Finally, something BIG to get excited about in hotel technology
There’s little new in hotel distribution, but that’s not to say that there’s little interesting going on elsewhere in the industry when it comes to technology.
There has been much talk and PowerPoint presentations about using the vast amount of data – social, semantic, transactional, loyalty profiles – that are available about travelers to craft more meaningful offers to improve conversion and increase revenues and profits.
However, there are precious few that actually delivering in that promise (I’m not counting Orbitz offering higher priced lodging to Mac users, that’s correlation, not causality).
Cue the good news.
Real(ly) big data
So, enter a little company called Nor1, based in the heart of Silicon Valley but seemingly not clammering for the attention of the local tech press which some its counterparts in the area attempt to do constantly.
The company, created in 2005, is trying to build a business at the intersection of personalization and predictive analytics with respect to upsell in order to increase ADR and RevPAR for hotels.
I hadn’t heard of the company before meeting with them at the HITEC event this year, but after spending some time with Jason Bryant, president and COO and Nor1’s data scientist Andrew Hines, I think they’re on to something.
For one, Nor1’s data team started with the recognition that the traditional CRM profile is flawed. Not that the attributes that are often used are wrong, but as soon as they are put into the system, they are outdated.
As Hines states: “[The] weakness of standard variables associated with hospitality profiles is that they are declarative, not dynamic”.
Translating that to layman’s terms, what a person tells you about themself at a single point in time is not necessarily and indicator of what they want in the future.
To that end, they built an engine that focuses on emphasizing the actual behavior of the traveler and let the accumulated experiences guide the offers that you put in front of the customer.
As Hines adds:
“When you look at the traveler profile and attempt to make conclusions, you are assuming causal relationships based on static information. What you don’t pay attention to are the outcomes over time.”
Nor1’s PRiME engine utilizes 250 variables (though 16 carry the most weight) and measures what upgrades are chosen and not chosen, enabling the system to make different offer decisions over time.
The results have been impressive, according to Bryant:
- Over 70 Million hotel guests exposed to Nor1 offers, to date – current pace 1.5-2.0 million guests exposed in per month.
- Between 20%-25% of exposed guests commit to pay for an upgrade.
- Incremental Upsell Demand per Booking = $79
- Incremental ADR Boost (without affecting occupancy) = 1+%
- Increases original amount of booking by 24%
The Conn is yours
From Dr Daystrom’s M-5, to Wargames’ WOPR. and SkyNet, science fiction has painted stories of computers taking over the jobs that people used to do…all while making better decisions.
Not surprisingly, Nor1 feels that its real-time decisioning engine can help shift the emphasis of where revenue management staff spend their time.
Hines projects that over the next five years, the big change in the industry will be to let machines make tactical decisions and let people be strategic.
What does that mean you may ask? Well, let the machines automate manual processes and crunch numbers to uncover insight from information.
Math is an important skill for global competitiveness, but there is little value in having people repeatedly crunch calculations, especially as the data you collect increases dramatically and the speed at which you need to process the data rises.
The result is that hoteliers will better know their guest and be able to use that data at the point of decision in real-time to better service them.
His belief is predicated on the belief that the more decisions you make, the more you learn and the better the next decision can be. And that rate of learning is critical and that favors machines.
Hines claims machines provide “provably exponential learning” capacity.
What does strategic mean you may ask? That will require a little introspection on the part of the hotelier.
Where can their people create the most impact for the company and for the guest? Devising new channel strategies? Pricing? Design differentiated experiences?
Will hoteliers bite?
In order to decide, hoteliers have to determine how willing they are to trust the data and the process. There are three stages of trust with data according to Nor1:
- Use data in order to inform decision making. This is the least intrusive use of data.
- Let a machine recommend a decision. The tool says “I looked at occupancy level” and recommends pricing strategy. The individual chooses whether to accept it and/or modify. Requires a higher level of trust or understanding
- Lastly, automated decision making. The tool computes, recommends and then executes the decision. It requires the highest level of process automation, sophistication and trust in the process.
So far 10,000 properties have signed on to the concept. If you’re a hotelier, is this something that excites you are worries you?
NB: Hotel fireworks image via Shutterstock.
Glenn Gruber is a contributing Node to Tnooz and senior mobility strategist at Propelics , an enterprise mobile strategy firm.
Previously Glenn was AVP travel technologies at Ness Technologies, responsible for developing the company’s strategy and solutions for the travel industry.
Prior to Ness he held leadership roles at Symphony Services, Kyocera and Israeli startups Power Paper Ltd and Golden Screens Interactive Technologies. He also writes a personal blog, Software Industry Insights.