technologyrevenue management

32 | March 7, 2016 | hotelbusiness.com

By Nicole CarlinoSenior Associate Editor

NEW YORK—Revenue management is one of the most important fields in the hospitality technology world these days, so it’s no surprise that it’s an area that’s garnering a lot of focus and inno-vation. The latest entrant into the field? LodgIQ, a NYC-based startup that has secured $5 million in seed funding led by Highgate Ventures, a venture capital platform focused on early stage tech-nology investments in the hospitality industry, and Trilantic Capital Part-ners, a global private equity firm.

“LodgIQ is really about us funda-mentally disrupting the traditional revenue management technology paradigm in terms of how demand is forecasted, in terms of how pricing is calculated and then how we can actually use all of these different data sources and interpret the right signals at the right time for decisions and rec-ommendations,” said Ravneet Bhan-dari, CEO of LodgIQ.

Bhandari elaborated on some of the challenges hotels can face. “There are so many different signals that are com-ing at hotels or at revenue managers. Customers are shopping more than they ever have, and rates have more transparency in the marketplace than they ever have,” he said. “One of the fundamental tenets of revenue man-agement always was that you could price differentiate and put fences around different rate types. That’s gone. Customers now react to not just price, but they also react to things like review scores and content—specifically images.” Bhandari noted that a reve-nue management platform should take that into account. “That’s one of the fundamental reasons why we wanted to do this. If you’re able to incorporate all of the different decision and data elements into a single composite al-gorithm, by definition, you’re making better decisions,” he said.

The CEO has more than 20 years of domain expertise in leadership and ad-visory roles with companies like Hyatt, Caesars Entertainment and Starwood Capital, and noted that his team has similar industry experience. According

to Bhandari, many of the established, legacy players use forecasting and op-timization algorithms that are two de-cades old. “The operating environment has changed, and there’s a lot of noise out there,” he said, noting that there is a lot of data available now with re-gard to buyer intent. “The vision from the beginning was that we would be able to create a multi-source, big data infrastructure and then disrupt the forecasting and optimization meth-odologies through advanced machine learning. What that really enables us to do is to incorporate this notion of buyer intent and all of the signals that are out there into the revenue optimi-zation decision, which eventually really means customer-based or customer-specific optimal pricing.”

LodgIQ deployed its platform at five New York City hotels last month. The company has two products: a web-based system and a mobile RMS. “Our full-featured mobile RMS is perceived as our entry level product for smaller hotels and select-service properties,” noted Bhandari. “Very specifically, we want to initially stay focused on the top 50 global lodging markets. We are targeting everything from full-service hotels to select-service properties, independents, asset management com-panies and the larger brands.”

LodgIQ incorporates a range of data, from flight schedules and weather pat-terns to events into its platform. “We are consuming, for instance, vacation rental demand data. We’re consuming forward-looking market demand data,” said Bhandari. “Obviously, we have competitive rate shopping. We are con-suming things like newsfeeds in real time from Associated Press and a few other data sources.”

The heart of LodgIQ RM is its machine-learning platform, which in-cludes Databricks, a company founded by the creators of Apache Spark, and which is used by companies like Amazon, Netflix and Google. “We can throw any unstructured data at Databricks, and it’s very quickly able to establish the relationships between disparate data sets,” explained Bhan-dari. Dato provides the second part of the machine-learning platform. “That

allows us to do things like semantic keyword extraction,” said Bhandari. “For instance, to take a very specific example, how do you, in terms of a real time newsfeed, extract the key-words that are relevant to being able to forecast either market demand or perceived price elasticity? How do you establish positive and negative correla-tion to news or events or other stuff happening in your marketplace? One of the fundamental ethos or constructs of our data schemer is this notion of any data, any source, and that’s some-thing we’ve tried very hard to be able to incorporate.”

A user-friendly user interface (UI) was also a focus for the company. Bhandari noted, “We have so much data, and so many different data sources, that we have to make sure we present it in a manner that is easily consumable.” Bhandari also said that a vast majority of users of revenue man-agement technology are Millennials and Gen Xers—people well versed in user interfaces in the B2C world—so LodgIQ wanted to bring B2C intelli-gent usage into the B2B environment. “We focused very heavily not just on design, but on this notion of intelligent design. Our UI actually morphs intel-ligently to different user types,” he said, adding that a revenue manager and a GM would have different needs from a revenue management platform. “If you’re a GM, very quickly, in about 10 days, the UI will recognize what your clickstream is, and it’s only go-ing to present information relevant to you,” he said. “Our objective is for people to spend time in the system, not use the system as just another tool

to export information into an Excel environment where they marry it with other data and try to make decisions based off of that.”

Bhandari looked toward the future of the revenue management platform and the field in general, noting that dynamic behavioral segmentation is something hotels should focus on. “Traditional revenue management… totally ignores the behavioral com-ponents of customers’ demand. He pointed out that leisure travelers flying from the U.K. to New York, who book flights 30 days out and hotels seven to 15 days before a trip, are a unique customer segment. “What is the price elasticity of that behavioral segment vs. someone else? That’s the notion of dynamic behavioral segmentation, and it’s something we have incorporated into our technology,” he said.

“The perfect manifestation of revenue management is one-on-one pricing at the point of purchase as opposed to using this notion of a hurdle rate or marginal rate or the BAR rate, which is one price typically charged to most customers depending on what market segment they belong to,” he continued. “How do you eventually make the opti-mal decision at the point of purchase in real time when the customer’s in the act of booking? That’s where we have to go and we’re setting the stage for that.” HB

Using machine learning to move toward customer-specific pricing

LodgIQ incorporates a range of data into its platform.