Hotel industry is data rich industry that captures huge volumes of different types of data. However, for most hoteliers data remains under-used and under-appreciated asset. Many capture loyalty information, but few go deep in analytics in order to deepen their customer knowledge and develop of more granular understanding of customer's needs. preferences, and identify new opportunities to attract new patrons.
Analytics in hotel industry is often used to segment guests according to booking trends, behavior and other factors in order to reveal their likelihood to respond to promotions and emerging travel trends. It is vitally important for hoteliers to be able to understand guest preferences (locations, activities, and room types), purchase behavior (frequency, length of stay, time of year) and profit potential in order to increase the brand loyalty and wallet share of their most valuable guests.
To maximize profits, hotels need to increase the loyalty and wallet share of their most valuable guests by marketing to their preferences and encouraging repeat visits. Focusing on the wrong guests reduces profitability across the enterprise. For example, if a hotel targeted guests who would likely take advantage of spa services, golf and restaurants, rather than guests who only generate room nights, they could significantly increase revenues and profitability.
Unfortunately, money often gets spent on blanket campaigns that don’t target individual guests or segments with offers they’re most likely to respond to. As a result, guests may feel that the hotel doesn’t care about them, or simply doesn’t offer services designed to meet their needs. It becomes easy for those guests to switch to a competing hotel.
For analytics to truly be a game changer, hospitality organizations need to recognize the difference between reactive and proactive decision making. Using their historical data they can create reports, drill-downs or alerts to keep a finger on the pulse of their patrons, but real difference is made by using advanced analytics that can help hotels figure out why things are happening, show what will happen next, and even lead to the best alternative action considering all of the operating constraints.
Following are some examples of analytical application in hotel industry:
For the hotel, initial task identify the unique cluster groups and to then conduct a separate value segmentation exercise for each cluster. Hotelier could then examine its current business customer base and once again establish unique groups of business customers. For example, they may know that there are groups of business customers that simply use the hotel for overnight stays, while others are there for longer term events held at the hotel. It may be possible to further segment these groups based on some other behavioral and affinity factors.
Customer Profiling Customer profiling is accomplished through in-depth analysis of guest demographics and lifestyle characteristics. Attributes such as income levels, family status, age and sports and cultural interests, if known, can be appended to model guests. Customer profiling can be used to create an e-mail listserv for targeted marketing of current as well as prospective clientele. Prospect profiles can be especially useful in identifying those folks most likely to respond to marketing and/or promotional offers. Profiling can also be important in determining which market segments are most productive and profitable.
Menu Engineering An analysis of menu item sales and contribution margins can be helpful to continuous, successful restaurant operations. While menu engineering deals with menu content decisions, data mining can produce reports to indicate menu item selections, by customer segment, as a basis for operational refinement. For example, Applebee’s has been described as employing data mining expressly for the purpose of determining ingredient replenishment quantities based on a menu optimization quadrant analysis that summarizes menu item sales. Through such analysis the company then decides which menu items to promote.
Productivity Indexing By correlating order entry time with settlement time hotelier can more accurately estimate elapsed production and service times. This data provides insight into average service time relative to customer turnover as well as waiting line statistics. While productivity data is difficult to ascertain, this analysis provides factual data to assist management in fine tuning operations.
Customer Associations and Sequencing Advanced big-data analytics can uncover affinities between isolated events. For example, a guest purchasing the restaurant house specialty is likely to also purchase a small antipasto salad and glass of Chardonnay. Paired relationships provide a basis for bundling menu items into a cohesive meal that simplifies ordering while ensuring customer satisfaction. Menu design can also be manipulated to feature such combinations as unique opportunities for customers. Data associations are often credited with a means for influencing customers to spend more than anticipated or upselling.
Forecasting Forecasting can enable restaurants to better plan to exceed the needs of its clientele through more efficient staffing, purchasing, preparation and menu planning.
Customer Value Within the travel industry, customers have always considered their time at a hotel as an experience rather just a visit. Activities such as fine dining, nightly entertainment, spas, corporate seminars or meetings nurture ccustomer experience. This range of activities is going to have varying levels of appeal among a given clientele. Here, role of analytics can be quite significant in helping hotels to better understand these varying client needs.
In the hotel industry world, analytics can also be used for internal operations. Energy consumption accounts for 60 to 70% of the utility costs of a typical hotel. However, costs can be controllable, without sacrificing guest comfort, by using energy more efficiently. At present times, smart data can help managers to build energy profiles for their hotels.
There are modern software solutions that gather data from multiple sources, including weather data, electricity rates and a building’s energy consumption to build a comprehensive ‘building energy profile’. Through a cloud-based, predictive analytics algorithm, the software can fine-tune whether power comes from the grid or an onsite battery module.
The right room at the right rate
Yield management is nothing new in the hotel industry. Providing different rates to different customers has been done for ages and with success. Big Data offers hotels the possibility to take revenue management a giant leap forward and start offering truly personalized prices and rooms to guests. According to some industry studies, the hotel chain Marriot has been using Big Data Analytics to start predicting the optimal price of its rooms to fill its hotels. They do this by using improved revenue management algorithms that can deal with data a lot faster, by combining different data sets and making these insights available to all levels to improve decision-making.
The American hotel chain Denihan goes even a step further. They used analytics to maximize profit and revenue across thousands of their rooms by combing their own data sets and data from for example review sites, blogs and/or social network website. They understand the likes and dislikes of their guests, optimize their offering and adjust the room rates accordingly.