The Revenue Management & Pricing track features analytics leaders from top companies in traditional and non-traditional industries such as travel, hospitality, transportation, entertainment, high tech, media and retail. This track promotes and disseminates the latest developments in pricing and revenue management with opportunities to learn from the industry experts. Discover how to use advanced analytics and operations research to better understand and target your customers, improve your pricing practices and demand forecasts, and drive revenue and market share growth. Learn more about confirmed presentations in this track below.
Prescriptive Analytics in Retailing
The use of analytics has become ubiquitous in modern retailing. The growth of online retailers such as Amazon and brick-and-mortar retailers like Zara is greatly explained by their successful implementation of data-driven processes. We will present a general framework to approach business analytics initiatives in practice. We then apply the framework to several examples in the retail industry with an emphasis on controlled field experiments to measure impact.
Banking on Revenue Management to Optimize Shopper Marketing
When it comes to Shopper Marketing, brands and retailers have to overcome an increasing cacophony of noise – across an ever-expanding range of channels — to effectively reach people as they make their buying decisions. Shopper Marketing is no longer just limited to promotions; it also includes generating brand awareness, building relationships and loyalty, and ultimately a profitable sales lift.
In this context, Revenue Management has to help optimize every single customer engagement, whether via an ad or a promotion, to delight shoppers and drive sales and profits for retailers and brands – not to mention industry partners like Catalina who help execute such campaigns. The scope of a sound Revenue Management program encompasses:
- The creation and rankings of consumer segments to optimize Return On Ad Spend
- The ability to forecast campaign outcomes
- The ability to dynamically change audiences and other campaign elements in-flight to deliver stronger results
Net, Revenue Management done right will optimize every touchpoint with a given target, identifying the best channels and timing for an ad or promotion to turn shoppers into buyers, and buyers into loyal fans.
Democratizing Revenue Management for Hotels: Technical and Commercial Challenges by Pace Revenue Management
Revenue management has a long history in the hospitality industry. However, despite years of evangelization and marketing, most hotels perform a lot of manual work or simply do not price dynamically. Pace was founded with the belief that advances in data science and technology could be used not only to improve current revenue management tools and techniques but to make most of those tools accessible to every hotel.
In this talk, we will discuss the challenges that we have encountered in that journey, and how we have tackled them to serve hundreds of properties 2 years after our first pilot customers went live. At a technical level, we will discuss the software architecture and technology stack that allows for a real-time response to demand signals. Thinking of our Machine Learning models as a complex software system, we will explain how it was built in a customer-centric way, with statistical rigor but also with the ideas of interpretability and reproducibility at its core. At a commercial and cultural level, we will discuss the role of change management in the implementation and adaptation to utilizing an RMS with advanced science capabilities. We will conclude with a discussion of the central role that the incorporation of self-learning capabilities plays in the Pace product, as developed and implemented by our Customer Success team.
Integrating Machine Learning and Artificial Intelligence into Pricing and Revenue Management
To prepare for its next 100-year journey, Hilton has committed to building a leading force in Data and Analytics by standing up its first Operations Research and Data Science team. The availability and advancement of data processing and compute resource have fueled the team’s ability to bring values to the business. We will share the first efforts in our journey to combine traditional Operations Research approaches with the power of ML/AI that would enable data-centric predictive and prescriptive decision making. A couple of use cases, contributions, and challenges will be discussed to illustrate how Operations Research has changed and challenged the way business decisions were made traditionally.
Pricing a Subscription Service to Maximize Customer Lifetime Value
As more and more businesses switch to subscription based offering, traditional pricing & revenue management models need to factor in the long term effect of price on customer’s lifetime value. In this presentation, we demonstrate a way to combine the effect of pricing on acquisition of new subscribers as well as the effect of pricing on renewal likelihood to promote a pricing structure that maximizes the overall lifetime value of a customer. Incorporating the effect of price on customer’s likelihood of renewal in subsequent years allows the business to make better pricing that creates a higher value customer pool that is more likely to renew the subscriptions without additional incentives. Early results from the implementation of this model indicates a 5 – 10% increase in number of subscriptions along with a 2 – 5% revenue increase in the first year.