Recent technological advances have brought online and offline retailers new tools to better understand customer behavior, streamline inventory management, and improve pricing optimization. 

But while retailers can gain a great deal from existing analytical techniques, artificial intelligence capabilities such as machine learning add insights and improve performance even further. McKinsey estimates that AI can add 87%, or $600 billion, of value to retailers, by optimizing core business processes to drive growth and reduce costs. 

With’s automated machine learning platform, retail businesses can add autoML (automated machine learning) to their business toolset. allows retailers to create effective models that predict demand and analyze pricing data, so that they can maintain ideal stock levels and hit optimum pricing for all products, on all channels, without the need for a robust data science team. 

Here are the two most important use cases for automated machine learning in the retail sector. 

Sales and demand forecasting

Overstocking leads to a loss of income through unmade sales, and the unfortunate waste of valuable inventory space on items that don’t sell. Yet when retailers run out of stock for high-demand items, they miss out on valuable sales now, and see an ongoing drop in revenue as customers abandon them for their better-stocked competitors. 

With demand forecasting, you can predict product sales accurately, so that you can always have the right amount of stock on hand. McKinsey notes that predictive forecasting improves accuracy by 10-20% for consumer goods retailers, which translates into an average 5% drop in inventory costs and 2-3% rise in revenue. 

Demand forecasting with autoML helps retailers to overcome the fluctuations of seasonality and periodicity, improve their accuracy and granularity in inventory management, and maximize the efficiency of stock replenishment cycles. 60% of retailers want to take advantage of machine learning for sales and demand forecasting. empowers you to quickly and simply build a time-sensitive model that balances all factors for streamlined, cost-effective inventory management. 

Pricing optimization

Retailers want to sell each product for the highest possible price, while customers want to pay the lowest possible amount. Predictive pricing optimization helps you to find that sweet spot which maximizes your profit without disenchanting customers. 

Inevitably, the ideal price for each item fluctuates according to the weather, stock markets, promotions by local competitors, and seasonal elements. What’s more, the ideal price for an item in a brick and mortar store is rarely the same as for the same item sold online. 

Big retailers apply autoML algorithms to stay ahead in the ever-changing, multi-channel, price optimization wars. With, you can create accurate, sensitive machine learning models that predict changes in the marketplace to calculate the optimum price for every product on every channel. delivers new efficiency for the retail industry

The retail industry can use autoML to boost profits and implement savings. By applying machine learning models, retailers can improve sales and demand forecasting to save money on overstocking and avoid stockouts, while also raising revenue through smart pricing optimization.’s easy to use automated platform brings machine learning-powered inventory and pricing insights within easy reach of every retail business, without the need for a robust data science department.