• Zev Isert

AI in Retail and ECommerce

When the average person thinks of AI, they probably think of robots or sci fi movies. But realistically, AI can be applied to so much more. Below we detail the top 5 uses of AI in retail and ecommerce that can be used to improve your business operations.


Inventory Management


Retail and ecommerce stores can use AI to optimize their inventory management strategies. Algorithms can analyze historical sales data, seasonal shopping patterns, and many other factors in order to predict demand for products. Predicted demand can help stores make accurate stocking decisions and reduce the supply/demand gap. Software can also be used to monitor inventory levels, rather than having to check manually, saving valuable time for staff and allowing them to allocate time to more productive tasks.


Pricing Optimization


AI can also be used to optimize your business’ pricing strategy. An algorithm can analyze your competition’s prices, historical trends, macroeconomic variables, customer buying power, demand and more to choose the best price for each item in your store or ecommerce platform. You can even go further and have AI analyze and show outcomes for different pricing strategies, and strategize moving forward based on that analysis. AI can even predict the best promotional pricing strategies for the future.


Another popular use for AI in pricing optimization is creating a dynamic pricing strategy, where the price of an item will change depending on factors such as competitor pricing and demand. Using AI in pricing is useful in identifying trends and acting on them quickly with your pricing strategy.


Chatbots


Another common use for AI in retail is chatbots. Chatbots can often handle customer service concerns quickly at any hour, and help customers get faster service. Chatbots help streamline your customer service systems: they can handle multiple different conversations simultaneously. They can also be used to resolve complaints quickly and efficiently. Lastly, chatbots can be used to provide the personalization that modern customers desire. They can easily track shipments and give updates on customer transactions.


Personalized recommendation engines


In recent years, customers have come to expect a customized shopping experience catered to their interests. AI is frequently used to provide this experience, through the use of personalized recommendation engines. This frequently increases the amount of purchases made, as well as customer loyalty. The personalized engine will use data to recommend products to users. The AI will recommend based on browsing history, the demographics of the user, historic purchases and the interests of similar users. There are many examples of extremely successful personalized recommendation engines being used to drive revenue and business growth. Netflix even offered $1 million in prize money for those who could improve their recommendation engine because it was such a valuable business initiative for them.


Examples:

  • Amazon

  • Amazon has successfully leveraged personalized shopping. Amazon’s entire shopping experience has several channels of personalization, you can get recommendations on your home page, see which items are frequently bought together, as well as see what other people who have bought the item are interested in. Amazon is one of the most valuable companies in the world so something must be working!

  • Stitch Fix

  • Stitch Fix has built their entire company and brand around a personalized shopping experience. Customers receive clothing based on the recommendations of an algorithm combined with a personal stylist after each customer fills out a customized style profile. Stitch Fix is a great example of how companies can innovate using personalized recommendation engines.


Customer Sentiment Analysis


Customer sentiment analysis is the process of determining the tone of a text segment, whether it is positive, negative or neutral. Retail and ecommerce brands can use sentiment analysis in order to understand customer opinions on things such as product quality, marketing campaigns and trending topics. Large chunks of data are scraped from a variety of sources to perform sentiment analysis, including social media, review forums, websites and more. AI algorithms analyze each piece of information, looking for positive/negative words and then giving the statement a score ranging from positive to negative.


Customer sentiment analysis can be used in retail and ecommerce companies for a variety of operational improvements including:

  • Choosing which products to stock based on online trends

  • Improving products based on online reviews

  • Understanding and improving marketing campaigns based on consumer response