Customers today are embracing technology at an exciting pace. Their needs and expectations are constantly evolving and brands today need to focus on building experiences for the individual, and not just for the mass-market. Shopping is dominated by the visual elements of the product. Even when shopping at physical stores, it is the look of the product that captures our attention. In our daily conversations, we are increasingly using pictures or videos to convey context. A visual search technology powered by artificial intelligence combines all the aesthetics of a product that cannot be captured by text alone.
When shopping online for clothing or furniture, more than 85% of respondents respectively put more importance on visual information than text information. The Intent Lab
Modern visual search understands the content and context of physical world images and gives a list of relevant results.
The source of visual media could be a picture of a handbag at the mall, a photo shared by a friend on Instagram or Facebook, video of a favourite character in a web series on Netflix—literally anywhere. What is important is that it caught the eye of the customer, who intends to buy it. Using visual search technology, a customer or retailer can look for a product online or in-store and also see recommendations for complementary products. AI plays a critical role in visual tools and makes the visual search experience more seamless and user friendly for the customer.
Visual search shortens the quest for the product.
After product inspiration strikes, the next logical step for an inspiration-driven customer is to use keywords to search for the product online. Seems easy—but at this point the customer is guessing the keywords and getting a wide variety of results back that are mostly irrelevant. The customer might become disappointed with the quest, the intent to buy may weaken, and the product search postponed to a future visit to the mall.
A lot of times text-based searching is limited by keywords, filters and categories. However, an AI powered visual search improves the way customer finds a product online.
A recent study found out that 62.2 percent of millennials prefer visual search over traditional search methods. Pinterest
On a visual search platform, the customer does not need to guess the brand, style or colour. An image is enough to do all the work. From there, the AI service extracts the product attributes from the image such as colour, style, pattern etc. The Machine Learning models then correlate the attributes with the catalogue inventory to display visually similar results. This enables a shorter product discovery journey that helps inspiration-driven shoppers to stay focused on the product.
A bridge from digital world to physical world
Visual technology connects online and offline experiences. Now let’s pretend that our inspiration-driven customer decides to continue with his product quest. And this time he wants to try the physical world. He visits the mall, armed with a product image in his phone and is walking from store to store to find it. After visiting innumerable stores, and looking at the product aisles, he is still not seeing the results. He even makes use of store assistance because obviously the staff would know all their products. This results in him looking at a lot of products, but not the product he wants.
61% of respondents say visual search elevates their experience while in-store browsing. Pinterest
Visual search technology can connect customers to the product they want in real-time and ensure thst they are no longer offered products that are inappropriate. AI-driven visual search can enable the retailers to sell products as well as an experience that includes recommending multiple products from multiple brands. So instead of just helping shoppers buy a pair of shoes worn by a social media influencer, visual search can assist shoppers in assembling an entire outfit that compliments the look. In case of out of stock items at the store, visual search can save the sale by presenting shoppers with similar products that are in stock, creating another opportunity for the retailer to build a satisfying shopping experience for the customer. In the midst of all this, the customers have the freedom to discover a list of products that match their inspiration and buy exactly what suits their taste and budget.
Frictionless product discovery for a satisfying customer experience
The visual search market is expected to exceed $25 billion this year—and by 2021, data indicates retailers that are early adopters of visual search are projected to increase their digital commerce revenue by 30%.
AI-powered visual search technology is impressive and can have a great impact on how a customer sees the brand. It has already gained momentum in retail industry. Target and Pinterest were early adopters of the visual search technology, and almost all major ecommerce brands in India today have launched some form of visual search. But the tool is only effective when the visual search enables the customers to discover the products and efficiently sift through extensive product catalogues. Machine learning teaches the tool to understand the image and go beyond the usual price and customer history based suggestions for an improved user understanding. It enables the brand to fulfill the needs of individual user segments to differentiate between user behaviour and interests for creating effective marketing strategies.“ The brands that will be successful won’t be those who deploy visual search first, but the ones who deploy it best. The underlying foundation of algorithms, data models and real-time analytics should solve customer's problems.” explains Pradeep Tiwari, the Principal Architect, AML team at Fynd. Visual search has to be extremely accurate in aiding customers to discover products, increase brand engagement and lead to an enduring customer relationship.
Farooq is the co-founder of Fynd. Fynd is India's largest O2O fashion platform. At Fynd he looks after the overall strategic vision, and heads product, engineering, and growth. Formerly, he worked as a management and big data analytics consultant in New York and San Diego. Graduated from IIT Bombay in 2008 with a Masters in Structural Engineering and Bachelor in Civil Engineering. Author of several papers and a book on structural engineering. In his spare time, he likes travelling, playing sports, trekking, scuba diving and photography. Last year he completed the Everest Basecamp Trek.