In the world of online shopping, its all about details and transparency. How a brand presents its products is of paramount importance. Online shoppers are no longer willing to dig through the countless array of products in search of the specific colour, size, or neckline they desire. This is where the art of detailing features becomes indispensable.
It’s about providing your consumers with comprehensive information about your products, enabling them to know exactly what they are buying.
Transparency, in this context, is the foundation of trust. When consumers have detailed product information, they are more likely to have faith in your brand to make informed choices. This transparency not only benefits the consumer but also makes the shopping process much more streamlined.
By listing precise product features, brands not only make it easy for consumers to sort through their offerings but also increase the product’s visibility and thereby the chances of it being purchased.
However, the task of providing such detailed listings is not as straightforward as it may seem. It requires meticulous effort to present accurate information, which is a significant challenge in the industry of fashion.
The Slow and Steady Tag Race: Why Manual Tagging is a Fashion Faux Pas:
While manual tagging was once the norm, this conventional approach has now become untenable. There are compelling reasons as to why manual tagging is no longer a sustainable practice:
Time-Consuming: Manual tagging is an exceedingly time-consuming process. Given the constant influx of new products and trends, the sheer volume of tagging has become an overwhelming burden for fashion businesses. As per our research it takes over 4.6 days for every 10 SKUs to get tagged when done manually. Resulting in opportunities loss due to slow go to digital shelves.
Low Scalability: As fashion enterprises expand and diversify, manual tagging becomes increasingly unscalable. The process cannot easily accommodate the addition of new products or the exploration of new markets without eating up a substantial amount of time and resources.
Lack of Accuracy: Manual tagging, which is prone to human error, jeopardises the ‘richness’ and ‘accuracy’ of data associated with each product. Incomplete or inaccurate tags pose a direct obstacle to customers to discover the products they want.
In response to the industry’s pressing need for heightened speed and accuracy, AI-generated fashion tagging by BRIO has emerged as a pivotal solution. The existing industry standard accuracy rate of 37.5% in fashion product listings is soon getting replaced by BRIO with its accuracy rate of 94%.
A low accuracy rate can have detrimental effects on retailers. necessitating the need for artificial intelligence to enhance this critical factor in the fashion world.
From Pixels to Product listings:
The process of AI tagging unfolds in three sequential stages:
Image Classification: The initial step in recognizing the content of an image, where AI identifies the primary category, forming the foundation for further tagging.
Image Feature Extraction: After classification, AI delves deeper into the image to identify specific features, utilising deep learning algorithms and neural networks to capture attributes.
Image Feature Tagging: Building upon classification and feature extraction, this stage translates visual information into a textual context. Through training with vast datasets, the system generates descriptive tags, aiding consumers in making informed purchase decisions.
AI’s closet conundrum:
The journey from image classification to product tagging represents a synergy of data, algorithms, artificial intelligence and deep learning. It brings a new level of sophistication to the realm of retail.
However, the complexity of the fashion industry is something else. The actual classification of a simple clothing such as a ‘red dress’ is almost always far more detailed than it may seem at first glance. It has an array of attributes to consider, from the fabric and style to the brand, size, and occasion for which it’s intended.
Moreover, our AI models have been rigorously trained in recognizing intricate embroideries and embellishments with remarkable accuracy, efficiency, and unwavering consistency. The rapid analysis and precise categorization of complex designs by BRIO feature extraction proves to be invaluable to industries in retail.
Sari-nade of Styles:
But a whole new layer of complexity arises, when we venture into the world of Indian clothing. Indian attires are known for its tapestry of colours, styles and materials. Indian ethnic wear is known for its intricate embroidery, beading, and embellishments. These details can be very intricate and varied, making it difficult to capture them all in a tagging system.
However, BRIO rises to this challenge, demonstrating its capability to accurately capture and categorise the multifaceted beauty of Indian clothing,
There are approximately 22 distinct embroidery styles, not to mention countless minute and unique variations in embellishments alone. Effectively and accurately tagging this is a big challenge, therefore the need for a model specialised in Indian ethnic wear is imperative!
Indian ethnic wear encompasses a wide range of clothing styles, from sarees and salwar kameez to lehengas and sherwanis. Each style can have a multitude of variations and designs, making it challenging to create a comprehensive tagging system.
BRIO’s feature extraction capabilities, represents a groundbreaking development in the field. It stands as the first model comprehensively trained to navigate the intricacies of Indian ethnic wear, delivering results with unparalleled accuracy and reliability. This innovation ushers a new era in the efficient analysis and recognition of the diverse, intricate and exquisite details that define Indian fashion.
Unlock profit potential with BRIO’s feature extraction
BRIO has got you covered from all sides. Leveraging Image feature extraction can prove to be highly advantageous for brands:
SEO Optimization: By employing well-tagged images, BRIO significantly enhances your brand’s search engine optimization. This, in turn, makes it simpler for potential customers to discover your products online, thereby elevating your online visibility and potentially leading to increased sales.
Listing Health: It’s not just about your products; it’s about how you present them.With automated product tagging, BRIO ensures that your product listings remain accurate and up-to-date. This is of the utmost importance as it fosters trust and satisfaction among your customers, ultimately enhancing their overall shopping experience.
Competition: However the crux of this feature lies in the discovery of Similar Products:
The complexity of competitive mapping in retail is difficult as it is. Manually scrutinising and analysing each product is extremely time consuming. Brio’s automated tagging can streamline this process by effectively linking similar tags thereby enabling us to find closely comparable products for your brand. But Brio doesn’t stop there. Its advanced pricing engines meticulously analyses pricing of your competitors and will then recommend the optimum price to be set for your product after taking into consideration all dynamic factors in the market! This real time insight helps you set competitive prices, ensuring that your brand stands out in the market without sacrificing your profit margins. BRIO’s automation and data-driven pricing recommendations represent a formidable asset in your pursuit to thrive in the retail landscape! BRIO’s image feature extraction is not just a buzzword in the retail industry; it’s a game-changer. Its ability to provide accurate and detailed tagging of products is crucial for your brand’s overall health online. And as the industry continues to evolve, embracing image feature extraction is not just a choice but a necessity for retailers looking to stay competitive and thrive in this dynamic market landscape.