Nand Kishor Contributor

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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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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How A.I. Shopping Tools are Changing E-Commerce Purchasing Behaviors

May 24, 2017 | 2715 Views

Consumers are leveraging A.I. to become more effective online shoppers.

It's no secret that e-commerce has become an integral part of our economy - consumers spent over $51.5 billion in clothes, shoes, and handbags online in 2015. Clothing sales rose by 19%, largely because of the increase in mobile usage and free shipping options. But with access to billions of products, it's becoming increasingly difficult for online shoppers to keep it all straight.

When asked what e-commerce trend to expect in 2017, Tracey Wallace, editor of BigCommerce, said: "We know consumers are shopping in micro-moments. They see an item they like (on Instagram or Facebook), they research it, they find other brands selling something similar, they compare prices, they read reviews and then they finally make a decision."

Because the shopping experience is prompted through a number of mediums (email, social media, search, etc.,) many consumers are turning to apps that simplify and organize the shopping process. Jonathan Friedman, CEO of Shoptagr, believes that "the A.I. Shopping Assistant will become the future of e-commerce and online retail as we know it. While retailers have been leveraging A.I. for years, it is now the consumer who will capitalize on machine learning and automation tools for efficiency."

The Current State of Online Shopping is an Organizational Nightmare

Constantly pestered with email advertisements, hyper-targeted banner ads and social media promotions, the pressure to make a purchase is constant for consumers. Using the internet can feel like an advertising minefield. If you need to search for a wedding gift, you will be served wedding ads everywhere you go online for weeks.

But the bombarding ads are an essential part of driving sales online. Consumers are scattered when they shop through digital mediums. They open different tabs, accidentally close windows that had active shopping carts, or forget where they saw an item. It can take weeks or months before a decision to purchase is made, during which time other online retailers could steal that consumer's attention and win the sale.

Consumers are fighting back against the clutter by seeking out shopping assistants that reduce the clutter and help them to optimize their search efforts. Until recently, this was not possible, but artificial intelligence is paving the way.

The Opportunity Lies in Artificial Intelligence

According to Gartner, 85% of customer interactions will be handled without a human by 2020. The level of sophistication that A.I. has today already closely mirrors human interaction in the offline world.

"Online shopping today is actually much like it is offline," says Friedman. "We all 'run' around between stores looking for a specific product at the right price, overwhelmed by all the options, and we are always late to a good deal. The digital space can and should be more organized, which is where A.I. shopping assistants are gaining traction."

Dr. Keng Siau, chair and professor of business and information technology, writes in a new research paper that retailers are replacing their salespeople with "AI, robotics, and machine learning," or, as Siau calls them, "sales machines."

"In a face-to-face environment, people may still prefer to interact with another human," says Siau. "In a virtual environment or a non-face-to-face context, people could not care less about who or what is on the other end of the line or responding to online queries."

A.I. shopping assistants could be the future of e-commerce and online retail. As the next generation of consumers enters the market, they will be looking for omnichannel tools that simplify the shopping experience.

The Future of Online Shopping Trends

Spending an estimated $632 billion in online retail by 2020, consumers will refer to A.I. shopping bots not just for shopping, but as an overall efficiency tool for all of their goods.

"Much of the tiring pre-purchase journey, which ranges from finding what you're looking for at the right price point to customer support and checkout, can actually be fully automated with the help of a personal A.I. shopping assistant without any human intervention," said Friedman.

From marketing to personalization to A.I.-driven shopping bots, the online retail experience for consumers and retailers is set to change dramatically. Shoppers are already able to price hunt fairly easily, but tracking capabilities, push notifications, behavioral analytics, and automation are just a few ways shoppers will find browsing online easier. Read More

Source: Inc
Nand Kishor Contributor

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 
Follow on

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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