4 Most Important Data science Concept For Hotelier

“What value will I get using your product ??  What are the immediate revenue stream your product produce ??”

So these were some of the most asked question we came across in our sales process and will the try to explain in the most lucid form. Apart from providing the most simplest way to check-in , our expertise lies in crunching data and providing insights from it. The travel industry is growing by leap and bound we believe that data will play an integral part in the growth strategy.

Introducing Data science for Hoteliers.

The Hotel business is an information rich industry that catches colossal volumes of information of various kinds. Discover how Customer Segmentation, Energy Consumption, Investment Management, and Resource Allocation for it tends to be reformed utilizing enormous information examination.

1. Identifying the Right Customer: For the hotel industry, identifying the unique cluster groups then conduct a separate value segmentation exercise for each cluster . For example if we cluster the group in high valued or pampered group (using spa or other luxurious services ), a group for foodie and a group for explorer. This segmentation can be used for marketing strategy which is data driven. In terms of Data science clustering(https://en.wikipedia.org/wiki/Cluster_analysis ) is an unsupervised algorithm which learns from data having similarity , where similar group of people can be clustered as one group.

2. Forecasting: Forecasting is the process of making future prediction with the use of past and present data Forecasting is one of the strengths of data mining and enables hoteliers to better plan to exceed the needs of its guests. Forecasting helps in more efficient staffing, purchasing, preparation and menu planning . For example what would be the guest count for a hotels during a festival season can be a  forecasted based on previous festival season trends. Forecasting is an important tool used in data science, algorithm such as time series(https://machinelearningmastery.com/time-series-forecasting/ ) is used for forecasting.

3. Process Improvement: Digital foot print of ticket logs can largely improve the the process. Average time of opening and closing of a ticket talks a lot about process .  This data can be traced and be used to reduced operational cost and minimize bad experience caused due to delayed service. Feature selection (https://en.wikipedia.org/wiki/Feature_selection ) is an important step in data science to identify the features which have the maximum influence on the outcome Similarly data will help to identify the cause of delays with in the hotel.

 

4. Personalization: With the use of data mining capabilities customer’s behaviour can be tracked and processed to provide an ultra-personalized experience. For example, a guest is a frequent business traveller and have a special sweet spot for your restaurant. Tracking this information, you can offer a special discount for food order on this next visit through a marketing email.

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