What are Personalized Recommendations?
Personalized recommendations are product suggestions tailored to individuals based on their unique preferences, browsing habits, and purchase history. These suggestions help online businesses create meaningful connections with their shoppers while encouraging additional purchases throughout the buying journey. Personalized recommendations use a variety of data including browsing history, previous purchases, location, and demographics to provide relevant options. Many personalized recommendation tools are powered by AI as the technology can efficiently create highly relevant recommendations at scale.
The Benefits of Personalized Recommendations
Personalized recommendations foster a more engaging and efficient shopping experience. Here are some of the most impactful ways this benefits businesses and customers:
Higher Conversion Rates
Shoppers are more likely to make a purchase when they see products aligned with their preferences and needs. Personalized recommendations provide this while also simplifying the shopping journey. As a result, more visitors result in sales conversions when exposed to personalized recommendations.
Increased Average Order Value (AOV)
Making it easier for shoppers to find complementary products leads to customers adding more items to their carts This can result in increased average order values (AOV) as your store’s orders include more items per transaction on average. Similarly, personalized recommendations can help surface relevant premium and higher-end products. These too can lead to higher order totals when acted on by customers.
Enhanced Customer Retention
Providing thoughtful, personalized suggestions makes customers feel valued and understood. This positive experience builds trust and loyalty. The more loyal customers are, the more likely they are to return for additional orders. Because repeat customers are more profitable than the cost to acquire new customers, this can have a major positive impact on the business
How Do Personalized Recommendations Work?
Personalized recommendations use machine learning (ML) algorithms that analyze customer data to determine what to recommend for each user. Some of the common forms these suggestions take include:
- Browsing history-based suggestions: Recommends products related to recent searches or purchases.
- Location-based recommendations: Recommends suggestions relevant to the user’s area. For example, apparel for the local sports team.
Affinity-based recommendations: Shows products that other users with similar interests have purchased.