Sunday, November 12

How the Machine Learning Based Mobile App Development Can Make Retailing Easier

Shopping excites us all! But is it really exciting in real? In fact, it’s not. We all know it. The market, apart from the goods we want to buy, is also heavily stuffed with crowds. Little space is there for more legs to walk, in fact. You observe the market carefully during weekends or festivals and, you will see almost no difference between the crowds in stores and bees in a hive.

So shopping nowadays is a struggle. And the shopping of groceries can even be the mission of day! But there is a way to avoid all this. The escape from going to market and buying everything by visiting from one store to other is your mobile. A smartphone with right apps can open stores for almost everything you buy by visiting the market.

A shopping app for retailing provides categories, product-search, item cart, and online payment and then gets purchased products delivered to the desired address.

This may inspire many retailers to launch their m-shopping stores. Yet, many retailers, who already own retailing apps, have also expressed their dissatisfaction with their online shopping apps. They say that because customers are not able to look and feel the color or texture of an item, they avoid using apps and prefer visiting a store, instead.

There is no doubt m-commerce apps still lack at providing people with senses of feeling, touching, and trying a product before making purchasing it. Apps do not also provide the effective ways to get a different color for a product with the selected design or the same color in different designs. It’s just limited there. But it’s not impossible to do that. Yes, when blending Artificial Intelligence and Machine Learning technology with the retail app development, it’s easy to come up with a retail app that lets customers virtually sense a product before purchasing it.

This will happen for sure. A report from Gartner says that 85% of customer-interaction in retailing will be managed by artificial intelligence by 2020. Some retailers have also started to test and implement AI and ML technologies at different operation levels. The India fashion retailer, Myntra has also started to use AI and ML technologies at its platform. The company has implemented the technology helping customers choose a t-shirt completely designed by software. There would be no intervention from designers. This will help customers enjoy a more convenient and richer shopping experience.

1-800-Flower.com and Macy’s are two leaders in their industries. They have also started to test cognitive computing capabilities to intensely serve their customers’ wishes. Staples, a chain retailer with office supplies, furniture, equipment and more has plans to implement the IBM designed AI computing technology, Watson to bring to life its Easy Button. It will allow customers to place orders via voice, text, email, messaging or a mobile app.

The future of shopping and retail is AI! Also, we will see customers to prefer a shopping bots-enabled platform that delivers enhanced shopping experience and effectively runs various other courses. AI can automate processes like product-recommendations, checkout, and support. The technology will help shoppers easily hunting best-priced products, along with having improved tracking capability, automation of several steps perform during an online purchase.

Other benefits that AI/ML can deliver to the retailing industry:

  • Offline stores powered by the cognitive computing and location-based software can help shoppers in internal-navigating of stores.
  • AI can avoid losses caused by item theft or non-scanning.
  • The biggest place where AI can really cut crowds from exists is the self-checkout.

Author Bio :- James Stewart is a digital marketing expert in Mobilmindz, a prominent mobile app development company which provides Android and iOS app development services across the global. He loves to write about latest mobile trends, mobile technologies, startups, and enterprises.

Leave a Reply

Your email address will not be published. Required fields are marked *