Revenue Management and Pricing

Kirby Bosch

Vice President, Technology

Practical Approaches using Customer Web Behavior to Maximize Hospitality and Gaming Revenues

A key metric in hospitality is RevPAR, Revenue Per Available Room.  Practical approaches using customer web behavior can maximize RevPAR on digital channels.  As customers interact with your online offering, they leave behind digital breadcrumbs.  Tapping into this data can discover customer behavior patterns, which in turn can predict customer willingness-to-pay by product.  Leveraging these customer insights in real-time, as they shop, can capture additional revenues by personalized pricing and product assortment on your digital channels.  The path toward lifting RevPAR with consumer web behavior need not be an all or nothing approach.  A fully operational machine learning pipeline built by a bleeding-edge team of data scientists and engineers is not a barrier to entry.  A range of implementation solutions are available to best fit your business.  Nor is the price to pay handing over all revenue management decisions to a machine.  These solutions enact your decisions to solve real-time, online personalization at scale.


Kirby Bosch, Nor1’s Vice President of Technology is responsible for managing Nor1’s skilled engineering, product, and data sciences teams globally, as well as leading product design, development and launch for all of Nor1’s Merchandising Technology solutions. Kirby’s positive attitude and strong work ethic, in addition to his communication and analytical skills, and insight into customer needs help position Nor1’s solutions as market leaders in Upsell Merchandising Technology for the Hospitality and Travel industries. Prior to joining Nor1 as a Product Manager, Kirby spent more than 6 years as a computer engineer at FAAC Incorporated where he managed software development teams in the design, implementation, and delivery of tactical training systems for military personnel and consulted on product development and strategy. Kirby earned his BSE in Computer Engineering from University of Michigan and his MBA in Corporate Innovation & Finance from Indiana University – Kelley School of Business.

Pavan Kapur

Senior Vice President, Global Gaming

Co-Presenting with Kirby Bosch

Pavan Kapur is Senior Vice President, Global Gaming, at Nor1. Mr. Kapur is responsible for the leadership of the Global Gaming team and driving sales of the Nor1 Merchandising Platform throughout the Global Gaming Industry. Mr. Kapur has most recently held positions as SVP, Analytics and Revenue Optimization at Atlantis, Paradise Island, Bahamas; and VP of Revenue Optimization, Enterprise Analytics at Caesars Entertainment Corporation. He received his MBA in Finance from Florida Atlantic University and a BS in Statistics from the University of Florida. In addition to his expertise with Revenue Optimization and Analytics, Mr. Kapur has an extensive background in predictive modeling, optimization, data mining, business intelligence, customer behavior analysis and competitive intelligence.

Angie Dobney

Vice President of Pricing and Revenue Management
The Rainmaker Group

Vegas Predicts:  How the Prophets of Gaming Called the Trend for Profit Optimization

Hotel revenue optimization has been covered extensively in academic work and at conferences. But the focus has usually fallen on approaches that optimize revenue by allocating room inventory based on predicted room revenue. Casinos have led the way on total profit optimization – where rooms are allocated according to total predicted revenue. This has long been necessary in casinos, where customers’ gaming and resort revenue frequently eclipse room revenue, and where integrated loyalty programs allow detailed segmentation and loyalty offers. Where casinos have led, non-casino hotels are now following as they become increasingly sophisticated in diversifying their revenue streams. The need to optimize total revenue is greater than ever before. In this session Tammy Farley – co-founder and President of the Rainmaker Group will explain the principles of total profit optimization and will draw on numerous examples – from hospitality and elsewhere – to demonstrate their applicability throughout multiple industries.


Angie Dobney is the vice president or pricing and revenue management for The Rainmaker Group An industry veteran with more than two decades of experience in revenue management and hospitality operations, she provides hands-on optimization of total resort profit to Rainmaker’s industry-leading gaming and hospitality clients.

As a former Rainmaker customer who turned the tables and joined the market leader in 2014, Dobney brings to her role a user’s perspective on the product. Having worked in both manual and automated environments, she knows firsthand the challenges customers face and draws from her personal experience to guide her clients in best practices.

A recognized expert in total asset and portfolio optimization, and as enthusiastic as she is knowledgeable, Dobney is frequently tapped to speak at conferences around the globe. She is a guest lecturer on hotel and hospitality asset and revenue management at the University of Nevada, Las Vegas, her alma mater, where she was named Mentor of the Year in 2007. She continues to seek out opportunities to get involved in other university programs in her quest to inspire and motivate the next generation of revenue managers.

Xingchu Liu

President/General Manager
BlackLocus, Home Depot Innovation Lab

Scalable Analytics in Interconnected Retail

Today’s consumers are becoming more instrumented, interconnected and intelligent. Thus, on the front end, Brick & Mortar retailers are heavily investing into a seamless omnichannel customer experience. However, on the back end, business decisions and operations are still facing enormous challenges. In this talk, we will discuss these challenges from analytics perspective, and solutions to leverage big data technology and advanced machine learning to optimize pricing as well as other areas (such as assortment, space, supply chain) in order to truly deliver an interconnected retail.


Xingchu Liu is a seasoned executive focusing on building scalable analytics solutions in pricing, assortment, space, supply chain, and digital marketing to a wide range of industries.

He received his bachelor’s degrees in Automation and Business Administration, a master’s degree in Automatic Control from Tsinghua University, and a doctoral degree in Industrial Engineering from Texas A&M University.

David Simchi-Levi

Professor of Engineering Systems

The New Frontier in Price Optimization

Retail is fast evolving into a complex multi-channel highly competitive environment. Companies are relying on a combination of accessible data, ability to experiment and analytic technologies to enable a new approach to various processes such as inventory management and pricing. There have been breakthroughs in the development of models that combine machine learning and optimization for pricing that significantly improve revenue and reduce inventory risks. These trends and breakthroughs will surely effect retailers, and their suppliers and customers, in 2017 and beyond. Collaborating with Groupon, we developed a dynamic pricing model where the demand function is unknown but belongs to a known finite set. The data suggested that we can approximate the true demand function by a collection of linear demand functions.
Groupon allows for a limited number of price changes during the selling season. We demonstrate a pricing policy that observes consumers making buy/no-buy decisions and update the demand functions on-the-fly. Implementation of our algorithm at Groupon shows significant impact on revenue and market share. In the second part of the presentation we extend the model to a network revenue management problem where an online retailer aims to maximize revenue from multiple products with limited inventory. This model is motivated by collaboration with retailer Rue La La where the retailer does not know the expected demand at each price point and must learn the demand information from sales data. We propose an efficient and effective dynamic pricing algorithm, which builds upon the Thompson sampling algorithm used for multi-armed bandit problems by incorporating inventory constraints into the pricing decisions. The algorithm proves to have both strong theoretical performance guarantees as well as promising numerical performance results when compared to other algorithms developed for the same setting. In the last part, we report on implementation of our methods and algorithms at B2W Digital, a large Latin America retailer. An important opportunity at B2W is product bundling. We show that bundling can be used as a form of price experimentation, that is, a mixed bundling scheme allows the firm to quickly learn the customer valuation distributions without having to change any prices. We then introduce a simple price bundling scheme that takes into account customer valuations and product cost.


David Simchi-Levi is a Professor of Engineering Systems at MIT and Chairman of Opalytics, a cloud analytics platform company. He is considered one of the premier thought leaders in supply chain management and business analytics. His research focuses on developing and implementing robust and efficient techniques for operations management. He has published widely in professional journals on both practical and theoretical aspects of supply chain and revenue management. His Ph.D. students have accepted faculty positions in leading academic institutes including U. of California Berkeley, Columbia U., Cornell U., Duke U., Georgia Tech, Harvard U., U. of Illinois Urbana-Champaign, U. of Michigan, Purdue U. and Virginia Tech. Professor Simchi-Levi co-authored the books Managing the Supply Chain(McGraw-Hill, 2004), the award winning Designing and Managing the Supply Chain (McGraw-Hill, 2007) and The Logic of Logistics(3rd edition, Springer 2013). He also published Operations Rules: Delivering Customer Value through Flexible Operations (MIT Press, 2011). He served as the Editor-in-Chief for Operations Research (2006-2012), the flagship journal of INFORMS and for Naval Research Logistics (2003-2005). He is an INFORMS Fellow, MSOM Distinguished Fellow and the recipient of the 2014 INFORMS Daniel H. Wagner Prize for Excellence in Operations Research Practice; 2014 INFORMS Revenue Management and Pricing Section Practice Award; 2009 INFORMS Revenue Management and Pricing Section Prize and Ford 2015 Engineering Excellence Award. Professor Simchi-Levi has consulted and collaborated extensively with private and public organizations. He was the founder of LogicTools which provided software solutions and professional services for supply chain optimization. LogicTools became part of IBM in 2009. In 2012 he co-founded OPS Rules, an operations analytics consulting company. The company became part of Accenture in 2016.

Lori Sinn

Director, Cargo Revenue Management
American Airlines

It Shouldn’t Be This Complicated, But It Is

Moving a box shouldn’t be so difficult, but yet an entire industry is built around moving boxes. And from a cargo revenue management perspective at a passenger airline, maximizing the profitability of the cargo space can be complex. For example, before we put capacity out for sale, we need to know how much capacity we might have after accounting for fluctuations in passenger count, bags, and fuel. When those metrics fluctuate, the risk of overbooking a flight can have a big impact on the operations. Forecasting demand is also a challenge – show up rates will vary by commodity type and customer, and sometimes shipments show up significantly bigger or smaller than what was booked. And given the complexity of cargo pricing (by weight break, volumetric size, commodity, etc.), cargo yield management systems need to be able to reconcile multi-dimensional pricing structures in order to maximize profitability. In this session, I plan to share the challenges I’ve experienced in cargo with forecasting and building a yield management system. Having worked in passenger revenue management, never did I think yield managing a bunch of boxes would be this complicated.


Lori Sinn is an airline professional with over 10 years of experience at American Airlines. She currently leads the Cargo Revenue Management department responsible for pricing and capacity management with the goal to maximize cargo’s contribution to the airline. She began her career on the Passenger side of the business learning all about the complexity of airline pricing, demand forecasting and inventory management in Passenger Revenue Management. Later she held leadership positions across different commercial and finance departments, including Alliances developing network strategies and strategic partnerships with other airlines to grow American’s footprint across the globe, and Commercial Planning supporting American’s cross-divisional projects including American’s efforts through its 2012 restructuring and integration of American Airlines and US Airways. Three years ago, Lori moved over to the Cargo division, as the Director of Strategic Alliances responsible for developing a more cargo relevant network on top of the passenger network through partnerships with other airlines and transportation organizations. She holds a degree in Economics from the University of Rochester (NY) and an MBA from Georgetown University (Washington DC).