with Natalie Osborn, Senior Industry Consultant, Hospitality and Gaming Practice, SAS

This week, we continue our fall “back to the basics” refresher series on analytics for hoteliers. Last week, in part one, Natalie and I reviewed the analytic methods that can be utilized by hoteliers. This week we will explore how key functions within the hotel can leverage predictive analytics. Let’s get started with Pricing and Revenue Management.

Pricing and Revenue Management

Revenue management is generally the most data and analytics intensive department within the typical hotel today. To accurately price rooms, revenue management systems forecast demand, and then optimizes the price and availability of rooms to maximize the revenue from the limited capacity of hotel rooms. Within this process, statistical analysis is used to model no-shows and cancellations, “unconstrain” demand and calculate price sensitivity. At this point, most hotels use a revenue management system that has been specifically designed to execute the complex analytics required to deliver an optimal price. The analytics processes are configured to the hotels’ specific operating conditions and connected to selling systems to deliver price and availability controls.

Still, there are many analyses that revenue management might conduct outside of the revenue management system. For example, they might want to use descriptive analytics to analyze demand for a restaurant or spa as part of a total hotel revenue management program, or data mining to understand how consumers value different attributes of the room to better configure rate spectrums.

Marketing and Customer Loyalty

Marketing and customer loyalty are fast following in the footsteps of revenue management when it comes to utilizing predictive analytics. To develop a better relationship with the guest, segmentation and profiling models are used to group guests in segments that have similar characteristics, whether they are business defined segments or demographic or behavioral defined segments. Return trip models are used to calculate the probability or a guest returning to the property in a specific period of time. Lastly, one of the most widely used predictive techniques for marketing and loyalty is that which calculates the customer lifetime value, determining how much a guest is worth during the expected lifetime of his/her relationship with your company. Understanding a guests predicted lifetime value can help determine the treatment of those guests, including any incentives in the form of promotions and discounts.

Marketers are using automated solutions for the campaign process, from designing the campaign, predicting response rates, executing the campaign and then tracking performance of the campaign. When analytic results are incorporated into this automated process, targeting improves and campaigns generate more lift.

The emerging area of opportunity for marketing is in digital intelligence. Marketers are using performance statistics from online channels to understand consumer behavior and better design the click through to conversion process. Hotels are beginning to use profile information combined with search context to identify what a guest may be looking for and surface relevant content, as well as follow up if they don’t convert.   A hot topic for marketers today is attribution modeling, which is a statistical technique to identify which channels or partners contributed to an eventual conversion. With rising costs of distribution, it is more important than ever to have a clear picture of who is contributing to actual sales and how much they are contributing.

Operations

Forecasting is particularly valuable to operations. Accurate demand forecasts can support labor scheduling and supply ordering. Revenue forecasts assist budgeting and planning. Statistical modeling can be used to understand the drivers of guest satisfaction, or the effectiveness of training programs. Text analytics interpret the content and sentiment of reviews and open ended guest survey questions to identify service improvement opportunities or design new offerings. Optimization can produce a labor schedule that minimizes labor costs while maintaining service levels. There are some automated systems, such as for labor management, but many hotel companies also utilize tools for ad hoc analysis. Compelling visualizations that communicate descriptive statistics plus analytical results are particularly useful for operations, since they need to stay focused on the next best action to take to properly serve guests, rather than spending a lot of time analyzing complex spreadsheets.

You can see from the departmental descriptions above that there could be some synergies between the data and analytics that are used by each group. Revenue management could benefit from knowing expected response rates to campaigns, and marketing could design better campaigns if revenue management let them know when to expect need periods. Demand forecasts could be leveraged across the organization, and certainly budgeting would run much more smoothly if portions of that process could be automated and supported by analytics rather than gut feel. The most advanced hotel companies today are moving towards a more integrated decision making, where data and results from each department are leveraged across the organization.

Many hotels are broadly implementing visualization tools for descriptive analytics. Revenue management departments are heavy users of advanced analytics in their systems, and some marketers are applying basic segmentation analysis or customer value calculations. Those hotel companies that are striving for competitive advantage will go beyond descriptive tools to apply advanced, predictive analytics, moving the entire organization from reactive to proactive decision making.