Originally published on Phocuswire.com.
Data, data, data. It’s all you’ve been hearing about for the past couple years.
And now that we’re amid a travel pandemic forcing hoteliers to look for additional insights on any available demand, data mining has moved from “important” to “critical.”
But, as we love to ask these days: What does it all mean? What data is out there, where can hoteliers find it, and – once they have it – what can they do with it to run a more successful business?
Often, the idea of collecting and analyzing data falls on the lap of a hotel company’s revenue team. Revenue leaders are tasked with seeking out the most relevant data for them, analyzing it, and then actioning it.
They often lean on tools – revenue management systems and business intelligence platforms – to help.
These tools are crucial simply because there are so many data points available that determining which are most relevant to your property and then crunching the numbers to find the best results simply cannot be done by a human.
As a simple example, imagine you were considering raising your Best Available Rate by $5. What effect would that have on the demand for each of your segments? For each of your channels? How would your competitors react?
A computer can run millions of “what-if” models in one second and spit out these answers to help you make the right decisions.
This, in essence, is the basics of “data science.” It’s about using data to create as much impact as possible for your business, whether that’s optimizing the business more efficiently or building data products more intelligently.
Data science typically follows the following process:
- Collecting hundreds of thousands of data points
- Exploring and transforming the data
- “Cleaning” the data to detect anomalies and determine what matters most to your specific problem
- Analyzing the data
- Determining the best way to apply “deep learning” to that data, whether it be through neural networks or other models.
Taking it a step further, perhaps the most important piece to data science is that machines can find and ask questions of the data that you may not have even thought to ask.
In the full article found on Phocuswire.com, David covers:
- Data science applied to Revenue Management,
- Data science and forecasting,
- Taking the first step