Effortlessly cool and chic, the Mango Spring 2013 collection is made for sophisticated urban women. Comprised of a simple colorway including the ever-staple monochromatic black and white as well as mint green and camel, the collection is perfect for the upcoming season. Not to mention that it is chock full of ensembles revolving around layers, which is helpful during temperamental weather.
The Mango Spring 2013 lookbook particularly showcases slim, androgynous silhouettes that are suited for stylish professionals. "A fresh mix of lightweight knits, form-fitting pants, casual denim and double-breasted jackets," as noted by Fashion Gone Rogue, the looks have a relaxed vibe about them that will encourage a casual carefree attitude for the new year.
The pieces in the Mango Spring 2013 lookbook and collection will also complement existing wardrobes nicely.
Effortlessly Chic Fashion
The Mango Spring 2013 Lookbook is Cool and Refreshing
Trend Themes
1. Effortless Chic Fashion - Disruptive innovation opportunity: Create a sustainable fashion line that incorporates effortless chic styles with eco-friendly materials.
2. Layered Ensembles - Disruptive innovation opportunity: Develop a clothing rental service that offers curated collections of layered ensembles, encouraging consumers to try new styles without committing to a purchase.
3. Androgynous Styles - Disruptive innovation opportunity: Design a gender-inclusive fashion line that embraces androgynous styles, catering to a diverse range of customers.
Industry Implications
1. Fashion Retail - Disruptive innovation opportunity: Implement augmented reality technology in fashion retail stores to allow customers to virtually try on clothes and explore different outfit combinations.
2. Sustainable Fashion - Disruptive innovation opportunity: Establish a platform that connects consumers with sustainable fashion brands, promoting transparency and ethical practices.
3. Clothing Rental - Disruptive innovation opportunity: Develop an AI-powered clothing rental platform that uses personalized data to suggest outfits based on individual preferences and occasions.