The Role of AI and Machine Learning in the E-Commerce Landscape

Tick-tock, tick-tock… the clock doesn’t stop.

You’ve heard the age-old adage “time is money,” but how do we get more of it? Acquiring more time requires more efficiency — which almost always comes at a cost. Activities like inventory research and comprehensive pricing strategy development are time-consuming and sometimes require a data scientist. Luckily, artificial intelligence (AI) and machine learning can eliminate these mundane daily tasks and produce effective automated processes.

To solve inventory challenges, for example, AI can be used to automatically generate purchase orders for products where demand is in a trending cycle. Sellers then have the potential to notify the customer when specified items run out of stock. AI can build useful reports to highlight high-demand items and produce automated actions. In turn, retailers can worry less about investing time in estimating stock or having excess inventory.

AI and E-Commerce

Multiple marketplaces are already adhering to these AI strategies. Amazon’s AI is impacting third-party (3P) sellers by leveraging tools when consumers search for certain brands. eBay’s AI created Image Search and Find It Now. Google’s newly founded Brain Team develops global collaboration for new AI innovations.  

Another time challenge for sellers is winning the Buy Box and maintaining a consistent price across all marketplaces. Repricers alone can select the best price, but often don’t adhere to individual marketplace pricing agreements. ChannelAdvisor’s Price Manager is an AI feature that allows sellers to implement a multichannel pricing strategy to consistently win the buy box and maximise margin while complying with marketplace parity standards.

Being able to accurately depict inventory quantity and order handling can also be extremely daunting for sellers. However, machine learning, in conjunction with AI, finds patterns in data by making predictions and creating new data that can be responsive to the internal functions of the user’s tool. ChannelAdvisor’s Demand Forecaster, which applies machine learning to accurately predict sales on expanding marketplaces, forecasts upcoming seasonal inventory needed to support sales and provides a detailed SKU-level analysis of what to expect during the sales cycle, thus allowing time to pivot product if necessary. Clients obtain critical insights into product-level demand, supplying an accurate forecast of future sales and enabling highly profitable, data-driven, tactical inventory decisions.  

In conclusion, AI and machine learning encompass a multitude of cost-effective tools, saving both time and money for sellers. For optimal solutions, retailers look at past experiences and determine what they want to test. Once designed, an analysis is created to get a sense of what users do or do not want. Reports are then evaluated using an analytics platform that allows more adaptable variables to be chosen and can be configured as well as used interchangeably on multichannels. These beneficial tactics are utilised in ChannelAdvisor’s platform to maximise your potential on marketplaces and optimise your inventory management while using algorithmic repricers, price parity, marketplace/social advertising, drop-ship integrations and fulfilment services.  

Since deep learning emerged 30 years ago, many technologies have allowed companies and businesses to flourish well above the expected norm, making 2018 a year to join in AI successes. With the influx of ever-changing e-commerce trends, AI and machine learning will continue to grow. Our recently released AI/deep learning platform features allow retailers the opportunity to recapture and reallocate more time back into their businesses.