PRiME® is the real-time, data-driven pricing and merchandising engine that serves as the intelligence engine of Nor1’s Merchandising Platform. PRiME’s automated approach to Machine Learning/Artificial Intelligence and offer generation enables hospitality companies to create more meaningful interactions, post-booking, with their guests that build loyalty and drive long term revenue.
This patented “decision intelligence” engine is a technology first for the hospitality industry.
PRiME is built with the most advanced mathematics and technology in Silicon Valley. Using sophisticated algorithms trained on millions of historical transactions, it can better predict guests’ willingness to pay for upgrades and services over and above the amount they already paid for their confirmed reservations.
Unlike a mere recommendation engine, PRiME accurately identifies in real-time what products and pricing to show a particular guest and then actually presents an offer set 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 information such as purchase history or travel preferences is utilized to create a dynamic customer experience, a deeper personal relationship develops between you and your guest. That’s why PRiME considers hundreds of variables and interactions, including
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.
