Imagine that you spot a piece of jewelry, a funky top, or a floral pattern t-shirt, or even a polka-dotted skirt in an offline store. Now, you want to search for the exact product online. How do you describe the details in the search bar? You could try typing some related keywords, but chances are you might not find the exact match.
Wouldn’t it be great if the search bar could understand images? This could help shoppers simply show what they want, helping businesses increase conversions and revenue. According to research from The Intent Lab, 36% of consumers have conducted a visual search.
With visual search, shoppers can snap a picture or upload screenshots of what they want. The technology quickly identifies features like color, pattern, and style, finding visually similar products in no time.
62% of millennials prefer visual search over any other new technology and this makes complete sense as ‘Seeing’ has always been our natural way of searching.
Text search requires users to articulate what they’re looking for using specific keywords or phrases. This process can be challenging, especially if they’re unable to accurately describe the item they want. It involves trial and error, sifting through various descriptions, and hoping to stumble upon a matching product.
Visual search, on the other hand, allows consumers to simply show an image of what they desire. This method bypasses the need for precise verbal descriptions and allows users to rely on visual cues.
Consumers rely on visual cues to make informed decisions, often seeking a seamless transition from what they see in their physical environment to online platforms. This behavior underscores the necessity for e-commerce sites to embrace visual search functionalities that mirror real-world experiences.
55% of consumers say visual search contributed to influencing their style and taste.
Data analytics in ecommerce allows for the extraction of invaluable insights. By examining search queries, click-through rates, and conversion paths, businesses gain a comprehensive understanding of user preferences and behaviors. For instance, a study by McKinsey notes that companies utilizing advanced analytics witness up to a 126% profit increase over their competitors.
AI keeps enhancing search features, making them stronger. In ecommerce, image search stands out as a powerful tool.
Thanks to better AI, buyers can take a picture of what they want, and then, with personalized suggestions, find great offers. This tech boosts finding products and keeping customers interested, leading to more sales.
In fact, revenues of ecommerce websites that are early adopters of visual search are projected to increase their digital commerce revenue by 30%.
AI-driven enhancements in ecommerce search ensure several benefits like accuracy, speed, and relevance.
The decision to implement visual search should stem from understanding the potential impact on user experience and sales.
The benefits are compelling. Here are some other ways your ecommerce business will get a boost if you decide to implement visual search:
Ultimately, the decision to implement visual search should be based on aligning with customer preferences and enhancing the overall shopping experience, which data suggests can have a significant positive impact on sales and customer satisfaction.
Meesho offers a wide variety of products at competitive prices for its customers. A lot of its customers are new to online shopping, especially from smaller cities in India. Meesho’s app is super popular, with over 500 million downloads.
Meesho wanted to make it easier for them to search, especially for young people who follow celebs and want to find similar styles effortlessly.
ViSenze enables Meesho to do exactly this.
Over the last couple of years, Meesho’s customers have done almost a billion visual searches annually. By ensuring that Meesho customers find what they want faster, AI-powered Smart Visual Search helped Meesho unlock more revenue.
NLP enables e-commerce platforms to decipher and process user queries in a more nuanced manner. By analyzing language patterns, contextual cues, and semantics, NLP understands the intent behind user queries. For instance, when a user searches for “women’s running shoes under $50,” NLP comprehends the specific criteria and retrieves relevant results.
An example showcasing NLP’s impact on search relevancy is evident in how Amazon’s recommendation system operates. Through NLP-powered algorithms, Amazon interprets user queries and past behavior to generate personalized recommendations. This approach significantly boosts relevancy, contributing to approximately 35% of Amazon’s sales.
Tags play a crucial role in aiding shoppers to discover products effortlessly.
Ecommerce stores can enhance online product information by using AI-powered tagging. With detailed descriptions to help shoppers navigate easily, they simplify the shopping experience by ensuring all your products are readily accessible and purchasable.
According to a report by Segment, 49% of consumers have made impulse purchases after receiving a personalized recommendation from AI. This emphasizes the crucial role personalized search plays in creating a compelling and engaging shopping journey, fostering customer loyalty and satisfaction.
Mobile optimization is key in today’s retail landscape due to the exponential growth of mobile usage in shopping. Statista reports that mobile commerce is expected to account for 60% of total e-commerce sales by 2023 emphasizing the dominance of mobile devices in online shopping. Mobile optimization is important in retaining users and preventing bounce rates, significantly impacting conversion rates and revenue.
Real-time analytics are pivotal in refining search algorithms in ecommerce. By analyzing user interactions, click-through rates, and search patterns instantaneously, ecommerce platforms can swiftly adapt and optimize their search algorithms.
A study by McKinsey suggests that companies leveraging real-time analytics achieve a competitive advantage in terms of profit margins. Real-time data empowers ecommerce platforms to swiftly identify trends, understand user behavior, and adjust algorithms to provide more accurate and relevant search results.
User feedback serves as a cornerstone for continuous improvement in visual search. Platforms that actively solicit and integrate user feedback witness notable enhancements in their search functionalities. For example, Amazon encourages users to rate and review product searches, contributing significantly to algorithm adjustments and product recommendations.
Direct user input, such as ratings, reviews, and search queries, allows e-commerce platforms to fine-tune their algorithms, ensuring that search results align more closely with user preferences.
The implementation of visual search, AI-driven algorithms, and real-time analytics not only improves user experiences but also increases conversion rates and customer satisfaction.
The future of search in e-commerce is poised for continuous evolution, driven by technological advancements and shifting consumer behaviors. Visual search, powered by AI and machine learning, will increasingly dominate the e-commerce landscape.
Get in touch with us now at Visenze and get to know the best solutions for product discovery.