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What machine learning can bring to the e-commerce sector

Machine learning can effortlessly quantify buying behavior over and over again, each time digging deeper into trends. In nutshell, it is a powerful tool to recommend what your customers didn't know they wanted.

Tags: Machine learning, e-commerce, customer, warehouse, buying behaviour, demand, online

BY Guest author  |  May 29, 2018  |  comments ( 0 )  | 
Machine learning

By: Ishan Gupta, MD India - Udacity

Rising smartphone and high-speed internet penetration have indelibly changed the way Indians transact. With more and more individuals coming online from tier II and III cities boosting India’s e-commerce market to grow at a 30% CAGR and hit a gross merchandising value of USD 200 billion by 2026. Part of this extended period of growth is expected to be driven by the use of artificial intelligence and machine learning to optimize India’s e-commerce sector.

Machine learning algorithms have the capacity to absorb, analyze, and derive insights from information at a much faster rate than any human being is capable of. E-commerce platforms have a lot of data-driven functions that, regardless of the effort expended and the thought applied, will never hit peak efficiency because of human limitations. Coordinating all the different moving parts – managing sellers, customers, inventory, logistics, warehousing, etc. – requires an inhuman ability to engage with information. By analyzing their unbelievably large quantities of data, machine learning systems can impact the e-commerce ecosystem in a variety of ways. These systems are already aiding the sector in customer acquisition, process optimization, supply chain management, and customer service. Here are some of the ways in which machine learning systems are impacting the e-commerce sector, both in the present and the future.

 

Displaying the right wares to the right audience

Product recommendations are one of the important use cases of machine learning in e-commerce. The implausible ability of machines to predict your choices and requirements has now become the backbone of the e-commerce industry. Machine learning algorithms are thus using customer data to make personalized offerings, which increases their sales and profitability. For e.g. Amazon claims its tremendous amount of sales from product recommendation engine.  Machine learning can effortlessly quantify buying behavior over and over again, each time digging deeper into trends. In nutshell, it is a powerful tool to recommend what your customers didn’t know they wanted.

 

Pricing, inventory, and supply chain management

Machine learning systems can also make accurate predictions that can help e-commerce platforms target a product in a manner that optimizes sales. It can draw a clear pattern between price fluctuations and sales, determining over time at which price there is the largest profit for the e-commerce platform. It can also wary the price to lessen demand when the stock of the product is low or increase demand when there is too much of a product in the warehouse. Further, it can help companies organize the collection of raw materials, organize the logistics and transportation of goods, and handle all these complex functions in the most efficient way possible.

 

These machine learning functions and systems are in the process of being implemented on various e-commerce platforms all across the world, creating a massive demand for professionals with the expertise to create and manage these artificially intelligent systems. The hyper-competitive nature of the sector means that stakeholders in India’s e-commerce industry are always on the lookout for resources that can help them rise above their rivals. Investing in re-skilling yourself in the field of machine learning is, therefore, an assured way to make yourself more attractive for employers in this sector, as they will be keen to deploy you to integrate machine learning systems into their current business model.

 

Dynamic pricing and pricing optimization

Many companies now moving towards Dynamic Pricing Model with the help of pricing algorithms i.e. increase prices when the demand is high and decrease them when the demand is low. While demand is one factor, dynamic pricing also has plenty of other variables that can also be used to estimate optimal prices, such as prices of competitors, time of day, warehouse stock, or season. Machine learning technology can change prices to account for many factors at once.

 

Getting the right pitch to make the sale

Machine learning’s capacity to predict customer wants and desires extends to the arena of digital marketing. There are so many contextual ads that exist on search page results, social media, and web pages. However, with so many ads present everywhere, individual products and services being promoted by e-commerce companies don’t always get noticed. However, using machine learning algorithms, digital marketing can become better and better at finding the right words and right products to sell to an individual customer, based on their profile. It can also provide insight into the time of day, the channel of communication, and the exact content of the message that would make the difference between an ignored ad and a completed sale.

 

 

 

 





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