Membership Travel Services

Timeline:

6 Weeks

Results:

Booking Rate 2X

Team Size:

2 People

Industry:

Travel

Company Profile

A national member services association and service
organization offering travel and tourism services was
looking to combat shifting market dynamics.
Corporate leaders had an objective to target
prospective customers with pinpoint precision by
investments in data science and machine learning
(ML) to better target the right customer, with the
right offer, in the right place, at the right time.

The Problem

The travel division of a member services organization required guidance to emerge from the recent mass disruptions in consumer travel due to the COVID-19 pandemic, while struggling to compete with smaller, more agile, technology-driven travel start-ups. Leadership, and key stakeholders, quickly realized that the approximately $10M in advertising it was spending annually was becoming increasing ineffective and sought to reverse its declining profitability, energize slumping advertising redemption rate of 1% – 1.5% and strategize how to better target prospective customers.

The Skylab74 Solution

Skylab74 was selected to scope, develop and
implement a more quantitatively rigorous approach
to targeting customers. The team developed a set of
generalized linear models that were integrated into
the client’s CRM, marketing and operations
technology suite that more accurately identified
members most likely to redeem travel offers. As a result, the organization saw 15% growth in
travel booking and a significant decline in customer
acquisition costs. Skylab74 allowed the client to
successfully achieve their desired objective: grow its
travel business at lower acquisition costs by
implementing a more targeted approach to
customer acquisition.

"Skylab74 allowed the client to successfully achieve their desired objective: grow its travel business at lower acquisition costs by implementing a more targeted approach to customer acquisition.“

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