Growing Airport Revenue and Improving the Trip Experience

Even with passenger volumes rising, ride-sharing is hitting airports where it hurts – parking revenue. Traditionally the top revenue source for airports, parking revenue cannot be replaced by $1 to $4 fees from ride-share companies like Uber and Lyft. Meanwhile in some markets, increases in passenger volume mask the overall shift away from parking–but still may require significant expansion of parking facilities. 

This article explores how leveraging the same dynamic pricing technology used by ride-sharing companies can help airports increase funding for operations, avoid costly infrastructure investment, and even improve the traveler experience.

McDonald’s recent acquisition of the Tel Aviv-based AI startup, Dynamic Yield, is an exciting move, demonstrating that the 63-year old fast-food chain is looking for ways to stay competitive. Like many large enterprises, McDonald’s has long recognized the value in data, collecting large amounts of data on everything from tractions to customer behavior. However, data is only as valuable as what you can do with it. The acquisition of this new machine learning and AI technology demonstrates the company’s intent to put customer data to work.

As pricing technology becomes more and more sophisticated, incorporating complex data analysis and using AI and Machine learning to optimize and deploy pricing, we’re seeing a wider acceptance of Dynamic Pricing.

Industries like airlines, hospitality, and ride-sharing have led advances in dynamic pricing, but other industries are now opening up to the possibilities. Both B2C and B2B companies are looking at incorporating Dynamic pricing into their strategies. We’ll talk about less obvious industry examples like Vail Resorts’ dynamic ticket pricing, and Lyft’s B2B strategy.

True Dynamic Pricing goes hand in hand with AI, as the real-time analysis and automated deployment of prices go beyond the physical capabilities of a pricing team. Whether pricing is being adjusted hour-to-hour or only week-to-week, AI can simulate and predict demand to optimize pricing for changing market conditions.

Vail Resorts Image

Vail Resort’s “Epic Pass” changed the ski industry when it was first released a decade ago–I would argue, for the better–even though it was directly coupled with massively more expensive day passes (sometimes 2x prior prices). Since it first launched in 2008, the pass has grown, and as a result, the pricing structure became more convoluted, with too many options to choose from. However, their latest update to pricing features a streamlined pricing structure and demonstrates how a well-crafted pricing strategy can evolve over the years.    

Buying Dynamic Pricing software, or software that takes enterprise data and turns it directly into real-time pricing actions, is necessarily involved. Frequently, the biggest challenge is change management–after all, you are fundamentally changing how your organization makes critical decisions. The Value Exploration Workshop (or VEW) is a crucial step in the decision process . It  is so essential in fact, that we as a company will not move forward with a prospective customer until we have flown in and run through a VEW in-person. 

Dynamic Pricing software takes data and turns it into actionable real-time prices. At Perfect Price, we've learned a lot about how executives assess and decide on using our software. We think this process could work well for evaluating any software that an enterprise is trusting to make decisions on its behalf.

Netflix raised prices $2 yesterday, and as a direct result, their shares added 6.5%. As a CEO, CFO, or controlling shareholder, wouldn't it be nice to do the same thing? Add $2 to prices and add $9.5 billion to your market capitalization? Move over Warren, I get the cover shot on Barron's this month. 

I am delighted to announce that Perfect Price has won the runner-up Phocuswright Award for Travel Innovation at its recent flagship conference in Los Angeles. Phocuswright is among the most internationally-respected sources for research and analysis in the travel, tourism, and hospitality industries; its flagship event is now in its 25th year and attracts thousands of senior executives from around the world, all of whom are there to learn about the latest trends in travel technology.

Selected from a pool of dozens, Perfect Price was among a handful innovative startups to present at the conference and was selected as a winner by a panel of industry expert judges (nicknamed "dragons") including, among others: 

• Tom Botts, Chief Commercial Officer of UpLift
• Erik Blachford, Venture Partner at Technology Crossover Ventures
• Todd Henrich, Head of Corporate Development at Booking Holdings
• Jim Hornthal, Managing Partner at Hornthal Investment Partners as well as Cofounder and Chairman of Zignal Labs, LaunchPad Central, M34 Capital, and Triporati
• Regi Vengalil, Head of Corporate Development at Expedia

I want to thank the panel of "dragons" not only for choosing Perfect Price but also for their insightful questions and candor, which we hope will help shed light on the power and promise of dynamic pricing. We are particularly  excited about this achievement because it again shows conclusively that the traditional way of doing pricing is no longer sufficient in the digital economy—and that AI-powered dynamic pricing is the next logical step forward for travel brands.

Below is our winning presentation, followed by a short Q&A. I encourage you to watch, share, and let us know your thoughts by dropping us a note here.

Perfect Price asks all candidates who move past the phone screen interview stage to complete a short assessment project before we bring them in to meet the team. This requires some of the candidates’ time (not too much!) and you might wonder, why do we do ask for this?

I’m very excited to announce that Perfect Price has closed $5.8 million in seed funding, which will enable us to expand our team here in the San Francisco Bay Area as well as a new UK office for our increasingly global footprint; increasingly work with more complicated datasets at larger enterprises; and, most importantly, continue to deliver world-class AI solutions that solve critical business challenges, including pricing.