Roadblocks To a Successful Visual Search in eCommerce Store
“A picture is worth a thousand words”
eCommerce sites are anchoring this phrase by adopting a visual search feature.
The human brain processes visuals 60,000 times faster than text and 90 percent of the information transmitted to the brain is visual. Hence, there is untapped potential in visual search that can be predicted even before experimentation. Moreover, when you enter a clothing brand there are a variety of options in front of you. As a customer, you select your preferences and even ask for advice from the salesperson to show you a similar or better product. Virtually, visual search is the same as the sales guy, analyzing the product and showcasing the options from the whole variety of products.
Additionally, the application of visual search is just a few taps away on your mobile. As for eCommerce, Google Lens gives consumers the capability to scan a product and provide more information on it. Moreover, it suggests which site is selling the same product. However, as you read along you’ll explore the hurdles eCommerce stores face while adopting visual search features.
Algorithm Stuck in Black Box
The soul of an AI-based site is its algorithm. However, we have long since adopted it. Humans do not fully understand why algorithms behave the way they do. But what are the consequences of not knowing the algorithm’s functioning? To illustrate, if you are shopping for a rope, the site will suggest chairs and a ceiling fan. The actual problem is contextual recognization. The site’s algorithm will tell you the most relevant frequently bought together items, but won’t understand its meaning. The conclusions at the output of the visual search algorithm are so complex, obscured, and multi-faceted that the greatest engineers struggle to keep track.
An algorithm that is not able to understand the meaning of data is like a blindfolded runner. The runner will never know the right direction. In fact, the challenge for tech companies is no longer necessary in just creating neural networks that can understand an image as effectively as a human. Rather to better understand the mechanism in the network and correct it in case it is wrong.
The future of eCommerce companies depends on efficiently discovering consumer patterns and filtering the patterns according to the set standards. The goal of these standards must be to keep the algorithm relevant in the wake of a shifting market. As with the visual search function in eCommerce sites, data scientists and engineers are working to replicate the real-world shopping experience in the cloud. So that the algorithm knows only to recommend the products as per your preferences. This is also essential when using a digital dressing room. The AI mirror function will recognize you and will know exactly what your preferences are because there is a replica inside the database of the past transactions and your shopping pattern. This way, businesses can directly present relevant information and products to you, based on your exact choice.