Automating Product Recommendation Systems using Artificial Intelligence
Client
Perla PliCategory
Product developmentDate
February, 2024Our Partner
Perla Pli is a professional natural haircare cosmetics brand committed to sustainability. In addition to selling high-quality natural products, they are dedicated to delivering a personalized experience for their customers. To enhance this aspect of their business, they offer product recommendations tailored to each customer's needs, determined by a questionnaire they complete. This personalized approach elevates their brand and enhances the overall customer experience.
The Challenge
The current process of product recommendation involves a professional manually generating the results from a questionnaire. This is time and resource consuming for the company and also reduces the conversion rate, as responses are sent a few days after the questionnaire is completed. Furthermore, the deisgn of the form is limited to the restraints of the e-commerce platform, making it impossible to make it a multistep form that could generate a higher number of leads.
The Proposal
The project consists on automating the recommendation process by introducing a database and product matching algorithms that will generate the response and send it via email to the user in a matter of seconds. The questionaire will be fully redesigned to become a multistep form which allows saving the user's progress obteianing valuable leads. Artificial intelligence will be incorporated to enhance guidance and generate better personalized responses that align with each user's needs.
The Solution
We developed a custom React + Next.js application integrated into their Shopify platform using an iFrame, enabling complete customization of the form's user interface (UI) and user experience (UX). The product recommendation process is fully automated, with recommendations automatically sent via Mailchimp services. This setup not only saves leads but also facilitates the automatic generation of marketing campaigns based on user interactions. Additionally, Open-AI's text generation API was integrated to create personalized responses tailored to each user's preferences and needs.
Tools and libraries
Results
Automation and Efficiency
By automating the recommendation process and introducing a database with product matching algorithms, Perla Pli significantly reduces manual effort and time spent on generating recommendations. This automation streamlines the process, enabling quicker response times to customer inquiries and reducing the delay between questionnaire completion and receiving recommendations.
Improved User Experience and Engagement
Redesigning the questionnaire into a multistep form enhances the user experience by allowing users to save their progress. This not only improves convenience for users but also captures valuable leads for Perla Pli. The multistep format can engage users more effectively, leading to increased completion rates and potentially higher conversion rates.
Enhanced Personalization with AI
Incorporating artificial intelligence into the recommendation system enables Perla Pli to provide more accurate and personalized responses tailored to each user's specific needs and preferences. AI algorithms can analyze user data more effectively, leading to better product recommendations that align with customer preferences and behaviors.
Business Growth and Marketing Opportunities
The streamlined recommendation process and improved user experience contribute to overall business growth. With a more efficient system in place, Perla Pli can capitalize on increased customer engagement and satisfaction, potentially leading to higher conversion rates and increased sales.
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