The client is a leading integrated travel and travel-related financial services company with a global footprint. Offering a comprehensive suite of services—from leisure and business travel to foreign exchange, visa assistance, and travel insurance—the company serves millions of customers worldwide. With a strong omnichannel presence and deep expertise in cross-border financial solutions, the company is uniquely positioned to support customers at every stage of their journey—whether for business, leisure, or expatriate needs.
As a leader in integrated travel and travel-related financial services, the company sought to enhance operational efficiency by accurately predicting customer conversion rates. However, their vision was hampered by fragmented and insufficient data. Customer interactions flowed in from multiple touchpoints—web platforms, mobile apps, and support centers—but the data lacked consistency, structure, and completeness.
The Chief Information Officer (CIO) faced mounting pressure as legacy systems and out dated data repositories made it difficult to create a unified view of the customer journey. Without proper data preparation and enrichment, predictive models lacked reliability, and key business questions around conversion remained unanswered.
The company recognized that meaningful forecasting required more than just collecting data—it required refining and aligning it for AI-readiness. Identifying information gaps, enriching datasets, and uncovering behavioral patterns were critical first steps. Only then could the business transition from reactive operations to proactive, insight-driven decision-making—unlocking real value across customer engagement, marketing strategy, and revenue optimization.
To help the client move from fragmented data to actionable intelligence, Datamatics deployed its AI/ML-powered platform, TruAI, as part of an integrated data science solution. The first step was to identify high-performing branches across the client’s operations and shortlist datasets that could offer the most reliable insights. This approach ensured a focused, quality-first foundation for the analytics model.
To uncover hidden conversion patterns, the Datamatics team applied advanced machine learning techniques, including Artificial Neural Networks (ANN) and Random Forest algorithms. These models enabled the team to process large volumes of structured and unstructured data, revealing nuanced behavioral trends and journey-level insights.
A custom polling engine was also implemented to evaluate the performance of different models and select the most accurate outputs. By automating this model-selection process, the team ensured consistency, speed, and precision in forecasting customer conversion rates.
The TruAI platform seamlessly brought together AI, machine learning, and predictive analytics—transforming raw, disconnected data into actionable business intelligence the client could trust.
Accuracy achieved
Improvement in existing conversion rate
Established for positive/negative impact of customers
Of customer conversion identified
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