Business Analytics in the Travel Industry

The travel industry makes extensive use of business analytics to drive loyalty, promotions, revenue management – and ultimately, the customer experience.  

These are not your grandfather’s type of slice and dice analytics, but use a combination of data science, big data and business analytics visualization tools.  Below are a few examples of business modeling in travel and hospitality:

The Booking Performance Model

A predictive model of booking performance was redesigned from scratch: adding new functionality while reducing error and the time to update and run the model by twenty-fold compared to the previous version. This model is responsible for determining:

- Targets and forecasts
- Of bookings and arrival
- Calculated at the book date, arrival data pair level

The model is a multiplicative pick up model, expanded to include:

- Stationarization
- Seasonal decomposition
- Outlier detection
- Frequency domain analysis
- SARIMAX
- Synthetic booking and cancellation curves

The Strategic Business Simulation

This model "looks at" the data and business questions in a unique way, to answer questions that are largely immune to statistical and trend extrapolation models.

The Deep Learning Model

This is a long short-term memory (LSTM) recurrent neural network (RNN) model. It will extract and predict spatial, temporal, and agent patterns of behavior.  

Rapid Response Analytics

Improve the rapid response analytics process to give better data faster by creating high-quality, complex data extracts and aggregations that can be quickly customized for unusual data pulls. This saves days of effort for several very urgent descriptive / diagnostic analytics requests, while drastically reducing turnaround time.

Data analytics revolve around these types of business subjects:

- Sales promotion targeting and performance
- Booking channels
- Competitive analysis
- A/B testing of new initiatives
- Segmentation of guests, assets, and prices
- Analysis of pricing strategies

May 24th, 2017

© 2017 RM Dayton Analytics, Ltd. Co.