Effective Hotel Revenue Management Strategies

Revenue management is one area that is evolving rapidly to keep pace with an increasingly technologically complex hospitality environment.

To get some insights on the future of revenue management and new best practices for hoteliers, we spoke with Nick Molitor, the Director of Account Management at IDeaS, the global leader in revenue optimization technology.

Nick, the topic of (big) data in revenue management is a hot one in the hotel industry these days, but it seems that many hotels are still struggling with the concept of data collection and using the right data to improve the hotel’s strategy. Can you break it down for us?

Data presents a tremendous opportunity for hoteliers to improve their decision-making around revenue management (and sales, and marketing), but it’s only useful to the degree that you can harness and leverage the data in a meaningful way.

This means being selective. The challenge big data presents isn’t so much of a quantity problem, but a quality problem. It’s about finding the data that is the most relevant – and the most reliable – to your hotel.

A hotel struggling with Big Data needs to first set realistic expectations for themselves. A hotel using an analytics platform, like IDeaS, can expect to handle more data than a hotel that isn’t. In either case, data quality is the key concern.

To successfully navigate through the data, a hotelier needs to understand the degrees of reliability the data provides – and think of data in context of the customer journey. Take regrets and denials for example. This is data people have been using forever, and some in the industry are lionizing it as essential to calculate price sensitivity.

But we need to think about when it’s being collected – either on your website or on the phone.

Research from Cornell tells us that by the time the customer has come to your website, they’ve already decided your hotel is in their price range, and now they are looking to justify the value.

They’re looking at photos, descriptions and reviews to decide if they’re going to purchase. If you get a regret, perhaps it’s because you have bad photos, or someone wrote a bad review. You may have a marketing opportunity, not a pricing opportunity. If you’re using regrets and denials for price sensitivity, it’s not always an accurate representation.

So we need to really be careful about generalizing the customer experience with big data, and make sure to appreciate that guests are looking at different things at different stages of their buying process. We need to be more critical of that when we look at data.

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