Data Science / Travel

Membership travel booking rate increases by 15%

A national member services organization used a more rigorous targeting approach to rebuild travel performance.

Travel bookings↑ 15%
Annual advertising spend$10M
Prior redemption rate1–1.5%

Company profile

The opportunity

A national member services association and service organization offering travel and tourism services was looking to combat shifting market dynamics. Corporate leaders aimed to target prospective customers with pinpoint precision through investments in data science and machine learning, helping reach the right customer with the right offer, in the right place, at the right time.

The problem

The travel division needed guidance to emerge from the mass disruption to consumer travel caused by the COVID-19 pandemic while competing with smaller, more agile, technology-driven travel startups. Leadership and key stakeholders realized that approximately $10 million in annual advertising spend was becoming increasingly ineffective. They needed to reverse declining profitability, improve a slumping advertising redemption rate of 1% to 1.5%, and better target prospective customers.

The solution

SDG was selected to scope, develop, and implement a more quantitatively rigorous approach to customer targeting. The team developed generalized linear models that were integrated into the client’s CRM, marketing, and operations technology suite to more accurately identify members most likely to redeem travel offers. The organization saw 15% growth in travel bookings and a significant decline in customer acquisition costs, allowing it to grow its travel business through a more targeted approach to customer acquisition.

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