Big Data Analytics in Hotel Industry

The Hotel industry is another data rich industry that captures huge volumes of data of different types. Find out, how Customer Segmentation, Energy Consumption, Investment Management, and Resource Allocation for it can be revolutionized using big 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 clients. 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.

 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 clients 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 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 up-selling.

As mentioned previously, forecasting is one of the strengths of data mining and enables restaurants to better plan to exceed the needs of its clients. 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 clients. 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 clients, 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.