INSUBCONTINENT EXCLUSIVE:
Sowmiya Chocka Narayanan
Contributor
Sowmiya Chocka Narayanan is Co-founder and CTO at Lily AI, a leading
AI platform helping brands - retailers understand individual customer emotional context
Prior to joining Lily AI, Sowmiya helped to build Box and worked on numerous projects at Yahoo! and Pocket Games.
Consumer expectations
are higher than ever as a new generation of shoppers look to shop for experiences rather than commodities
They expect instant and highly-tailored (pun intended?) customer service and recommendations across any retail channel.
To be
forward-looking, brands and retailers are turning to startups in image recognition and machine learning to know, at a very deep level, what
each consumer current context and personal preferences are and how they evolve
But while brands and retailers are sitting on enormous amounts of data, only a handful are actually leveraging it to its full potential.
To
provide hyper-personalization in real time, a brand needs a deep understanding of its products and customer data
Imagine a case where a shopper is browsing the website for an edgy dress and the brand can recognize the shopper context and preference in
other features like style, fit, occasion, color etc., then use this information implicitly while fetching similar dresses for the
user.
Another situation is where the shopper searches for clothes inspired by their favorite fashion bloggers or Instagram influencers using
images in place of text search
This would shorten product discovery time and help the brand build a hyper-personalized experience which the customer then rewards with
loyalty.
With the sheer amount of products being sold online, shoppers primarily discover products through category or search-based
However, inconsistencies in product metadata created by vendors or merchandisers lead to poor recall of products and broken search
This is where image recognition and machine learning can deeply analyze enormous data sets and a vast assortment of visual features that
exist in a product to automatically extract labels from the product images and improve the accuracy of search results.
Why is image
recognition better than ever before?
While computer vision has been around for decades, it has recently become more powerful, thanks to
the rise of deep neural networks
Traditional vision techniques laid the foundation for learning edges, corners, colors and objects from input images but it required human
engineering of the features to be looked at in the images
Also, the traditional algorithms found it difficult to cope up with the changes in illumination, viewpoint, scale, image quality, etc.
Deep
learning, on the other hand, takes in massive training data and more computation power and delivers the horsepower to extract features from
unstructured data sets and learn without human intervention
Inspired by the biological structure of the human brain, deep learning uses neural networks to analyze patterns and find correlations in
unstructured data such as images, audio, video and text
DNNs are at the heart of today AI resurgence as they allow more complex problems to be tackled and solved with higher accuracy and less
cumbersome fine-tuning.
How much training data do you need?
