Retail industries deal with both products and services. However, while products are consumed, services are experienced. This is the simple but important thing to understand for their customers to be a successful part of today’s retail world because the customer is the most valuable piece of the puzzle in a retail market. Providing the best customer shopping experience is the complete first step to gaining loyal customers. Loyal customers are, without a doubt, the backbone of every retailer on which their businesses survive.
Plain and simple, it leads to profits. It improves customer retention by 5% to 10% and improves your sales by 45-95%, depending on the industry. As a result, most E-commerce businesses have improved the quality of the customer shopping experience. However, there’s still a painful gap between inspiration and product discovery that doesn’t feel as organic as it does in a brick-and-mortar store.
AI-powered Visual search has been around for a few years. Still, the technology shows its impact on helping retailers boost their customer experience and improve their business revenue and growth. Find out how incorporating visual search and product discovery can transform your shopping journey to increase your average order value per transaction.
Visual search is a Computer vision-based technique that uses real-world images such as screenshots, Internet images, or photos as the stimuli for online searches. Modern visual search technology uses Artificial Intelligence algorithms (AI) to understand the content and context of these images and return a list of similar photos.
In the retail industry, customers use a visual search application to find out products they want to buy or similar products by using images instead of keywords generally used in search engines. For example, customers can take a photo of something they want to buy and upload it to a particular retail shop app that has integrated with the visual search application. They can immediately see visually similar items available to purchase. Visual search allows retailers to search and recommend products customers want to improve customer experience by reducing the struggle of searching a product using a text query alone.
Visual search has the potential to transform the way we communicate with the world around us. This technology opens new opportunities in scenarios where the customer doesn’t understand how to define the product she’s looking for but has a visible reference. The visuals already dominate humans, so it seems natural to use an image to start a search.
We are visual creatures able to process pictures faster than text. The human brain processes images hundreds of thousands of times quicker than text, and most knowledge conveyed to the brain is visual. So it doesn’t surprise that customers are quickly adopting this visual search behavior to allow them to search for information with visuals.
That means Visual search enables retailers to propose the same and similar products to customers visually which are struggling to search for the products that they want by using traditional text-based inquiries alone.
Visualization is vital to social media marketing, but it’s relatively critical for every touchpoint in the customers’ journey. This statement is particularly true for the product discovery process in the retail industry, where customers are wasting and unsatisfied with their traditional searching process with text.
This is why visual search technology has grown so great.
We have already discussed that we are all able to process images faster than text. So it doesn’t surprise that users are quickly adopting this search behavior to allow them to search for information with visuals.
AI-powered visual search is expected to significantly influence eCommerce and retail marketing by transforming the way shoppers shop for visual products online and increasing customer experience expectations.
Few examples of how visual search shows the impact on the world and retail industry :
People have been speaking about visual search for a long time, but it has actually come into its own over the past few years. Compelling smartphones, increasingly smart artificial intelligence, and customer interest drive the growth of this exciting technology. But how does visual search work?
Visual search requires Artificial Intelligence techniques to feature detection and pattern matching. AI technologies run in the background to analyze shapes, colors, and patterns of the uploaded image. Afterward, it will apply the same technique on the entire retail shop product inventory images to rank those product images that resemble the uploaded image from closest to farthest. With all pictures mapped to this space, finding similar items becomes as simple as finding which coordinates are the most relative to the uploaded product image.
The number of outcomes of the visual search application you display to your consumers can be easily customized based on your needs. However, it is essential to remember that shoppers like a choice as long as it is tailored and relevant to their intent. The AI will continue learning your individual shopper’s intents and choices. As they interact more with your store, your store’s visual search application recommendations will become more personalized.
As the search technique develops beyond its traditional modes and new technologies remain introduced, it is essential to know and take advantage of it as quickly as possible.
In the retail world, visual search has the power to make a better shopping experience much easier and quicker for customers—and that can generate some profound benefits for retailers and store owners.
So more than 45% of retail industry owners are planning to adapt visual search in the future, getting ahead from their competitive market because realization is better early than late. Let’s look at some primary benefits for retailers who implement a visual search engine.
Visual search executes product discovery easier by enabling customers to search with images for the closest possible or accurately the exact representation of the product they are looking for.
It supports answering questions that are hard to verbalize, such as “I’m looking for a dress like this one that I see in some function” or “what’s the best price for this kind of sofa that I found in my friend’s house” etc.
Visual search implements the technology for a product recommendation system based on actual similarity and not other customers’ choices. Brands utilize this power to propose similar products to offer broader choices to their customers.
Visual search can help decrease the shopping cart abandon rate by offering an alternative to products out of stock.
Visual search improves product discovery, delivers where text search fails, increases conversions, and decreases shopping cart abandonment while also offering a rich media experience to users.
When we think of visual search technology, a couple of players immediately come to mind. It’s not so mysterious that almost every large brand company is experimenting with visual search or researching what computer vision can bring for their company.
Pinterest is a platform that revolves around pictures, so it’s no wonder that it has made the greatest steps towards this change.
A few years ago, the company began investing loads in computer vision, AI, and ML, which ultimately led to Lens. Continuous development brought things like Shop the Look, the initial visual shopping tool of its kind.
You may remember when Google Lens started, which was the mega-industry’s first entry into the visual search market. Now essential to Android smartphone cameras, it worked instantly on these devices for the first time.
Since most people use visual search for shopping, Google has moved down this path too. With the ‘Style Match’ feature, customers can take a picture on their mobile device and find the product to purchase online.
Bing is very effective in the visual search area. Microsoft has been doing a lot of analysis and making lots of data freely available. It’s also worth discussing Bing because they also have a web-based visual search platform similar to the one created by Pinterest.
Amazon uses its visual search technology regularly to provide other platforms with a way of visually shopping for stuff. For Amazon, visual search is great as it gives them a new way to have customers shop. Now, if we see something in a store, we can take a photo of it with our mobile camera then the Search functionality inside the Amazon app will search for us. It shows us all the relevant products available on Amazon related to our uploaded photo, and we can improve our search via visual attributes if you want.
These big companies and many medium and small industries are also adopting this visual search technology into their business to improve their customer service, customer satisfaction, customer experience, etc. For example, Facebook works on developing an AI-powered version of their Marketplace. They even bought a visual search technology start-up called GrokStyle that could drive that development. Apple also bought several organizations active in the visual search space, mostly to enhance their photo apps, while their ARKit developer program has very attractive options for working with visuals. Both Snapchat and Instagram let us buy stuff on Amazon by pointing our camera at an object. And many industries are ready to adopt this technology to see the same benefits as above mentioned companies.
Visual search is a great tool to make the whole customer shopping experience quicker, easier, and fun for shoppers in the retail business. It allows customers to search by images that interest them and eliminates sorting through irrelevant results. For example, the fashion results are all shoppable visual recommendations, thereby ensuring a delightful customer experience. The below are the few use cases of visual search for retailers.
Target company decided to enter the visual search by allowing Pinterest Lens, Pinterest’s visual search tool, into their mobile app. By visiting a Target store, the customers can snap photos of any product on the app to get related products. This way, the consumers will be able to browse products quickly instead of going through the complete product catalog. This technology can also help customers check out the full list rather than just the available products in one store.
This brand has taken the visual search path by exploring the visual way of thinking about products and purchases. The President of Forever 21 says that Visual search technology connects the gap between the comfort of customer shopping and the rich discovery experience of the regular retail industry.
Forever 21 combined visual search and artificial intelligence (AI) to improve the eCommerce customer shopping experience. For example, the customers can search for the products using different attributes like shirt neckline, color, or dress length.
Other use cases :
Advanced technology allows piracy content identification in real-time, thus supporting media owners to protect their content rights. It seems to be an outstanding solution for piracy content detection and distribution prevention.
Brand similarity search helps brands monitor and identify counterfeits and fight them. In addition, visual search-powered solutions aim to detect fake products and prevent shoppers from buying them.
Another use case that needs to be mentioned here is ordering food or purchasing grocery items. For example, if the customer knows his local language but doesn’t speak the English language. He got one incident in that he had to order his afternoon meal; he knew the name of the meal in their native language and a picture of that item but not known in-app language. In that case, visual search technology is more helpful for him.
If restaurant industries adopt this technology into their app, then customers easily search for the meal by uploading images that they have. If that particular meal is not available, the app will recommend a similar meal to your customer.
Not the above-mentioned use cases; there are so many use cases of visual search technology to improve your customer experience and journey in the retail industry.
Till now, we discussed visual search, such as the benefits of visual search, how it was working and how it will enhance customer experience, etc. We know that AI algorithms will give systems able to deliver ultimate outcomes that are not just accurate but relevant and timely. However, how does AI deliver with such capability?
That power is all because of data annotation. It’s only through the process of data labeling/annotation that modules could differentiate between a grocery item and clothes, rice and a skirt, or an apple from an orange. Without data annotation, every single image would be the same for systems because machines don’t have any kind of information or knowledge about anything in the world.
In the real world, nearly 95% of the data is unstructured. In simple words, the unstructured dataset can be all over the place and is not properly defined. For example, images of grocery store products or dresses in the malls are all unstructured data. There are no proper tags for each image to differentiate from other images of products. If we are building an AI-based visual search model, we need to feed quality annotated data to an AI algorithm to process and deliver outputs and inferences.
So basically, suppose we want to start a new AI/ML initiative like visual search, we quickly realize that not only is feeding data not important, but we must and should be finding high-quality training data with proper labels and all kinds of data. So the output of visual search, not only this technology, every AI & ML model is as much as the data we use to train it. So the precision that we apply to data aggregation and the tagging and identifying of that data is important.
The quality of the image dataset has to be good quiet for the visual search systems to recognize the individual components within every image.
At Predictly Tech Labs, we combine Active learning technology with human care to provide high-quality text, audio, image, and video annotations. Our data annotation team maintains quality while processing & labeling the image and text data which can be used effectively for various AI and ML initiatives.
Adding visual search technology is an excellent way to increase revenue. According to research, more than 69% of retail and e-commerce industries prefer visual search. They are the biggest growing group of customers, and visual search will only become more and more of a necessity in the future.
Gartner also forecasts that by 2021, early adopters of this visual search technology that redesign their product discovery experience to support visual search will increase their e-commerce revenue by 30%. These research results mean visual search leverages the retail and E-commerce industries to enhance your customer shopping experience for your brand. So, why not try it now!