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One of the most neglected sources of energy loss is streetlights that generate too much light in areas where it is not required. Energy waste has enormous economic and environmental effects. In addition, due to the conventional manual nature of operation, streetlights are frequently seen being turned ‘ON’ during the day and ‘OFF’ in the evening, which is regrettable even in the twenty-first century. These issues require automated streetlight control in order to be resolved. This study aims to develop a novel streetlight controlling method by combining a smart transport monitoring system powered by computer vision technology with a closed circuit television (CCTV) camera that allows the light-emitting diode (LED) streetlight to automatically light up with the appropriate brightness by detecting the presence of pedestrians or vehicles and dimming the streetlight in their absence using semantic image segmentation from the CCTV video streaming. Consequently, our model distinguishes daylight and nighttime, which made it feasible to automate the process of turning the streetlight ‘ON’ and ‘OFF’ to save energy consumption costs. According to the aforementioned approach, geo-location sensor data could be utilised to make more informed streetlight management decisions. To complete the tasks, we consider training the U-net model with ResNet-34 as its backbone. Validity of the models is guaranteed with the use of assessment matrices. The suggested concept is straightforward, economical, energy-efficient, long-lasting and more resilient than conventional alternatives.

Customer segmentation is an important research area that helps organizations to improve their services according to customer needs. With the increased information that shows customer attitudes, it is much easier and also more necessary than before to analyze customer responses on different campaigns. Recency, frequency, and monetary (RFM) analysis allows us to segment customers according to their common features. In this chapter, customer segmentation and RFM analysis are explained first, then a real case application of RFM analysis on customer segmentation for a Fuel company is presented. At the end of the application part, possible strategies for the company are generated.

This study attempts to identify and explicate the unique segmentation of the increasingly growing virtual reality (VR) user market based on the user experience. Consequently, it collects five hundred forty-five online survey questionnaires through the Prolific platform and deploys cluster analysis to identify mutually exclusive groups of VR users. The research variable, user experience, contains 16 indicators explained by four dimensions. As a result, this study is able to unveil three mutually exclusive markets which are labeled as (1) beginner, (2) aficionado, and (3) utilitarian. The unique features of these three groups are further compared based on their VR tour behaviors. In the conclusion section, it offers managerial implications for devising novel marketing strategies.

Effective strategic internal communication is instrumental for meaningful relationships and productive work climates within organizations and, therefore, for overall organizational success. Currently, demographic shifts, the dramatically changing work environments because of pandemics and technology, and longer working lives challenge internal communication experts to assess the implications of generational differences for respective workforce target audiences. Effectively addressing the target audiences requires a focus on and understanding of their needs and behaviours. Surprisingly, little research has been conducted on the segmentation criteria of internal stakeholders, and even less on the implications of different generations for internal communication management. This study is novel as it discusses the value and practicality of different generations as a segmentation criterion for effective internal communication from the perspective of internal communication professionals. A multi-method qualitative research approach was employed, including 49 unstructured interviews, eight focus groups and 13 semi-structured interviews with internal communication professionals in Swiss companies on a strategic and operational level. The results show, first, that the segmentation of the internal audiences is dominated by traditional one-dimensional top-down stakeholder-oriented approaches. Second, while an awareness of the importance of generations is high and the need for multi-dimensional segmentation is evident, formal segmentation by generation is rarely implemented and remains controversial. Third, internal communication can foster more inclusive and productive workplaces by better considering the needs and preferences of the different internal segments and by simultaneously standardizing and customizing communication. The study also discusses inherent limitations and suggests avenues for future research.

Segmentation is an important marketing concept that identifies and analyzes different needs and wants of buyers as well as their buying behavior. Two different perspectives on how buyers and potential customers should be approached have emerged over the last two decades: the transactional perspective and the relational perspective. The two approaches differ in their overall understanding of the customer and how to address the customer. The two approaches therefore hold different implications for how segmentation should take place and how markets should be monitored.

This chapter aims to compare two data collection methods to ascertain whether the method impacts the segments derived for events. A total of 1,180 onsite surveys were collected during a music festival and 522 online surveys were completed by attendees a fortnight after the event ended. Both sets of data were analyzed separately using TwoStep cluster analysis. Four valid segments were identified in both models. In each case 4 segmentation bases and 12 variables represented the final solutions with minimal but observable differences. Of note, a difference in the size of the clusters was identified. Furthermore, in a few instances a category that was ranked first for the onsite approach fell to second, or vice versa. Both online and onsite data collection methods are capable of providing data for segmentation purposes with a high degree of accuracy observed between data collection methods. Researchers must be aware that the choice of data collection will have a minor influence on the segments derived. Ideally researchers should seek to collect data using multiple data collection methods to gain a more holistic view of festival attendees.

This chapter provides an overview of the marketing strategy development process in the commercialization of breakthrough technologies. Important concepts and elements that are considered critical when developing market applications are presented with emphasis on three key decisions: target market selection, segmentation, and positioning. These strategic decisions will guide the more tactical considerations relating to the specific elements, or marketing mix, of the product's marketing strategy. Marketing strategy development is a dynamic process impacted by many factors. This chapter highlights the dynamic nature of this process as well as provides insight into the fundamental considerations in strategy formulation.

This chapter presents an investigation into the theory of labour market segmentation and income inequality in the Ghanaian mining sector. Mining activity especially gold mining has been a significant component of exports as well as employment and income earning in the three major mining regions of Ghana. While income growth is an economic benefit, the high incomes associated with the mining sector may lead to greater income inequality. This chapter provides an analysis of mining activity and income inequality in the Western, Eastern, and Ashanti regions of Ghana. The application of labour market segmentation and the Gini coefficient (a measure of inequality) for personal income are found to be significantly associated with the type and levels of mining employment. However, this observation is not linear as income inequality initially increases with mining activity before decreasing at medium to high levels of mining employment, thus following a Kuznets curve pattern. Segregating datasets for indigenous and expatriate staff reveals very different patterns of income inequality. It poignantly increases with indigenous and/or local community personnel relative to expatriate technical personnel at high levels of mining employment; income inequality is lower among the local community residents relative to nationals from other regions and/or from neighbouring countries. This means segmented labour markets (SLM) within the mining industry are likely to be a problem as they result in increased income inequality among locales relative to foreign expatriates.