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Amazon To Use Machine Learning To Increase Its Footprint In The ‘Kirana’ Market

Amazon To Use Machine Learning To Increase Its Footprint In The ‘Kirana’ Market


Early in 2018, Amazon ambitiously started working in the area of grocery delivery operations in India. With its Amazon Pantry section, it started hyperlocal grocery deliveries, including fresh vegetables, fruits and whole food in Bengaluru, Delhi-NCR, Mumbai and Hyderabad. Amazon is taking the local kirana stores by storm and is challenging the interests and profits of domestic retailers. The company is quite clear with its aim to increase its kirana footprint in India, for which it is majorly resorting to technologies such as machine learning to mark larger benefits.



How Machine Learning Comes Into Picture

Grocery has become the next frontier for e-commerce players with many big and new names trying to capture the market. Amazon too has been experimenting with its grocery play in India and trying out multiple formats to get the best bet. It is quite a unique move by the company as it has not tried something around grocery in any other geography.

The company had earlier mentioned that it aims to increase their engagement with kirana stores from where a shopper can get food and other essentials delivered from nearby stores within 2-4 hours of placing an order. The company had also said that they are looking to increase the company’s delivery network and increase their reach of areas where products can be delivered.

With this new delivery service, the owners of kirana shops can upload their inventories onto the Amazon website for local shoppers to see what’s available in nearby stores. Once the products are identified, they can either be delivered through the company’s delivery service or through logistics companies. All of this will be facilitated by the new age technologies, especially machine learning.

While Amazon has been pushing the use of machine learning in a lot of its initiatives, the recent adoption comes with an aim to increase its kirana footprint in India. As the company promises its commitment to put customer expectations at the heart of every transaction, machine learning will play an important role to help deliver the same.

During a recent conference and addressing a gathering of Amazon executives as a part of its Women In Technology conference, Amit Agarwal, Senior Vice President, India Consumer Business, Amazon said that the company is using machine learning to identify defects in its products. “We use it to check for defects in pages that list these products,” he had said. The company would earlier do it manually which was a cumbersome task as humans would manually scour through pages of information to search for defects. Similarly, in various other inside workings, it uses ML.

It is heavily relying on the use of technology, and thousands of data scientists being hired to work on ML and improvise on the processes, yield a better result and streamline the process.

Amazon’s History With Machine Learning

This is not the first time that tech and e-commerce giant has used machine learning in its functioning. Reportedly the company has invested over Rs 13,800 crore in India, and they are relying heavily on ML and AI to boost up their market share in the country. The company has been using machine learning for more than 20 years, with thousands of engineers being hired to in the new tech space.

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They are adopting transformative technologies in several ways to improve customer experience. Some of the areas where it is heavily adopting ML are product returns, improve the speed and accuracy of product deliveries, provide more relevant search results and improve efficiency in other areas of its business.

Rajeev Rastogi, director, machine learning at Amazon India had said that there are a lot of problems that are India-specific where you need machine learning. He gave an example of address quality, as he suggested that addresses in India are highly unstructured and ML can be used to improve it drastically.

It has also been using AI for product-delivery, location-related queries, standardising sizes in fashion products, market search, advertising, improving catalogue, and more.


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