Our client is a well-known name in the Banking and Financial Services industry, that also deals in corporate travel management, with a vast and diverse clientele ranging from small businesses to multinational conglomerates.
Client's travel business unit is committed to providing personalized travel experiences and maximizing client satisfaction, it has propelled them to seek innovative solutions for enhancing their service delivery.
The goal was to increase user engagement, improve customer satisfaction, and ultimately boost business growth through more efficient and personalized services.
Data Quality and Quantity: ensuring the quality and quantity of historical data. Incomplete or inconsistent data could lead to inaccurate recommendations and hinder the effectiveness of the recommendation engine.
Diverse Customer Preferences: Capturing diverse travel preferences accurately and accommodating the nuances posed a challenge in building a one-size-fits-all recommendation system.
Cold Start Problem: Recommending travel options for new customers with limited historical data presented a challenge, often leading to less accurate suggestions. This "cold start" problem needed a solution to provide useful recommendations for new users.
After analyzing different approaches, Ezycon implemented a Hybrid Method, i.e., a combination of collaborative and content-based filtering using data classification and clustering. The rationale behind this decision was the volume and diversity of data at our disposal, which could benefit from the advantages of data classification and clustering and provide highly accurate and personalized travel recommendations.
The hybrid recommendation engine was developed in the following steps:
Data-Driven Insights: data collected and analyzed for the recommendation engine to provide valuable insights into customer behavior, preferences, and trends. These insights could guide business decisions and strategies.
Data Collection and Preprocessing: Gathered data, which included user profiles, past travel details, user feedback, hotel ratings, and more. The data was then cleaned, normalized, and classified for further processing.
Feature Extraction and Selection: Relevant features were extracted from the data, and important features were selected using techniques like Principal Component Analysis (PCA) for model training.
Model Training: The hybrid recommendation system was trained using a combination of collaborative and content-based filtering. This involved training two separate models and then combining their outputs.
Model Validation and Optimization: The model was validated using cross-validation techniques and optimized using parameter tuning methods like grid search.
Integration and Deployment: The trained model was then integrated with Client's existing system and deployed for real-time recommendation generation.
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