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Revolutionize Your Ecommerce Business with AI

Artificial intelligence isn’t just a novel technology implemented for its “cool factor.” Implementing AI has the potential to impact any number of business functions across your organization.




To understand how it could impact your business, it helps to have an understanding of the components of artificial intelligence.


The definition of AI is broad, and encompasses data mining, natural language processing, and machine learning.


  • Data Mining refers to the gathering of both current and historical data to inform predictions.

  • Natural Language Processing focuses on human-computer interaction and how computers interpret natural human language.

  • Machine Learning concerns using a collection of algorithms to apply past experience or provide examples to solve a problem. Deep learning “involves layering algorithms in an effort to gain greater understanding of the data.”

Over the past couple of years, AI technology has matured and become a powerful tool to boost sales and optimize operations. Even many small ecommerce businesses are using technology with some kind of AI capability.


AI Use Cases in Ecommerce

There are plenty of use cases in ecommerce for AI, and you’re probably familiar with a lot of them — you just might not know that the technology they’re built on is actually related to AI.


Here are six of the most common:


  • Personalized product recommendations.

  • Pricing optimization.

  • Enhanced customer service.

  • Customer segmentation.

  • Smart logistics.

  • Sales and demand forecasting.


1. Personalized product recommendations.

It’s easier than ever to collect and process customer data about their online shopping experience. Artificial intelligence is being used to offer personalized product recommendations based on past customer behavior and lookalike customers.


Websites that recommend items you might like based on previous purchases use machine learning to analyze your purchase history. Retailers rely on machine learning to capture data, analyze it, and use it to deliver a personalized experience, implement a marketing campaign, optimize pricing, and generate customer insights.


Over time, machine learning will require less and less involvement from data scientists for everyday types of applications in ecommerce companies.


2. Pricing optimization.

AI-enabled dynamic pricing is a strategy of changing your product price based on supply and demand. With access to the right data, today’s tools can predict when and what to discount, dynamically calculating the minimum discount necessary for the sale.


3. Enhanced customer service.

With virtual assistants and chatbot technology, you can deliver the appearance of higher touch customer support. While these bots aren’t completely self-reliant, they can facilitate simple transactions, leaving live support agents able to focus on more complex issues.


Virtual agents also have the advantage of being available 24/7, so low-level questions and issues can be addressed at any time of day, without making your customer wait.


4. Customer segmentation.

Access to more business and customer data and processing power is enabling ecommerce operators to understand their customers and identify new trends better than ever.


In an insight from Accenture, they write, “AI systems can explore highly complex and varied options for customer engagement very quickly, and continuously optimize their performance as more data becomes available. This means marketers can set parameters and allow the AI to optimize and learn to achieve precision.”


5. Smart logistics.

According to a report from Emerging Tech Brew, “Machine learning’s predictive powers shine in logistics, helping to forecast transit times, demand levels, and shipment delays.”


Smart logistics or intelligent logistics, is all about using real-time information through sensors, RFID tags, and the like, for inventory management and to better forecast demand. Machine learning systems become smarter over time to build better predictions for their supply chain and logistics functions.


6. Sales and demand forecasting.

Particularly in a world during and after COVID-19, you’ll want to plan your inventory on both real-time and historical data. Artificial intelligence can help you do just that. A recent McKinsey report suggests that investment in real-time customer analytics will continue to be important to monitor and react to shifts in consumer demand that can be harnessed for price optimization or targeted marketing.

Benefits of Using Artificial Intelligence in Ecommerce

1. More targeted marketing and advertising.

2. Increased customer retention.

3. Seamless automation.

4. Efficient sales process.


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