Hoken — Web2 meet Web3 for More Efficient Travel Inventory
Tokenize hotel inventory -- "It’s a put option on a hotel room financially. Tangential benefits only super close to stay date to de-risk a previous purchase"
Travel industry is one of the largest industries in the world -- contributing ~$5 trillion+ to our global GDP every year -- and even with the advent of globalism/global interconnectedness, internet proliferation, and financialization of assets (not you can rent your unused car and RV, full house or a single room and even your garage and parking space today and soon tokenization/fractionalization of private assets/IP/sports player future contracts/event-based futures and more), distribution of travel booking inventory (hotel, car rental, flight, and travel packages) is still heavily centralized, and one can argue, perhaps, monopolized/duopolized by a small handful of distribution players.
There are 3 major distribution channels for any hotel, car rental, flight operators -- these are:
OTA (online travel agent // Expedia/Booking.com)
GDS (global distribution system //Amadeus, Sabre, Travelport)
Indirect/Direct (on operator website and/or via internet indices such as Google Travel).
A global distribution system (GDS) puts all sorts of travel-related bookings under one umbrella, not just hotel rooms. For example, it lets corporate customers book everything they need for one trip – airline seats, hotel rooms, group tours, rental cars and more. a GDS can also be an OTA via a channel manager. On the other hand, Online travel agent (OTA) are mostly consumer facing.
Most GDS and OTA (will refer to them as "the group" going forward) operate on a small SaaS pricing model (i.e. pay $x per month/year to list your inventory on my website) and a variable rate pricing model (these GDS/OTA usually take 10-30% of the booking as service fee, which is rather heavy). These take rates are not uncommon for most marketplaces today -- Airbnb, eBay, StockX, GOAT and other marketplaces usually take 10-20% of gross revenue as service fee.
The group can commend such as high take rate because of their ability to consolidate traffic (via indexing, brand equity and other non-tangible value adds) and from the consumers perspective, the ability to have a wide range of optionality is a strong value-add. On the flip side -- depending on the corporate structure of these hotel operators and the likes i.e. chain hotel vs boutique hotel, direct gross margin of their product/service (hotel room) is not excessively high given the inflation-linked labor intensive nature of the service model (net margin is usually 0-10% +/- 5% range) so having the group take 10-20% of gross revenue is, to say the least, painful. Not to mention most US cities and states take another 10-15% of gross revenue as hotel occupancy taxes (usually paid monthly or quarterly).
Giving the ill-structured cost dynamic of this industry -- operators have seek other ways to drive growth and profitability -- including driving more direct booking (save 10-20% on the top line gross revenue) by offering a memorable stay experience, credit card-based/general loyalty programs and preferred rate to corporate clients (i.e. if the average hotel daily rate is $200 on GDS/OTA, operators can offer $190 to direct/corporate users b/c they don't have to pay the 10-20% on marketplace take rate). Loyalty programs are oftentimes maintained through the sponsored card mechanism (i.e. Costco Card linked to membership benefits and Marriot Bonvoy below)
Index players such as Google has recently ventured into aggregation of travel inventory (direct booking for flight inventory but no direct booking for hotel inventory. I suspect the rationale to incentivize more direct booking for these operators is second order to that of driving more marketing advertisement (reference the screenshot below for advertisement revenue). Google has also ventured into job aggregation years back but Indeed (and all the tangentially related companies Glassdoor and other highly traffic job aggregation site acquired by Indeed/by their JP-based parent company) but it is still excessive challenging for Google to monetize their job aggregation product.
HOKEN -- Web2 meet Web3
for More Efficient Travel (hotel) Inventory
Beyond the distribution and margin dynamics mentioned above -- travel operators also have to balance the rather complex pricing dynamics. In fact, pricing for this industry is complex and proactive (unlike restaurants or retail industry where product pricing only get updated 1-2x every quarter/year, travel order prices get updated almost daily/weekly -- based on a handful of key variables, including historical/forecasted revenue seasonality, weekdays/weekends, shock events such as Austin F1 Race/SXSW/Mardi Gras/New Years, inventory of comps, inventory in general and more). Admittedly, I am only vaguely familiar with the pricing complexity of this space.
Having a strong pricing team is critical to optimizing your top-line gross revenue as there the max inventory is fixed -- based on the # of rooms you have in your hotel building (unlike B2B and B2C software where the amount of users you can onboard is theoretically unlimited, well, realistically, there are constraints due to database scale, customer success team and other variables). Not to mention average occupancy rate is, directionally, usually in the 65-75% range for most hotel inventory and 30-60% for most Airbnb inventory.
Having a strong grasp of revenue seasonality is critical to managing the pure cashflow swing of the operators business -- this is further amplified by shock events -- most travelers book shock events 60-120 days in advance and 30-60 days in advance for non-shock events.
Here comes Hoken -- a NFT marketplace for RWA asset -- hotel rooms (can be applied to other travel verticals with lighter regulation -- so flight tickets might not work too directly), for shock events/peak hotel revenue seasonality.
Broad strokes similar market pricing dynamic to concert ticket sales.
Hotel inventory and pricing dynamic is similar to that of concert ticket inventory. Both inventory is capped (based on the # of hotel rooms in the building and # of concert tickets available/max # occupancy in the performance venue), pricing dynamic is slightly similar (more stable on a longer duration basis vs most concert tickets get acquired within a compress timeframe), secondary market is very different (non-existent for hotel product vs lots of non-official secondary market txns).
Hoken has an amazing UI -- tie NFT/Web3 technology on the backend, but totally abstract it away from the frontend. This is the way to bring next group of consumers into Web3 (used very loosely here to signify the technology behind it). Even better, future state, it would be nice tie in Apple/Android wallet UI with 1x or ongoing loyalty program into the product experience.
Below is hotel room inventory for SXSW 2023.
The Hoken model provides a tremendous improvement to all hotel operators -- from more upfront cash collection (can use to float to do ops and capex investment) and recoup ongoing x% secondary market royalty. This allows the operator to smooth out their cashflow seasonality overtime -- especially if they have assets in cities with a handful of shock events every year (i.e. Austin, NYC, Miami, LA, Seattle, Dallas)
Cancellation policy is likely not going to be addressed with this model given the complexity of primary and secondary market (i.e. if the 1st buyer bought the hotel room on primary market for $500/night but it's selling for $1,200/night on secondary market -- and a refund is needed from the second buyer -- hotel operator only gets the initially $500 + x % of secondary market txns as revenue, so a full refund of $1,200/night is not financially possible).
More balanced Supply and demand dynamics -- it's hard to forecast with 100% certainty of shock event hotel room pricing so theoretically, if the pricing team can forecast with 90%+ certainty on the primary market, the secondary market x% royalty take rate will increase that certainty level to > 90%, which is ultimately a win for hotel operator revenue optimization effort.
Operators do take some downside pricing risks -- i.e. they projected the occupancy rate for their SXSW 2023 room is 95% @ $850 RevPAR for in Nov 2022, but it's really 98% @ $1000 RevPAR -- they do lose some on the upfront end, but balance some with higher demand from secondary market royalty.
Best of all -- the Hoken model has the potential to lessen hotel operator's dependency on GDS/OTA long-tail...if their product reach some level of PMF product-market-fit, and have the ability/value add to have the operators bring on their non-shock event inventories into this new business model.
Overall, I am optimistic about companies like Hoken who is looking to help both the travel operators and travelers alike. I think similar dynamics can also be applied to concert/performance ticket sales -- but with a slightly different market dynamics -- distribution and venue are not owned directly to the artist or concert goers themselves.
Other things worth mentioning:
City/state hotel occupancy taxes (usually 12-15% of gross booking) might be a bit complex. Tag on hotel taxes on primary but not secondary to avoid double collection? Or don’t charge tag on primary but secondary but then hotel assumes risk for no-secondary ones Or primary pays the base 12-15%, secondary pays the delta if it’s a premium, hotel gets secondary royalty + delta hotel occupancy tax or hotel wins if it’s a discount
1099 tax form obligation is another point to address with regards to capital gain/loss from these transactions, which upstream also implies the collection of users PII (Level I KYC: full name, physical and email address, Level II KYC: prior plus SSN and W-9/W-8 form) and upstream from that is utility vs security token argument.
Thoughts from an Experienced Travel Pricing Leader
For venues they guarantee x amount of revenue and are more worried about maximizing revenue with generally unlimited capacity (very low chance of sellout), so they under price to de risk and guarantee revenue to the acts. That’s the arbitrage opportunity.
Lodging predicts demand and adjusts accordingly as well has having cancelation policies. Here, you’re mainly operating on the idea that you need more flexibility than the hotel will allow, it’s a much smaller secondary market.
And the opportunity for arbitrage is lower. Essentially, you’d have to be experienced in travel inventory pricing, realize a hotel under priced, buy the room and know it’ll sell on the secondary marketplace below future prices but above my purchase price.
"It’s a put option on a hotel room financially. Tangential benefits only super close to stay date to de-risk a previous purchase" - Jordan Locke (https://www.linkedin.com/in/jordanlocke)
One interesting way to use Web3/blockchain to benefit everyone and create cross-platform collaboration is to track the asset and allow revenue sharing across multiple transactions.
For instance, every room/unit is perishable and can only be sold once per night. Once that night passes the room expires and can’t be sold and cannot be recovered for resale. So, revenue management is looking to optimize that one-time sale … price low enough to sell enough volume not so high as to not sell the inventory at all.
But if blockchain/Web3 allows revenue sharing on future transactions, it would allow hedging at each stage. A hotel could offer low prices initially, de-risking their inventory and guaranteeing a base of revenue early, without giving up all revenue upside. Instead of holding inventory at higher prices and adjusting to demand signals they’d offer lower prices initially, guarantee the base of revenue, and get a %of the secondary market resale profits should the demand go higher.
It would be an additional tool in the strategy of a revenue manager.
Feel free to reach out to me via this blog or my other social media accounts -- love to hear your thoughts!