How Does Machine Learning Benefit E-Commerce Businesses?

Muneeb Chawla

· E-Commerce

A rapidly evolving area of computer science, machine learning involves using algorithms and data to imitate the ways humans learn, gradually improving its efficiency and accuracy. Machine learning is a form of artificial intelligence that utilizes large amounts of data to make predictions or decisions.

Over time, the more data the technology receives, the more accurate the output becomes. In this way, machine learning algorithms seem to “learn.” This is slightly different than artificial intelligence (AI), which refers to the wider range of technology that exhibits human traits. While machine learning falls under this umbrella, AI might also refer to sensing or adapting instead of exclusively learning.

The applications of machine learning to online retail and E-Commerce are numerous. Because online retailers depend on algorithms to sort through large amounts of data, machine learning can help online business owners increase efficiency and improve outcomes across many areas.

First, machine learning can help online retailers develop effective marketing campaigns. With more access to customer data than ever before, e-commerce companies can sort through customer data to target specific groups with relevant marketing materials. For example, if a customer browses a clothing website and adds a pair of jeans to the cart but does not buy, the company can utilize the customer’s email address in a re-targeting campaign. In this case, the company might send a reminder email or a link to a similar pair of jeans.

Machine learning can also help online retailers increase their conversion rates. When someone visits an online retail shop, the conversion rate measures how many shoppers make a purchase.

Converting browsers into buyers comprises a key aspect of online retailers’ business strategy.

Machine learning algorithms can create targeted search results for online shoppers, ensuring they see items they truly want to find. Natural language processing allows algorithms to understand what shoppers type in the search bar and offer accurate results, even if the person did not type a specific brand or product.

Machine learning delivers smart product recommendations by recognizing patterns in shopping behavior over time. The algorithm learns which content a customer interacts with and which websites and products the customer browses. The next time the customer searches, they receive recommendations for items related to previous searches.

When a customer experiences a problem with a purchase or shipment, machine learning can make the service experience more efficient. Online chatbots can answer common questions about products or provide personalized advice, reducing the need for human agents. These messaging bots can increase customer engagement across e-commerce sites and various social media channels.

Along with improving customer-facing processes, machine learning can help online retailers improve operational efficiencies. Maintaining the appropriate inventory in optimal amounts can be challenging, as retailers cannot always accurately predict customer demand. Algorithms can analyze customer data to predict future demand and calculate online sales, tax considerations, and warehousing costs. Using these data-informed insights, e-commerce companies can make decisions that ultimately save money and streamline operations.

Machine learning allows business owners to make more confident decisions supported by large amounts of data. Whether they are adding a new product line to their inventory or exploring the financial viability of drop shipping, machine learning processes large volumes of information quickly and delivers actionable results.

 

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