Prem Kiran
Prem Kiran
Founder and CEO

Machine Learning is a subfield of AI that use algorithms and data sets to help forecast and formulate conclusions of AI. An online grocer used machine learning in e-commerce to implement inventory and pricing practices that reduce food waste by 40%.

Manual implementations of pricing and inventory controls are two of many machine learning use cases that can be prone to error and inefficiency when done manually. With machine learning, e-commerce businesses can reduce considerable time and labor costs while enhancing user interactions, leading to better experiences and increased profits. 

The key to unlocking the algorithmic power of machine learning requires a subset of data science skills that are challenging to implement but employed in even the biggest, most complex e-commerce firms with specialized software (as in this AI Demo). 

This article highlights the many use cases in which e-commerce businesses can leverage machine learning algorithms to drive higher profits and improved user experiences.

Key Takeaways

  • Businesses can use machine learning algorithms to analyze complex data sets to improve business processes and increase profits
  • Machine learning algorithms enhance the cost-effectiveness of what would otherwise take manual labor, considerable time, and costs for the same result.
  • E-commerce can leverage machine learning algorithms to improve processes in a variety of use cases 

Machine Learning for Product Recommendations and Customer Predictions

Machine Learning for Customer Marketing

E-commerce businesses utilize product recommendations to enhance the user experience and cart value by displaying product recommendations that the customer might not have encountered otherwise. You might have experienced this yourself shopping online: when you enter a product page, don’t quite like what you see, decide you’re going to click off, but suddenly see a product recommendation that catches your eye. 

Customers might not always buy into recommendations, but product recommendations accomplish two goals: the customer stays on-site longer, creating more selling opportunities, and if the customer does decide to buy, you’ve just increased your sales to what would have otherwise been a lost opportunity. 

Using Machine Learning to Improve Product Recommendations

Intelligent product recommendation algorithms can be difficult to implement and cannot easily factor in varied customer preferences without back-end programmers having to build out impressive libraries of code. However, machine learning algorithms can utilize e-commerce data sets to predict user tastes and shopping habits more accurately. Conclusions drawn from machine learning algorithms can then be implemented on-site for more accurate product recommendations and increased sales.

Machine Learning on Search Products Results

Like product recommendations, online store search results can use machine learning algorithms to process data from online search results. Algorithms return products that are most relevant to the consumer. Better yet, with a large enough data set, companies can optimize customer journeys to cross-sell items that the customer might not realize they wanted!

Targeted Marketing and Customer Lifetime Customer Value

Machine Learning for Customer Marketing

Depending on the industry, new customer acquisition can be costly when factoring in the cost of marketing. Businesses can leverage machine learning to help forecast when and how often customers reorder. 

Imagine you’re operating a beauty supply chain, with the customer ordering the same number of soaps and creams every other month. Two months pass, and the customer checks in but orders nothing. You are likely not to notice as a store owner, but an ML system could.

A machine learning-guided AI could process a data set, compare it to previous values and deduce that it’s the correct time to email the customer a personal discount to use on the next order. E-commerce businesses can leverage machine learning algorithms to help foster customer loyalty and reinforce the brand.

Machine Learning for Inventory Management

E-commerce can leverage machine learning to optimize inventory controls. Have you ever tried ordering food online, and your favorite item is out of stock? Mismanaged inventory is the quickest way to lose a potential sale from out-of-stock items. Having too much inventory isn’t any better. Read how Walmart sold items at a discount to combat extra inventory.

Inventory management can be a complex, logistical process involving item reordering, coordinating suppliers, dealing with manufacturers, accounting for shipping times, predicting demand, and factoring warehousing space and costs. Depending on the organization’s size, it can take tremendous time and labor to manually manage inventory. Wouldn’t it be nice if there was a better way to manage inventory?

Machine Inventory Management

The complexity of interactions that must be tracked and analyzed for proper inventory management is the exact thing machine learning excels at! Data points don’t limit machine learning algorithms, which can crush, analyze, and optimize them to produce an inventory management system that can forecast item reorders more effectively.

Predicting Client Size with Machine Learning

Machine learning systems can process large data arrays, including purchase frequency and the number of items customers buy. AI algorithms can use the data to estimate a client’s size! Not only does this help with inventory management and allows e-commerce follow-ups that offer more personalized offers and let marketers determine which customers require more focus.

Machine Learning for Chatbot Support

Machine Learning for Inventory Management

Customer support can be costly to maintain, and trouble tickets can be difficult to process promptly (depending on the size of the organization.) Machine learning algorithms indeed power adaptable chatbot applications. These chatbots, however, tend to fall short of the complex cognitive requirements needed to resolve unique problems. Luckily, with the proper support team, AI chatbots can augment the customer support team’s ability to resolve client issues.

Advancements in natural language processing algorithms can help resolve basic support questions while leaving the more complex interactions to customer support teams. Chatbots take time to train but can even help upsell, offer suggestions, and create customized coupons. 

Unleash the Power of Machine-Driven E-commerce Solutions with Hypersonix Today!

Hypersonix is an AI-driven product that implements machine learning algorithms in a prescriptive, predictive, and measurable manner. Hypersonix provides autonomous personalization, pricing and demand forecasting, assortment intelligence, shopper feedback & more. Take your e-commerce business to the next level with Hypersonix and request your demo today.