Predictive Customer Loyalty Analytics for a Global Premier Hotel Chain
Overview
With a portfolio of 550+ hotels globally, one of the world’s largest premier hotel chains sought to maximise revenue from its current loyalty program members through selective tier upgrades.
Since just 2.31% of the 3.8 lakh Tier 4 members in the base achieved tier upgrades annually, indicating significant untapped potential, the client aimed to adopt a methodical customer loyalty data analytics solution to determine which guests were likely to upgrade.
Solution
1- Through a comprehensive Exploratory Data Analysis (EDA) on the client’s loyalty program data, we identified particular time-sensitive, spending velocity, booking channel preferences and behavioural patterns.
2- Using advanced machine learning techniques for a customer loyalty analytics solution, we built a propensity-to-upgrade model to-
- Target guests who upgraded within 1 month
- Create lookalike profiles of successful upgraders
- Give propensity scores (0-100%) for each Tier 4 guest
3- We launched three distinct campaigns based on guest propensity scores, such as-
- High propensity (90-100%)– Those already on the verge of upgrading, requiring more awareness and convenience.
- Medium-High propensity (80-90%)- Guests who require slight encouragement and moderate incentives with emphasis on value reinforcement.
- Medium propensity (70-80%)- Guests who need more specific discounts and offers to reach their spending targets.
Impacts Delivered
- ₹3.3+ crores of total incremental revenue generated from targeted campaigns
- Zero acquisition or brand-building costs and only pure incremental value from existing members
- Higher member lifetime value through accelerated tier progression and customer retention data analytics
- Time-sensitive interventions to increase conversion rates among the high-propensity visitors.
