How traditional business leverage data analytics: Starwood Hotel

Data analytics in hotel industry

Burdened by legacy, the incumbent players in the traditional industries are invariably not technically savvy as the emergent disruptive tech startups. However developing the data analytics capacity seems to be an inevitable pathway for the incumbents.

Starwood Hotel, now under Marriott, is one of the earlier adopters of data analytics in the traditional hotel industry to improve their customer experience. Back in 2012, the analytics trend was quite imminent in the backdrop of the disruption from tech-enabled Airbnb which easily harnesses massive listing data at scale and low cost. With <400k rooms, 1300 properties, 11 brands globally, Starwood has a relatively sizable database. A big chunk of its revenue comes from the corporate accounts which their most profitable and core assets. Their highly-acclaimed and well-received loyalty program SPG also helps to retain a high level of return customers. The comprehensive and repetitive data pattern has laid a foundation for them to revamp the data gathering and integration processes and to develop deeper connections with the customers

As a real estate/hotel company, they figure the best way to improve the service quality is through pulling customers feedback. They designed a Guest Experience Index (GEI) based on the post-stay surveys, social media reviews gathered from various websites and constituted a proprietary index to guide the hotel managment of disparate properties to tackle the areas of improvement more accurately. The personalization level of the service is enhanced as they are able to quickly identify trivial customer need i.e whether a customer prefers sparkling or still water or a chocolate mousse cake at the check in counter and preempt for it even before the customers arrive. What’s more, they expand the data gathering funnels to be more real time through an interactive digital chat box during customers’ stay. The box will alert the hotel staff what the customers at stay need. The accumulated pre and post stay data engenders Starwood to address customers’ needs without delay resulting in an increase of loyal customer retention rate, which differentiates them from Airbnb-like competitors.

Another analytics advancement introduced by Starwood in 2014 towards its Revenue Optimizing System is probably more proliferated nowadays among the hospitality industry – a dynamic pricing system. According to Venturebeat, the software platform is named DORM inside Starwood. It is comprised of a multi-tier architecture that uses R, the statistical modeling language, IBM’s C-Plex optimization software, as well as proprietary machine learning algorithms. Software components for variables such as forecasts and booking windows are self-contained within modules. As you click through the booking system, DORM will render a dynamic pricing suggestion for the particular stay and the particular group by cross-checking precedents and latest reservation data, booking pattern data, cancellation data, occupancy data, room type, daily rate, as well as transient or group status, competitive pricing data, weather and climate data, and booking patterns on other sites. The algorithm is also able to alert hotels when it recognizes upcoming neighbor events such as concerts, sports games that are likely to influence the pricing. Ultimately pricing is only part of the package delivered to the customers and will commoditize eventually. The ability to leverage the dynamic data to offer service that’s more sophisticated and convenient is vital. Ironically, similar analytics are also being aggressively developed and refined by disruptors in the hotel value chain namely the intermediators OTAs, i.e Priceline, Expedia and Airbnb which boasts 2MM listings today. Despite the minor to moderate difference in the business model and profit formula, the latter’s advantage as an online platform, asset light model enables them to scale up faster, process transaction cheaper and generate higher ROI. There is a structural constraint on what asset heavy hotels can do, or in a broader sense, the traditional industries can do in face of the challenges from the “late movers”. But I think these traditional businesses cannot be easily replaced. The bigger challenge for them is how to effectively empower their internal data analytics to generate organic growth. As the survey conducted by EIU pointed out, only 2% of the corporates who have invested in data analytics agree there is a broad and transformative impact from the investments.

Ironically, similar analytics are also being aggressively developed and refined by disruptors in the hotel value chain namely the intermediates OTAs, i.e Priceline, Expedia and Airbnb which boasts 2MM listings today. Despite the minor to the moderate difference in the business model and profit formula, the latter’s advantage as an online platform, asset light model enables them to scale up faster, process transaction cheaper and generate higher ROI. There is a structural constraint on what asset heavy hotels can do, or in a broader sense, the traditional industries can do in face of the challenges from the “late movers”. But I think these traditional businesses cannot be easily replaced. The bigger challenge for them is how to effectively empower their internal data analytics to generate organic growth. As the survey conducted by EIU pointed out, only 2% of the corporates who have invested in data analytics agree there is a broad and transformative impact from the investments.

 

 

As a layman, I found below two readings/links quite interesting as an extension to this thread:

https://www.zs.com/-/media/files/publications/public/broken-links-why-analytics-investments-have-yet-to-pay-off.pdf?la=en

https://blog.kissmetrics.com/how-airbnb-uses-data-science/

Source:

http://www.cio.com/article/3070384/analytics/starwood-taps-machine-learning-to-dynamically-price-hotel-rooms.html

https://venturebeat.com/2016/02/09/starwood-hotels-luxury-at-scale-requires-emotional-analytics/

http://nerds.airbnb.com/redesigning-search/

https://hbr.org/2003/12/the-one-number-you-need-to-grow

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