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How the Luxury, Fashion, and Beauty Sectors Evolved from Fixed Pricing Models to AI-Driven Strategic Pricing Systems
Technology in Fashion
25 July, 2025
Table of contents
In an era defined by economic uncertainty, shifting consumer expectations, and digital acceleration, pricing has emerged as a powerful strategic lever for brands across the luxury, fashion, and beauty sectors. Historically, pricing was guided by intuition, category benchmarks, and rigid seasonal updates. However, the rise of e-commerce, inflationary pressures, and digitally fluent consumers forced the industry to evolve.
Over the last decade, digital-native brands have reshaped expectations by experimenting with dynamic discounts, flash sales, and hyper-personalised offers. This has pushed even heritage luxury houses and conglomerates to integrate artificial intelligence (AI) into their pricing functions - not as a cost-cutting tool but as a core strategic asset. From protecting brand equity to driving conversion and loyalty, AI is transforming how companies set and adapt prices - product by product, market by market, and even customer by customer.
AI-powered pricing is reshaping the economics of retail and luxury by offering a level of speed, precision, and personalisation that traditional methods cannot match. It addresses three critical shifts in today’s market:
Consumer expectations for price transparency and fairness
Volatile macroeconomic conditions, including inflation and supply chain disruptions
Omnichannel complexity, especially across global markets
Real-Time Responsiveness
Brands are using AI-driven dynamic pricing can adjust thousands of SKUs in real time based on factors like regional demand, inventory turnover, and competitive shifts. This responsiveness is especially critical during periods of economic uncertainty or during viral social trends.
Retailers applying machine learning for pricing have seen up to 8% margin improvement. AI algorithms help retailers identify optimal pricing windows - maximising full-price sell-through and reducing over-discounting.
Recent work by Google Cloud and Salesforce shows that AI is enabling brands to create micro-segments and deliver personalised pricing based on user behaviour, engagement history, and lifetime value. Sephora and L’Oréal have both used such models to dynamically adjust offers and bundles, increasing average order value.
Generative AI tools are now being tested to simulate consumer response to price changes. For example, in pilot projects at fashion tech incubators like Stitch Fix and Farfetch, brands are using synthetic data to model multiple price elasticity scenarios before launch.
Together, these capabilities transform pricing from a reactive, rule-based function into a strategic system that can learn, adapt, and optimise continually - ensuring alignment with consumer expectations and commercial goals.
Luxury pricing is about far more than margin - it’s a deliberate reflection of exclusivity, brand heritage, and long-term value.
Chanel has strategically positioned its handbags as long-term investments by systematically raising prices over the past five years. Its Classic Flap bag now retails for over €10.000 in many markets - nearly double the 2019 price. Chanel's pricing contributed more than half of the company’s sales growth since 2019. The brand uses AI-powered resale tracking and data monitoring tools to align its primary market pricing with secondary market values, ensuring its products retain perceived investment-grade quality.
Chanel has started pulling back on future price hikes as macroeconomic conditions tighten, but will continue using predictive analytics to forecast demand sensitivity across regions and SKUs.
Hermes pursues a different strategy rooted in scarcity, craftsmanship, and geographic control. Instead of frequent price hikes, Hermès relies on AI to manage demand forecasting, in-store allocation, and production planning. According to Vogue Business, the brand uses internal AI models to analyse sales velocity by item and boutique - helping maintain scarcity while matching regional purchasing patterns.
Reuters confirmed in February 2024 that Hermes outperformed peers with +17,5% sales growth, driven by stable pricing and strong demand for ultra-luxury items. The brand’s restraint on price increases, paired with smart allocation through data, reinforces its market leadership and desirability.
Rolex exemplifies how secondary market intelligence can serve as a pricing control mechanism. Although Rolex does not publicly confirm the use of AI, industry sources, including WatchPro and WatchCharts reveal that AI-driven platforms like EveryWatch track resale prices in real time - monitoring over 600 Rolex models globally.
These tools feed market sentiment and resale data back to Rolex and authorised dealers, supporting strategic decisions around model availability and regional pricing consistency. The brand uses this intelligence to reduce cross-border arbitrage, maintain value retention (averaging +12,8% YoY in 2025 according to WatchCharts), and preserve its reputation for stability and prestige.
Together, these brands demonstrate how luxury pricing has become an exercise in precision - where data and AI quietly reinforce exclusivity, equity, and long-term brand value.
Fashion retailers operate in one of the most volatile environments - where seasonality, trend cycles, and consumer sentiment shift rapidly. AI allows brands to dynamically recalibrate prices at speed and scale.
Zara uses proprietary AI and data science tools to assess SKU-level data, including sales velocity, returns, local weather, and store footfall. Dynamic pricing decisions are made weekly based on these real-time insights. Zara’s use of predictive analytics enables more accurate pricing windows and has reduced unnecessary markdowns by approximately 21% across its global network.
Inditex SA executives confirmed that AI is embedded in both online and offline systems, feeding data into the company’s vertically integrated supply chain to reduce inventory carry-over and improve sell-through rates.
H&M announced a strategic partnership with Google Cloud to accelerate its data-driven transformation. The initiative focused on building a secure, scalable enterprise data backbone to enhance customer experiences and supply chain performance. While the company has yet to disclose concrete outcomes related to AI-driven pricing or markdown optimisation, the collaboration positions H&M to integrate predictive analytics across retail functions, including future applications in dynamic pricing.
Levi's is using data analytics and machine learning to optimise pricing, sizing, and customer recommendations as part of its broader AI transformation. According to an interview with Levi’s Global Head of Data, Analytics & AI, the company is building a unified architecture across channels to dynamically serve both strategic and operational decisions. While exact margin or sell-through improvements are not disclosed, Levi’s has confirmed AI’s role in tailoring pricing decisions and supporting omnichannel retail execution.
Fast Retailing, Uniqlo’s parent company, has made major investments in digital transformation and supply chain agility. The 2023 annual report mentions plans for AI-supported inventory control, but does not specify applications in price optimisation or dynamic promotions. References to regional repricing based on weather events, including Japan's rainy season, have not been substantiated in public reports
In the beauty industry, AI-powered pricing is being used to drive personalisation, increase average order value (AOV), and maximise margin across online and offline channels. This sector’s inherent complexity - ranging from product bundling and seasonal releases to loyalty-driven promotions - makes it fertile ground for machine learning models that can adapt pricing based on behaviour and intent.
Sephora deploys AI-driven competitive intelligence and recommendation engines, powered by platforms like Salesforce and Google Cloud. Their dynamic pricing adjusts promotions based on cart behaviour, loyalty tier, and market data. A recent case study notes how AI-powered pricing and bundling improved campaign effectiveness and deepened loyalty. While specific AOV figures aren’t publicly disclosed, the impact on promotional precision and customer engagement is strong.
L'Oréal Paris leverages its ModiFace AI/AR platform combined with Google Cloud’s GenAI tools to personalise beauty consultations and product suggestions. These digital experiences directly influence pricing by tailoring offers to consumers' preferences. According to a 2025 report, their internal CREAITECH Lab supports generating up to 50,000 AI-created images and 500 videos monthly - boosting ROI and enabling market-responsive bundle pricing.
Additionally, Novi Labs' analysis describes how L'Oréal Paris’s virtual-try-on mirrors support omnichannel pricing adjustments by providing data on shade popularity and in-store behaviour - helping reduce returns and inform regional pricing decisions.
Estée Lauder has established an AI Innovation Lab in collaboration with Microsoft Azure OpenAI to develop generative-AI tools across its prestige brands. This includes chatbots and GPT-powered systems that analyse consumer data and trend signals to inform localised pricing strategies and campaign decisions.
Their AI initiatives also support marketing efficiency and launch speed, enabling smarter segmentation - essential inputs for pricing and promotion planning.
While AI-powered pricing delivers strategic gains, it also presents three critical challenges - limiting its effectiveness if not addressed properly.
Perception Risk: A Harvard Business Review analysis highlighted backlash from major companies - like Wendy’s and JetBlue - when dynamic and surge pricing felt unfair, prompting public criticism.
Consumer Fairness Gap: A survey across 17 countries found that ~49% of consumers believe dynamic pricing is unfair, especially for entertainment and travel - demonstrating potential reputational risks.
Algorithmic Discrimination Risks: Studies show that pricing algorithms can engage in price discrimination - charging customers differently based on inferred traits. A 2024 Nected.ai guide warns that without careful design, pricing AI can exploit personal data and create opaque, unfair outcomes.
Oversight Imperative: The AI Now Institute (2024) found that systems lacking sufficient human oversight exhibited 2,4× more bias than supervised models.
To balance AI advantages with these risks, many brands adopt constrained AI pricing, which layers algorithmic recommendations with human oversight and rule-based guardrails.
These hybrid models ensure pricing decisions align with brand strategy, fairness principles, and compliance needs - while retaining analytical support from AI.
Brands embedding controls - such as limiting price change frequency, requiring manual approval above thresholds, logging reasons for price movement, or anonymising data segments - can mitigate backlash and build trust.
The next frontier in AI-driven pricing is autonomous pricing systems - fully agentic engines capable of setting, adjusting, and executing prices without human intervention.
AI-powered pricing is moving from pilot-phase to full deployment. By 2025, an estimated 75% of retail companies are expected to have adopted such systems across significant portions of their portfolios - up from 25% in 2022.
These systems analyse real-time data (demand, competitors, availability) to adapt prices continuously, optimising revenue and margin in dynamic environments.
“Agentic” AI - autonomous agents that independently carry out multi-step tasks - is quickly redefining retail. By 2025, Gartner expects these agents to handle up to 15% of everyday business decisions, especially within pricing, merchandising, and supply chain domains.
NRF predicts a surge in AI-driven shopping agents able to autonomously adjust product pricing based on real-time data - heralding a new age of pricing autonomy.
Generative AI can cut support-function costs by up to 20% and reduce goods-related expenses by 1-2 percentage points - impacting pricing decisions and enabling more agile strategic margin management.
Autonomous pricing systems promise to streamline workload for buyers and pricing teams, freeing them to focus on high-impact strategic tasks.
Though powerful, autonomous pricing poses significant risks: algorithmic bias, collusion effects, and limited transparency. Academic research warns of pricing algorithms inadvertently coordinating high pricing under competitive conditions.
From heritage luxury houses to high-street fashion retailers and global beauty giants, the transition from static price lists to intelligent, adaptive pricing systems is transforming how value is delivered and perceived in the consumer market.
AI-powered pricing has evolved beyond operational support - it is now a strategic cornerstone. Whether protecting the prestige of a €10.000 handbag or responding to unpredictable fast-fashion cycles, AI enables brands to optimise margins, align pricing with consumer intent, and retain competitiveness in real time. The case studies across Chanel, Hermes, Rolex, Zara, Levi's, Sephora, and L'Oréal Paris all demonstrate one clear trend: intelligent pricing is not just about discounts - it is about precision, relevance, and agility.
Moreover, as AI matures, brands are pushing toward agentic and autonomous pricing models - capable of continuous self-optimisation based on market signals, behavioural data, and real-time inventory. Yet, with power comes responsibility. The risks of consumer backlash, unfair treatment, and regulatory scrutiny are real and growing. Leading companies are mitigating these with constrained AI models, embedding human oversight and ethical governance to preserve brand integrity.
Looking ahead, pricing will no longer be just a lever pulled by finance or merchandising teams - it will be a cross-functional, AI-enhanced capability that integrates marketing, loyalty, supply chain, and even creative. In an industry where perception defines value, and timing defines margin, AI-driven pricing will become the ultimate differentiator.
Cover Image: World branding Forum.