Automated processes and improved customer experience.
ChallengeTrain recommendation algorithm based on sparse data
SolutionPredictive analytics module for e-commerce platform
Technologies and toolsPython, Scikit-learn, Implicit, Docker
The Client is an e-commerce provider who cooperates with more than 50 brands. The Client was interested in a solution for generating single-brand recommendations. The business goal was to use machine learning (ML) to increase sales, revamp customer experience, attract new clients, and retain loyal users of the online platform. ESSID Solutions was entrusted to provide a high-end solution, so this case study highlights ESSID Solutions’ proven expertise in the e-commerce niche.
Challenge: train recommendation algorithm based on sparse data
The Client challenged our team to create a custom predictive analytics and recommender system to enhance the existing e-commerce platform and improve sales. And building a decent recommender system, ideally, requires having a large and diverse dataset of user behavior that can include purchase history, product page views, likes, ratings, reviews, etc.
At the start, we faced data limitations. We had only purchase history available at that point. To add more, a substantial part of users had quite a small number of purchases. And that was the only type of user data that we could work with. So, the data shortage complicated our task of tailoring an e-commerce analytics system and training a recommendation model. The challenge was to tap into the limited data on users’ purchasing history and harness it for predictive modeling.
Solution: predictive analytics module for e-commerce platform
We based our development approach on collaborative filtering technique based on matrix factorization used in recommender system. We didn’t use any specific data about the users or products. We gathered only historical data on user-product interactions and also retrieved information about positive preferences of users to item. So we decided to stick with a simple yet well-proven implementation called implicit ALS to train our model on sparse data. Also, we had a reasonable amount of data on the number of user transactions that was provided by the Client. The collected data was used to train the ML model that would power a custom recommender system.
We used the confidence metric to train the model to emphasize items purchased several times over items purchased only once. Also, we had a significant number of users with few purchases, which was not enough for the recommendation model to give a reliable prediction. So, we implemented several additional techniques to enhance the recommendations given by the model. As a result, we made the algorithm to better understand user preferences and avoid recommending the same items to all users.
We applied several filters to make the model more accurate and relevant to the business goals:
- Recommended items have the tag “in stock”
- Users haven’t already bought recommended items
- Users opt-in for email
As a result, relevant categories of users will get recommendations that will make them feel more satisfied with the Client’s services.
Result: ML-based solution to automate processes and improve customer experience
We delivered the model to fit in with the Client’s business needs and be used for completing the following tasks:
- Recommend items to a given user
- Find similar users based on items preferences
- Recommend most likely users to purchase a given item
- Recommend similar items
Our solution perfectly met the following Client’s business needs:
- Make the search process highly personalized
- Automating routine tasks of shop assistants
- Ensure awesome online shopping experience
- Boost customer loyalty
With the help of professional predictive analytics development services provided by the ESSID Solutions’ team, the Client acquired an AI-powered MVP.