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Case Study
Data ScienceData-Driven Marketing Strategy Transformation
Overview
JashDS revolutionized a travel company's marketing strategy by implementing advanced machine learning models, resulting in a 20% reduction in marketing costs and a 20% increase in passengers served. The solution integrated customer interaction scoring, product ranking, and lifetime value prediction to deliver highly personalized marketing experiences and optimize resource allocation across various customer touchpoints.
About the Client
A leading travel company specializing in authentic cultural experiences for semi-retired American travelers, offering nearly 100 different trip options.
The Challenge
The client followed a product-driven marketing strategy, identifying sets of customers to market specific products to each week. This approach lacked personalization and efficiency, leading to suboptimal marketing effectiveness and resource allocation.
Key Results
- Reduced annual marketing costs by 20% (from $20M to $16M)
- Increased annual passengers served by 20% (from 95,000 to 114,000)
- Improved profitability by optimizing discount strategies
- Enhanced customer loyalty through a more coherent, personalized marketing approach
Our Solution
JashDS implemented a machine learning-driven strategy to shift the focus from products to customers:
- Developed a scoring system for customer interactions across multiple touchpoints:Website browsing data (product, section, time spent, date and time of visit)
Call center data (product, date of call)
Physical survey data (product and date of survey)
Events data (product and date of event) - Created a three-tiered modeling approach:Logistic regression model to score each interaction
Aggregation model to rank products for each customer at the customer-product level
Predictive model to determine the likelihood of booking within 30 days, classifying customers in their booking cycle - Implemented a customer lifetime value (LTV) model based on Recency, Frequency, and other factors
- Designed a comprehensive marketing and customer service strategy combining the booking cycle and LTV models:Personalized marketing materials based on customer classification
Customized website landing pages
Prioritized call routing for high-LTV customers
Tailored special treatments (upgrades, priority boarding, holiday presents) for high-LTV customers
Optimized discounting strategy
Technologies Used
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