“We could not be happier with Nor1 eStandby. Our front desk agents look forward to managing the requests each day. The program is very simple and they get very excited about seeing
the additional revenue they bring into the hotel with each upgrade they are able to award. It’s also fun to see the reaction from our guests at check in when we congratulate them on their upgrade.”
Assistant General Manager
Country Inns and Suites, Mall of America
PRiME® is the data-driven pricing and merchandising engine that serves as the critical backbone of Nor1’s upsell solutions. This patented decision intelligence engine is a technology first for the hospitality industry. PRiME’s true purpose is to maximize ancillary revenue for hotels while maintaining the rate integrity of their perishable inventories.
Unlike a recommendation engine, PRiME accurately identifies the right product selection, upsell pricing, and display ranking decisions for each individual guest based on sophisticated algorithms trained on millions of historical transactions and then actually makes the offer to the guest on behalf of the hotel.
It’s this “decision intelligence” that will increase a property’s revenue and strengthen customer loyalty. When used with eStandby® Upgrade and Front Desk Upsell, PRiME customizes upsell offers for each hotel reservation. With eReach®, our communication suite of services, PRiME enables hotels to engage their customers like never before via Mobile and eMail with relevant, targeted connections.
PRiME identifies which variables and interactions matter most when applying pricing and merchandising strategies to upsell offers. The data points that matter are typically a combination of booking-specific variables, guest-specific information, and information that’s specific to the property.
Each of the variables have a certain weighting, and the weighting itself changes for each reservation based on the unique intersection of the booking, guest, and property – there is no one size fits all.
PRiME enables hoteliers to better predict how offers for similar bookings by similar guests at similar properties performed in the past, which enables us to determine, in real-time, which offer to make when a new booking comes through the system. Essentially, we can infer the likely product preferences and price sensitivities of the guest even if we have no historical data for the specific property or guest. The result is that PRiME produces excellent results right from the start.
PRiME can optimize offer sets to help channel demand to under-utilized inventory, while ensuring its in-line with consumer preferences. And that’s another key element of the intelligence in PRiME – choosing prices and setting prices that interact with each other to achieve the optimal outcome.
We use equations to make decisions that balance the trade-offs that matter. The trade-offs are balanced formally using mathematical optimization, but the concept is simple. For example, one important trade-off has to do with long-term revenue versus short-term gain. We use a specific and proprietary system of equations, called the Loyalty Lattice™, to ensure that eStandby Upgrade increases revenue and guest satisfaction in the long-term as well as the short-term.
The Loyalty Lattice is a mathematical construct that describes the cumulative experience of any given guest’s interaction with a Nor1 Solution. Every guest we’ve ever touched has a specific position on the Loyalty Lattice, and with each contact that position changes every time. PRiME uses the guest’s position on the Loyalty Lattice to estimate the likelihood of different behaviors at future touch-points with the guest. It helps PRiME decide what offer to make in order to induce the desired future behavior.
Once there is evidence that a particular type of guest or a particular property is significantly different from existing models used to initialize a property, PRiME automatically produces a new model to target the specific property or guest type. Then, as even more data are accumulated, PRiME will automatically produce even finer-tuned versions of the guest type or property-specific models.
Real-time reservation data is merged with historical data on the property and the guest, and statistically classified in real-time.
Price sensitivities and product preferences are detected and described mathematically, in the same instant, capturing the interactions between product selection and price sensitivity.
Millions of possible product and price combinations are evaluated in less than 70 milliseconds, and the optimal pricing and merchandising is selected and displayed.