Online shopping is now part of everyday life. Customers often visit e-commerce websites to browse different products, compare prices, read reviews and find products that fit their needs. However, there can often be thousands of products on one website so it can take some time to find the right product.
Artificial Intelligence (AI)
Many online stores use AI powered Product Recommendations to help them understand what customers are interested in and suggest products that are relevant. This uses the customer’s browsing history, previous purchases, products searched for and shopping behaviour.
For example, if a customer purchases a mobile phone, an online store may recommend a phone case, screen protector or wireless headphones.
AI Product Recommendations allow online stores to create a better shopping experience, increase engagement and make it easier for customers to find products.
In this blog, we will look at how AI Product Recommendations work, what types there are, the benefits and how they can be used in an e-commerce business.
What are AI Product Recommendations?
AI Product Recommendations are recommendations suggested by an Artificial Intelligence system based on a customer’s interests, preferences and shopping behaviours.
Rather than showing the same products to all customers, AI recommendation systems use customer data to determine which products would be relevant to them.
For example, if a customer frequently searches for fitness items, then an online store may suggest dumbbells, yoga mats, protein shakers or other fitness accessories.
These recommendations can be shown throughout the online store including:
Recommended products on the homepage
Similar products on product pages
Products often bought together
Personalised recommendations via email
Related products at checkout
To help customers find products that are relevant to them.
How do AI Product Recommendations work?
AI product recommendation systems use customer and product data to understand their shopping behaviours and suggest products that are relevant.
Here is how they work:
Collecting Customer Data
Firstly, customer data is collected.
This can include browsing history, purchases made, products searched for, items added to the basket and customer ratings.
If a customer often browses the laptop section, the system can identify they are interested in laptops and accessories.
Understanding Customer Behaviour
Once data has been collected, AI will analyse customer behaviours to determine patterns and favourite items.
This will help the recommendation system understand which products are being looked at, purchased and which ones catch their eye.
Analysing Product Data
AI will also analyse product data such as categories, product description, pricing, brand, features and customer ratings.
This will help determine which products are similar and suggest relevant products.
Giving Recommendations
Once the system has analysed the customer behaviour and product data it will give recommendations.
For example, if a customer purchases a digital camera, they may be given recommendations for camera bags, memory cards, tripods and lenses.
As the customer continues to browse and purchase products, the system can use the extra data to improve future recommendations.
5 Types of AI Product Recommendations
There are various types of recommendations that can be used to help e-commerce businesses recommend products based on customer behaviours.
Personalised recommendations
Recommendations based on customer interests, browsing behaviour and previous purchases.
For example, if a customer frequently purchases skincare products, an online store may suggest moisturisers, cleansers and other skincare products.
Helps customers discover products relevant to them.
Similar Products
AI will help online stores determine which products are similar. This can be based on features, product categories, brand or specification.
For example, if a customer looks at a specific pair of running shoes, they may be shown similar products from different brands or price points.
This helps increase the options available to the customer.
Often Bought Together
AI can analyse previous purchase behaviours to determine which products customers often purchase together.
For example, if customers purchase a laptop they often purchase a laptop bag, wireless mouse or keyboard.
Online stores can use this to suggest related products.
Trending Products
AI can analyse the latest sales, searches and interactions to determine which products are trending.
For example, during the winter season an online clothing store may recommend jackets, jumpers and winter items based on increased customer demand.
This helps businesses identify products that customers are interested in.
Personalised Emails
AI can help businesses personalise Product Recommendations within emails.
For example, if a customer frequently browses home furniture items, an online store can send them emails with furniture ranges, related products and relevant offers.
This helps make emails more useful.
Benefits of AI Product Recommendations
Using AI powered Product Recommendations can provide many benefits to online stores. It helps understand customer behaviours and create engaging shopping experiences.
Improved Customer Experience
Customers won’t need to spend too long searching through hundreds of products. AI will suggest relevant products based on their interests.
Increased Sales
Customers will be more likely to look into further products if they see products related to their interests.
Increased Customer Engagement
Personalised recommendations will encourage customers to look into more products.
Improved Customer Retention
Customers may be more likely to come back to the same shopping platform if they regularly find useful products and relevant recommendations.
Increased Understanding of Customer Preferences
AI can help businesses identify which products are popular, what customers are interested in and what they are purchasing. This can help with marketing and product planning decisions.
Examples of AI Product Recommendations
AI Product Recommendations can be useful for lots of different e-commerce industries.
Fashion Stores – Recommend clothing, shoes and accessories based on Customer Preferences.
Electronics Stores – Recommend compatible accessories and similar products.
Beauty & Skincare Stores – Recommend products based on previous purchases and customer interests.
Grocery Platforms – Recommend products that are often purchased.
Home Decor Stores – Recommend furniture, lighting and décor items based on browsing.
This shows how businesses can use AI to help customers find relevant products.
Challenges
Whilst using AI Product Recommendations can help improve the shopping experience, there are a few challenges to consider.
Customer Data – Businesses must ensure they are handling customer data properly and in line with data protection regulations.
Data Accuracy – AI needs accurate product data and customer data to provide useful recommendations.
Technical Implementation – Businesses may need to integrate the recommendation system with their e-commerce website, CRM and product database.
Cost – Customised AI systems can take an investment.
Customer Satisfaction – Businesses shouldn’t over recommend or recommend products that aren’t relevant. Customers should feel comfortable.
A well planned recommendation system should focus on helping customers.
How can online stores use AI Product Recommendations?
Businesses can start using AI recommendations by doing the following.
Identify the customer’s requirements.
Ensure you have accurate product data – descriptions, categories, pricing and specifications.
Choose the correct recommendation system.
Integrate the AI system with your e-commerce website.
Monitor the performance.
Analyse the results regularly and improve the recommendations.
Businesses can start off with basic recommendations and build up to more advanced AI powered shopping experiences.
The Future of AI Product Recommendations
As online shopping develops, AI Product Recommendations will become more intelligent.
Recommendations may soon understand customer requirements through natural language. This means customers can describe what they need.
AI shopping assistants may also help customers compare products, understand specifications and find suitable items.
Using technologies such as machine learning, predictive analytics and AI agents online stores will have more opportunities to provide personalised shopping experiences.
The future of e-commerce will be more than just showing products, it will help customers find the right products.
Conclusion
AI Product Recommendations are changing how online stores engage with customers. Using browsing history, shopping behaviours and product data AI helps businesses suggest relevant products.
Personalised recommendations, similar products and products often bought together will help customers find products easily and create extra sales opportunities.
However, businesses must ensure they have accurate data, integration and an understanding of customer requirements.
For e-commerce businesses looking to increase customer engagement and create better shopping experiences, investing into AI product recommendation systems can be a great option.
FAQs
What are AI Product Recommendations are personalised recommendations based on Artificial Intelligence. They use customer interests, browsing and purchasing behaviours to suggest relevant products.
How do AI recommendations work in online stores?
AI systems will analyse customer behaviours, product data and purchasing behaviours to determine what products are relevant and give personalised recommendations.
How do AI Product Recommendations increase sales?
It helps customers find related products, similar products and products relevant to their interests. This creates extra sales opportunities.
Can small e-commerce businesses use AI recommendations?
Yes. Small online stores can use pre-built AI recommendations or customised solutions based on their website requirements and budget.
What data is needed for AI Product Recommendations?
Typically, browsing history, previous purchases, products searched for, items added to basket, product categories and customer interactions.
What is the future of AI recommendations in e-commerce?
AI recommendations will become more personalised using machine learning, chatbot shopping assistants, predictive analytics and AI agents.
