Competing With Data & Analytics
How does a global company such as Intercontinental Hotel Group (IHG) get its individual hotel operators to embrace analytic decision making? How does it decide how and when to deploy its search-engine bid-management platform? How does it keep competitors from borrowing the techniques it builds?
MIT Sloan Management Review raised these questions with four leaders across IHG’s Global Sales and Marketing team: Larry Seligman, vice president of advanced consumer analytics; Jim Sprigg, director for database marketing and analytics; Angela Galeziowski, vice president of worldwide sales, strategic insights and planning; and Dev Koushik, vice president of global revenue optimization.
It comes down to managing complexity, the IHG team noted. If you can effectively address complexity in modern marketing, you gain a competitive advantage that can take a lot of time for competitors to replicate.
The interview was conducted by Sam Ransbotham, an associate professor of information systems at the Carroll School of Management at Boston College and the MIT SMR guest editor for the Data and Analytics Big Idea Initiative. Answers by individual IHG team members have been bundled together.
Hotels and airlines were early leaders in using analytics in their operations. What is IHG doing today that you think the rest of the world would find different and interesting and novel?
We have done a tremendous amount of work in trying to focus our efforts towards how to improve pricing performance. This includes things like discounts and anchoring-off our best available rate. We work with a lot of regional revenue management teams and the hotels at the unit level, to educate them on what right pricing strategy and pricing structure should be in place, and to give individual hotels ways to set prices in an automated fashion.
We developed a capability called Price Optimization, which revolves around some advanced analytics. We can embed the current business practices and account both for our demand for cash for the next 365 days and for the price sensitivity of the retail demand to different price points. This capability also accounts for what comparative rates look like. All of these things are done in an automated fashion and come up with an optimal rate at the property level for every day for the next 365 days.