BigCommerce Personalized Recommendations: Using AI To Suggest Products To Your Customers

A Man Holding a Credit Card for an Online Purchase

Artificial Intelligence (AI) has revolutionized the way businesses operate in the digital world. The eCommerce industry is no exception, with more and more online retailers realizing the importance of using AI technology to improve their customer experience. One of the latest additions to the AI-driven eCommerce toolkit is personalized product recommendations. In this article, we will explore how BigCommerce is using AI to suggest products to your customers through personalized recommendations.

What Are Personalized Recommendations?

Personalized recommendations are product suggestions that are tailored to a specific customer’s needs and preferences. These recommendations are generated using AI algorithms that analyze the customer’s browsing and purchase history to predict what they might be interested in buying next. For online retailers, personalized recommendations can help increase sales, improve customer retention and loyalty, and ultimately drive overall business growth.

How Does BigCommerce Personalized Recommendations Work?

BigCommerce is a leading eCommerce platform that enables online retailers to build and grow their online stores. To offer personalized recommendations to customers, BigCommerce uses a proprietary AI algorithm that is built into its platform. The algorithm analyzes a customer’s browsing and purchase history, their interactions with other customers, and their demographic information to suggest products that are most relevant to them.

Data Collection

To generate personalized recommendations, BigCommerce collects data on a customer’s behavior and preferences. This includes their browsing history, purchase history, and product interactions. The platform also collects data on other customers who have similar preferences and behavior to the customer being analyzed. This data is used to create a customer profile that can be used to generate personalized recommendations.

Predictive Analytics

The BigCommerce AI algorithm uses predictive analytics to analyze a customer’s behavior and identify patterns that might suggest what products they would be interested in buying. The algorithm uses machine learning techniques such as clustering and collaborative filtering to make accurate recommendations.

Dynamic Content

Personalized recommendations are displayed on a customer’s homepage, product pages, and checkout page. The recommendations are displayed as dynamic content that changes based on the customer’s behavior on the website. This allows BigCommerce to deliver the most relevant recommendations to each customer at the right time, increasing the chances of conversion.

Why Use Personalized Recommendations?

Personalized recommendations have become a popular tool for eCommerce businesses for a number of reasons.

Increased Sales

By suggesting products that a customer is more likely to buy, personalized recommendations can increase the chances of conversion. The more relevant the recommendation, the more likely a customer will be to add it to their cart and complete the purchase.

Improved Customer Experience

Personalized recommendations provide customers with a better shopping experience by suggesting products that are most relevant to their needs and preferences. This can improve customer satisfaction and loyalty, leading to repeat business and positive reviews.

Cost Savings

Personalized recommendations can also save retailers money by reducing the cost of acquiring new customers. By retaining customers and encouraging them to make repeat purchases, businesses can reduce their marketing and advertising spend.

Best Practices For Using Personalized Recommendations

While personalized recommendations can be beneficial for eCommerce businesses, it’s important to use them in the right way. Here are some best practices for using personalized recommendations to improve your customer experience and increase your sales.

Use Customer Data Responsibly

When collecting customer data, it’s important to respect their privacy and use the data in accordance with data protection laws. Retailers should be transparent about how they are collecting and using data, and give customers the option to opt out of personalized recommendations if they choose to do so.

Segment Your Customer Base

Not all customers have the same preferences, so it’s important to segment your customer base based on their behavior and demographics. This will allow you to generate more accurate recommendations that are tailored to each customer segment.

Use A/B Testing

A/B testing can help you identify which personalized recommendations are the most effective at driving sales. By comparing different versions of your recommendations, you can optimize your strategy and improve your conversion rates.

Continuously Analyze Your Data

Keeping abreast of your customer data is crucial to generating accurate and relevant recommendations. It’s important to continuously analyze your customer data so that you can adapt your recommendations to changing customer behavior and preferences.


Personalized recommendations are a powerful tool for eCommerce retailers looking to improve their customer experience and drive sales. Using AI-driven algorithms, BigCommerce is helping online retailers generate more accurate and relevant recommendations for their customers. By following best practices for using personalized recommendations, businesses can improve customer satisfaction, encourage repeat business, and ultimately grow their business.

Scroll to Top