We continue our fall “back to the basics” refresher series on analytics for hoteliers. Last week, we 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.

Read full article at:  SAS