The diffusion of digital technologies has shifted brand marketing from mass communication toward individualized consumer dialogue, and personalization has become a central capability for competitive differentiation. This paper examines how global brands design and implement personalization strategies in digital marketing environments, using L'Oréal, the world's largest cosmetics group, as a case. Drawing on a qualitative, single-case design and secondary data, including corporate reports, press releases, and peer-reviewed research, the study analyzes L'Oréal's personalization initiatives across four dimensions: data infrastructure and artificial intelligence (AI)-based diagnostics, augmented reality (AR) virtual try-on services, omnichannel customer relationship management, and personalized content delivered through influencers and social platforms. The findings indicate that L'Oréal's approach rests on proprietary technology ownership, systematic integration of AI and AR into the consumer journey, and organizational structures that connect data science with brand management. The case also reveals tensions involving privacy, algorithmic bias, and digital exclusion that condition the effectiveness of personalization. The study contributes an integrated framework that links AI capabilities to personalization touchpoints and brand outcomes, and offers managerial guidance for consumer goods firms seeking to scale personalization without eroding consumer trust.

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Large amounts of public and private money go into professional sports and into major sporting events, and the spenders usually believe it is worth it in economic terms. Two ways of hosting sport are compared in this paper in order to check that belief. In the first, the mega-event model of the Super Bowl, the Fédération Internationale de Football Association (FIFA) World Cup and the Olympic Games, the host city pays for stadiums, security, transport and other infrastructure, and leagues, federations, broadcasters and outside businesses collect most of the income. According to economic studies and published host-city budgets, costs exceed the budget nearly every time, the jobs created are temporary and mostly filled by outsiders, ordinary tourists stay away because of crowding and higher prices, and the venues built for the event are little used once it ends. In the second, the grassroots model of Village BA, a village-run basketball tournament in Taijiang County, Guizhou Province, China, the games are played on existing open-air courts, entry is free, volunteers run the event, and income from licensing, stalls, parking and agricultural products stays with the village collective. The figures reported for the county and for the village show more tourists, higher collective and household income, migrant workers returning home and better local governance. The comparison suggests that how much is spent has little to do with the economic and social value of sport, and that small events owned locally are a lower-risk and more inclusive way to develop.

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Nowadays, using AI technology to improve efficiency is one of the most important research subjects. Researchers found that Sephora use AI and AR technology to improve the efficiency of shopping. However, the reason behind efficiency improvements and their broader implication is not been unified. Therefore, this study collects data, including sales growth and so on, and compare it with initial data to examine how the adoption of these technologies enhancing Sephora's operational efficiency. According to the survey, AI technology has as well as enhanced sales efficiency and the experience of consumers transformed the structure of rights and the relationship between AI and consumers. These positive impacts demonstrate that Sephora must continue to use these technologies and refine them further. Besides, these advanced technologies may lead to data privacy leakage and declining consumer trust issues as well. Therefore, solving these issues has also become part of Sephora's efforts to refine these technologies.

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This study examines how expected return, investment risk, investment cost, and time horizon shape enterprise investment decisions. It integrates the literature review and methodology into a single analytical design and compares four investment episodes using academic research, company disclosures, and financial reporting: Alibaba's acquisition of UCWeb, Shanghai Wanye Enterprises' derivatives exposure, Taiwan Semiconductor Manufacturing Company's U.S. expansion, and Berkshire Hathaway's long-term holding of Coca-Cola. Secondary evidence shows marked differences in scale and outcome. UCWeb reported more than 500 million quarterly active users; Wanye recorded a RMB55.593 million derivative mark-to-market loss; TSMC's announced cumulative U.S. commitment increased from US$12 billion in 2020 to US$165 billion in 2025 as project scope expanded; and Berkshire's annual Coca-Cola dividend rose from US$75 million to US$704 million between 1994 and 2022. The comparison indicates that return, risk, cost, and time are interdependent rather than separately rankable. An integrated screen should combine discounted cash-flow analysis, financing capacity, downside and reversibility tests, life-cycle cost, and a holding period consistent with the value mechanism. The evidence is illustrative rather than causal, but it yields testable propositions for later quantitative research.

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