Every now and then, a novel technology emerges that disrupts a specific industry or niche. Take AI, for example. AI-powered technologies are currently reshaping almost every possible sector, and the businesses that don’t implement ML- and AI-based solutions are basically doing an enormous favor to their direct competitors, missing out on visual search algorithm success.
This is especially true for the fast-paced world of e-commerce, where staying ahead means embracing innovative new technologies early on.
One such advancement that has revolutionized the online shopping landscape is visual search algorithms and AI-powered product discovery tools.
Visual search algorithms empower users to search for products using images rather than text-based keywords. This helps shoppers effortlessly and quickly get to the products they want, even those they didn’t know they wanted, while it also helps e-commerce businesses skyrocket their sales and other metrics critical for thriving across the online retail realm.
This technology leverages AI and ML to analyze visual characteristics such as color, shape, pattern, and texture, delivering accurate and relevant results in real time. Read this guide to learn more about visual search and product discovery for e-commerce.
Now, let’s see what it takes for online retailers to reach visual search algorithm success.
Tip 1: Invest in High-Quality Images
Even though AI-based technologies are becoming more powerful by the day, we must help them reach their full potential. This is why every online retailer should make sure their product images are:
- High-resolution
- Well-lit
- Clear
It significantly enhances the performance of visual AI search and is the first step toward visual search algorithm success.
Quality images are vital for these algorithms to effectively recognize, understand, and match various objects, shapes, and patterns in product images in order to simplify the way users interact with search mechanisms within e-commerce search platforms.
Tip 2: Choose the Right Feature Extraction Method for Visual Search Algorithm Success
Selecting the appropriate feature extraction technique, such as SIFT, SURF, or ORB, is among the crucial steps in improving visual search precision and locking the visual search algorithm success for your platform.
Each method has its unique advantages, and choosing the right one ensures that the algorithm can accurately identify and fetch exactly the items your shoppers are looking for. In terms of content-based image retrieval (CBIR) optimization and visual search platforms, the feature extraction method really does influence the visual search algorithm success, making it essential for e-commerce decision-makers to carefully consider their options.
Tip 3: Utilize Pre-trained CNN Models
Making the most out of pre-trained CNN models like Inception or ResNet can significantly improve the development of visual search algorithms. These models come equipped with a wealth of visual knowledge, having been rigorously trained on vast datasets such as ImageNet.
Through these pre-existing networks, developers can bypass the time-consuming task of building a neural network from scratch and speed up the development timeline, ensuring precision and effectiveness levels that might be challenging to achieve otherwise.
Tip 4: Implement Smart Filtering and Relevance Feedback
Smart filtering and relevance feedback are essential tools for improving the accuracy and customization of search outcomes in visual search platforms.
They help companies ensure their visual search solutions meet and exceed consumer’s expectations, creating a reliable and engaging space for them to discover the products that match their taste and engage with content that resonates with them.
These sophisticated tools elevate the efficiency of visual search technologies, refining the search experience and improving user satisfaction.
Tip 5: Consider Image Preprocessing Techniques
Image preprocessing prepares images for analysis. Methods such as resizing, cropping, color adjustment, and noise elimination transform images, making them more uniform and accessible for the algorithm to analyze effectively.
These preprocessing activities reduce the variety found in unprocessed images, thus boosting the algorithm’s ability to accurately identify and classify objects within those images.
Tip 6: Implement Miros.ai Visual Search and Product Discovery Infrastructure
Miros.ai is an easy-to-integrate AI-powered visual search solution that allows shoppers to more easily discover products that match their taste and style. It understands their buying intent through browsing behavior and shows a whole wall of relevant products.
It typically works like this:
- The shopper inputs a quick text-based search
- Taps on a couple of items that are close to the one they desire
- Miros.ai then reads the product images and delivers only relevant results
- The shopper makes a purchase because they quickly found the product they wanted
If you think it feels almost like mind reading, you won’t be far from the truth. It’s like your shoppers have their own personal shopping assistant who knows their taste and style.
Check out this quick demo to see how it works:
Want to try Miros for your online store? Book a quick demo with our team and learn all the benefits of AI-powered visual search infrastructure.
