Identifying true value in 2025 fashion trends requires a data-driven approach, moving beyond fleeting fads to understand consumer behavior, market analytics, and long-term sustainability for smart style investments.

Navigating the ever-evolving world of fashion can feel like a whirlwind, especially when trying to discern fleeting fads from lasting styles. For 2025, understanding how to identify 2025 fashion trends data is more crucial than ever to make informed choices that truly add value to your wardrobe and lifestyle.

Understanding the Shift Towards Data-Driven Fashion

The fashion industry, traditionally driven by intuition and creative vision, is undergoing a profound transformation. Data analytics now plays a pivotal role in shaping everything from design and production to marketing and retail. This shift isn’t just about efficiency; it’s about understanding the consumer on a deeper level and predicting what truly resonates.

Gone are the days when designers alone dictated trends. Today, algorithms analyze vast amounts of data, including social media sentiment, sales figures, web searches, and even climate patterns, to forecast future preferences. For consumers, this means a more informed approach to shopping, moving beyond mere aesthetics to consider longevity, ethical production, and genuine personal expression.

The Rise of Predictive Analytics

Predictive analytics in fashion leverages historical data and machine learning to anticipate upcoming trends. This helps brands minimize waste and produce items that are more likely to sell, aligning with sustainable practices. It also empowers consumers to identify investments rather than ephemeral purchases.

  • Reduced Overproduction: Brands use data to match supply with demand, lessening environmental impact.
  • Personalized Recommendations: Tailored suggestions based on individual preferences and purchasing history.
  • Early Trend Spotting: Identifying emerging styles before they hit mainstream popularity.

This data-driven evolution isn’t just a corporate strategy; it’s a new paradigm for how we engage with fashion. By understanding its mechanisms, we can better navigate the landscape of 2025 fashion trends and make choices that are both stylish and sensible.

Leveraging Social Media Analytics for Trend Identification

Social media platforms are undeniable epicenters of emerging fashion trends. What starts as a niche aesthetic among a few influencers can quickly snowball into a global phenomenon. However, distinguishing genuine trends from fleeting viral moments requires a keen eye and a strategic approach to social media analytics.

Observing engagement rates, hashtag performance, and the longevity of certain styles across diverse demographics provides invaluable insights. It’s not just about what’s popular, but how that popularity is sustained and adopted by different communities, signaling true staying power.

Tracking Influencer Engagement and Micro-Trends

Influencers, particularly micro-influencers with highly engaged niche audiences, are powerful indicators of nascent trends. Their followers often represent early adopters, and their content can reveal styles gaining traction before they appear in mainstream media.

  • Authenticity Over Reach: Focus on influencers with genuine engagement, not just large follower counts.
  • Cross-Platform Consistency: Look for trends appearing consistently across Instagram, TikTok, and Pinterest.
  • Comment Section Insights: Analyze discussions for consumer sentiment and specific product interest.

Beyond individual influencers, monitoring broader social media narratives can reveal shifts in consumer values, such as a growing preference for sustainable materials or gender-neutral designs. These overarching themes often underpin the most significant and enduring fashion movements.

Analyzing E-commerce Sales Data and Consumer Preferences

E-commerce sales data offers a direct and unfiltered look into what consumers are actually buying, providing a robust foundation for identifying valuable 2025 fashion trends. This goes beyond mere popularity; it reveals purchasing patterns, price sensitivities, and the categories where investment is truly happening.

By examining sales volume, return rates, and customer reviews across different product types and brands, we can pinpoint items that not only attract attention but also satisfy buyers over time. This data helps differentiate between items bought on impulse and those representing a considered purchase.

Fashion analysts reviewing consumer data and trend prediction algorithms

Identifying Best-Selling Categories and Repeat Purchases

Focusing on best-selling categories provides a clear picture of consumer priorities. Are people investing more in loungewear, formal attire, or perhaps versatile transitional pieces? Understanding these shifts is key to anticipating future demand.

  • High-Volume Categories: Pinpoint product types consistently showing strong sales.
  • Low Return Rates: Indicators of customer satisfaction and perceived value.
  • Repeat Purchases: Reveals items or brands that foster customer loyalty and enduring appeal.

Furthermore, analyzing consumer reviews and feedback offers qualitative insights into why certain products succeed or fail. Comments on fit, fabric quality, durability, and versatility are goldmines for understanding what truly constitutes value in the eyes of the consumer, informing your own purchasing decisions for 2025 fashion trends.

The Role of AI and Machine Learning in Predicting Future Styles

Artificial Intelligence (AI) and machine learning (ML) are revolutionizing fashion trend forecasting, moving beyond human intuition to provide highly accurate and granular predictions. These technologies can process vast datasets at speeds impossible for humans, uncovering subtle patterns and correlations that signal emerging styles.

From analyzing runway shows and street style photography to deciphering global economic shifts and cultural movements, AI models can identify the foundational elements of future fashion. This allows both brands and savvy consumers to make more strategic decisions, investing in pieces with genuine staying power rather than fleeting fads.

AI-Powered Trend Spotting Platforms

Numerous platforms now utilize AI to assist in trend prediction. These tools can track visual elements, color palettes, fabric textures, and silhouette changes across millions of images and articles, providing a comprehensive view of the evolving fashion landscape.

  • Visual Recognition: AI identifies recurring patterns in clothing, accessories, and styling.
  • Natural Language Processing: Analyzes fashion articles, blogs, and social media text for sentiment and keyword trends.
  • Cross-Industry Analysis: Connects fashion trends with parallel movements in art, design, and technology.

The precision offered by AI and ML means that by 2025, identifying truly valuable fashion trends will be less about guesswork and more about informed, data-backed insights. This empowers consumers to curate wardrobes that are both contemporary and timeless, reducing impulse buys and promoting more conscious consumption.

Understanding Consumer Search Behavior Through Keyword Data

What consumers search for online provides a powerful indicator of their interests, intentions, and emerging desires in fashion. Keyword data, derived from search engines, offers a direct window into the collective mind of the marketplace, revealing what styles, brands, and values are gaining traction.

Analyzing search volume, related queries, and long-tail keywords can help distinguish between broad interest and specific demand. A sudden spike in searches for a particular fabric, color, or garment type can signal an impending trend, long before it becomes widely visible in retail stores.

Smartphone displaying social media fashion trend analytics dashboard

Identifying Emerging Style Preferences

Beyond generic terms, specific keyword combinations reveal nuanced preferences. For instance, a rise in searches for “sustainable organic cotton dresses” indicates a consumer base prioritizing both ethics and comfort, far more detailed than just “dresses.”

  • Long-Tail Keywords: Reveal specific, high-intent consumer interests.
  • Geographic Search Data: Pinpoints regional trend variations and adoption rates.
  • Comparative Search Trends: Shows how interest in one style compares to another over time.

By paying attention to these digital breadcrumbs, both individuals and brands can proactively adapt their strategies. For consumers, this means being ahead of the curve, spotting 2025 fashion trends that align with their personal values and aesthetic long before they saturate the market.

The Importance of Sustainability Metrics in 2025 Fashion

In 2025, true value in fashion extends far beyond aesthetics and immediate popularity; it deeply intertwines with sustainability. Consumers are increasingly conscious of the environmental and social impact of their clothing choices, leading to a significant shift in what constitutes a “desirable” trend. Data-driven insights into sustainability metrics are becoming non-negotiable for identifying valuable fashion.

This involves scrutinizing supply chains, material sourcing, production processes, and brand transparency. Trends that align with ethical production, circular design principles, and reduced environmental footprints are gaining substantial ground, indicating long-term relevance and consumer loyalty.

Evaluating Brand Transparency and Ethical Practices

Brands that openly share their sustainability efforts, from carbon footprint reduction to fair labor practices, are building trust and garnering support. Data on certifications, audit results, and material origins are crucial for consumers to make informed choices.

  • Certifications: Look for recognized labels like GOTS, Oeko-Tex, or Fair Trade.
  • Circular Economy Initiatives: Brands offering repair services, recycling programs, or upcycled collections.
  • Material Innovation: Trends favoring recycled, organic, or biodegradable textiles.

Investing in sustainable fashion isn’t just a moral choice; it’s a practical one. These items often boast higher quality, greater durability, and a timeless appeal, making them smart, data-backed choices for any wardrobe seeking true value in 2025 fashion trends.

Demographic and Psychographic Data: Tailoring Trends to Audiences

Fashion is deeply personal, and understanding who is adopting certain trends – and why – is critical for identifying true value. Demographic data (age, location, income) combined with psychographic data (values, interests, lifestyle) provides a nuanced picture of consumer segments and their relationship with fashion.

A trend might be highly popular among Gen Z in urban centers, while another resonates with Millennials in suburban areas. By segmenting data, we can see which trends have broad appeal versus those that cater to specific, influential niches, helping us predict longevity and impact.

Understanding Generational Fashion Preferences

Each generation often exhibits distinct fashion preferences influenced by cultural, technological, and economic factors. Analyzing these differences helps in identifying which 2025 fashion trends are likely to appeal to specific age groups, ensuring relevance and adoption.

  • Gen Z: Often gravitates towards sustainable, expressive, and comfort-driven styles.
  • Millennials: Value authenticity, quality over quantity, and versatility in their wardrobes.
  • Gen X & Boomers: Prioritize classic styles, comfort, and investment pieces with timeless appeal.

Furthermore, psychographic data uncovers the motivations behind purchases. Is it status, self-expression, comfort, or practicality? Aligning trend identification with these deeper psychological drivers ensures that the chosen styles not only look good but also genuinely fulfill consumer needs and desires, making them truly valuable.

The Intersection of Culture, Art, and Fashion Data

Fashion is a mirror of society, constantly reflecting and influencing cultural movements, artistic expressions, and global events. Identifying truly valuable 2025 fashion trends requires looking beyond immediate sales figures and delving into the broader cultural landscape through data analysis.

Trends often originate from shifts in art, music, film, and even political climates. Data can help connect these seemingly disparate dots, revealing how socio-cultural phenomena translate into specific garment styles, color palettes, and aesthetic sensibilities that resonate deeply with the public.

Analyzing Cultural Shifts and Artistic Influences

Monitoring major art exhibitions, music festival aesthetics, popular film genres, and even interior design trends can provide early signals for fashion direction. Data analytics can quantify the influence of these cultural touchstones, showing their impact on consumer preferences.

  • Emerging Art Movements: How new artistic styles inspire prints, silhouettes, or fabric choices.
  • Music Subcultures: The influence of specific music genres on street style and youth fashion.
  • Global Events: How major societal shifts or historical anniversaries influence sartorial expressions.

By understanding the symbiotic relationship between fashion and broader culture through data, we can better predict which trends will not only capture attention but also hold significant meaning and longevity for 2025. This holistic perspective ensures that fashion choices are not just trendy, but culturally resonant and truly valuable.

Forecasting Longevity: Distinguishing Fads from Enduring Trends

One of the biggest challenges in fashion is distinguishing between a fleeting fad and an enduring trend that offers true value. A data-driven approach is essential here, allowing us to move beyond the initial hype and assess the potential longevity of a style. It’s about investing wisely in pieces that will remain relevant beyond a single season.

Analyzing historical data on trend cycles, adoption rates across different demographics, and the underlying drivers of a trend can provide clues. Fads often emerge quickly and disappear just as fast, driven by novelty. Enduring trends, however, are usually underpinned by deeper cultural shifts, functional benefits, or a timeless aesthetic.

Metrics for Assessing Trend Durability

Several data points can help forecast a trend’s staying power. Look for consistent growth in search interest, sustained engagement on social media, and continued sales performance over multiple seasons, rather than just an initial boom.

  • Sustained Search Interest: Google Trends data showing consistent or growing search volume over 12+ months.
  • Cross-Generational Adoption: A trend embraced by more than one age group suggests broader appeal.
  • Versatility and Adaptability: Styles that can be easily integrated into diverse wardrobes and occasions tend to last longer.

Ultimately, a trend’s longevity is often tied to its ability to evolve and adapt. Styles that can be reinterpreted, accessorized differently, or integrated into various aesthetics are more likely to endure. By focusing on these data-backed indicators, you can confidently identify 2025 fashion trends that offer lasting value.

Key Trend Identification Method Brief Description
Social Media Analytics Analyze influencer engagement and hashtag performance for emerging styles.
E-commerce Sales Data Examine best-selling categories and low return rates for consumer preference.
AI/Machine Learning Leverage AI to process vast datasets for accurate future style predictions.
Sustainability Metrics Assess brand transparency and ethical practices for long-term value.

Frequently Asked Questions About 2025 Fashion Trends

How can I use data to make smarter fashion purchases for 2025?

By analyzing social media engagement, e-commerce sales, and keyword search data, you can identify trends with genuine staying power. Look for consistent interest and positive consumer feedback, focusing on items that align with your personal style and values, ensuring longevity and reducing impulsive buys.

What role does AI play in forecasting 2025 fashion trends?

AI and machine learning analyze vast amounts of data, including visual trends, sales figures, and cultural shifts, to predict future styles with high accuracy. This helps identify emerging patterns and distinguishes between fleeting fads and enduring trends, offering valuable insights for both brands and consumers.

Why is sustainability data important for identifying valuable fashion trends?

Sustainability metrics provide insights into a garment’s ethical and environmental impact. Trends aligning with transparency, ethical sourcing, and circular design offer long-term value, appealing to conscious consumers and often indicating higher quality and durability, which contributes to their lasting relevance.

How do I differentiate between a fad and an enduring trend?

Fads typically have rapid, short-lived popularity driven by novelty. Enduring trends show sustained interest across demographics and seasons, often supported by deeper cultural shifts or practical benefits. Data like consistent search volume and cross-generational adoption are key indicators of longevity.

Can demographic data influence the adoption of fashion trends?

Absolutely. Demographic and psychographic data help understand which trends resonate with specific age groups, locations, or lifestyles. This segmentation reveals if a trend has niche appeal or broader adoption, guiding decisions on whether a style will be a personal or widespread valuable investment.

Conclusion

The fashion landscape of 2025 is undeniably complex, but by embracing data-driven strategies, consumers can confidently navigate beyond the hype and identify true value. From leveraging social media insights and e-commerce analytics to understanding the power of AI, sustainability metrics, and cultural influences, the tools are available to make informed and impactful wardrobe decisions. Investing in fashion no longer needs to be a gamble; it can be a strategic, value-conscious endeavor that aligns with personal style, ethical considerations, and long-term relevance. By focusing on what the data truly reveals, you can build a wardrobe that is both contemporary and enduring, reflecting genuine style and smart choices for the future.

Lara Barbosa

Lara Barbosa has a degree in Journalism, with experience in editing and managing news portals. Her approach combines academic research and accessible language, turning complex topics into educational materials of interest to the general public.

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category-shopping-tips tag-2025-fashion tag-data-driven-fashion tag-fashion-analytics tag-fashion-trends tag-shopping-tips tag-style-insights tag-sustainable-fashion tag-value-fashion"> <div class="container"> <div class="row"> <div class="col-md-8 col-lg-8 mx-auto"> <div class="section section-featured js-section-featured"> <div class="card card-featured card-top m-0"> <div class="card-body"> <h1 class="card-title">Beyond the Hype: 8 Data-Driven Ways to Identify True Value in 2025 Fashion Trends</h1> <div class="card-text">Identifying true value in 2025 fashion trends requires a data-driven approach, moving beyond fleeting fads to understand consumer behavior, market analytics, and long-term sustainability for smart style investments.</div> <div class="card-author"> <p>By: <b>Lara Barbosa</b> on May 30, 2025 <strong>Last updated on:</strong> October 14, 2025</p> </div> </div> <div class="card-preview mt-8"> <img class="card-pic" src="https://ChicTrendsNow.com/wp-content/uploads/2025/10/data-driven-2025-fashion-trends-1024x558.webp" alt="Beyond the Hype: 8 Data-Driven Ways to Identify True Value in 2025 Fashion Trends" /> </div> </div> </div> <div class="section section-content"> <div class="center"> <div class="content"> <p><!-- SEO_TITLE: Beyond the Hype: 8 Data-Driven Ways to Identify True Value in 2025 Fashion Trends --></p> <p class=summarization>Identifying true value in 2025 fashion trends requires a data-driven approach, moving beyond fleeting fads to understand consumer behavior, market analytics, and long-term sustainability for smart style investments.</p> <p><!-- Main SEO Title: Beyond the Hype: 8 Data-Driven Ways to Identify True Value in 2025 Fashion Trends --></p> <p> Navigating the ever-evolving world of fashion can feel like a whirlwind, especially when trying to discern fleeting fads from lasting styles. For 2025, understanding how to identify <strong>2025 fashion trends data</strong> is more crucial than ever to make informed choices that truly add value to your wardrobe and lifestyle. </p> <p></p> <p><!-- COVER_IMAGE: A stylish woman in an elegant, modern outfit, standing in a futuristic fashion showroom with holographic displays showing data analytics and trend insights. The setting is bright and clean, emphasizing innovation and sophisticated design. --></p> <h2>Understanding the Shift Towards Data-Driven Fashion</h2> <p> The fashion industry, traditionally driven by intuition and creative vision, is undergoing a profound transformation. Data analytics now plays a pivotal role in shaping everything from design and production to marketing and retail. This shift isn&#8217;t just about efficiency; it&#8217;s about understanding the consumer on a deeper level and predicting what truly resonates. </p> <p> Gone are the days when designers alone dictated trends. Today, algorithms analyze vast amounts of data, including social media sentiment, sales figures, web searches, and even climate patterns, to forecast future preferences. For consumers, this means a more informed approach to shopping, moving beyond mere aesthetics to consider longevity, ethical production, and genuine personal expression. </p> <h3>The Rise of Predictive Analytics</h3> <p> Predictive analytics in fashion leverages historical data and machine learning to anticipate upcoming trends. This helps brands minimize waste and produce items that are more likely to sell, aligning with sustainable practices. It also empowers consumers to identify investments rather than ephemeral purchases. </p> <ul> <li><strong>Reduced Overproduction:</strong> Brands use data to match supply with demand, lessening environmental impact.</li> <li><strong>Personalized Recommendations:</strong> Tailored suggestions based on individual preferences and purchasing history.</li> <li><strong>Early Trend Spotting:</strong> Identifying emerging styles before they hit mainstream popularity.</li> </ul> <p> This data-driven evolution isn&#8217;t just a corporate strategy; it&#8217;s a new paradigm for how we engage with fashion. By understanding its mechanisms, we can better navigate the landscape of <a href="https://ChicTrendsNow.com/2025-celebrity-red-carpet-trends">2025 fashion trends</a> and make choices that are both stylish and sensible. </p> <div class="video-container" style="position: relative; padding-bottom: 56.25%; height: 0; overflow: hidden; max-width: 100%; margin-bottom: 20px;"> <iframe style="position: absolute; top: 0; left: 0; width: 100%; height: 100%;" width="560" height="315" src="https://www.youtube.com/embed/-0JQEckDswE" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen><br /> </iframe> </div> <h2>Leveraging Social Media Analytics for Trend Identification</h2> <p> Social media platforms are undeniable epicenters of emerging fashion trends. What starts as a niche aesthetic among a few influencers can quickly snowball into a global phenomenon. However, distinguishing genuine trends from fleeting viral moments requires a keen eye and a strategic approach to social media analytics. </p> <p> Observing engagement rates, hashtag performance, and the longevity of certain styles across diverse demographics provides invaluable insights. It&#8217;s not just about what&#x2019;s popular, but how that popularity is sustained and adopted by different communities, signaling true staying power. </p> <h3>Tracking Influencer Engagement and Micro-Trends</h3> <p> Influencers, particularly micro-influencers with highly engaged niche audiences, are powerful indicators of nascent trends. Their followers often represent early adopters, and their content can reveal styles gaining traction before they appear in mainstream media. </p> <ul> <li><strong>Authenticity Over Reach:</strong> Focus on influencers with genuine engagement, not just large follower counts.</li> <li><strong>Cross-Platform Consistency:</strong> Look for trends appearing consistently across Instagram, TikTok, and Pinterest.</li> <li><strong>Comment Section Insights:</strong> Analyze discussions for consumer sentiment and specific product interest.</li> </ul> <p> Beyond individual influencers, monitoring broader social media narratives can reveal shifts in consumer values, such as a growing preference for sustainable materials or gender-neutral designs. These overarching themes often underpin the most significant and enduring fashion movements. </p> <h2>Analyzing E-commerce Sales Data and Consumer Preferences</h2> <p> E-commerce sales data offers a direct and unfiltered look into what consumers are actually buying, providing a robust foundation for identifying valuable <a href="https://ChicTrendsNow.com/end-of-season-fashion-deals">2025 fashion trends</a>. This goes beyond mere popularity; it reveals purchasing patterns, price sensitivities, and the categories where investment is truly happening. </p> <p> By examining sales volume, return rates, and customer reviews across different product types and brands, we can pinpoint items that not only attract attention but also satisfy buyers over time. This data helps differentiate between items bought on impulse and those representing a considered purchase. </p> <p><img decoding="async" class="aligncenter size-large" src="https://ChicTrendsNow.com/wp-content/uploads/2025/10/fashion-analytics-team-meeting.webp" alt="Fashion analysts reviewing consumer data and trend prediction algorithms"></p> <h3>Identifying Best-Selling Categories and Repeat Purchases</h3> <p> Focusing on best-selling categories provides a clear picture of consumer priorities. Are people investing more in loungewear, formal attire, or perhaps versatile transitional pieces? Understanding these shifts is key to anticipating future demand. </p> <ul> <li><strong>High-Volume Categories:</strong> Pinpoint product types consistently showing strong sales.</li> <li><strong>Low Return Rates:</strong> Indicators of customer satisfaction and perceived value.</li> <li><strong>Repeat Purchases:</strong> Reveals items or brands that foster customer loyalty and enduring appeal.</li> </ul> <p> Furthermore, analyzing consumer reviews and feedback offers qualitative insights into why certain products succeed or fail. Comments on fit, fabric quality, durability, and versatility are goldmines for understanding what truly constitutes value in the eyes of the consumer, informing your own purchasing decisions for 2025 fashion trends. </p> <h2>The Role of AI and Machine Learning in Predicting Future Styles</h2> <p> Artificial Intelligence (AI) and machine learning (ML) are revolutionizing fashion trend forecasting, moving beyond human intuition to provide highly accurate and granular predictions. These technologies can process vast datasets at speeds impossible for humans, uncovering subtle patterns and correlations that signal emerging styles. </p> <p> From analyzing runway shows and street style photography to deciphering global economic shifts and cultural movements, AI models can identify the foundational elements of future fashion. This allows both brands and savvy consumers to make more strategic decisions, investing in pieces with genuine staying power rather than fleeting fads. </p> <h3>AI-Powered Trend Spotting Platforms</h3> <p> Numerous platforms now utilize AI to assist in trend prediction. These tools can track visual elements, color palettes, fabric textures, and silhouette changes across millions of images and articles, providing a comprehensive view of the evolving fashion landscape. </p> <ul> <li><strong>Visual Recognition:</strong> AI identifies recurring patterns in clothing, accessories, and styling.</li> <li><strong>Natural Language Processing:</strong> Analyzes fashion articles, blogs, and social media text for sentiment and keyword trends.</li> <li><strong>Cross-Industry Analysis:</strong> Connects fashion trends with parallel movements in art, design, and technology.</li> </ul> <p> The precision offered by AI and ML means that by 2025, identifying truly valuable fashion trends will be less about guesswork and more about informed, data-backed insights. This empowers consumers to curate wardrobes that are both contemporary and timeless, reducing impulse buys and promoting more conscious consumption. </p> <h2>Understanding Consumer Search Behavior Through Keyword Data</h2> <p> What consumers search for online provides a powerful indicator of their interests, intentions, and emerging desires in fashion. Keyword data, derived from search engines, offers a direct window into the collective mind of the marketplace, revealing what styles, brands, and values are gaining traction. </p> <p> Analyzing search volume, related queries, and long-tail keywords can help distinguish between broad interest and specific demand. A sudden spike in searches for a particular fabric, color, or garment type can signal an impending trend, long before it becomes widely visible in retail stores. </p> <p><img decoding="async" class="aligncenter size-large" src="https://ChicTrendsNow.com/wp-content/uploads/2025/10/social-media-fashion-analytics.webp" alt="Smartphone displaying social media fashion trend analytics dashboard"></p> <h3>Identifying Emerging Style Preferences</h3> <p> Beyond generic terms, specific keyword combinations reveal nuanced preferences. For instance, a rise in searches for &#x201C;sustainable organic cotton dresses&#x201D; indicates a consumer base prioritizing both ethics and comfort, far more detailed than just &#x201C;dresses.&#x201D; </p> <ul> <li><strong>Long-Tail Keywords:</strong> Reveal specific, high-intent consumer interests.</li> <li><strong>Geographic Search Data:</strong> Pinpoints regional trend variations and adoption rates.</li> <li><strong>Comparative Search Trends:</strong> Shows how interest in one style compares to another over time.</li> </ul> <p> By paying attention to these digital breadcrumbs, both individuals and brands can proactively adapt their strategies. For consumers, this means being ahead of the curve, spotting <a href="https://ChicTrendsNow.com/celebrity-airport-fashion-2025">2025 fashion trends</a> that align with their personal values and aesthetic long before they saturate the market. </p> <h2>The Importance of Sustainability Metrics in 2025 Fashion</h2> <p> In 2025, true value in fashion extends far beyond aesthetics and immediate popularity; it deeply intertwines with sustainability. Consumers are increasingly conscious of the environmental and social impact of their clothing choices, leading to a significant shift in what constitutes a &#x201C;desirable&#x201D; trend. Data-driven insights into sustainability metrics are becoming non-negotiable for identifying valuable fashion. </p> <p> This involves scrutinizing supply chains, material sourcing, production processes, and brand transparency. Trends that align with ethical production, circular design principles, and reduced environmental footprints are gaining substantial ground, indicating long-term relevance and consumer loyalty. </p> <h3>Evaluating Brand Transparency and Ethical Practices</h3> <p> Brands that openly share their sustainability efforts, from carbon footprint reduction to fair labor practices, are building trust and garnering support. Data on certifications, audit results, and material origins are crucial for consumers to make informed choices. </p> <ul> <li><strong>Certifications:</strong> Look for recognized labels like GOTS, Oeko-Tex, or Fair Trade.</li> <li><strong>Circular Economy Initiatives:</strong> Brands offering repair services, recycling programs, or upcycled collections.</li> <li><strong>Material Innovation:</strong> Trends favoring recycled, organic, or biodegradable textiles.</li> </ul> <p> Investing in <a href="https://ChicTrendsNow.com/sustainable-celebrity-brands-2025">sustainable fashion</a> isn&#8217;t just a moral choice; it&#8217;s a practical one. These items often boast higher quality, greater durability, and a timeless appeal, making them smart, data-backed choices for any wardrobe seeking true value in 2025 fashion trends. </p> <h2>Demographic and Psychographic Data: Tailoring Trends to Audiences</h2> <p> Fashion is deeply personal, and understanding who is adopting certain trends &#x2013; and why &#x2013; is critical for identifying true value. Demographic data (age, location, income) combined with psychographic data (values, interests, lifestyle) provides a nuanced picture of consumer segments and their relationship with fashion. </p> <p> A trend might be highly popular among Gen Z in urban centers, while another resonates with Millennials in suburban areas. By segmenting data, we can see which trends have broad appeal versus those that cater to specific, influential niches, helping us predict longevity and impact. </p> <h3>Understanding Generational Fashion Preferences</h3> <p> Each generation often exhibits distinct fashion preferences influenced by cultural, technological, and economic factors. Analyzing these differences helps in identifying which 2025 fashion trends are likely to appeal to specific age groups, ensuring relevance and adoption. </p> <ul> <li><strong>Gen Z:</strong> Often gravitates towards sustainable, expressive, and comfort-driven styles.</li> <li><strong>Millennials:</strong> Value authenticity, quality over quantity, and versatility in their wardrobes.</li> <li><strong>Gen X &amp; Boomers:</strong> Prioritize classic styles, comfort, and investment pieces with timeless appeal.</li> </ul> <p> Furthermore, psychographic data uncovers the motivations behind purchases. Is it status, self-expression, comfort, or practicality? Aligning trend identification with these deeper psychological drivers ensures that the chosen styles not only look good but also genuinely fulfill consumer needs and desires, making them truly valuable. </p> <h2>The Intersection of Culture, Art, and Fashion Data</h2> <p> Fashion is a mirror of society, constantly reflecting and influencing cultural movements, artistic expressions, and global events. Identifying truly valuable <a href="https://ChicTrendsNow.com/celebrity-formal-wear-2025">2025 fashion trends</a> requires looking beyond immediate sales figures and delving into the broader cultural landscape through data analysis. </p> <p> Trends often originate from shifts in art, music, film, and even political climates. Data can help connect these seemingly disparate dots, revealing how socio-cultural phenomena translate into specific garment styles, color palettes, and aesthetic sensibilities that resonate deeply with the public. </p> <h3>Analyzing Cultural Shifts and Artistic Influences</h3> <p> Monitoring major art exhibitions, music festival aesthetics, popular film genres, and even interior design trends can provide early signals for fashion direction. Data analytics can quantify the influence of these cultural touchstones, showing their impact on consumer preferences. </p> <ul> <li><strong>Emerging Art Movements:</strong> How new artistic styles inspire prints, silhouettes, or fabric choices.</li> <li><strong>Music Subcultures:</strong> The influence of specific music genres on street style and youth fashion.</li> <li><strong>Global Events:</strong> How major societal shifts or historical anniversaries influence sartorial expressions.</li> </ul> <p> By understanding the symbiotic relationship between fashion and broader culture through data, we can better predict which trends will not only capture attention but also hold significant meaning and longevity for 2025. This holistic perspective ensures that fashion choices are not just trendy, but culturally resonant and truly valuable. </p> <h2>Forecasting Longevity: Distinguishing Fads from Enduring Trends</h2> <p> One of the biggest challenges in fashion is distinguishing between a fleeting fad and an enduring trend that offers true value. A data-driven approach is essential here, allowing us to move beyond the initial hype and assess the potential longevity of a style. It&#8217;s about investing wisely in pieces that will remain relevant beyond a single season. </p> <p> Analyzing historical data on trend cycles, adoption rates across different demographics, and the underlying drivers of a trend can provide clues. Fads often emerge quickly and disappear just as fast, driven by novelty. Enduring trends, however, are usually underpinned by deeper cultural shifts, functional benefits, or a timeless aesthetic. </p> <h3>Metrics for Assessing Trend Durability</h3> <p> Several data points can help forecast a trend&#8217;s staying power. Look for consistent growth in search interest, sustained engagement on social media, and continued sales performance over multiple seasons, rather than just an initial boom. </p> <ul> <li><strong>Sustained Search Interest:</strong> Google Trends data showing consistent or growing search volume over 12+ months.</li> <li><strong>Cross-Generational Adoption:</strong> A trend embraced by more than one age group suggests broader appeal.</li> <li><strong>Versatility and Adaptability:</strong> Styles that can be easily integrated into diverse wardrobes and occasions tend to last longer.</li> </ul> <p> Ultimately, a trend&#8217;s longevity is often tied to its ability to evolve and adapt. Styles that can be reinterpreted, accessorized differently, or integrated into various aesthetics are more likely to endure. By focusing on these data-backed indicators, you can confidently identify <a href="https://ChicTrendsNow.com/celebrity-capsule-wardrobe-2025">2025 fashion trends</a> that offer lasting value. </p> <p><!-- Start of minimalist table area --></p> <div style='text-align: center; margin-bottom: 20px; margin-top: 20px;'> <!-- Main table --></p> <table style='border-collapse: collapse; margin: 0 auto; display: inline-table; border: 1px solid #000000; font-family: Arial, sans-serif; font-size: 14px;'> <!-- Table Header --></p> <thead> <tr style='background-color: #000000; color: white;'> <th style='text-align: center; width: 30%; border: 1px solid #000000; padding: 8px;'>Key Trend Identification Method</th> <th style='border: 1px solid #000000; padding: 8px; text-align: center;'>Brief Description</th> </tr> </thead> <p> <!-- Table Body --></p> <tbody> <!-- Row 1 --></p> <tr style='border-bottom: 1px solid #000000; background-color: #f9f9f9;'> <td style='font-weight: bold; text-align: center; border: 1px solid #000000; padding: 8px;'>Social Media Analytics</td> <td style='border: 1px solid #000000; padding: 8px;'>Analyze influencer engagement and hashtag performance for emerging styles.</td> </tr> <p> <!-- Row 2 --></p> <tr style='border-bottom: 1px solid #000000;'> <td style='font-weight: bold; text-align: center; border: 1px solid #000000; padding: 8px;'>E-commerce Sales Data</td> <td style='border: 1px solid #000000; padding: 8px;'>Examine best-selling categories and low return rates for consumer preference.</td> </tr> <p> <!-- Row 3 --></p> <tr style='border-bottom: 1px solid #000000; background-color: #f9f9f9;'> <td style='font-weight: bold; text-align: center; border: 1px solid #000000; padding: 8px;'>AI/Machine Learning</td> <td style='border: 1px solid #000000; padding: 8px;'>Leverage AI to process vast datasets for accurate future style predictions.</td> </tr> <p> <!-- Row 4 (Optional, if necessary for the topic 'Beyond the Hype: 8 Data-Driven Ways to Identify True Value in 2025 Fashion Trends' ) --></p> <tr style='background-color: #ffffff;'> <td style='font-weight: bold; text-align: center; border: 1px solid #000000; padding: 8px;'>Sustainability Metrics</td> <td style='border: 1px solid #000000; padding: 8px;'>Assess brand transparency and ethical practices for long-term value.</td> </tr> </tbody> </table> </div> <p><!-- End of minimalist table area --></p> <h2>Frequently Asked Questions About 2025 Fashion Trends</h2> <p><!-- FAQ Item 1 --></p> <div class='faq-item'> <div class='faq-question'>How can I use data to make smarter fashion purchases for 2025?<span class='arrow'>▼</span></div> <div id='faq-answer-1' class='faq-answer'> <p>By analyzing social media engagement, e-commerce sales, and keyword search data, you can identify trends with genuine staying power. Look for consistent interest and positive consumer feedback, focusing on items that align with your personal style and values, ensuring longevity and reducing impulsive buys.</p> </div> </div> <p><!-- FAQ Item 2 --></p> <div class='faq-item'> <div class='faq-question'>What role does AI play in forecasting 2025 fashion trends?<span class='arrow'>▼</span></div> <div id='faq-answer-2' class='faq-answer'> <p>AI and machine learning analyze vast amounts of data, including visual trends, sales figures, and cultural shifts, to predict future styles with high accuracy. This helps identify emerging patterns and distinguishes between fleeting fads and enduring trends, offering valuable insights for both brands and consumers.</p> </div> </div> <p><!-- FAQ Item 3 --></p> <div class='faq-item'> <div class='faq-question'>Why is sustainability data important for identifying valuable fashion trends?<span class='arrow'>▼</span></div> <div id='faq-answer-3' class='faq-answer'> <p>Sustainability metrics provide insights into a garment&#8217;s ethical and environmental impact. Trends aligning with transparency, ethical sourcing, and circular design offer long-term value, appealing to conscious consumers and often indicating higher quality and durability, which contributes to their lasting relevance.</p> </div> </div> <p><!-- FAQ Item 4 --></p> <div class='faq-item'> <div class='faq-question'>How do I differentiate between a fad and an enduring trend?<span class='arrow'>▼</span></div> <div id='faq-answer-4' class='faq-answer'> <p>Fads typically have rapid, short-lived popularity driven by novelty. Enduring trends show sustained interest across demographics and seasons, often supported by deeper cultural shifts or practical benefits. Data like consistent search volume and cross-generational adoption are key indicators of longevity.</p> </div> </div> <p><!-- FAQ Item 5 --></p> <div class='faq-item'> <div class='faq-question'>Can demographic data influence the adoption of fashion trends?<span class='arrow'>▼</span></div> <div id='faq-answer-5' class='faq-answer'> <p>Absolutely. Demographic and psychographic data help understand which trends resonate with specific age groups, locations, or lifestyles. This segmentation reveals if a trend has niche appeal or broader adoption, guiding decisions on whether a style will be a personal or widespread valuable investment.</p> </div> </div> <h2>Conclusion</h2> <p> The fashion landscape of 2025 is undeniably complex, but by embracing data-driven strategies, consumers can confidently navigate beyond the hype and identify true value. From leveraging social media insights and e-commerce analytics to understanding the power of AI, sustainability metrics, and cultural influences, the tools are available to make informed and impactful wardrobe decisions. Investing in fashion no longer needs to be a gamble; it can be a strategic, value-conscious endeavor that aligns with personal style, ethical considerations, and long-term relevance. By focusing on what the data truly reveals, you can build a wardrobe that is both contemporary and enduring, reflecting genuine style and smart choices for the future.</p> </div> </div> </div> <div class="author-bio-section"> <div class="author-avatar"> <img alt='' src='https://secure.gravatar.com/avatar/50334f8816afcbb433f3c41be8eb55a245bf4bb34fcc130d2ffdbbd30f22c413?s=80&#038;d=mm&#038;r=g' srcset='https://secure.gravatar.com/avatar/50334f8816afcbb433f3c41be8eb55a245bf4bb34fcc130d2ffdbbd30f22c413?s=160&#038;d=mm&#038;r=g 2x' class='avatar avatar-80 photo' height='80' width='80' decoding='async'/> </div> <div class="author-info"> <h3 class="author-name">Lara Barbosa</h3> <p class="author-description">Lara Barbosa has a degree in Journalism, with experience in editing and managing news portals. 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like Chrome/[0-9.]+ (?!Mobile)",SurfaceTablet:"Windows NT [0-9.]+; ARM;.*(Tablet|ARMBJS)",HPTablet:"HP Slate (7|8|10)|HP ElitePad 900|hp-tablet|EliteBook.*Touch|HP 8|Slate 21|HP SlateBook 10",AsusTablet:"^.*PadFone((?!Mobile).)*$|Transformer|TF101|TF101G|TF300T|TF300TG|TF300TL|TF700T|TF700KL|TF701T|TF810C|ME171|ME301T|ME302C|ME371MG|ME370T|ME372MG|ME172V|ME173X|ME400C|Slider SL101|\\bK00F\\b|\\bK00C\\b|\\bK00E\\b|\\bK00L\\b|TX201LA|ME176C|ME102A|\\bM80TA\\b|ME372CL|ME560CG|ME372CG|ME302KL| K010 | K011 | K017 | K01E |ME572C|ME103K|ME170C|ME171C|\\bME70C\\b|ME581C|ME581CL|ME8510C|ME181C|P01Y|PO1MA|P01Z|\\bP027\\b|\\bP024\\b|\\bP00C\\b",BlackBerryTablet:"PlayBook|RIM Tablet",HTCtablet:"HTC_Flyer_P512|HTC Flyer|HTC Jetstream|HTC-P715a|HTC EVO View 4G|PG41200|PG09410",MotorolaTablet:"xoom|sholest|MZ615|MZ605|MZ505|MZ601|MZ602|MZ603|MZ604|MZ606|MZ607|MZ608|MZ609|MZ615|MZ616|MZ617",NookTablet:"Android.*Nook|NookColor|nook browser|BNRV200|BNRV200A|BNTV250|BNTV250A|BNTV400|BNTV600|LogicPD Zoom2",AcerTablet:"Android.*; \\b(A100|A101|A110|A200|A210|A211|A500|A501|A510|A511|A700|A701|W500|W500P|W501|W501P|W510|W511|W700|G100|G100W|B1-A71|B1-710|B1-711|A1-810|A1-811|A1-830)\\b|W3-810|\\bA3-A10\\b|\\bA3-A11\\b|\\bA3-A20\\b|\\bA3-A30|A3-A40",ToshibaTablet:"Android.*(AT100|AT105|AT200|AT205|AT270|AT275|AT300|AT305|AT1S5|AT500|AT570|AT700|AT830)|TOSHIBA.*FOLIO",LGTablet:"\\bL-06C|LG-V909|LG-V900|LG-V700|LG-V510|LG-V500|LG-V410|LG-V400|LG-VK810\\b",FujitsuTablet:"Android.*\\b(F-01D|F-02F|F-05E|F-10D|M532|Q572)\\b",PrestigioTablet:"PMP3170B|PMP3270B|PMP3470B|PMP7170B|PMP3370B|PMP3570C|PMP5870C|PMP3670B|PMP5570C|PMP5770D|PMP3970B|PMP3870C|PMP5580C|PMP5880D|PMP5780D|PMP5588C|PMP7280C|PMP7280C3G|PMP7280|PMP7880D|PMP5597D|PMP5597|PMP7100D|PER3464|PER3274|PER3574|PER3884|PER5274|PER5474|PMP5097CPRO|PMP5097|PMP7380D|PMP5297C|PMP5297C_QUAD|PMP812E|PMP812E3G|PMP812F|PMP810E|PMP880TD|PMT3017|PMT3037|PMT3047|PMT3057|PMT7008|PMT5887|PMT5001|PMT5002",LenovoTablet:"Lenovo TAB|Idea(Tab|Pad)( A1|A10| K1|)|ThinkPad([ ]+)?Tablet|YT3-850M|YT3-X90L|YT3-X90F|YT3-X90X|Lenovo.*(S2109|S2110|S5000|S6000|K3011|A3000|A3500|A1000|A2107|A2109|A1107|A5500|A7600|B6000|B8000|B8080)(-|)(FL|F|HV|H|)|TB-X103F|TB-X304X|TB-X304F|TB-X304L|TB-X505F|TB-X505L|TB-X505X|TB-X605F|TB-X605L|TB-8703F|TB-8703X|TB-8703N|TB-8704N|TB-8704F|TB-8704X|TB-8704V|TB-7304F|TB-7304I|TB-7304X|Tab2A7-10F|Tab2A7-20F|TB2-X30L|YT3-X50L|YT3-X50F|YT3-X50M|YT-X705F|YT-X703F|YT-X703L|YT-X705L|YT-X705X|TB2-X30F|TB2-X30L|TB2-X30M|A2107A-F|A2107A-H|TB3-730F|TB3-730M|TB3-730X|TB-7504F|TB-7504X|TB-X704F|TB-X104F|TB3-X70F|TB-X705F|TB-8504F|TB3-X70L|TB3-710F|TB-X704L",DellTablet:"Venue 11|Venue 8|Venue 7|Dell Streak 10|Dell Streak 7",YarvikTablet:"Android.*\\b(TAB210|TAB211|TAB224|TAB250|TAB260|TAB264|TAB310|TAB360|TAB364|TAB410|TAB411|TAB420|TAB424|TAB450|TAB460|TAB461|TAB464|TAB465|TAB467|TAB468|TAB07-100|TAB07-101|TAB07-150|TAB07-151|TAB07-152|TAB07-200|TAB07-201-3G|TAB07-210|TAB07-211|TAB07-212|TAB07-214|TAB07-220|TAB07-400|TAB07-485|TAB08-150|TAB08-200|TAB08-201-3G|TAB08-201-30|TAB09-100|TAB09-211|TAB09-410|TAB10-150|TAB10-201|TAB10-211|TAB10-400|TAB10-410|TAB13-201|TAB274EUK|TAB275EUK|TAB374EUK|TAB462EUK|TAB474EUK|TAB9-200)\\b",MedionTablet:"Android.*\\bOYO\\b|LIFE.*(P9212|P9514|P9516|S9512)|LIFETAB",ArnovaTablet:"97G4|AN10G2|AN7bG3|AN7fG3|AN8G3|AN8cG3|AN7G3|AN9G3|AN7dG3|AN7dG3ST|AN7dG3ChildPad|AN10bG3|AN10bG3DT|AN9G2",IntensoTablet:"INM8002KP|INM1010FP|INM805ND|Intenso Tab|TAB1004",IRUTablet:"M702pro",MegafonTablet:"MegaFon V9|\\bZTE V9\\b|Android.*\\bMT7A\\b",EbodaTablet:"E-Boda (Supreme|Impresspeed|Izzycomm|Essential)",AllViewTablet:"Allview.*(Viva|Alldro|City|Speed|All TV|Frenzy|Quasar|Shine|TX1|AX1|AX2)",ArchosTablet:"\\b(101G9|80G9|A101IT)\\b|Qilive 97R|Archos5|\\bARCHOS (70|79|80|90|97|101|FAMILYPAD|)(b|c|)(G10| Cobalt| TITANIUM(HD|)| Xenon| Neon|XSK| 2| XS 2| PLATINUM| CARBON|GAMEPAD)\\b",AinolTablet:"NOVO7|NOVO8|NOVO10|Novo7Aurora|Novo7Basic|NOVO7PALADIN|novo9-Spark",NokiaLumiaTablet:"Lumia 2520",SonyTablet:"Sony.*Tablet|Xperia Tablet|Sony Tablet S|SO-03E|SGPT12|SGPT13|SGPT114|SGPT121|SGPT122|SGPT123|SGPT111|SGPT112|SGPT113|SGPT131|SGPT132|SGPT133|SGPT211|SGPT212|SGPT213|SGP311|SGP312|SGP321|EBRD1101|EBRD1102|EBRD1201|SGP351|SGP341|SGP511|SGP512|SGP521|SGP541|SGP551|SGP621|SGP641|SGP612|SOT31|SGP771|SGP611|SGP612|SGP712",PhilipsTablet:"\\b(PI2010|PI3000|PI3100|PI3105|PI3110|PI3205|PI3210|PI3900|PI4010|PI7000|PI7100)\\b",CubeTablet:"Android.*(K8GT|U9GT|U10GT|U16GT|U17GT|U18GT|U19GT|U20GT|U23GT|U30GT)|CUBE U8GT",CobyTablet:"MID1042|MID1045|MID1125|MID1126|MID7012|MID7014|MID7015|MID7034|MID7035|MID7036|MID7042|MID7048|MID7127|MID8042|MID8048|MID8127|MID9042|MID9740|MID9742|MID7022|MID7010",MIDTablet:"M9701|M9000|M9100|M806|M1052|M806|T703|MID701|MID713|MID710|MID727|MID760|MID830|MID728|MID933|MID125|MID810|MID732|MID120|MID930|MID800|MID731|MID900|MID100|MID820|MID735|MID980|MID130|MID833|MID737|MID960|MID135|MID860|MID736|MID140|MID930|MID835|MID733|MID4X10",MSITablet:"MSI \\b(Primo 73K|Primo 73L|Primo 81L|Primo 77|Primo 93|Primo 75|Primo 76|Primo 73|Primo 81|Primo 91|Primo 90|Enjoy 71|Enjoy 7|Enjoy 10)\\b",SMiTTablet:"Android.*(\\bMID\\b|MID-560|MTV-T1200|MTV-PND531|MTV-P1101|MTV-PND530)",RockChipTablet:"Android.*(RK2818|RK2808A|RK2918|RK3066)|RK2738|RK2808A",FlyTablet:"IQ310|Fly Vision",bqTablet:"Android.*(bq)?.*\\b(Elcano|Curie|Edison|Maxwell|Kepler|Pascal|Tesla|Hypatia|Platon|Newton|Livingstone|Cervantes|Avant|Aquaris ([E|M]10|M8))\\b|Maxwell.*Lite|Maxwell.*Plus",HuaweiTablet:"MediaPad|MediaPad 7 Youth|IDEOS S7|S7-201c|S7-202u|S7-101|S7-103|S7-104|S7-105|S7-106|S7-201|S7-Slim|M2-A01L|BAH-L09|BAH-W09|AGS-L09|CMR-AL19",NecTablet:"\\bN-06D|\\bN-08D",PantechTablet:"Pantech.*P4100",BronchoTablet:"Broncho.*(N701|N708|N802|a710)",VersusTablet:"TOUCHPAD.*[78910]|\\bTOUCHTAB\\b",ZyncTablet:"z1000|Z99 2G|z930|z990|z909|Z919|z900",PositivoTablet:"TB07STA|TB10STA|TB07FTA|TB10FTA",NabiTablet:"Android.*\\bNabi",KoboTablet:"Kobo Touch|\\bK080\\b|\\bVox\\b Build|\\bArc\\b Build",DanewTablet:"DSlide.*\\b(700|701R|702|703R|704|802|970|971|972|973|974|1010|1012)\\b",TexetTablet:"NaviPad|TB-772A|TM-7045|TM-7055|TM-9750|TM-7016|TM-7024|TM-7026|TM-7041|TM-7043|TM-7047|TM-8041|TM-9741|TM-9747|TM-9748|TM-9751|TM-7022|TM-7021|TM-7020|TM-7011|TM-7010|TM-7023|TM-7025|TM-7037W|TM-7038W|TM-7027W|TM-9720|TM-9725|TM-9737W|TM-1020|TM-9738W|TM-9740|TM-9743W|TB-807A|TB-771A|TB-727A|TB-725A|TB-719A|TB-823A|TB-805A|TB-723A|TB-715A|TB-707A|TB-705A|TB-709A|TB-711A|TB-890HD|TB-880HD|TB-790HD|TB-780HD|TB-770HD|TB-721HD|TB-710HD|TB-434HD|TB-860HD|TB-840HD|TB-760HD|TB-750HD|TB-740HD|TB-730HD|TB-722HD|TB-720HD|TB-700HD|TB-500HD|TB-470HD|TB-431HD|TB-430HD|TB-506|TB-504|TB-446|TB-436|TB-416|TB-146SE|TB-126SE",PlaystationTablet:"Playstation.*(Portable|Vita)",TrekstorTablet:"ST10416-1|VT10416-1|ST70408-1|ST702xx-1|ST702xx-2|ST80208|ST97216|ST70104-2|VT10416-2|ST10216-2A|SurfTab",PyleAudioTablet:"\\b(PTBL10CEU|PTBL10C|PTBL72BC|PTBL72BCEU|PTBL7CEU|PTBL7C|PTBL92BC|PTBL92BCEU|PTBL9CEU|PTBL9CUK|PTBL9C)\\b",AdvanTablet:"Android.* \\b(E3A|T3X|T5C|T5B|T3E|T3C|T3B|T1J|T1F|T2A|T1H|T1i|E1C|T1-E|T5-A|T4|E1-B|T2Ci|T1-B|T1-D|O1-A|E1-A|T1-A|T3A|T4i)\\b ",DanyTechTablet:"Genius Tab G3|Genius Tab S2|Genius Tab Q3|Genius Tab G4|Genius Tab Q4|Genius Tab G-II|Genius TAB GII|Genius TAB GIII|Genius Tab S1",GalapadTablet:"Android [0-9.]+; [a-z-]+; \\bG1\\b",MicromaxTablet:"Funbook|Micromax.*\\b(P250|P560|P360|P362|P600|P300|P350|P500|P275)\\b",KarbonnTablet:"Android.*\\b(A39|A37|A34|ST8|ST10|ST7|Smart Tab3|Smart Tab2)\\b",AllFineTablet:"Fine7 Genius|Fine7 Shine|Fine7 Air|Fine8 Style|Fine9 More|Fine10 Joy|Fine11 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1027",HCLTablet:"HCL.*Tablet|Connect-3G-2.0|Connect-2G-2.0|ME Tablet U1|ME Tablet U2|ME Tablet G1|ME Tablet X1|ME Tablet Y2|ME Tablet Sync",DPSTablet:"DPS Dream 9|DPS Dual 7",VistureTablet:"V97 HD|i75 3G|Visture V4( HD)?|Visture V5( HD)?|Visture V10",CrestaTablet:"CTP(-)?810|CTP(-)?818|CTP(-)?828|CTP(-)?838|CTP(-)?888|CTP(-)?978|CTP(-)?980|CTP(-)?987|CTP(-)?988|CTP(-)?989",MediatekTablet:"\\bMT8125|MT8389|MT8135|MT8377\\b",ConcordeTablet:"Concorde([ ]+)?Tab|ConCorde ReadMan",GoCleverTablet:"GOCLEVER TAB|A7GOCLEVER|M1042|M7841|M742|R1042BK|R1041|TAB A975|TAB A7842|TAB A741|TAB A741L|TAB M723G|TAB M721|TAB A1021|TAB I921|TAB R721|TAB I720|TAB T76|TAB R70|TAB R76.2|TAB R106|TAB R83.2|TAB M813G|TAB I721|GCTA722|TAB I70|TAB I71|TAB S73|TAB R73|TAB R74|TAB R93|TAB R75|TAB R76.1|TAB A73|TAB A93|TAB A93.2|TAB T72|TAB R83|TAB R974|TAB R973|TAB A101|TAB A103|TAB A104|TAB A104.2|R105BK|M713G|A972BK|TAB A971|TAB R974.2|TAB R104|TAB R83.3|TAB A1042",ModecomTablet:"FreeTAB 9000|FreeTAB 7.4|FreeTAB 7004|FreeTAB 7800|FreeTAB 2096|FreeTAB 7.5|FreeTAB 1014|FreeTAB 1001 |FreeTAB 8001|FreeTAB 9706|FreeTAB 9702|FreeTAB 7003|FreeTAB 7002|FreeTAB 1002|FreeTAB 7801|FreeTAB 1331|FreeTAB 1004|FreeTAB 8002|FreeTAB 8014|FreeTAB 9704|FreeTAB 1003",VoninoTablet:"\\b(Argus[ _]?S|Diamond[ _]?79HD|Emerald[ _]?78E|Luna[ _]?70C|Onyx[ _]?S|Onyx[ _]?Z|Orin[ _]?HD|Orin[ _]?S|Otis[ _]?S|SpeedStar[ _]?S|Magnet[ _]?M9|Primus[ _]?94[ _]?3G|Primus[ _]?94HD|Primus[ _]?QS|Android.*\\bQ8\\b|Sirius[ _]?EVO[ _]?QS|Sirius[ _]?QS|Spirit[ _]?S)\\b",ECSTablet:"V07OT2|TM105A|S10OT1|TR10CS1",StorexTablet:"eZee[_']?(Tab|Go)[0-9]+|TabLC7|Looney Tunes Tab",VodafoneTablet:"SmartTab([ ]+)?[0-9]+|SmartTabII10|SmartTabII7|VF-1497|VFD 1400",EssentielBTablet:"Smart[ ']?TAB[ ]+?[0-9]+|Family[ ']?TAB2",RossMoorTablet:"RM-790|RM-997|RMD-878G|RMD-974R|RMT-705A|RMT-701|RME-601|RMT-501|RMT-711",iMobileTablet:"i-mobile i-note",TolinoTablet:"tolino tab [0-9.]+|tolino shine",AudioSonicTablet:"\\bC-22Q|T7-QC|T-17B|T-17P\\b",AMPETablet:"Android.* A78 ",SkkTablet:"Android.* (SKYPAD|PHOENIX|CYCLOPS)",TecnoTablet:"TECNO P9|TECNO DP8D",JXDTablet:"Android.* \\b(F3000|A3300|JXD5000|JXD3000|JXD2000|JXD300B|JXD300|S5800|S7800|S602b|S5110b|S7300|S5300|S602|S603|S5100|S5110|S601|S7100a|P3000F|P3000s|P101|P200s|P1000m|P200m|P9100|P1000s|S6600b|S908|P1000|P300|S18|S6600|S9100)\\b",iJoyTablet:"Tablet (Spirit 7|Essentia|Galatea|Fusion|Onix 7|Landa|Titan|Scooby|Deox|Stella|Themis|Argon|Unique 7|Sygnus|Hexen|Finity 7|Cream|Cream X2|Jade|Neon 7|Neron 7|Kandy|Scape|Saphyr 7|Rebel|Biox|Rebel|Rebel 8GB|Myst|Draco 7|Myst|Tab7-004|Myst|Tadeo Jones|Tablet Boing|Arrow|Draco Dual Cam|Aurix|Mint|Amity|Revolution|Finity 9|Neon 9|T9w|Amity 4GB Dual Cam|Stone 4GB|Stone 8GB|Andromeda|Silken|X2|Andromeda II|Halley|Flame|Saphyr 9,7|Touch 8|Planet|Triton|Unique 10|Hexen 10|Memphis 4GB|Memphis 8GB|Onix 10)",FX2Tablet:"FX2 PAD7|FX2 PAD10",XoroTablet:"KidsPAD 701|PAD[ ]?712|PAD[ ]?714|PAD[ ]?716|PAD[ ]?717|PAD[ ]?718|PAD[ ]?720|PAD[ ]?721|PAD[ ]?722|PAD[ ]?790|PAD[ ]?792|PAD[ ]?900|PAD[ ]?9715D|PAD[ ]?9716DR|PAD[ ]?9718DR|PAD[ ]?9719QR|PAD[ ]?9720QR|TelePAD1030|Telepad1032|TelePAD730|TelePAD731|TelePAD732|TelePAD735Q|TelePAD830|TelePAD9730|TelePAD795|MegaPAD 1331|MegaPAD 1851|MegaPAD 2151",ViewsonicTablet:"ViewPad 10pi|ViewPad 10e|ViewPad 10s|ViewPad E72|ViewPad7|ViewPad E100|ViewPad 7e|ViewSonic VB733|VB100a",VerizonTablet:"QTAQZ3|QTAIR7|QTAQTZ3|QTASUN1|QTASUN2|QTAXIA1",OdysTablet:"LOOX|XENO10|ODYS[ -](Space|EVO|Xpress|NOON)|\\bXELIO\\b|Xelio10Pro|XELIO7PHONETAB|XELIO10EXTREME|XELIOPT2|NEO_QUAD10",CaptivaTablet:"CAPTIVA PAD",IconbitTablet:"NetTAB|NT-3702|NT-3702S|NT-3702S|NT-3603P|NT-3603P|NT-0704S|NT-0704S|NT-3805C|NT-3805C|NT-0806C|NT-0806C|NT-0909T|NT-0909T|NT-0907S|NT-0907S|NT-0902S|NT-0902S",TeclastTablet:"T98 4G|\\bP80\\b|\\bX90HD\\b|X98 Air|X98 Air 3G|\\bX89\\b|P80 3G|\\bX80h\\b|P98 Air|\\bX89HD\\b|P98 3G|\\bP90HD\\b|P89 3G|X98 3G|\\bP70h\\b|P79HD 3G|G18d 3G|\\bP79HD\\b|\\bP89s\\b|\\bA88\\b|\\bP10HD\\b|\\bP19HD\\b|G18 3G|\\bP78HD\\b|\\bA78\\b|\\bP75\\b|G17s 3G|G17h 3G|\\bP85t\\b|\\bP90\\b|\\bP11\\b|\\bP98t\\b|\\bP98HD\\b|\\bG18d\\b|\\bP85s\\b|\\bP11HD\\b|\\bP88s\\b|\\bA80HD\\b|\\bA80se\\b|\\bA10h\\b|\\bP89\\b|\\bP78s\\b|\\bG18\\b|\\bP85\\b|\\bA70h\\b|\\bA70\\b|\\bG17\\b|\\bP18\\b|\\bA80s\\b|\\bA11s\\b|\\bP88HD\\b|\\bA80h\\b|\\bP76s\\b|\\bP76h\\b|\\bP98\\b|\\bA10HD\\b|\\bP78\\b|\\bP88\\b|\\bA11\\b|\\bA10t\\b|\\bP76a\\b|\\bP76t\\b|\\bP76e\\b|\\bP85HD\\b|\\bP85a\\b|\\bP86\\b|\\bP75HD\\b|\\bP76v\\b|\\bA12\\b|\\bP75a\\b|\\bA15\\b|\\bP76Ti\\b|\\bP81HD\\b|\\bA10\\b|\\bT760VE\\b|\\bT720HD\\b|\\bP76\\b|\\bP73\\b|\\bP71\\b|\\bP72\\b|\\bT720SE\\b|\\bC520Ti\\b|\\bT760\\b|\\bT720VE\\b|T720-3GE|T720-WiFi",OndaTablet:"\\b(V975i|Vi30|VX530|V701|Vi60|V701s|Vi50|V801s|V719|Vx610w|VX610W|V819i|Vi10|VX580W|Vi10|V711s|V813|V811|V820w|V820|Vi20|V711|VI30W|V712|V891w|V972|V819w|V820w|Vi60|V820w|V711|V813s|V801|V819|V975s|V801|V819|V819|V818|V811|V712|V975m|V101w|V961w|V812|V818|V971|V971s|V919|V989|V116w|V102w|V973|Vi40)\\b[\\s]+|V10 \\b4G\\b",JaytechTablet:"TPC-PA762",BlaupunktTablet:"Endeavour 800NG|Endeavour 1010",DigmaTablet:"\\b(iDx10|iDx9|iDx8|iDx7|iDxD7|iDxD8|iDsQ8|iDsQ7|iDsQ8|iDsD10|iDnD7|3TS804H|iDsQ11|iDj7|iDs10)\\b",EvolioTablet:"ARIA_Mini_wifi|Aria[ _]Mini|Evolio X10|Evolio X7|Evolio X8|\\bEvotab\\b|\\bNeura\\b",LavaTablet:"QPAD E704|\\bIvoryS\\b|E-TAB IVORY|\\bE-TAB\\b",AocTablet:"MW0811|MW0812|MW0922|MTK8382|MW1031|MW0831|MW0821|MW0931|MW0712",MpmanTablet:"MP11 OCTA|MP10 OCTA|MPQC1114|MPQC1004|MPQC994|MPQC974|MPQC973|MPQC804|MPQC784|MPQC780|\\bMPG7\\b|MPDCG75|MPDCG71|MPDC1006|MP101DC|MPDC9000|MPDC905|MPDC706HD|MPDC706|MPDC705|MPDC110|MPDC100|MPDC99|MPDC97|MPDC88|MPDC8|MPDC77|MP709|MID701|MID711|MID170|MPDC703|MPQC1010",CelkonTablet:"CT695|CT888|CT[\\s]?910|CT7 Tab|CT9 Tab|CT3 Tab|CT2 Tab|CT1 Tab|C820|C720|\\bCT-1\\b",WolderTablet:"miTab \\b(DIAMOND|SPACE|BROOKLYN|NEO|FLY|MANHATTAN|FUNK|EVOLUTION|SKY|GOCAR|IRON|GENIUS|POP|MINT|EPSILON|BROADWAY|JUMP|HOP|LEGEND|NEW AGE|LINE|ADVANCE|FEEL|FOLLOW|LIKE|LINK|LIVE|THINK|FREEDOM|CHICAGO|CLEVELAND|BALTIMORE-GH|IOWA|BOSTON|SEATTLE|PHOENIX|DALLAS|IN 101|MasterChef)\\b",MediacomTablet:"M-MPI10C3G|M-SP10EG|M-SP10EGP|M-SP10HXAH|M-SP7HXAH|M-SP10HXBH|M-SP8HXAH|M-SP8MXA",MiTablet:"\\bMI PAD\\b|\\bHM NOTE 1W\\b",NibiruTablet:"Nibiru M1|Nibiru Jupiter One",NexoTablet:"NEXO NOVA|NEXO 10|NEXO AVIO|NEXO FREE|NEXO GO|NEXO EVO|NEXO 3G|NEXO SMART|NEXO KIDDO|NEXO MOBI",LeaderTablet:"TBLT10Q|TBLT10I|TBL-10WDKB|TBL-10WDKBO2013|TBL-W230V2|TBL-W450|TBL-W500|SV572|TBLT7I|TBA-AC7-8G|TBLT79|TBL-8W16|TBL-10W32|TBL-10WKB|TBL-W100",UbislateTablet:"UbiSlate[\\s]?7C",PocketBookTablet:"Pocketbook",KocasoTablet:"\\b(TB-1207)\\b",HisenseTablet:"\\b(F5281|E2371)\\b",Hudl:"Hudl HT7S3|Hudl 2",TelstraTablet:"T-Hub2",GenericTablet:"Android.*\\b97D\\b|Tablet(?!.*PC)|BNTV250A|MID-WCDMA|LogicPD Zoom2|\\bA7EB\\b|CatNova8|A1_07|CT704|CT1002|\\bM721\\b|rk30sdk|\\bEVOTAB\\b|M758A|ET904|ALUMIUM10|Smartfren Tab|Endeavour 1010|Tablet-PC-4|Tagi Tab|\\bM6pro\\b|CT1020W|arc 10HD|\\bTP750\\b|\\bQTAQZ3\\b|WVT101|TM1088|KT107"},oss:{AndroidOS:"Android",BlackBerryOS:"blackberry|\\bBB10\\b|rim tablet os",PalmOS:"PalmOS|avantgo|blazer|elaine|hiptop|palm|plucker|xiino",SymbianOS:"Symbian|SymbOS|Series60|Series40|SYB-[0-9]+|\\bS60\\b",WindowsMobileOS:"Windows CE.*(PPC|Smartphone|Mobile|[0-9]{3}x[0-9]{3})|Windows Mobile|Windows Phone [0-9.]+|WCE;",WindowsPhoneOS:"Windows Phone 10.0|Windows Phone 8.1|Windows Phone 8.0|Windows Phone OS|XBLWP7|ZuneWP7|Windows NT 6.[23]; ARM;",iOS:"\\biPhone.*Mobile|\\biPod|\\biPad|AppleCoreMedia",iPadOS:"CPU OS 13",SailfishOS:"Sailfish",MeeGoOS:"MeeGo",MaemoOS:"Maemo",JavaOS:"J2ME/|\\bMIDP\\b|\\bCLDC\\b",webOS:"webOS|hpwOS",badaOS:"\\bBada\\b",BREWOS:"BREW"},uas:{Chrome:"\\bCrMo\\b|CriOS|Android.*Chrome/[.0-9]* (Mobile)?",Dolfin:"\\bDolfin\\b",Opera:"Opera.*Mini|Opera.*Mobi|Android.*Opera|Mobile.*OPR/[0-9.]+$|Coast/[0-9.]+",Skyfire:"Skyfire",Edge:"\\bEdgiOS\\b|Mobile Safari/[.0-9]* Edge",IE:"IEMobile|MSIEMobile",Firefox:"fennec|firefox.*maemo|(Mobile|Tablet).*Firefox|Firefox.*Mobile|FxiOS",Bolt:"bolt",TeaShark:"teashark",Blazer:"Blazer",Safari:"Version((?!\\bEdgiOS\\b).)*Mobile.*Safari|Safari.*Mobile|MobileSafari",WeChat:"\\bMicroMessenger\\b",UCBrowser:"UC.*Browser|UCWEB",baiduboxapp:"baiduboxapp",baidubrowser:"baidubrowser",DiigoBrowser:"DiigoBrowser",Mercury:"\\bMercury\\b",ObigoBrowser:"Obigo",NetFront:"NF-Browser",GenericBrowser:"NokiaBrowser|OviBrowser|OneBrowser|TwonkyBeamBrowser|SEMC.*Browser|FlyFlow|Minimo|NetFront|Novarra-Vision|MQQBrowser|MicroMessenger",PaleMoon:"Android.*PaleMoon|Mobile.*PaleMoon"},props:{Mobile:"Mobile/[VER]",Build:"Build/[VER]",Version:"Version/[VER]",VendorID:"VendorID/[VER]",iPad:"iPad.*CPU[a-z ]+[VER]",iPhone:"iPhone.*CPU[a-z ]+[VER]",iPod:"iPod.*CPU[a-z ]+[VER]",Kindle:"Kindle/[VER]",Chrome:["Chrome/[VER]","CriOS/[VER]","CrMo/[VER]"],Coast:["Coast/[VER]"],Dolfin:"Dolfin/[VER]",Firefox:["Firefox/[VER]","FxiOS/[VER]"],Fennec:"Fennec/[VER]",Edge:"Edge/[VER]",IE:["IEMobile/[VER];","IEMobile [VER]","MSIE [VER];","Trident/[0-9.]+;.*rv:[VER]"],NetFront:"NetFront/[VER]",NokiaBrowser:"NokiaBrowser/[VER]",Opera:[" OPR/[VER]","Opera Mini/[VER]","Version/[VER]"],"Opera Mini":"Opera Mini/[VER]","Opera Mobi":"Version/[VER]",UCBrowser:["UCWEB[VER]","UC.*Browser/[VER]"],MQQBrowser:"MQQBrowser/[VER]",MicroMessenger:"MicroMessenger/[VER]",baiduboxapp:"baiduboxapp/[VER]",baidubrowser:"baidubrowser/[VER]",SamsungBrowser:"SamsungBrowser/[VER]",Iron:"Iron/[VER]",Safari:["Version/[VER]","Safari/[VER]"],Skyfire:"Skyfire/[VER]",Tizen:"Tizen/[VER]",Webkit:"webkit[ /][VER]",PaleMoon:"PaleMoon/[VER]",SailfishBrowser:"SailfishBrowser/[VER]",Gecko:"Gecko/[VER]",Trident:"Trident/[VER]",Presto:"Presto/[VER]",Goanna:"Goanna/[VER]",iOS:" \\bi?OS\\b [VER][ ;]{1}",Android:"Android [VER]",Sailfish:"Sailfish [VER]",BlackBerry:["BlackBerry[\\w]+/[VER]","BlackBerry.*Version/[VER]","Version/[VER]"],BREW:"BREW [VER]",Java:"Java/[VER]","Windows Phone OS":["Windows Phone OS [VER]","Windows Phone [VER]"],"Windows Phone":"Windows Phone [VER]","Windows CE":"Windows CE/[VER]","Windows NT":"Windows NT [VER]",Symbian:["SymbianOS/[VER]","Symbian/[VER]"],webOS:["webOS/[VER]","hpwOS/[VER];"]},utils:{Bot:"Googlebot|facebookexternalhit|Google-AMPHTML|s~amp-validator|AdsBot-Google|Google Keyword Suggestion|Facebot|YandexBot|YandexMobileBot|bingbot|ia_archiver|AhrefsBot|Ezooms|GSLFbot|WBSearchBot|Twitterbot|TweetmemeBot|Twikle|PaperLiBot|Wotbox|UnwindFetchor|Exabot|MJ12bot|YandexImages|TurnitinBot|Pingdom|contentkingapp|AspiegelBot",MobileBot:"Googlebot-Mobile|AdsBot-Google-Mobile|YahooSeeker/M1A1-R2D2",DesktopMode:"WPDesktop",TV:"SonyDTV|HbbTV",WebKit:"(webkit)[ /]([\\w.]+)",Console:"\\b(Nintendo|Nintendo WiiU|Nintendo 3DS|Nintendo Switch|PLAYSTATION|Xbox)\\b",Watch:"SM-V700"}},g.detectMobileBrowsers={fullPattern:/(android|bb\d+|meego).+mobile|avantgo|bada\/|blackberry|blazer|compal|elaine|fennec|hiptop|iemobile|ip(hone|od)|iris|kindle|lge |maemo|midp|mmp|mobile.+firefox|netfront|opera m(ob|in)i|palm( os)?|phone|p(ixi|re)\/|plucker|pocket|psp|series(4|6)0|symbian|treo|up\.(browser|link)|vodafone|wap|windows ce|xda|xiino/i, shortPattern:/1207|6310|6590|3gso|4thp|50[1-6]i|770s|802s|a wa|abac|ac(er|oo|s\-)|ai(ko|rn)|al(av|ca|co)|amoi|an(ex|ny|yw)|aptu|ar(ch|go)|as(te|us)|attw|au(di|\-m|r |s )|avan|be(ck|ll|nq)|bi(lb|rd)|bl(ac|az)|br(e|v)w|bumb|bw\-(n|u)|c55\/|capi|ccwa|cdm\-|cell|chtm|cldc|cmd\-|co(mp|nd)|craw|da(it|ll|ng)|dbte|dc\-s|devi|dica|dmob|do(c|p)o|ds(12|\-d)|el(49|ai)|em(l2|ul)|er(ic|k0)|esl8|ez([4-7]0|os|wa|ze)|fetc|fly(\-|_)|g1 u|g560|gene|gf\-5|g\-mo|go(\.w|od)|gr(ad|un)|haie|hcit|hd\-(m|p|t)|hei\-|hi(pt|ta)|hp( i|ip)|hs\-c|ht(c(\-| |_|a|g|p|s|t)|tp)|hu(aw|tc)|i\-(20|go|ma)|i230|iac( |\-|\/)|ibro|idea|ig01|ikom|im1k|inno|ipaq|iris|ja(t|v)a|jbro|jemu|jigs|kddi|keji|kgt( |\/)|klon|kpt |kwc\-|kyo(c|k)|le(no|xi)|lg( g|\/(k|l|u)|50|54|\-[a-w])|libw|lynx|m1\-w|m3ga|m50\/|ma(te|ui|xo)|mc(01|21|ca)|m\-cr|me(rc|ri)|mi(o8|oa|ts)|mmef|mo(01|02|bi|de|do|t(\-| |o|v)|zz)|mt(50|p1|v )|mwbp|mywa|n10[0-2]|n20[2-3]|n30(0|2)|n50(0|2|5)|n7(0(0|1)|10)|ne((c|m)\-|on|tf|wf|wg|wt)|nok(6|i)|nzph|o2im|op(ti|wv)|oran|owg1|p800|pan(a|d|t)|pdxg|pg(13|\-([1-8]|c))|phil|pire|pl(ay|uc)|pn\-2|po(ck|rt|se)|prox|psio|pt\-g|qa\-a|qc(07|12|21|32|60|\-[2-7]|i\-)|qtek|r380|r600|raks|rim9|ro(ve|zo)|s55\/|sa(ge|ma|mm|ms|ny|va)|sc(01|h\-|oo|p\-)|sdk\/|se(c(\-|0|1)|47|mc|nd|ri)|sgh\-|shar|sie(\-|m)|sk\-0|sl(45|id)|sm(al|ar|b3|it|t5)|so(ft|ny)|sp(01|h\-|v\-|v )|sy(01|mb)|t2(18|50)|t6(00|10|18)|ta(gt|lk)|tcl\-|tdg\-|tel(i|m)|tim\-|t\-mo|to(pl|sh)|ts(70|m\-|m3|m5)|tx\-9|up(\.b|g1|si)|utst|v400|v750|veri|vi(rg|te)|vk(40|5[0-3]|\-v)|vm40|voda|vulc|vx(52|53|60|61|70|80|81|83|85|98)|w3c(\-| )|webc|whit|wi(g |nc|nw)|wmlb|wonu|x700|yas\-|your|zeto|zte\-/i,tabletPattern:/android|ipad|playbook|silk/i};var h,i=Object.prototype.hasOwnProperty;return g.FALLBACK_PHONE="UnknownPhone",g.FALLBACK_TABLET="UnknownTablet",g.FALLBACK_MOBILE="UnknownMobile",h="isArray"in Array?Array.isArray:function(a){return"[object Array]"===Object.prototype.toString.call(a)},function(){var a,b,c,e,f,j,k=g.mobileDetectRules;for(a in k.props)if(i.call(k.props,a)){for(b=k.props[a],h(b)||(b=[b]),f=b.length,e=0;e<f;++e)c=b[e],j=c.indexOf("[VER]"),j>=0&&(c=c.substring(0,j)+"([\\w._\\+]+)"+c.substring(j+5)),b[e]=new RegExp(c,"i");k.props[a]=b}d(k.oss),d(k.phones),d(k.tablets),d(k.uas),d(k.utils),k.oss0={WindowsPhoneOS:k.oss.WindowsPhoneOS,WindowsMobileOS:k.oss.WindowsMobileOS}}(),g.findMatch=function(a,b){for(var c in a)if(i.call(a,c)&&a[c].test(b))return c;return null},g.findMatches=function(a,b){var c=[];for(var d in a)i.call(a,d)&&a[d].test(b)&&c.push(d);return c},g.getVersionStr=function(a,b){var c,d,e,f,h=g.mobileDetectRules.props;if(i.call(h,a))for(c=h[a],e=c.length,d=0;d<e;++d)if(f=c[d].exec(b),null!==f)return f[1];return null},g.getVersion=function(a,b){var c=g.getVersionStr(a,b);return c?g.prepareVersionNo(c):NaN},g.prepareVersionNo=function(a){var b;return b=a.split(/[a-z._ \/\-]/i),1===b.length&&(a=b[0]),b.length>1&&(a=b[0]+".",b.shift(),a+=b.join("")),Number(a)},g.isMobileFallback=function(a){return g.detectMobileBrowsers.fullPattern.test(a)||g.detectMobileBrowsers.shortPattern.test(a.substr(0,4))},g.isTabletFallback=function(a){return g.detectMobileBrowsers.tabletPattern.test(a)},g.prepareDetectionCache=function(a,c,d){if(a.mobile===b){var e,h,i;return(h=g.findMatch(g.mobileDetectRules.tablets,c))?(a.mobile=a.tablet=h,void(a.phone=null)):(e=g.findMatch(g.mobileDetectRules.phones,c))?(a.mobile=a.phone=e,void(a.tablet=null)):void(g.isMobileFallback(c)?(i=f.isPhoneSized(d),i===b?(a.mobile=g.FALLBACK_MOBILE,a.tablet=a.phone=null):i?(a.mobile=a.phone=g.FALLBACK_PHONE,a.tablet=null):(a.mobile=a.tablet=g.FALLBACK_TABLET,a.phone=null)):g.isTabletFallback(c)?(a.mobile=a.tablet=g.FALLBACK_TABLET,a.phone=null):a.mobile=a.tablet=a.phone=null)}},g.mobileGrade=function(a){var b=null!==a.mobile();return a.os("iOS")&&a.version("iPad")>=4.3||a.os("iOS")&&a.version("iPhone")>=3.1||a.os("iOS")&&a.version("iPod")>=3.1||a.version("Android")>2.1&&a.is("Webkit")||a.version("Windows Phone 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