Hotel distribution is a complex business with an intricate web of connections linking hotel room suppliers to hotel distributors which includes bed banks, hotel room wholesalers, hotel switches, online travel companies, metasearch engines as well as the big Global Distribution Systems (GDSs). Selling travel is very competitive and hoteliers need to supplement their direct sales channels (i.e. brand.com and call centres) with a number of different third party channels in order to reach their target audiences across the globe.
No one can sit back and expect to be found, but is better off working with partners who have built strong distribution networks. This is where intermediaries such as accommodation wholesalers and bed banks come in. This article looks at the role of hotel distributors and presents 5 fundamental data sets that they need to be able to optimise their operations and avoid costly mistakes and missed business opportunities.
The Connected World of Hotel Distribution
Travel agents appreciate access to the many hotel suppliers that can be reached through a single intermediary and conversely the individual hotel appreciates the many agents that it can reach through the wholesaler. It’s proved to be a great business model for both large and small hotel companies. It’s worth noting that travel distribution is technology intensive and time sensitive. Bed banks and wholesalers have systems and expertise in abundance. After all they don’t own the room, or indeed the consumer, but they are an invaluable conduit to getting one to the other before the sell-by date and profit opportunity expires.
So hotel distributors such as bed banks and wholesalers play a pivotal role in linking accommodation supply to demand, especially with regards to diverse travel agents or niche tour operators. The distributor is focused on getting the right inventory deals from the supply side for the right prices and sell them on to their clients for the best prices achievable. To be successful requires solid relationships with the hotels (so the right deals can be contracted) and a robust system architecture capable of handling heavy search and response traffic, largely using standard XML formatted data. Direct connects in the shape of APIs between suppliers, intermediaries and customers provides the plumbing for millions of messages flowing between providers and consumers. APIs streamline the many to many connections that exist today.
The customers benefiting from this electronic infrastructure are largely B2B travel businesses such as agencies, tour operators, metasearch engines and even other wholesalers. It means inventory and reservation systems can be linked up enabling real-time inventory management and instantaneous booking confirmations. With online travel a growth industry and Look-to-Book ratios still soaring, enormous system and network capacity is required as well as performance expectation.
Of course, each channel carries certain costs with it, while bringing varying degrees of benefit. Indeed choosing the right mix of channels with revenue streams that yield sustainable profit is an important balance to strike throughout the supply chain. Where channel proliferation is the norm, becoming skilled at assessing which ones contribute to profit and which ones don’t is the challenge. In short, we’ve identified that the complex task of hotel distribution by bed banks and wholesalers brings with it three critical operational demands:
- Monitoring the infrastructure (connectivity, capacity, responsiveness)
- Managing the supply (inventory, availability, rates)
- Measuring channel contribution (relevance, margins)
Introducing the 5 Must Have Data Sets for Hotel Distributors
So what does a bedbank or wholesaler need to effectively optimise his operations to meet these challenges? Having good systems and processes in place is just part of the answer, but the real key to business success is data and how the data is used to make a difference. Specifically I am referring to operational performance data and business intelligence derived from XML data streams. We believe there are 5 critical data sets that are an essential part of every distributor’s toolkit when managing daily distribution challenges:
- Timeouts: how and why they occur and their financial impact
- Response Times: measuring and manging responsiveness to opportunities
- Inventory: managing what’s available against market demand
- Margins: understanding impact of cost and prices on profit
- Search metrics: using search metrics to measure channel value
These five key insights are based on the raw data that is part of every distribution operation. So it’s not a question of the data not being available but a case of harnessing it for best use. This is where a platform that not only measures operational performance but is also capable of extracting wide ranging intelligence from XML messages is an important competitive differentiator.
Let’s take a closer but high level look at each of the insights we have listed to appreciate its significance to the daily life of a hotel distributor.
Millions of requests, offers confirmations, cancellations and payments pass through the network of connected travel partners 24/7. Speed of information exchange is critical and timeouts occur when a system gives up waiting for a response. Networks, servers, applications and even errors in requests can all be at fault which means offers to requests don’t get included and sales opportunities are lost. To mitigate this the distributor needs to know when it happens, pinpoint why, where and how and then fix it as quickly as possible. But timeouts nearly always equate to missed opportunities to do business – and therefore lost revenue. The bigger the channel partner is in terms of doing business the higher the opportunity costs especially if poor responses lead to being locked out for a period of time. In this Timeout Use Case we give an example of the financial damage timeouts can cause especially when considered accumulatively over a business day, or longer.
- Response Times
These are measured to make sure performance stays within set boundaries, and serve as useful indicators of the health of the systems and applications. Measuring speeds and feed from end-to-end not only reveals technology performance but user (or customer) experience. Since response times are so important, for example when monitoring timeout occurrences, or monitoring the time requests take to process, then taking the average across a time period isn’t accurate enough for this speed driven business. Using percentiles to look at data rather than just average ranges becomes more meaningful. In the case of timeouts, a 90% percentile view for example can capture more of the timeouts that occurred over a given period than the average view, where a number of timeouts disappear from the graph, and end up literally off the radar. In this Response Time Use Case we use charts and graphs to explain how looking at response data with a percentile lens broadens the message coming from response data analysis.
Distributors such as Bed banks and wholesalers distribute inventory, so having a real-time perspective on what is available, what the market is searching and having the right visibility into inventory to align the two is imperative. Getting the wrong balance or running out of popular products can be very costly. Our research shows that almost 20% of XML search requests (e.g. price and availability requests) either fail due to easily addressable or preventable errors such as system issues or errors contained in the request itself. It is not untypical for distributors to show no availability to a request, when availability does exist, or to genuinely run out of a popular product before taking action to replenish the stock. Monitoring system performance, error checking XML messages and analysing real-time search to keep inventory levels in line with demand are three simple steps that can save a distributor millions in annual revenue. Take a look at this 2 minute short video to see how the maths stacks up. Analysis of inventory performance, not only helps distributors manage inventory but also has the double benefit of arming marketers with data for campaign RoI and gives contracting departments the right insights to take to their negotiations. Our Inventory Management Use Case helps distributors understand the financial impact of inventory gaps as well as get tips to mitigate them.
- Margin Management
When contracting with many different suppliers, dealing with different room types and destinations, different costing and pricing structures apply, so different margin levels will also prevail. Distributors need to be aware of the different nuances that impact costs (i.e. what he is committed to paying for the room) and price (i.e. what he can sell the room for), all in a competitive environment. This is where margin management comes in where the opportunity to sell rooms has been achieved at the optimum margin. Success depends on getting the right accommodation at the best margin prices and securing room fulfilments at the best purchase prices. This all has to be done within the normal business parameters of real-time accurate pricing, and delivered to the buyer at optimum speed, taking of course account of the margin opportunity. Managing large inventory databases and squeezing the most profit out of them can become a high-wire balancing act of supply and demand. Not really possible without an analytics platform delivering an array of insights into system performance, product prices, product availability and relevance to demand. Our Margin Management Use Case calculates examples of margin management and demonstrates how intelligence from XML shopping streams can help wholesalers manage their inventory levels and fine tune their system business logic to factor in margin implications when responding to searches with their offers.
- Revenue per Search
Look-to-Book is the industry benchmark for measuring the number of searches in relation to actual bookings made. But only when a booking is made does the revenue begin to flow. Searches continue to rise, as does the overhead of being able to handle them. So Look-to-book, although a good indicator of activity level, does not alone tell you much about who is hitting your systems with searches and who is bringing you bookings. Nor does it tell you if those bookings are high in value, low in value or anything in between. Look-to-book is cost centric not revenue centric so cannot tell you which channel is wasting your resources and which ones bring the best rewards. A better way of evaluating activity levels is measuring searches coming from a particular channel against the revenue levels it brings. XML analytics can give distributors such as bed banks and wholesalers insights into which channel is loading their systems with high volume searches but poor bookings. This knowledge empowers them to manage the channel relationship in an optimum way. This may mean uncovering that some of the requests are for products they don’t have, therefore stop them, or turn the channel off altogether if a good match cannot be found. Our Revenue per Search Use Case describes with examples the insights to be gained from measuring searches against revenues.
Being successful in online travel is all about measuring and understanding distribution performance in real time. Five easy to get data sets, with the right analytics platform installed, can help distributors leverage their operational information such as timeouts and response times better and faster and gain deep insights into inventory, pricing margins and search RoI. Using the date ensures distributors can attain the optimal channel mix in an optimised operational environment. Without this data on their dashboards, distributors will continue to fall short of their potential and remain unaware of the cost of those missed opportunities.