How To Avoid The Product Recommendation “Bestseller” Trap π€
Andrew Figgins
Founder, AOV Lab . October 4, 2023

Key Takeaways π:
- AI for Product Recommendations: Grasp how AI can revolutionize personalized product recommendations, elevating the user experience and amplifying sales.
- Chatbots for Product Discovery: Uncover the utility of AI-powered chatbots in guiding customers to product recs they’ll adore.
- Avoid The “Bestseller” Trap: AI for product recommendations can get stuck in a loop of recommending best-selling products, which helps them sell better. It’s critical that the AI is trained in a way that allows you to introduce new and time-sensitive products that you want to sell.
Introduction π
Artificial Intelligence (AI) is dramatically reshaping the ecommerce landscape, particularly in the realm of product recommendation. With the power of AI, you can deliver highly personalized product recs that resonate with individual customer preferences, thereby boosting engagement and sales.
Strategy 1: AI-Powered Product Recommendations π―
The Imperative for Intelligent Product Recommendations π§
In today’s competitive ecommerce environment, personalized product recommendations are a game-changer. AI algorithms sift through a plethora of data to provide product recs that are not only pertinent but also have a high likelihood of conversion.
Implementation π
For effective product recommendation, integrate AI engines specializing in machine learning-based suggestions. These algorithms will analyze data points like user behavior, purchase history, and even social signals to deliver highly personalized product recommendations.
Key Metrics π
The key metrics to measure the effectiveness of your product recommendation strategy include the click-through rate on recommended items, conversion rates, and average order value. Seeing an uptick in these KPIs will signify the success of your AI implementation.
Case Study: Netflix’s AI-Powered Product Recs π
Netflix’s AI-driven recommendation engine is so efficient that it influences 80% of the content consumed on the platform. Employing similar AI technology in ecommerce can achieve analogous levels of user engagement (Source: Netflix Technology Blog).
Strategy 2: Chatbots for Intelligent Product Discovery π€
Enhancing Product Recommendation with Chatbots π¬
AI-powered chatbots can serve as virtual shopping assistants, guiding customers through your ecommerce store and offering intelligent recommendations based on their queries and preferences.
Implementation π
Integrate a chatbot that uses natural language processing (NLP) and machine learning to understand customer needs and suggest relevant product recommendations. Make sure the chatbot can also handle frequently asked questions to improve overall customer service.
Key Metrics π
Track engagement levels with the chatbot, the quality of recommendations made, and how often those recommendations lead to a sale. High engagement and conversion rates will indicate the chatbot’s effectiveness in product recommendation.
Case Study: Sephora’s Virtual Artist Chatbot π
Sephoraβs Virtual Artist chatbot offers personalized makeup product recommendations based on user selfies. This has led to a 200% increase in user engagement with the chatbot, showcasing the potential for AI in product recommendation (Source: Sephora Innovation Lab).
Strategy 3: Avoid Bestsellers Taking Over Everything π
Leveraging AI for Strategic Product Recs π
AI can also be used for upselling and cross-selling by analyzing customer behavior and offering product recommendations that complement their current or past purchases.
It’s critical that the algorithm is watched closely so that it doesn’t simply take bestsellers and regurgitate them. Early AI recommendation systems struggle with this problem.
Implementation π
Incorporate algorithms that can identify related or complementary products based on individual purchase histories and browsing behaviors. Position these product recs strategically on product pages, in shopping carts, or even post-purchase confirmation emails.
Key Metrics π
Monitor the average order value and the percentage of upsells and cross-sells completed successfully to gauge the efficacy of this strategy.
Case Study: Amazon’s “Frequently Bought Together” π
Amazon’s “Frequently Bought Together” feature, powered by AI, significantly boosts their average order value by complementing the customer’s current selection (Source: Amazon Data Services).
Conclusion π¬
AI is not just an add-on but a necessity in modern ecommerce for effective recommendations. By implementing intelligent algorithms and chatbots, you can significantly boost customer engagement, conversion rates, and ultimately, your bottom line.
Further Reading π
- “The Future of Personalization in Ecommerce” by Adobe Blog
- “Chatbots and Ecommerce: The AI Revolution” by Chatbots Magazine
- “Upselling and Cross-Selling Strategies” by Shopify Plus
Deep Dive
Related articles
How To Enhance Revenue with Effective Augmented Reality Strategies
Key Takeaways π: Introduction π Augmented Reality (AR) is no longer a futuristic concept; it’s a…
How To Leverage 3D Printing for Instant Home Delivery π¨οΈ
Key Takeaways π: Introduction π Why wait for delivery when you can print your purchase at…
How Mobile Wallets Dominates Secure Payments by 2030 πΈ
Key Takeaways π: Introduction π The future of secure payments is in your pocket. By 2030,…