Data Solutions for Hotel Industry

Machine Learning28.08.2023

Hotel industry is one of the largest that didn't start to go through Data Transformation.

When we talk with hotel managers around the globe they name lack of competencies and well-known cases of their competitors as main obstacles to get Data Solutions on board.

We hope this blog post will help some of the hotel groups to start experimenting with Data. At the very least, the costs of such an experiment will be negligible (there are no client expectations) while profits might be enormous.

This is the list of Data applications in hotel industry with the most potential:

1. Customer management

2. Resource optimization

3. Uncovered areas analysis

When we talk about Customer management we mean building a customer profile. For example, which outdoor views the customer prefers, usage of laundry, requirements for WI-FI quality, does the customer travel alone, together with a partner or within a large group of people. Most of this data is easy to collect and easy to act upon, but we haven't seen a single case of that. If a client visited a specific hotel group at least 3 times it's quite easy to understand her visiting profile. Whether it's business or leisure, average duration of stay, affordable price. All of these could be used to improve the offer for the client and win long-term relationship with her.

Resource optimization mostly comes down to prediction of hotel rooms usage and additional services like breakfast, spa and so on. This could lead to improvements in hotel unit economics.

Uncovered areas analysis implies the collection of Data to better understand where to open new hotels and the optimal size of the hotels which comes from the number of bookings that one might expect. These decisions are hard to change so we recommend the hotel groups to be as data-driven as possible in these choices.

Related Blog

Data Analytics05.09.2023

Tez Data was asked to help large e-grocery company to optimize its operational costs. We collected the data on client's competitors and defined pickers management as the main source of improvement. Our solutions were targeted at 2 goals.

First, we wanted to better predict the amount of work (basically, the amount of orders) that was required at a given hour.

Second, we tried to make the work more efficient.

First target was measured in standard bias and variance terms. Specifically, we picked MAE and WAPE metrics for that. Second target was measured as an average time to collect the order. To better predict the amount of orders we changed the existing approach in spreadsheets to more advanced Machine Learning based model. This allowed us to account for special occasions like holidays and to faster react to changed conditions. After many iterations our client were able to predict the amount of orders 20% better than it was done before.

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