Recommender System powered by Machine Learning
Aavatar’s Machine Learning solution features an innovative recommender engine that curates personalized product offerings, enhancing the customer experience. By leveraging data insights, we ensure that each user receives tailored recommendations that align with their preferences and needs.
Business problems solved
Develop a personalized list of recommended products for each client, tailored to their individual interests and profile. This customization enhances user engagement by presenting items that resonate with their preferences and shopping habits.
Provide special discounts for bundle purchases to encourage cross-selling, making it more enticing for customers to buy complementary products together. This strategy not only increases the average order value but also enhances customer satisfaction by offering better value.
Inform clients about new products that align with their previous purchases or interests to encourage up-selling. This approach keeps customers engaged and promotes additional sales by showcasing relevant offerings that enhance their experience.
Business applications
Showcasing tailored product recommendations on your website, in the office, or during contact center calls can significantly boost conversion rates by over 60%. This personalized approach enhances the customer experience, making it easier for clients to find products that meet their needs and encourages them to make a purchase.
Tailored promotional product packages designed to meet specific customer needs and preferences.
Identifying the best time slots for client communication to maximize engagement and response rates.
Enhancing the effectiveness of marketing campaigns through data analysis and targeted strategies.
Methods of making product recommendation
Product Hierarchy
When you purchase a printer, you’ll likely need to buy an ink cartridge as well.
Recommendations based on product features
If you’re a fan of Clint Eastwood’s action films, “The Good, the Bad and the Ugly” on Netflix is a must-watch that aligns perfectly with your taste.
Social and interest graph
This approach relies on trust and social interactions among individuals. For instance, if your friends enjoy Lady Gaga’s music, it’s likely that you will too. This strategy is effectively utilized by platforms like Facebook and LinkedIn.
Hybrid methods
Integrating any of the previously mentioned methods.
SOURCE DATA
for the recommender engine
