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Published on February 2nd, 2025 | by Bibhuranjan

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How to Use Generative AI for Product Recommendations?

Generative AI is not a buzzword anymore, especially in the eCommerce sector. It has become an integral part of business strategies, operations, and solutions, all aimed at one goal: enhanced customer experience. And including its capabilities, has significantly helped multiple brands like Amazon, Netflix, and Sephora improve the customer experience as well as business productivity. Gen AI is rapidly changing the entire eCommerce workflow–starting from how the product is designed, displayed to the customers, and delivered.

But one of the major use cases of Gen AI in the context of commerce is product recommendations. There are multiple ways it helps to improve the recommendation process, making it more personalized for customers and leading to a satisfactory experience.

Moreover, to seamlessly implement Gen AI capabilities within your system, you must get expert Generative AI strategy. The partnership helps navigate existing gaps and integrate custom AI models to power the recommendation procedures. Nevertheless, let us deep dive into how Gen AI can be used to fuel product recommendations.

What are Gen AI-powered Recommendation Engines?

Gen AI-powered recommendation engines are software solutions that leverage a combination of machine learning and deep learning algorithms to analyze a vast array of data points, including customer behavior, purchase history, browsing patterns, product views, customer ratings, and reviews, and then generate the best recommendations as an outcome.

These algorithms function particularly using supervised learning techniques, enabling the engine to create dynamic customer segments and allowing for more personalized recommendations. For example, Amazon released its very own recommendation engine, known as “Amazon Personalize” that uses Gen AI algorithms to make more personalized and accurate recommendations for each customer.

The idea of Gen AI-powered recommendation is to deliver the right message to the right customer at the right time so that businesses can significantly increase conversion rates and deliver an optimal customer experience.

Key Applications of Generative AI in Product Recommendations

Now that we understand what Gen AI-powered product recommendation is, this section will take it further with key applications of this technology in fueling the recommendation processes.

Personalized Shopping Experiences: The foremost application of Gen AI in product recommendation is to create personalized shopping experiences by recommending specific products based on user purchase history and browsing behavior.

Conversational Commerce: The next use case is conversational commerce, which is a highly popular commerce trend nowadays. Bringing this trend to life are the Gen AI-powered chatbots that suggest/recommend products to a customer during natural conversations. These suggested products result from the data the chatbot model has compiled based on the particular user’s purchase and browsing history.

Context-Aware Recommendations: Apart from personalization and conversational commerce, Gen AI-powered product recommendations are also tailored based on the context, like the current season, event, or a specific trend. The trained recommendation engine model will suggest summer clothes in June and winter clothes in December.

Cross-Selling and Upselling Opportunities: Gen AI in product recommendation also helps in improving your upselling and cross-selling efforts. It provides intelligent prompts for relevant, complementary products once an item is added to the cart. This helps to keep the customers engaged and encourages them to buy all the products in one go.

To implement these capabilities within your store, you will need Gen AI strategy services from a professional firm. They will understand your requirements and build a custom ecommerce recommendation system that boosts conversions, personalizes customer experience, and positions you for long-term growth.

How to Implement Generative AI for Product Recommendations

Now let’s understand the step-by-step process to implement Gen AI capabilities for the product recommendation use case.

Step 1: Define Recommendation Goals

Before implementing generative AI for product recommendations, clearly define your business objectives. Understanding your goals ensures that your AI solution aligns with the outcomes you aim to achieve.

The ideal recommendation goals can be:

  • Upselling: Suggest higher-value products based on user preferences
  • Cross-selling: Recommend complementary products to increase cart value
  • Customer Engagement: Deliver hyper-personalized recommendations to improve user experience
  • Inventory Optimization: Promote products that need to move faster
  • Seasonal Campaigns: Provide time-sensitive product suggestions

For example, If your goal is to increase customer lifetime value, the AI model can recommend subscription plans or higher-margin products based on purchase trends.

Step 2: Collect and Prepare Data

Once you have the goals in place, the next step is to collect and prepare the data which will be fed to the AI model for accurate recommendation. The reason why data collection and preparation is done is because Generative AI models require high-quality, diverse datasets to deliver accurate product recommendations.

The types of data you need to collect are customer interaction data like browsing history and click patterns, purchase history, product details, user demographics, and user feedback data.

Once all the data is gathered, now you need to prepare it. This will be done by cleaning the data, adding labels, and making defined segments.

Step 3: Choose a Generative AI Model

Now you have the goals and data covered, the next step is to choose the Gen AI model. The right model depends on your business needs, available resources, and technical expertise. Make sure to consider the scalability, compatibility, and customization capabilities of the model to make the final decision.

There are two popular model choices:

  1. Pre-trained models: These are the models like OpenAI’s GPT models or Google Cloud Recommendations systems, which do not require groundwork training and can be fed with the data to quickly implement the recommendation procedures.
  2. Custom Models: This is like developing a new customized model to cater to your unique business requirements. An experienced Gen AI consulting company like Successive Digital can build a custom AI model for product recommendation to align with the complex needs of your business.

Step 4: Train the Model on Collected Data

Once you choose the AI model, it will now need to be trained on your collected and prepared data. This is done to help the model learn patterns and generate relevant outputs. Also, continuously train the model with regular updates to ensure it evolves and provides better recommendations.

Step 5: Integration with eCommerce System

Once trained, the generative AI model needs to be seamlessly integrated with your eCommerce platform to deliver real-time recommendations. The Gen AI consulting company will help in this step.

These are the integration points where you can connect the Gen AI model to:

  • Product Pages: Suggest related products
  • Search Results: Offer personalized search results
  • Checkout Page: Recommend complementary products
  • Email Marketing: Generate personalized product suggestions
  • Chatbots: Enable conversational product recommendations

Step 6: Test, Monitor, and Optimize

Once the model is integrated, you will need to run tests to evaluate the model’s performance and accuracy. Remember that effective implementation requires continuous testing, monitoring, and optimization to maximize the effectiveness of recommendations.

Additionally, make sure to update training data regularly to reflect changing customer behavior and help the AI model provide more accurate results. For instance, if customers frequently ignore specific recommendations, adjust the model to provide more contextually relevant suggestions.

Conclusion

In the complex world of eCommerce, where delivering personalized shopping experiences is the major priority, Gen AI serves as a powerhouse fueling the business capability to not only personalize the customer journey but also connect with them better. The major use case of this robust technology in the eCommerce context is product recommendation. By utilizing machine learning and deep learning algorithms to analyze and study customer preferences and then provide suitable suggestions accordingly. Moreover, the key applications of Gen AI in the recommendation process extend beyond merely suggesting a product. It is also applied to:

  • Chatbots that suggest relevant items naturally
  • Context-based recommendations for specific events and seasons
  • Support cross-selling and upselling efforts to boost the profit margin on orders, increase customer engagement, and enhance the shopping experience.

In a nutshell, Gen AI can be the guiding angel for your business. So, are you ready to leverage it for your business? Connect with a Gen AI consulting company to bring this technology transformation to life for your business.

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About the Author

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Editorial Officer, technofaq.org I'm an avid tech enthusiast at heart. I like to mug up on new and exciting developments on science and tech and have a deep love for PC gaming. Other hobbies include writing blog posts, music and DIY projects.



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