Are you failing for the right reasons?
Written by Lori Grant, Director of Product Performance Analytics and Insight at IHG Hotels and Resort & Chakradhar Munjuluri, Product Manager Third Party Distribution at Choice Hotels
Emilie Galle, International Marketing and Distribution Director at Choice Hotels & Laurie Pumper, Director of Communications, HEDNA
Note: The opinions are those of the authors and not necessarily those of the companies where they are employed.
Current use of booking failure metric limits hotel chains to address macro issues
Today’s hotel distribution landscape is a complex web of connected internal or external systems. All of these systems have different levels of technological maturity. This complex ecosystem leads to points of failure in the distribution of inventory between partners. Any failure in this chain of systems can impact a hotel’s revenue. Minimizing the many points of failure can contribute to hotel profitability.
Identifying key metrics to track systems is important for troubleshooting. Hotel chains have established metrics, such as reservation failure rate. This rate is useful for tracking and resolving systemic issues, but tends to be retrospective, preventing proactivity. Depending on the magnitude of a problem, the booking failure rate ranges from alarmingly high to generally acceptable. The technical teams’ current operational procedures often explain the alarming errors and have measures in place to resolve these issues. However, for generally acceptable levels of failures due to business rules, the root causes are often identified through discovery calls, which take time and resources. Product and technical teams pay close attention to the list of business rules when designing system functionality and most Central Reservation System (CRS) or Property Management System (PMS) vendors can provide a list. of their business rule errors. However, they are not used to detect or fix problems unless the reservation failure rate exceeds an overall threshold. While these aggregate thresholds may be acceptable at first, they can quickly turn into alarming failure rates when analyzed at the channel, rate, or region level. Unresolved, these points of failure add up to a steady loss of bookings and revenue over weeks and months.
Look to reservation failure rate to solve micro-problems
The reservation failure rate provides a direct correlation to an underlying problem which may be systemic or inherent in connectivity. If used in real time, this metric can allow hotel chains to solve smaller problems in concrete ways. A closer look at the typical root causes of booking failures provides insight into what solutions should be put in place.
Three categories of booking failures:
|Reservation failure category||Type||The description||Example (root cause)|
|Work by design||In good health||These errors prevent or protect against revenue loss targeting “end user behavior” and “system behavior”.||
|Business Process/Product Configuration||Unhealthy||These errors indicate inaccurate or missing business processes and product configuration or activation, in one or more systems.||
|Software issues||Unhealthy||These errors are the unintended “bugs” fixed with more software coding. Impacts occur at multiple levels, including product (rate or room) and channel, and can be regional or global.||
For the last two categories, the failures are probably due to a problem of availability or data policy. But in the case of a last room availability model or a third-party cache model, a failure can fall into two categories at once, adding to the complexity of identifying and resolving it. For example, a product is sold out and a software issue prevents distribution of the availability change to a third-party partner. Although the reservation is rejected because there is no availability (operation by design), the root cause of the failure is that the availability was not shared (software problem). This is one of the most difficult scenarios to analyze, but valuable insights are gained by comparing all providers with the same availability pattern. Organizations can also identify a healthy threshold (because a cache pattern involves latency) and pursue that domain only when they exceed that threshold. For issues in many categories, the best course of action will be on a case-by-case basis, as the cost of such a complex model can outweigh the benefits.
Increase efficiency to solve micro-issues, using booking failure rate
The reservation failure rate reflects all upstream and downstream data discrepancies, on any system in the distribution chain. It provides granular data to leverage in real time to identify issues and alert the right teams to fix.
Using the steps below will allow organizations to use booking failures as a metric and formalize an appropriate real-time process/model for resolving minor issues.
- Categorization: Look at all reservation failures over a period of time and group them according to the previously identified categories (work by design, business process/product configuration, and software issues).
- Segmentation: Determine the consistency, frequency, and propagation of each category of reservation failures across channels, systems, products, properties, and terminate any failures that cannot be reduced.
- Operational efficiency:
- Reduce business process errors at an early stage. This will prevent the problems from getting worse over time. When root causes are identified as a training gap, reviewing training materials will be beneficial in addressing the process gap. (This may be more noticeable when new staff are in place.)
- Align errors with their corresponding configuration settings. As errors occur, review all hotels where the configuration exists, as they are likely subject to future errors. Ex: in the case of a new rate launch, if a reservation failure occurs on day 1, associate the error with the configuration to determine how many other hotels are affected by the omission of a configuration parameter .
- Threshold: Establish a healthy failure rate and update the threshold based on the new improvement.
- Product design: Based on the root cause identified in step 3:
- Product Enhancement: Install a potential enhancement in the underlying product/system. For example: data validation or mandate in the user interface.
- Process Automation: Install an automation enhancement to resolve errors. This would build a mature model to solve the identified problems.
The proposed process provides real-time updates and categorizes issues based on predefined rules. Depending on the process, the time it takes to identify and resolve the issue will vary depending on the efficiencies gained through automation and alerts to take appropriate action. In the following example, a fare that is supposed to be sold in a particular region does not sell. The estimated time to identify and resolve the missing mapping step ranges from 6 to 32 hours, depending on the maturity of the process.
Reservation Rejection Template
In conclusion, use booking failure rate as the primary metric to identify and act on small issues, and integrate it into a mature and consistent real-time process. These actions can lead to increased income.
HEDNA Hotel Analytics Working Group
The Hotel Analytics working group raises awareness of the opportunities offered by data analysis to optimize costs and conversion and thus enable hoteliers to collect, store, analyze and act on their data to make intelligent decisions regarding their distribution strategies. The group is currently co-chaired by Connie Marianacci at Accor and Anisha Yadav at Revinate. Click here to learn more and how to become a HEDNA member.