Data mining and analytics – techniques for exploration and analysis of large quantities of data in order to discover meaningful patterns, trends and rules – helps hotels sift through massive data sets for meaningful relationships, where they can anticipate, rather than simply react to, customer needs.

The hospitality industry is known as a highly customer-centered business and accumulates large amounts of customer data from central reservation systems (CRS), property management system (PMS), point-of-sale (POS), and guest loyalty program databases. Therefore, data mining application can play a huge role in the hospitality industry by assisting managers formulate marketing strategies, enhance guest experiences, increase retention and loyalty and ultimately, maximize profits.

However, simply investing in data-mining technology may not guarantee success.

Seven guidelines influence the effective management of data-mining technology

1. Match your IT priorities with a skilled provider who can turn data into useful information.

2.  Build segmentation and predictive models.  A customer could potentially fit into several categories, which poses a challenge for data-mining techniques. As a consequence, finding a provider who has experience creating models in the hotel industry is a major benefit, aided by the expertise of IT and marketing managers.

3. Collect data to support the models. Inadequate data gathering and input lead to a decrease in the value of any data warehouse, in addition to diminishing the value of proposed models.

4. Select the appropriate tools for analysis and prediction such as decision trees, neural networks and genetic algorithms.

5. Demand timely output as it varies widely among data-mining packages.

6. Refine the process. Data mining involves a continuous cycle of inputs and outputs based on models that must be modified and refined as conditions change in the competitive environment.

7. Hire a well-trained staff and a knowledgeable IT manager. Productive data mining requires two-fold proficiency among both IT managers and those who interpret the outputs.

Task categories

Once data-mining is properly managed, the tasks performed can be grouped into five categories:

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