IBM Watson has become a household name in AI. The software was originally created to beat the best human chess players, but has since morphed into a “do anything and everything” machine. Now, it is one of the most visible examples of artificial intelligence in our world today.
While AI feels futuristic because of examples like Watson, it's actually not a new concept within retail. AI and machine learning technologies have been in the industry for years, and both have already made a huge impact. Platforms like Amazon and Jet.com have been using this technology on the back end to adjust prices, monitor demand and even encourage consumers to buy more (see Jet.com’s dynamic pricing model, which lowers prices as consumers add to their cart).
Real-Time Price Adjustments
Prices on e-Commerce marketplaces (Amazon, Jet.com, eBay) change similarly to stocks on the trading floor — they fluctuate by the minute, experience “flash crashes,” and respond to constantly shifting market conditions. Adding to this dynamic environment is the fact that more sellers than ever are competing to sell the same products.
For these business owners to stay competitive, they have to adjust the prices of their SKUs to respond to every shift in market conditions. For sellers with only a few products, this can be done manually. But for high-volume sellers, this becomes a full-time job.
This is where AI comes in. These systems can monitor and assess market conditions to continually set prices that give the optimal balance of market share and profit margin for each individual product.
A Smoother And Automated Supply Chain
For e-Commerce businesses, running out of stock is one of the worst things that can happen. Even with the speed of today’s supply chain fulfillment and shipping options, stock can only be replenished so fast, and brand equity can take a heavy hit as a result. On the other hand, stocking too much inventory can drive up costs that aren’t recouped, as businesses face selling items at a loss or not at all.
However, forecasting inventory isn’t easy, as the level of demand from consumers and the breadth of industry competition also change frequently. In this new paradigm, the old methods of inventory planning (analyzing past sales with rudimentary software) are no longer effective.
AI can help sellers combat supply chain issues through the application of modern demand forecasting and predictive analytics that monitor market changes in real time to more accurately predict the volume of inventory needs. And the best part? These systems only get better with time, as they gather more data about each business and competitive environment.
Accurate Demand Planning
Once business owners have organized and updated their supply chain, the next step is to assess product assortment at a granular level. Ensuring that a product is simply available is a great first step, but this type of planning means that the right sizes, colors and more are always in stock. The pace of consumer demand and speed at which products are announced means that sellers need to know daily which products to keep, which products to add and which to get rid of.
Sellers need to be aware of specific market trends, demand and above all, product competition. Sometimes product categories are so competitive that only companies that do an incredible amount of sales can even break even, and sellers need to be aware of these categories, otherwise they risk losing money.
Accurate assortment forecasting requires a system that can assess the relationships across products, influences of sales events and the impact of competition and pricing, and make real-time recommendations. Companies like Walmart and Amazon constantly monitor this area and have entire teams of data scientists working on these tasks, but now individual companies and sellers can use these capabilities in their businesses through algorithmic tech and AI.
One of the most useful, but also most challenging, parts of online commerce is that everything is recorded. Every piece of purchase data, operational data, market data, competition data and more can be dissected, analyzed and learned from. Until recently, it was difficult to separate the signal from the noise, but advances in AI and machine learning have given companies the tools to dig into this information and come away with actionable insights.
Now, no marketplace seller or small business should go without these tools. Companies need to make these investments a priority, and when they do they will soon reap the benefits.
Before founding Feedvisor, Victor Rosenman was a founder of an innovative social media marketing startup and a senior R&D manager at Sun Microsystems. Victor holds a B.Sc. in Computer Science and an Executive MBA from Kellogg Northwestern.