules, cluster analysis is investigated possible customer profiles and association rules is implemented to explore customer preference patterns and rules on the value-in-eat creation process. Findings Data mining results show two patterns including Pattern 1: meal patterns and customer preferences for restaurant hospitality management and Pattern 2: customer relationship management (CRM) for restaurant hospitality management that customer profiles and preferences on meal patterns, service patterns and CRM are engaged to suggest effective Western restaurant hospitality management alternatives, such as proper bundles for restaurant types, meals, exotic atmosphere and services of hospitalities in terms of a value-in-eat creation process. Originality/value To the best of the authors’ knowledge, this study is the first study to investigate consumers’ behaviors in Western-style restaurants using the measurement of nominal and ordinal scale for questionnaire development and further to implement a data mining approach on selected data samples. In addition, this study illustrates the patterns/rules of Taiwan customer preferences that best explain the knowledge of how to manage Western-style restaurants from the perspective of customer hospitality using data mining.

Application of logistics model in analysing relationship marketing in travel agencies

A concept of relationship marketing in tourism implies creating quality relations among all participants in the tourist supply chain. Analyzing the assumptions of the development of relations and their impact on the overall performance of companies in tourism is especially important. In this regard, the subject matter of this research is the application of relationship marketing in travel agencies in Serbia and the identification of key factors of loyalty of users of tourist services. This paper aims to investigate the influence of relationship marketing assumptions (such as trust, customer complaint management, investment in internal marketing, implementation of information technology in agencies, business image and tradition of agencies, as well as socio-demographic characteristics of clients) on the choice of travel agency through which clients will travel. The contribution of the paper is in the application of the logistics model in the research of relationship marketing in agencies. The results of this research have confirmed that investing in relational determinants in tourism leads both to the development of a long-term relationship with customers and to business performance improvement. Also, the results showed that customer profiles are important in the implementation of relationship marketing to increase the number of loyal customers in tourism. The importance of the paper is in proposing an efficient model for the application of relationship marketing in order to increase the level of customer loyalty in travel agencies operating on the Serbian market.

A classification model for formal high-end menswear retailers in Italy

PurposeDespite menswear is gaining a significant relevance in terms of retail sales, it represents a neglected topic within the academic literature. Therefore, this paper aims at providing a better understanding of the formal menswear market by developing a tailor-made classification model for the identification of retailers' clusters and at discovering the critical success factors (CSFs).Design/methodology/approachThis research looked at most formal high-end menswear retailers in Italy adopting a methodology based on the Analytic Hierarchy Process and multiple case studies. Thirty interviews were conducted with experts, managers and shopkeepers from case studies.FindingsThe study develops for the first time a classification framework of formal high-end menswear retailers and a matching matrix to jointly analyse retailers' clusters and customer profiles in Italy. The results identify the CSFs pursued by menswear retailers and highlight the existence of four clusters of retailers (Differentiated Fashion Firms; Formal Menswear Leaders; Tailoring Firms and Luxury Brands) and seven customer profiles (Habitual Professionals, Special Events, Young People, Occasional Professionals, Foreigners, VIPs and Fashionistas).Originality/valueThe formal high-end menswear represents one of the most traditional Italian heritage markets while being almost ignored in the academic literature. Thus, the value of this research lays in deepening our understanding of this market from the retailers' perspective, by providing for the first time a taxonomy of its players and contributing to identifying the CSFs and the main customer profiles.

Analysis of Demography, Psychograph and Behavioral Aspects of Telecom Customers Using Predictive Analytics to Increase Voice Package Sales

In 2018, Telkomsel's core business shifted its main services from Telephone and SMS services to Data and Digital services, since a declining trend of revenue starting 2014. However, telephone service still contributed 28.4% to the revenue and was the second largest, while SMS gave 4.1%. This research predicts voice package buyers using predictive analytics to identify customer profiles and significant variables to form appropriate target customer segmentation. Logistic regression was used to predict customers who would buy voice packages using 15 input variables. Next, analytics was done by dividing the data into 70% training data sets and 30% testing data obtained from customer voice package user data. The model accuracy gained 97.2%, and the top seven significant variables were formed. Then five clusters of customer segmentation were formed based on top significant variables using the K-Means clustering technique. Based on the results of the prediction model and clustering, behavioral targeting was conducted to provide targeted gimmick products based on five segmentations formed, and then it was divided into two main target customers by considering the similarity of behaviors based on revenue voice, minutes of voice usage, voice transactions, day of voice usage and data payload, thus it was more targeted.