Nand Kishor is the Product Manager of House of Bots. After finishing his studies in computer science, he ideated & re-launched Real Estate Business Intelligence Tool, where he created one of the leading Business Intelligence Tool for property price analysis in 2012. He also writes, research and sharing knowledge about Artificial Intelligence (AI), Machine Learning (ML), Data Science, Big Data, Python Language etc... ...Full Bio
Nand Kishor is the Product Manager of House of Bots. After finishing his studies in computer science, he ideated & re-launched Real Estate Business Intelligence Tool, where he created one of the leading Business Intelligence Tool for property price analysis in 2012. He also writes, research and sharing knowledge about Artificial Intelligence (AI), Machine Learning (ML), Data Science, Big Data, Python Language etc...
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Three Ways Artificial Intelligence Will Transform Online Shopping
If you want to get a lot of retailers nodding their heads, ask them this: "Are you pursuing a personalization strategy?" You'll get a pretty resounding "yes!". In fact, personalization already drives a lot of activity on eCommerce sites. It often influences the products or offers featured on the home page, the order of products you see on a category or search results page, and makes product recommendations both on the site and in the digital marketing (email, retargeting) that follow. If you've ever been browsing a site and suddenly had the order of products change around on a product listing page, that was probably a decision driven by personalization.
But personalization is only as good as the data about products and the site's ability to influence a shopper. Shoppers who are logged in and whose behavior can be tied to past purchase history are likely to get the most relevant recommendations. But a lot of guesses can be made based on behavior alone. As long as a personalization engine can piece together similarities between how you shop compared to how other people before you have shopped, and has good access to product data, it can make pretty good guesses as to what other products you might also like to see and how to organize them.
But in some ways, personalization as it exists today is static. Yeah, it can come up with recommendations, but it's not necessarily categorizing the behavior to identify insights about that behavior beyond predicting the next best action for someone who is already engaged with you. You throw in some Artificial Intelligence or Machine Learning, though, and the dynamic of engaging with shoppers on a site will change. The question is, how much? Here are three big changes on the way. You decide how much they might impact the online shopping experience...
Dynamic filters / fuzzy attributes
Shopping a site that sells product categories where the facets have clearly not been optimized for the product you're trying to buy is enormously frustrating. Go to any home furnishings site and try to buy a round kitchen table that has an extension in it. You can select "round" and you can select "oval" but you often can't filter on one that could potentially be both. Amazon is actually one of the worst for providing relevant filters for different product categories. It's clear right away which categories they have someone paying attention to, with filters that reflect how consumers actually shop for products, versus those products they haven't figured out yet (hint: athletic vs. casual might be good a place to start in breaking down women's shorts).
Personalization as it exists today can only do so much here - it fully depends on the data that is already associated with a product. At its least sophisticated, personalization is basically just a difference engine - 72% of people who viewed this product also viewed these other 3 products, so it will show you these other 3 products too. Read More