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Review of the Machine Learning

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Many of the eCommerce players like Macy’s, Amazon, Flipkart are competing against one another, instituting niche technologies and personalizing every nuance of customer experiences to enhance revenue generation and stay ahead of other market players.

Machine learning, an application of Artificial Intelligence, is a technique that is being adopted by the e-commerce players recently, to help improve customer’s shopping experience and make it more efficient and engaging. Below are some of the areas in which ML can help improve revenue for the eCommerce players.

ML helps to define pricing policies based on customer’s price sensitivity and product’s demand. In detail, ML tracks every move of the whole customer experience: what the customer looks for, frequency of their visits, shopping patterns, navigating pages, cart items, recent purchases, customer’s geography, etc. and gives in-depth insights on pricing position, shopping cart abandonment rate, competitive data, historical pricing, revenue generated, etc. This analysis can help improve businesses to reposition their pricing policies, recover lost sales, boost conversions and generate high profits.

MI uses techniques such as marketing mix, the share of wallet (amount of business a company gets from specific customers), bundling (offering two or more complementary goods as a package deal), etc. to identify potential customers and boost the value of the purchase. These techniques allow businesses to suggest shopping recommendations for online shoppers, personalize promotions for repeat customers, and enable seasonal demand offers and cross-selling for targeted audiences. Companies use promotions, offers, and deals to achieve a fair trade-off between volumes of sales and profit percentage.

Optimally managing inventory is one of the key factors to manage revenue effectively. Based on customers past transactions, ML can figure out products on demand and supply chain processes. When the entire supply chain is accurately tracked from order to delivery, ML can suggest optimal procurement policies for stocking high performing, profitable products and avoids stockpiling low-value products. ML also suggests assorted products (mix of merchandise that suits customers), alerts on replenishment status (to avoid out-of-stock conditions) and helps in tackling cancellation looms.

For successful product launches, it is essential for businesses to understand the market segments and the target audience. ML offers 360-degree analysis of the size of the market, the scope of the product within the segment, accessibility of customers, homogeneity in terms of their preferences and characteristics, quantified results for product launches within the segment in the recent times. This in-depth research adds value to decisions along with insights from competitor analysis, proprietary analysis, promotion channels, and niche audience.

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