Data and Analytics Equal Big Bookings for Denihan Hospitality Group

| November 25, 2013

Denihan Hospitality Group  has announced are collaboration on a Big Data project with IBM by applying advanced analytics technologies to enhance strategic planning.  This impacts multiple aspects of the business, from improving the guest experience, and personalizing marketing campaigns, to boosting productivity of the revenue management team across Denihan’s 14-hotel portfolio.
 
Denihan's portfolio includes unique properties operated under The James and Affinia Hotels brands, as well as luxury independents The Surrey and The Benjamin, and several affiliates. Each brand and hotel is strategically designed to appeal to a specific customer segment.  Throughout its 50 years in the hospitality business, family-owned and operated Denihan has prided itself on an individualized approach to customer service.  

For Denihan, marketing and booking the right room for the right customer at the right time -- and very importantly, at the right rate -- is critical to the bottom line. Denihan places a great emphasis on personal customization across its brands, in order to continuously drive preference and customer loyalty.
 
With the continued growth of hotel rating websites, travel blogs and social media, customer service and pricing are more critical than ever for the hotel industry, allowing a hospitality business to either secure or lose a customer in seconds.  According to a 2012 Forrester study, the financial impact of customer service on hotels is $1.36 billion, with roughly $825 million of that revenue sparked by churn reduction, or efforts to keep guests from choosing other hotel brands. 

By applying IBM's analytics technology to Big Data, Denihan can now sift through massive amounts of information -- from customer feedback  to room price, length of stay and more --  to understand why customers choose their hotels and why they choose to return. By taking the pulse of guests' likes and dislikes, Denihan can also fine-tune its marketing campaigns to engage customers on an individual basis, reinforcing this notion of the "era of you" in the hospitality industry.
 
At Affinia Manhattan, Denihan utilized IBM analytics to dissect guest feedback and guest profile data that uncovered varied comments on what guests wanted in their guest rooms.   Affinia Manhattan is located in an area popular with both tourists and business travelers, and guests’ feedback reflected the need for flexible spaces that can be used for a variety of different needs.  As a result, Denihan remodeled each of the hotel’s rooms to create a relaxation zone, a work zone and a sleep zone.  Denihan then made a point of using flexible and comfortable furniture throughout the new guestroom design, adding such pieces as convertible sofas and mobile ottomans that can be moved by the coffee table or by the bed depending on the need.   In addition, feedback from women and family travelers revealed a desire for more storage in the bathroom, and in response,

How Big Data Enables Big Bookings

In an effort to further their success in the business and leisure travel sectors, Denihan was looking for a way to harness big data at a magnitude that would transform the company’s strategic direction.  They wanted a solution that would not only sustain business during periods of vulnerability, such as non-peak seasons, but boost revenue during positive economic times, as well. 

However, to increase revenue, reduce costs and improve customer experiences, they first needed to build an analytics culture within the ranks of their employees. The objective: place user-friendly analytics tools into the hands of its guest facing employees, including hotel management and corporate support teams. This has enabled Denihan to:

Increase Revenues – Denihan’s revenue management team is now able to anticipate the most beneficial type of business to book at a given time and understand precisely how far in advance it would do so.  They can also estimate what the room rate trend will be, through what channel it will be booked, and for what length of stay. This kind of information allowed one of Denihan’s New York City properties to outperform at double the room rate during a recent United Nations Assembly Week, one of the most profitable times of the year for New York City hotels.  Such ready reporting is available daily, providing a 40% productivity boost for their teams whose counterparts in many hotels can spend up to half a day finding and collating data.
 
Better Manage Expenses – Having detailed insight into all expense categories and occupied room metrics, Denihan realized a significant cost savings allowing more resources to be put towards innovative renovations and product offerings that enhance the guest experience.  The company is also able to analyze data such as payroll trends and employee overtime in the context of forecasted vs. actual occupancy.
 
Guide Strategic Direction – The key to guest retention and advocacy is understanding and delivering on customer preferences.  Guests surveys that obtain feedback on why guests choose their hotels, why they return, and how they make their booking decisions not only gives strategic insight into guest behavior, but provide valuable attitudinal knowledge for longer term planning. 

And, by quickly tapping into daily individual stay guest comments, management is able to easily identify what guests like about a stay and where they might wish to see improvement.  For example, outside noise was the number one challenge across the company’s 11 New York City hotels.  In response, Denihan management launched a “Put NYC on Mute” campaign, providing earplugs in nightstand drawers in all guestrooms, an action that has greatly improved guest feedback.

Analytics provides a win-win for both Denihan and its guests.  Through a win-back program that encourages valuable guests to return, the company has produced over 30 times the revenue it invested, while greatly increasing the loyalty of those customers.  Additionally, Denihan can now target the right offer to the right customer based on past customer data and feedback, and the predicted value they can bring in the future.  For example, a guest who has spent $40,000 in the past would receive a different offer than one who had spent $1,000.  While Denihan wants both to return, the offer becomes both customized and measureable.

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