Case Study 
AI & ML Services
Improving User Experience Through Bespoke Ingredients Recommendations!
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Overview
We worked on creating an ingredient recommender system for an e-commerce website that wanted to improve its user experience.
They wanted to utilize Machine Learning to provide the best recommendations to every client, as per different attributes, to improve results and get repeat customers.
Our Contribution
A leading beauty e-commerce store that offers multiple brands wanted to provide personalized recommendations as per users' skin concerns. So, we created a custom recommender system using AI and ML that suggests the ingredients to use per the client's features, like skin type, texture, etc.
Our Process
After collecting all the data in CVS files, we created numerous segregations to extract insights from the data and created the perfect database for machine learning. Now, depending upon the user's input, the machine can offer recommendations of ingredients identified and categorized to yield results for their skin and texture.
The resultant product was deployed on the client's website as a tool that customers could use to avail custom recommendations per their needs.
About the Business
Our client offers skincare products through an eCommerce site on the internet and physical stores.
They have a range of products with various ingredients that are marketed for their potent ingredients.
Requirements
With the lockdown underway, more users were shopping online, and they wanted a system that could offer bespoke recommendations like their experts in the physical stores while limiting cost.
What Problem Arose & Our Solutions?
Problem 1
Finding the correct sites
Solution
Any machine learning system is as good as its raw data. So, we only took data from the most credible sights.
Problem 2
Lack of Insights
Solution
While the client’s websites and a few others did offer essential data, there was a lack of insights. So, we had to create several segregations, quantify people’s perspectives using NLP and draw appropriate insights.
Problem 3
How to Extract User’s Requirements
Solution
To understand users’ requirements, we have created a survey that asks relevant questions and only offers recommendations.
The Results
A highly scalable skincare recommendation system that takes input from users to provide ingredient suggestions. It can also be applied to several other industries with only a few tweaks in data.
Creating the tool has helped the organization make bespoke recommendations, ensuring better results, and getting more repeat customers.