Sunday, August 9, 2015

Big-Data Analytics in hotel industry


 Hotel industry is another industry where effective use of analytics can change dramatically how business is run. It is another data rich industry that captures huge volumes of data of different types, including video, audio, and Web data. However, for most hoteliers data remains an underused and underappreciated asset. Hoteliers capture loyalty information, for example, but few go beyond loyalty tier in how they consistently view and take action with their guests. With analytical exploitation of their data, hoteliers can go beyond their traditional loyalty programs and deepen their knowledge of guests in order to develop a more granular understanding of segment behavior, needs, and expectations; identify profitable customer segments and their buying preferences; and identify opportunities to attract new guests. But all that starts with having clear customer-driven vision, before embarking on Integrating and standardizing guest data from multiple channels, systems and properties into a unified, accurate view of all interac­tions.  
Next phase is using analytics 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. 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.
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.
 
 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 your data to create reports, drill-downs or alerts helps you to keep a finger on the pulse of your business. But these things only show you what happened. They will not tell you why the problem is happening or what effect it will have in the future. Predictive analytics, like forecasting and optimization, can help you figure out why things are happening, show you what will happen next, or even lead you to the best alternative action considering all of your operating constraints. Hoteliers are starting to use more and more predictive analytics to move from reactive to proactive decision making, which would enable them to stay one step ahead of trends, set strategy and achieve goals. They gain advantage over the competition, increase value to shareholders, and continue to surprise and delight their guests. Following are areas where analytics can play essential role:
 
Customer Segmentation
For the hotel industry, a more useful approach might be to identify the unique cluster groups and to then conduct a separate value segmentation exercise for each cluster. For example, for a given hotel we identify 4 basic clusters or distinct customer groups such as tennis groups, ski group, pampered group (e.g. use spa and valet type services) and the nighthawk group ( fine dining and theatre goers). The segmentation approach might look as follows: initial learning from this type of segmentation could be used in developing a marketing strategy that is data-driven.   The hotel could examine its current business customer base and once again establish unique groups of business customers. For example, we 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 industry sector. We would certainly expect that local events held by the oil and gas industry might be more appropriate in one city, while financial services type events may be more prevalent somewhere else. Of course, all this supposition on what might define unique business segments would need to be determined quantitatively through clustering routines. By using the data and mathematics rather than intuitive judgment to define key customer segments, we can develop unique programs that are appropriately geared to different groups of unique business travelers. Customer experience has always been the overriding customer philosophy within the travel industry, long before the advent of data analytics. Yet, with data analytics, the travel industry can now use information to make better decisions regarding its customers. This enhanced decision-making capability enables hotels to be more proactive with its customers. Traditionally, success in the hotel industry has always been determined by superior customer service actions that address the immediate requested needs of the customer. The competitive advantage in today’s hotel industry is driven more by the ability to anticipate and proactively meet the needs of customers; an ability that can only be exercised through data analytics.
 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.
 Site Selection Data mining can also be essential to determining sound criteria for restaurant site selection given an index derived from an analysis of high-volume, successful units. Such items as demographics (customer profile) and psychographic (buying patterns), and related customer descriptors are used to delineate highly probable factors for site modeling. As a result, evaluation data and analytical profiling qualify companies to be better able to identify candidate sites.
Forecasting  Customer transactional data (segmented by menu item and day part) can be useful in the development of a forecasting model that accurately produces meaningful expectations. Regardless of whether a restaurant company relies on moving average or time series forecasting algorithms, data mining can improve the statistical reliability of forecast modeling. Estimating in advance how much and when menu items will need to be prepared is critical to efficient food production management. Data mining can provide a prognostication of product usage by day part given available sales data. In addition, knowing how much product was sold during any meal period can also be helpful in supporting an effective inventory replenishment system that minimizes the amount of capital tied up in stored products.
Customer Relationship Management  An effective CRM program can be a direct outcome of data mining applications. The ability to enhance CRM given rapid accessibility of more comprehensive management information should lead to satisfied clientele and improved sales performance. The ability to anticipate and affect consumer behavior (influence menu item sales and other promotions) can provide the restaurant with a competitive advantage. Having a signature item, for example, can be found to be a driver of improved relations while providing a product that customers do not perceive as having an equivalent elsewhere.
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 (POS time stamped) with settlement time, data mining is able to provide a reliable estimate of 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 (heart of the house and dining room staff).

 Customer Associations and Sequencing 
 Data mining 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 As mentioned previously, forecasting is one of the strengths of data mining and enables restaurants to better plan to exceed the needs of its clientele. Forecasting enables 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 / meetings nurture this notion of ‘customer experience’. This range of activities is going to have varying levels of appeal among a given clientele. The role of data mining and analytics can be quite significant in helping us to better understand these varying client needs. Our first task might be to conduct a basic customer value exercise in order to ultimately identify our best customers. As with many analytical exercises, the concept of seasonality needs to be considered here. Seasonality is a very significant factor within the hotel industry. Most analysts would agree that for the travel industry, the issue of seasonality can potentially have a significant impact on travel behaviour. For example, one traveler may spend $1,000 annually as a casual traveler throughout the year and is considered an “average customer”. Another traveler spends $1,000 annually, but on a tennis package for one week period. Both customers spend the same amount but are in fact very different types of customers. This notion of seasonality is significant when conducting any analytics exercise particularly if we consider that many hotels will offer tennis and golf packages in the summer and ski packages in the winter. In addition to the issue of seasonality, there are various services that may have more appeal to certain groups of customers. Fine dining and theatre may appeal to one group of customers while spas and perhaps valet type services appeal to another group. With varying interests amongst travel clientele, a “cluster type” segmentation exercise would be a very useful way to identify different groups of customers. Experts in the travel industry would certainly agree that there are distinct or homogenous customer segments. Using the data being captured on travel customers, we can apply some Science to identify truly distinct customer segments. How do we integrate the notion of customer value within the cluster segmentation approach? Typically we might conduct a value segmentation exercise on the entire customer base and then overlay the cluster segments to see how they align with customer value.
 Personalized Marketing and Website Optimization By tracking and processing your customer’s behavior and actions, you can provide them with personalized offers that are more effective and give a personal touch. Let say for example, you have a client that visits your hotel restaurant on a frequent basis due to business. When you are planning your next promotional campaign, make it targeted and personal. Send an email to this client saying “We know you have enjoyed our great restaurant in the past, so when you visit next week, here’s a coupon for a free appetizer and drink”. There are various marketing automation tools out there that facilitate this process and allow you to deploy an effective and personalized cross channel marketing strategy. Another area where you can use data in order to boost business is to optimize your website or landing pages through A/B testing. Are you implementing a marketing campaign, but the conversion rates of your landing pages are not as anticipated? An easy solution is to resort to A/B testing. A/B testing is the act of running a simultaneous experiment between two or more pages to see which performs or converts the best. 


Energy Consumption 

 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. 
 Investment Management 
 Another way to use analytics for the hotel industry is for financial performance and investment. When managers want to proceed to make capital investments, like refurbish the lobby or the rooms or renovate their restaurant, they can consider implementing a “Randomized Testing” strategy. How does this work? Basically the hotel chain would refurbish the lobby and rooms in only two or three “test” hotels. Then, they would monitor if there has been a difference in bookings and customer satisfaction. The data obtained from the test hotels can then be compared to the data of the other hotels that were not refurbished. Thus, managers can take a data driven decision and clearly see if it’s profitable to make the investment throughout the whole chain. In conclusion, data analytics can be a powerful force in transforming the hotel industry. From taking evidence based actions to developing customer centered marketing and pricing strategies, increasing the ROI of capital investments and generally empowering hoteliers to make bigger and better decisions.  There are however also some great examples of hotel chains moving in the right direction in respect to use of analytics. This can result in improved customer satisfaction, personalized marketing campaigns and offers so that the right guests book the right room at the right moment and at the right rate. In addition, it can boost in employee productivity and more efficient operations. The advantages of using analytics and data mining the hotel industry are enormous. Deep customer insights can lead to improved guest satisfaction and an unforgettable experience. Making these insights available to all levels and departments within the hotel is crucial. It allows the concierge to know which local tours to recommend that fit your preferences. It allows the restaurant departments to predict which menu items are likely to be ordered, based for example on the local weather. It allows the reservations department to predict the optimal rate for a room and sales and marketing to create tailored messages across different (social) networks and send truly personalized email campaigns. Let’s dive a bit deeper in some possibilities:
 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. The massive growth in booking websites, hotel review websites such as TripAdvisor and Yelp and the ever growing list of social media networks offer a lot of potential. Combined with hotels’ own CRM systems and/or loyalty programs there is a lot of data that can be used to optimize revenue management. 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 software 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.
 Mobile Big Data throughout the Hotel
More and more hotels have developed mobile Apps that guests can use to book a hotel room. These apps however offer vast more possibilities for guests if developed correctly. It could serve the key to your hotel room; it can be used to make reservations in restaurants and spa’s and for example to order room service. If hotels start using the vast possibilities of mobile application they can generate massive amounts of data that can be analyzed. So, from a guest perspective, mobile offers a lot of convenience. From an employee perspective, it can make life a lot easier for the staff while at the same time increase customer satisfaction. Providing the housekeeping department with smart devices for example will allow them to know in real time, that you prefer an extra pillow or an extra light. Kempinski and Hyatt in Dubai already use such applications for their hotels. Most of the staff within hotels do not have an office or a computer so providing them with real-time guest information should be done on-the-go. Although this requires a different approach and a different way of presenting the insights, placing user-friendly analytics in the hands of guest facing employees will definitely improve customer satisfaction.

More efficient hotel operations

 From a hotel operations point of view, big data offers also many different solutions. Big Data can be used to reduce your energy bill for example. By combining data from 50 different sources, including electricity rates, weather data and a building’s energy consumption, two InterContinental hotels in San Francisco managed to reduce their energy costs by 10-15%. They created detailed energy profiles for their buildings and using a predictive algorithm they decided whether to use an onsite battery module or receive power from the grid. Hotels should also use analytics more to help more efficiently running their  IT operations, which is especially relevant for chains that operate their own booking engine. A server that breaks down or a booking engine that is inaccessible could result in lost bookings and therefore lost revenue. IT operations analytics monitors a hotel’s complete IT environment, including the different relations between applications and hardware and can predict when things are about to go wrong. Advanced IT operations analytics can even solve problems automatically before they occur. This could save a lot of money because IT that’s not working will results in a bad customer experience. Of course the examples given here are just a few of the massive possibilities that analytics has to offer for the hotel industry. Data mining technology can be a useful tool for hotel corporations that want to understand and predict guest behavior. Based on information derived from data mining, hotels can make well-informed marketing decisions, including who should be contacted, to whom to offer incentives (or not), and what type of relationship to establish. Data mining is currently used by a number of industries, including hotels, restaurants, auto manufacturers, movie-rental chains, and coffee purveyors. Firms adopt data mining to understand the data captured by scanner terminals, customer-survey responses, reservation records, and property-management transactions. This information can be melded into a single data set that is mined for nuggets of information by data mining experts who are familiar with the hotel industry. However, data mining is no guarantee of marketing success. Hotels must first ensure that existing data are managed—and that requires investments in hardware and software systems, data mining programs, communications equipment, and skilled personnel. Affiliated properties must also understand that data mining can increase business and profits for the entire company and should not be viewed as a threat to one location. Since data mining is in its initial stages in the hotel industry, early adopters may be able to secure a faster return on investment than will property managers who lag in their decisions. Hotel corporations must also share data among properties and divisions to gain a richer and broader knowledge of the current customer base. Management must ensure that hotel employees use the data-management system to interact with customers even though it is more time consuming than a transactional approach.

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