4C - Predictive Analytics for Overbooking Optimization

Predictive analytics to enable hotel sales managers to achieve 100% occupancy while maintaining customer loyalty

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Inspiration

Each year, approximately 1 million guests are relocated due to overbooking. Walking guests costs the hotel industry an estimated $5 billion per year.

What it does

4C is a cloud-based application that leverages predictive analytics to get actionable insights for optimizing overbookings by hotel sales managers. It relies on historical occupancy data, current & future reservations, variable room costs and walk costs to forecast the optimal rate of overbooking.

How we built it

See architecture in gallery above

Challenges we ran into

  • Setting up authorization flow with 3rd-party hotel API
  • Getting access to data & sort through it
  • Narrow down problem scope
  • Develop algorithm & data science to get problem insights

Accomplishments that we're proud of

  • Team-wide contributions where each member was able & willing to help in their field of expertise
  • Fully-functional app that actually works
  • Working data pipelines to ingest & analyze real data in real-time from 3rd party APIs.
  • Rapid focus in defining problem so we can prioritize objectives, divide tasks, plan and execute in short timeframe
  • Fantastic team chemistry

What we learned

  • The power of working in a multi-functional team
  • The invaluable insights one can get from existing data

What's next for 4C - Predictive Analytics for Overbooking Optimization

  • Develop ability to input additional variables such as events and weather to algorithm to calculate cost and opportunity
  • Give hotels ability to view data by day of week, reservation type, loyalty status and more
  • Incorporate data from additional APIs to accurately forecast demand for similar properties
  • Leverage more machine learning to optimize forecast for all variables.
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