How Hotels use Big Data to Generate New Revenues

Hotel revenue management and use of analytics for room sales has remained largely unchanged for decades since the early 1980s when hotels started looking at yield and how they could optimize the revenue each room could generate. By the mid-1990’s, Marriott’s successful execution of revenue management strategies were adding between $150 — $200 million in annual revenue and thus marked the beginning of data intelligence to drive new revenue.

Fast forward to 2016 — and the part insight, part intuition, part data-driven approach to revenue management largely hasn’t moved into the new age of big data for most hoteliers.

There is a new application of data modelling hotels are utilizing to see big gains in RevPAR (Revenue Per Available Room) and this comes through price differentiation. That is — dynamically displaying different room rates for every person that views your hotel search price query.

To demonstrate how the data engine works behind the scenes — below is a basic visualized scenario on what a hotel search looks like with the use of analytical driven data — and the outcomes it provides where guests are happy with the prices and hotels are able to extract more revenue out of each guest.

Example:
Jeff is booking a room for a leisure trip to Hong Kong next week. Being a loyal member of brand.com, he logs onto the direct hotel website and logs into his frequent guest profile.

Jeff enters the dates and hits ‘Search’ — here is what happens:
The data systems kick into overdrive. Trillions of intensive number crunching calculations processed over the past days, weeks, months prior are all getting an intensive workout as reservations, yield management and data models all temporarily synchronize together to generate the ‘perfect price’ for Jeff. Using key data points, probability metrics and real-time data feed analytics being fed in from external sources — reservation systems bring together all the hard work to interpret exactly how much Jeff is willing to pay for this specifically hotel — before he books it.

Has Jeff ever stayed in Hong Kong? Where, and how long ago? Is there a trend to his stays? (If so, what did he pay last time? Base rate + ability to charge more based on previous spend $$++)

Is this a business or personal trip and who is paying? (Hotels can find this out by asking the user and through easily obtainable third party data — business stays can typically be at a higher rate, especially if not part of a larger company)

Did Jeff search using a corporate/promotional rate code? (if yes, is he eligible/has he used it previously, what % of rooms does he book using codes? what is our internal score likelihood of him booking a room WITH and WITHOUT a code? Price increase++)

What is the internal rating of Jeff as a guest? (High rating means less servicing effort, good customer with no fuss, bad customers = more $$.