Shoppers know exactly what they are looking for but have a hard time finding it using text search and are often recommended an overwhelming selection of unrelated products. Some shoppers just want to find the lowest price product they want (e.g., lightweight performance hiking shoes). While some shoppers have certain ideas about the product but don’t know exactly what types of products are available (e.g., all-terrain hiking boots, mountain hiking shoes), and some are looking for product ideas for their needs (e.g., hiking gear).
To solve this problem, ViSenze was launched with the mission to make finding products easier with smart AI. Working on deep learning and computer vision, the brand decided to create a breakthrough technology solution, an AI that starts with understanding what humans see so that their search can be more intuitive without the struggle of keyword guessing.
“Our first product launch was a visual search solution in 2014. And as we onboarded more brands and retailers and mapped different customer journeys, this helped us improve our product capabilities further to expand our product offerings to include search, recommendation, and overall product discovery. We launched our third-generation product – Discovery Suite, in 2020,” asserted Oliver Tan, CEO & Co-Founder, ViSenze.
“Today, we support over 900 retail clients globally in search and recommendation, including leading retailers in India. We work with retail brands, including ASOS, H&M, Myntra, Meesho, Rakuten, Zalora, and DFS. This year, to drive increased adoption, we further enhanced the Discovery Suite by ensuring seamless integration of product catalogs from websites built with the most popular e-commerce platforms such as Shopify, Bigcommerce, Magento, Woocommerce, and Salesforce,” he further added.
Betting Big on AI
ViSenze’s underlying AI technology is built on vector-based technology and an inferencing engine with over 500+ production-sized AI models to date to support a wide range of catalogs - from fashion apparel to furniture to eyewear, jewelry, and toys. Most of its models are trained using deep learning techniques.
“As an AI solutions company, we are helping retailers increase revenues. We see technology as an enabler and not an end to itself. It is easy to fall in love with technology and yet not make an impact in the real world. So we are very clear in its roadmap and values to measure the impact it ultimately makes with the AI solutions we create. We see the growing importance of AI technology in a few areas: personalization, retail analytics, automated inventory planning, and management,” he explained.
Personalization Gaining Prominence
Today, shoppers know what they are looking for. When retailers provide a personalized shopping experience coupled with curated style-driven product recommendations, it helps build customer loyalty, increases purchases, and reduces returns. 80 percent of shoppers are more likely to purchase from a company that offers personalized recommendations. Personalizing a shopper’s journey involves understanding the shopper’s intent, data, and consumer privacy and ensuring trust in user data collection. With such a significant volume of information available to retailers, making effective use and data-driven decisions can be challenging without the right tools.
“We offer personalization in two ways; the first is Visual AI Recommendations. These recommendations are personalized based on what the customer is currently seeing. We have seen visually similar recommendations perform better than: “You Might Like” or “Often Bought” or other standard recommendations that are mostly based on shoppers' last purchase. While “Shop The Look” allows e-commerce merchants to instantly style model photos, and “Complete the Look” acts as an AI stylist to recommend complementary products.
The second is a new type of recommendation called Session-Based Recommendations (SBR). SBR aims to capture short-term but dynamic user preferences to provide more timely and accurate recommendations sensitive to the evolution of their session contexts. In other words, it lets retailers deliver a personalized experience to shoppers without the need to collect personal data such as gender, age, and location,” he explained.
“Both types of personalized recommendations work well for all visitors, including known and unknown (i.e., not signed-in) visitors. We strongly believe this is a game changer as it removes the constant dependency on knowing shoppers’ data first and helps retailers to still comply with stricter consumer privacy laws.
Future of E-commerce in India
Gen Zers are the next new group of consumers that retailers can no longer ignore. Disruptive and distinctive, Gen Z shoppers are growing up around new shopping experiences such as live streaming, visual search, and BuyNowPayLater (BNPL), and these experiences have become second nature to them.
They consume content insatiably over platforms like Instagram, Snapchat, and other social media platforms, which all deliver a personalized content feed directly to them. More advanced forms of personalization are always needed, and the technology that can bring the most personalized experience with the least amount of input can win.
“We believe retail technologies, especially AI, can shape and influence personalization strategies. This will be most demanded and sought after by retailers and brands to reach these shoppers, as well as present-day mainstream shoppers who are just too fed up with irrelevant products recommended to them,” he concluded.