We’ve always been deeply committed to hostels and hybrids and are proud to have so many using our platform - over 200 properties and 100,000 beds to be more precise - but what many hoteliers don’t always appreciate is that hostelry is one of the toughest arenas in hospitality…
It goes without saying that it’s exciting to be a tech company using data science to solve technically tricky problems and hostels and hybrids are cases in point. For example, a typical hotel may have 200 rooms, fixed products and relatively stable demand. Hotel booking curves also tend to be nicely elongated and we can rely on certain patterns forming (cough…Covid pandemics excluded…cough).
In contrast, hostels and hybrids can have over ten times the range of inventory and things get complex quickly. Beds and rooms are competing under the same real estate. A room for a female is not the same as a bed for a male, which is not the same as a coworking space for a small team. Hopefully you get the jist - the permutations of products can sometimes seem endless.
Unfortunately, there is still a perception that revenue management - or let’s call it a deep focus on decision intelligence - is only necessary above a certain revenue threshold and designed for room based hotels. This couldn’t be more untrue!
What is slightly true, however, is that most revenue management systems don’t support smaller properties of under a couple of hundred units. That’s old school. Just so you know - when it comes to good data science, a strong algorithm can produce great results above around 10 units (below that the data just gets too thin). So come one come all!
We’ve also entered an era where hostels have become hybrids…and hybrids are the hot thing. We now have all these different types of hybrid players including The Student Hotel and Sonder who have moved beyond simply diversifying point of sale inventory and offering different accommodation categories within the same real estate.
The next few years will see massive growth in the space and with that we need a renewed understanding of having multiple types of inventory for so many different audiences. Brian Chesky of Airbnb talked a lot about it recently - how the pandemic and the need for flexibility has accelerated the blending of home and work spaces, particularly among young people or the merging of business and leisure into so-called ‘bleisure’. In fact, he believes the systemic negative impact to hospitality over the next few years as a result of the pandemic will be far outweighed by the opportunities presented by this new flexibility where the home is becoming less of a nucleus. We agree - we’re already seeing an explosion in demand for hybrid properties that facilitate this blended and remote model.
It’s clear that hybrids face unique challenges that include the complexity of different types of inventory in the same building but they also have particularly demanding customers as millennials expect things to be online, mobile and to ‘just work’…all seamlessly.
So how can hostels/hybrids play to their strengths? There’s a few things that we think you need to be doing if you want to effectively harness the opportunities out there.
- Data has to be your north star
Given that everything is so much more complex, you need to lean more and more into the data. That means having de facto live data that automatically updates and business intelligence to be able to harness and analyze that data.
- Rethink your KPIs
Data alone is not enough…you need new KPIs that effectively capture the strategic direction you want to go in. Perhaps a unifying KPI across your rooms and your beds? Could you try shifting from looking at RevPAR or RevPAB to something which allows you to actually think about square meter or square footage as a unit in itself? You need metrics that will let you compare and contrast your different inventories and understand what they’re yielding. Again, a strong native BI tool is essential here.
- Disruptive team
You need to have people in your team who are strategic in their thinking and more like commercial analysts than traditional revenue managers. We have different problems to solve today. We have way more complexity. We have new data. We have new metrics. So we need people who can actually make sense of it all…
- Innovative processes
This is where automation comes in. Over half of a typical RM’s day is spent building reports manually, typing prices in and performing glorified rate loading based on arbitrary and crude decrees by a GM (reduce by 20%/increase by 20%), which doesn’t cut it anymore. 90 percent of properties still use a manual process to price their rooms. The more complex your inventory becomes the less you can afford to keep doing this. We have automation to take care of much of that. More specifically, we can automate dynamic pricing.
- Strong technology partners
It goes without saying, you need technology and you can either build it in house or partner up with innovative providers. We recommend the latter…
Forecasting uncertainty with analytics
One of the default charts in Pace Analytics, our native BI platform, is the monthly historical forecast chart. Not only do we show the forecast but we also display how good our forecast has been historically. It seems straightforward but there’s some really hard stuff that we have to do in the backend to be able to give our customers this and it’s why few can provide it.
You can see the ‘Volume’ and ‘Revenue’ you actualize at your property over a given month. Given that we have a booking window, that revenue is going to materialize over time. So one way of visualizing forecast accuracy is to look at what the forecast was seven days out/fifteen days out/30 days out and compare that to the final actual revenue. The more data you get, the more they should converge.
Beyond high level forecasting, you can also start to dive into the BI platform and build new metrics with all the live data. In short, we can delve even deeper into the data.
We believe it’s important to keep the system output forecast separate from the human forecasting elements. Why? Because forecasting can mean many things to different people, from budgets to what the RM thinks to what the boss of the RM thinks and more, so we think it’s important to separate them all out and keep the system output forecast discrete. Furthermore, our platform automatically updates demand hourly so the forecast will immediately adjust, while a human may not be around to always be tweaking that.
On a separate note, what we are investing in over the next few months is developing our system to allow the manual forecast options to be even more granular so that users can also input more of their own data. We want to give revenue managers much better tools to look at the forecast in more granular detail. For example, to forecast occupancy, RevPAB or ADR by different segments and months and breaking that down into days…
Today, Pace users are already using the forecasting function and Pace Analytics to anticipate changes in demand.
Shifting inventory with Samesun Hostels
For example, Samesun Hostels needed to rapidly move inventory around at their Canadian and US properties based on sharp swings in demand and used BI to anticipate those changes.
“Pace Analytics is fundamental to our strategic decision making. We’re particularly keen to look at ‘revenue per square foot’ at our properties and to spot yearly trends. From that we can predict when we should switch dorms to private rooms and vice versa. Being able to rapidly flex our inventory is a game changer and a good BI tool affords us this opportunity. Post-Covid, our room inventory is quickly changing so we have created dashboards to help us pick those trends in advance and be prepared for those changes in demand.
For example, on a seasonal basis we can see that we are going to get more backpackers during the summer months so we can pivot to 6-bed and 8-bed dorms. While during the shoulder period we can see how many more private rooms we may need. PA let’s us plan for that. Right now we are 50% privates vs dorms. That used to be 10%…”
Space-time with The Student Hotel and Mews
With The Student Hotel (TSH) and our friends at Mews, we’ve been able to effectively separate the physical space and the products on top. In fact, TSH calls these spaces ‘cubes’.
So how can the team decide by month, week or night what product they should sell in that space? Furthermore, through what channel should they sell?
Pace helps visualize all this by showing the square meterage, what the inventory allocation is and how to ultimately shift that over time. You start to see a graph in your property of how much of your revenue or square meters is allocated to products like co-working, dormitories or rooms. You could go further and break things down by time unit also…Mews call it ‘space-time’ and it’s definitely the future.
We’ve been forecasting and optimizing pricing at a rate code level and have recently leveled up to the segment level. It’s been super hard to get this far as it takes a lot of data science to be able to achieve it. But the next step is to be able to have virtual inventories and multiple products forecasted for the same space so that the platform can recommend to you what you should be selling.
We need more sharp minds to get there! Oh by the way…we’re hiring!