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Today’s online retail industry is rapidly changing and presenting new challenges to ecommerce startups. The markets have become increasingly competitive, to the point where the price of each individual product must change frequently in response to market dynamics. Therefore, even for an online merchant with a couple hundred SKUs, continuous adjustment of prices quickly becomes a challenge.
Repricing merchandise strategically is particularly crucial on Amazon, where sellers constantly compete to land the Amazon Buy Box — a coveted spot that essentially guarantees its winner vast sales. To select products for placement, Amazon uses sophisticated algorithms to assess merchants’ performance metrics such as ratings, reviews, shipping, pricing, and quality of service. For these reasons, optimal pricing of merchandise on Amazon requires sellers to go much deeper than guesstimates.
AI solves this problem by repricing merchandise using complex learning algorithms that continuously assess the market dynamics and changes in competitive environment.
Managing inventory availability across channels is one of the biggest worries for ecommerce businesses. Being out of stock is a nightmare scenario, as it takes days to replenish products and can heavily affect merchants’ revenues. On the other hand, overstocking increases business risks and capital requirements.
The problem with forecasting inventory velocity in a rapidly changing market is that both demand and competition change quite frequently. In such markets, a hindsight perspective traditionally implemented with the help of BI technology is no longer sufficient. In order to reach operational efficiency, retailers must employ accurate demand forecasting and predictive analytics.
Artificial intelligence and learning algorithms can help with order velocity forecasting. They can identify key factors that affect the velocity of orders, and monitor the factors’ impact to accurately model velocity and inventory requirements. The beauty of learning systems is that they get smarter over time, enabling merchants to accurately predict their inventory needs.
The other key aspect of retailer operation is managing the assortment of products — that is, which products to keep selling, which products to add, and which products to discontinue. Like inventory planning, assortment planning requires a good amount of forecasting. Merchants need to monitor market trends and changes in demand to understand the competitiveness of products.
Although a person can analyze the past performance of products and categories, accurate forecasting requires a sophisticated algorithmic model. It must assess the relationships across products, influences of various events, and impact of competition and pricing.
Giants like Amazon and Walmart constantly monitor their product assortment and have a team of data scientists dedicated to this task. For the first time, these advanced capabilities are now available to ecommerce startups, thanks to advances in AI and algorithmic technology.
The beauty of online commerce is that it is completely digitalized. All the data from the operations, the market, and the competition can be consolidated and analyzed. It can be examined historically and now, with the help of AI technology, forecasted as well.
Now is the time for ecommerce businesses to get smarter and reach operational excellence. Logistics used to be the core competency of retail; today, algorithms constantly crunch data, predict market trends, and respond to market changes in real time. Such advancements are only possible because of AI.
This article was republished from: https://venturebeat.com/2017/06/19/3-ways-ai-is-already-impacting-ecommerce/