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. InData Labs was entrusted to provide a high-end solution, so this case study highlights InData Labs’ proven expertise in the e-commerce niche.
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.
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:
As a result, relevant categories of users will get recommendations that will make them feel more satisfied with the Client’s services.
We delivered the model to fit in with the Client’s business needs and be used for completing the following tasks:
Our solution perfectly met the following Client’s business needs:
With the help of professional predictive analytics development services provided by the InData Labs’ team, the Client acquired an AI-powered MVP.