8 Great Benefits of AI in eCommerce You Should Know

Artificial Intelligence (AI) is transforming eCommerce by offering numerous advantages for both businesses and consumers. Try to name any online industry that isn’t taking advantage of artificial intelligence (AI) and machine learning (ML) technology and I think you’ll be hard-pressed.

The benefits of machine learning and AI in eCommerce to automate tasks, analyze data, and generate innovative solutions are becoming clear to eCommerce vendors, and bit by bit they are revolutionizing the way we sell online.

In this article, we discuss some benefits of AI in eCommerce and the power of conversational AI in eCommerce.

Benefits of AI in eCommerce

Here are some innovative ways in which artificial intelligence (AI) and machine learning systems can benefit eCommerce stores.

1. Virtual Assistants

Virtual assistants are the public face of AI in eCommerce. They are becoming increasingly common across sectors from finance to legal services and beyond.

We’ve all been approached by one of these friendly helpers before. These are chatbots that can answer our questions regarding a product or service, and they are popular because they are a great low-effort way of engaging potential customers.

That being said, virtual assistants are not an all-out solution for your customer service, they are a useful branch of it. Make sure to allow customers the option to contact a human representative to discuss questions or products in detail.

Also, you can use data from the kinds of questions asked of your virtual assistants to evaluate how informative your product pages are.

2. Lead Generation

Behind the scenes of the eCommerce site, customer data is sometimes more valuable than regular dollars. Knowing what your customers like and how to improve your service to them leads to far increased revenues in the future, as long as you can interpret the data.

“That’s where AI comes in,” says Frances Marlinson, a tech writer at LastMinuteWriting and Writinity. “AI and machine learning are great at consuming vast amounts of data and revealing patterns and connections.

Using a machine learning system on your customer data can help you discover new specific purchasing trends in a much shorter timeframe than if you had human analysts working on the job.”

3. Reducing Cart Abandonment

Customers abandoning their purchases right before confirming the transaction is the bane of any eCommerce platform. All the work you’ve put into making a sale is suddenly lost for any number of reasons from slow loading, hidden costs, or cumbersome checkout procedures. 

Machine learning can help in two ways. Firstly, by analyzing multiple cases of cart abandonment it can point to certain moments of the checkout procedure that seem to deter people most, which you can then focus on with your UX.

Secondly, it can help to prevent cart abandonment in all instances by sending automated emails whenever a customer leaves their cart, perhaps including incentives like discount vouchers or free shipping offers to prompt them back to purchasing.

4. Inventory Management

On the flip side is the sold-out message; as annoying to customers as cart abandonment is to eCommerce stores.

In this case, the customer is set and ready to buy but is suddenly thwarted because their product is out of stock. Getting your hopes up and then being disappointed can disillusion your customers and may eventually push them to other stores.

Machine learning can help you avoid this scenario by predicting stock movements. A machine learning system can analyze purchase and inventory numbers to predict that a certain product will sell out at a certain time. This can help you order more inventory in anticipation of selling out and avoid losing that sale.

5. Personalized Search

Incorporating machine learning into your store’s search functionality can help customers gain a personalized experience with your store. Machine learning systems can be used to analyze past search data and suggest products based on the customer’s data portrait, allowing them to more easily find the products they’re looking for.

On top of that, search data can be used across customers to find keyword associations between related products. Some eCommerce experts believe that machine learning systems can be trained on keywords to present more relevant search results.

For example, a jacket might not have “waterproof” in its name, but based on previous searches the system knows that customers who are looking for a waterproof jacket click through and often buy this jacket. Therefore, subsequent searches for the waterproof jackets will show the jacket in question.”

6. Localized Searches

Another more specific angle of personalized searches is pushing certain products to localized customers. This involves the customer’s location as another data set in their profile, allowing you to predict if certain products might appeal to customers in specific areas.

This might be based on other customers’ purchases in the same area or perhaps based on delivery costs. As an example of the latter, eBay employs a system that gives preference to sellers closer to the buyer to reduce shipping costs and allow for ease of collection, each increasing sales.

7. Personalized Recommendations

The next implementation of personalization comes in the form of product recommendations. This is commonly associated with Amazon, with its “you might like” feature, or outside of eCommerce in media platforms like Netflix which recommend movies or TV shows to individual users. 

Both of these systems use machine learning to study customer purchases and notice patterns or categories. These are then compared to other customers who also opt for these patterns or categories, and suddenly there emerges a list of products that this customer may like.

As customers engage with this list the data can get more sophisticated and more personalized, promoting customer engagement, and driving sales.

8. An Affordable Tool

The bottom line of AI in eCommerce is that it is an affordable tool for making sales generation more efficient. All of the features in this list could be implemented by human designers, marketers, and customer service representatives; indeed, they have been for many years.

The difference is AI and machine learning systems can do them in half the time and for a fraction of the cost. But they are still tools, and like any tool, they have to be used correctly by a skilled person to do the job.

Conversational AI in eCommerce

Conversational AI in eCommerce is creating more engaging, personalized, and efficient shopping experiences. Through chatbots and virtual assistants, businesses can interact with customers in a natural and human-like way, addressing queries, providing recommendations, and facilitating purchases.

Here are some ways conversational AI in eCommerce is a gamechanger:

• Enhanced Customer Experience: Conversational AI offers 24/7 support, personalized recommendations, and quick issue resolution, leading to higher customer satisfaction.

• Increased Engagement: Chatbots and voice assistants encourage more interaction, leading to longer session times and higher conversion rates.

• Improved Sales: By understanding customer preferences and providing tailored product suggestions, conversational AI can boost sales and revenue.

• Deeper Customer Insights: Analyzing customer interactions provides valuable data on preferences, behaviors, and pain points, enabling data-driven decisions.

• Cost Reduction: Automating customer service tasks can significantly reduce operational costs.

Challenges of Conversational AI in eCommerce

While conversational AI offers immense potential, several challenges must be addressed for successful implementation:

1. Natural Language Processing (NLP)

Developing sophisticated AI models capable of accurately understanding and responding to a wide range of customer queries, including complex requests, nuances, and variations in language, remains a significant hurdle.

Ensuring that chatbots can effectively interpret and respond to user input is essential for providing a seamless and satisfying customer experience.

2. Integration

Integrating conversational AI seamlessly with existing e-commerce platforms, payment gateways, and other systems is crucial for a smooth customer journey. Challenges may arise in data synchronization, API compatibility, and ensuring consistent user experiences across different channels.

3. Privacy and Security

Protecting sensitive customer data is paramount. Implementing robust security measures, adhering to data privacy regulations, and building trust with customers is essential for maintaining a positive brand reputation.

Balancing the need for data collection to improve AI capabilities with customer privacy concerns requires careful consideration.

4. Scalability

As the volume of customer interactions increases, conversational AI systems must be able to handle the increased load without compromising performance.

Scaling infrastructure, optimizing algorithms, and ensuring efficient resource utilization are critical for maintaining responsiveness and meeting customer expectations.

5. Human-in-the-Loop

While AI can handle many customer inquiries, there will always be situations that require human intervention.

Effectively integrating human agents into the conversational AI workflow is essential for handling complex issues, providing exceptional customer service, and ensuring a seamless transition between AI and human support.

6. Training Data

High-quality training data is essential for developing accurate and effective conversational AI models.

Collecting, cleaning, and labeling data can be time-consuming and resource-intensive. Additionally, ensuring data diversity and representativeness is crucial for avoiding biases in the AI system.

7. Continuous Improvement

Conversational AI is not a static technology. To stay competitive, businesses must continuously monitor performance, gather user feedback, and refine AI models to improve accuracy, relevance, and user satisfaction.

Implementing feedback loops and leveraging analytics to identify areas for improvement are essential for ongoing optimization.

By carefully addressing these challenges, businesses can harness the full potential of conversational AI to create exceptional customer experiences and drive business growth.