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Includes column charts and icons indicating changes in luxury goods market distribution channels from 2019 to 2025F.
Market Analysis and Trends
Retail & E-commerce
Explores the transformation of the distribution landscape for luxury goods, emphasizing shifts towards online and direct channels post-COVID-19.
distribution, luxury, market, 2025, online
All brick-and-mortar channels dramatically hit in 2020, leading to a distribution eco-system transformation YoY ’19 – 20E 2020 … … and beyond • Monobrand stores & outlets suffered due to closures • Department stores and specialty stores: survival of the fittest – European department stores also deteriorated due to lack of tourism • Travel retail heavily impacted from world travel freeze • Online to become #1 channel, fueling omnichannel transformation • Outlet weight increase given value-driven purchases and spur from Chinese middle class • Monobrand stores to be reinvented as a g-local touchpoint with a new role of the store – Physical networks disruption (i.e. resized footprint & format) – Integration of digital in the physical • Department and specialty stores losing relevance (and surface) in ecosystems – Winners vs. losers game: sharp opinion, new calendar and updated business model key to thrive • Travel retail expected to recover to historical levels CAGR ‘19 – ‘25F D I S T R I B U T I O N E C O S Y S T E M T R A
All brick-and-mortar channels dramatically hit in 2020, leading to a distribution eco-system transformation YoY ’19 – 20E 2020 … … and beyond • Monobrand stores & outlets suffered due to closures • Department stores and specialty stores: survival of the fittest – European department stores also deteriorated due to lack of tourism • Travel retail heavily impacted from world travel freeze • Online to become #1 channel, fueling omnichannel transformation • Outlet weight increase given value-driven purchases and spur from Chinese middle class • Monobrand stores to be reinvented as a g-local touchpoint with a new role of the store – Physical networks disruption (i.e. resized footprint & format) – Integration of digital in the physical • Department and specialty stores losing relevance (and surface) in ecosystems – Winners vs. losers game: sharp opinion, new calendar and updated business model key to thrive • Travel retail expected to recover to historical levels CAGR ‘19 – ‘25F D I S T R I B U T I O N E C O S Y S T E M T R ABAIN-ALTAGAMMA 2020 WORLDWIDE LUXURY MARKET MONITOR
The slide is divided into two sections with a large green title at the top. The left side contains a block of text outlining the key findings related to AI risks and concerns, governance issues, and active management steps. On the right, a pie chart illustrates strategies organizations are implementing to manage AI risks, with percentages. The slide background is white with a green design element on the left and the number '18' at the bottom right corner.
Risk Assessment and Management
Technology & Software
This slide details the governance and risk management practices for generative AI implementation, identifying concerns like inaccurate results, copyright infringement, and systemic bias. It summarizes a survey's findings from Q4 2023 and presents management strategies adopted by organizations, such as monitoring compliance and conducting internal audits. This content is crucial for informing businesses about common risks associated with AI and the importance of establishing robust governance frameworks to manage these risks effectively.
generative AI, risk management, governance, compliance, audits, bias, data privacy, regulation, survey findings, Q4 2023
Specific generative AI risks and concerns include inaccurate results and information (i.e., “hallucinations”); legal risks such as plagiarism, copyright infringement, and liability for errors; privacy and data ownership challenges; lack of transparency, explainability and accountability; and systemic bias.
Specific generative AI risks and concerns include inaccurate results and information (i.e., “hallucinations”); legal risks such as plagiarism, copyright infringement, and liability for errors; privacy and data ownership challenges; lack of transparency, explainability and accountability; and systemic bias.Insights from the leading edge of generative AI adoption
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