How to close inventory gaps with XML search intelligence
Many online B2B travel organisations invest considerable amounts of money in connecting more clients, developing their distribution capabilities and marketing to generate ever increasing volumes of search traffic. All too often considerably less emphasis is placed on optimising their inventory. In part at least, this is due to inherent technical challenges in capturing and analysing large quantities of search traffic in real-time.
The Inventory Gap
It is highly unlikely that any online travel distributor will be able to supply product for every single search request that they receive. The key question is: how large is the gap between the inventory of available products and the searches that the system is currently servicing?
The average level of availability over time, measured as responses returning at least one product per search, is shown above. Whilst there are no dramatic dips in availability visible in this sample, the availability level shows an average of approximately 80% and there is some variance with peak levels around 90% and low points around 60%. The red region shows the times when no availability was returned at least once in every 4 requests.
The chart above shows the trend lines of searches and responses with availability. Dips in which the availability volume (blue) line drops significantly away from the search volume (red) line are the times when percentage availability levels drop. In this particular dataset there is quite a noticeable drop in availability levels during the peak trading part of each daily cycle.
Sizing the Financial Impact
A recent exercise undertaken for a medium sized online hotel wholesaler highlights the potential revenue opportunity associated with search responses that don't include available product. The flowchart below identifies some 30 million searches over a single working week (5 days) and how the lack of availability impacted the business in terms of revenue.
In practice, there are a significant number of reasons why requests can result in no availability responses. The chart below, which shows the frequency of the various types of errors, highlights just how dominant the lack of product (rooms) is and how it readily accounts for 75% of all XML level errors. For many online wholesalers this underlines a significant mismatch in inventory availability when compared to searches.
The chart below shows the top twenty hotel names ordered by the number of search requests that have failed to respond with room availability over our time period. In this case, the leftmost property, the River Rose Hotel, has returned the highest unavailability.
Whilst it would be reasonable to call the River Rose Hotel reservations team and seek further room allocations on this information alone, it is more appropriate that we understand which check in dates are most affected.
The chart above shows the River Rose Hotel's unavailability by check in date. The highest peaks are clearly occurring in the near term which is on the left of the chart. This is probably not surprising since the peaks are partly driven by search volume. The lower peaks are probably a by-product of lower searches currently being performed for mid and longer term check in dates. Independently of search volumes, it is clear that the River Rose Hotel is consistently showing poor availability for check in dates across an 12 month time frame.
The action here is to either resolve the nearly continuous lack of allocation with the River Rose Hotel and/or find alternate properties of a similar standard within in the same geographic area.
To read more about identifying inventory gaps and managing distribution channels download the White Paper: Closing the Multi Million Pound Inventory Gap with XML Search Intelligence that covers the topic more in depth.
*For confidentiality reasons hotel names have been changed
based business intelligence and operational analytics designed to help online travel companies meet the challenges and opportunities of today"s fragmented distribution landscape. Triometric technology is a powerful end-to-end web services monitoring and analytics platform that helps customers manage complex distribution dynamics by giving them deep insight into their search and booking traffic. This actionable intelligence enables travel companies including hotel wholesalers, online travel agencies, metasearch engines and hotels and airlines to improve their business performance by reducing costs and increasing revenue.
Triometric is a specialist provider of XML-
Triometric is a privately held company based in Surrey, United Kingdom. Customers in the travelsector include GTA, Hotelbeds, SERHS Tourism, Miki Travel, Bonotel and SunHotels.