Who is the Client

A US-based retailer with more than 700 departmental stores and 150 specialty stores, client is a major player in their industry and one of the oldest shopping destinations for their customers.

The Challenge

The client's existing rule-based product recommendation system was unable to handle product sequencing for a large number of products, as it was entirely a manual process. The client was aspiring for an automated product sequencing system that can extract data from multiple databases and process the information to rank the products based on the historical sales pattern.

The client wanted to deploy a Machine Learning (ML)-based trained model to overcome the limitations of the existing manual process (rule-based approach) for their non-personalized product recommendation system.

The Solution

GSPANN used machine learning techniques and automated the process while considering the historical product sales pattern. We replaced the rule-based system of product ranking and placement with automated Python scripts to deploy a more accurate and dynamic product ranking system.

GSPANN analyzed that the existing product recommendation system was rule-driven, manual, and suffers from various limitations. We provided a proof of concept for the ML-based trained model, which was automated, dynamic, scalable, flexible, and more accurate in ranking the products to achieve higher sales.

The ML-based hybrid system approach was a perfect fit for the new products (with no historical sales pattern) and old products (with available sales pattern) ranking on the e-commerce website.

GSPANN developed an ML trained model to display the product’s listing based on the historical sales pattern. We used Python for programming the ML model, which extracts the historical data from multiple databases and then processes the information to rank the products based on the probability of getting higher sales.

Business Impact

  • The product sequencing ML-trained model was more accurate than the traditional rule-based approach.
  • The ML-trained model was dynamic and more reliable in predicting the product sequence, which resulted in higher sales.

Technologies Used

SQL. A standard database language used to create, maintain, and retrieve a relational database
Apache Hive. A data warehouse software project built on top of Apache Hadoop to provide data query and analysis
Python. An interpreted, high-level, and general-purpose programming language that enables programmers to write clear and logical code

Related Capabilities

Utilize Actionable Insights from Multiple Data Hubs to Gain More Customers and Boost Sales

Unlock the power of data insights buried deep within your diverse systems across the organization. We empower businesses to effectively collect, beautifully visualize, critically analyze, and intelligently interpret data to support organizational goals. Our team ensures good returns on the big data technology investment with effective use of the latest data and analytics tools.

Do you have a similar project in mind?

Enter your email address to start the conversation